Housing Fiscal Impact of New Housing in Massachusetts
The Fiscal Impact of New Housing Development in
Massachusetts: A Critical Analysis
Robert Nakosteen, Ph.D.
Isenberg School of Management
University of Massachusetts, Amherst
James Palma, MRP, AICP
Research Manager
University of Massachusetts Donahue Institute
With:
Rebecca Loveland, MRP
Michael Goodman, Ph.D
Research Assistants:
Alexandra Proshina
Pamela Miller
Robert Lacey
Copyright 2003
The University of Massachusetts Donahue Institute
The contents of this publication may be reproduced only with permission of the authors.
The Fiscal Impact of
New Housing Development
in Massachusetts
A Critical Analysis
For Ci tizens’Housing and Planning Association
Fe bruary 2003
University of Massachusetts Donahue Institute
Economic and Public Policy Research Unit
Robert Nakosteen,Ph.D.
James R.Palma,MRP,AICP
with
Michael Goodman,Ph.D.
Rebecca Loveland,MRP
Research Assistants
Robert Lacey
Pamela Miller
Alexandra Proshina
Donahue Institute
AC KNOWLEDGEMENTS
Citizens’ Housing and Planning Association and University of Massachusetts Donahue
Institute would like to thank the following people for serving on our study advisory
committee:
Gene Clerkin, Massachusetts Housing Investment Corporation
Howard Cohen, The Beacon Companies
John Connery, Connery Associates
Larry Curtis, WinnDevelopment
Scott Dale, AvalonBay Communities
Robert Engler, Stockard & Engler & Brigham
Ben Fierro, Home Builders Association of Massachusetts
Joe Flatley, Massachusetts Housing Investment Corporation
To m Gleason, MassHousing
Fred Habib, Department of Housing and Community Development
Mark Leff, Salem Five
Robert Pyne, MassHousing
Mat Thall, Local Initiatives Support Corporation, Boston Office
Sarah Young, Department of Housing and Community Development
We would also like to thank Kurt Gaertner of the Executive Office of Environmental
Affairs and Elisabeth Krautscheid of the Department of Housing and Community
Development for reviewing drafts of the report.
CHAPA would like to especially thank MassHousing and Massachusetts Housing
Investment Corporation for providing critical funding for this report.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Table of Contents
1 INTRODUCTION.....................................................................................................9
1.1 METHODS AND DATA SOURCES .........................................................................10
1.2 LIMITATIONS OF THE STUDY ..............................................................................11
2 DEMOGRAPHIC AND MUNICIPAL REVENUE TRENDS...........................13
2.1 DEMOGRAPHIC CHANGES 1990-2000:...............................................................13
2.2 OUT-MIGRATION FROM MASSACHUSETTS .........................................................15
2.3 MUNICIPAL REVENUE TRENDS 1981-2001........................................................16
2.4 GEOGRAPHIC PATTERNS OF MUNICIPAL REVENUES, 1990 TO 2000..................18
2.5 DEMOGRAPHIC AND MUNICIPAL REVENUE TRENDS: CONCLUSIONS .................20
3 DEMOGRAPHIC ANALYSIS AND MUNICIPAL RANKINGS.....................21
3.1 INTRODUCTION ..................................................................................................21
3.2 RANKING MUNICIPALITIES BY POPULATION GROWTH .......................................21
3.3 RANKING MUNICIPALITIES BY KIND OF COMMUNITY ........................................22
3.4 DEMOGRAPHIC ANALYSIS OF THE CATEGORIES .................................................22
3.5 CONCLUSIONS ....................................................................................................23
3.6 TOTAL POPULATION BY CATEGORY ...................................................................24
3.7 TOTAL HOUSING UNITS BY CATEGORY .............................................................25
3.8 TOTAL VACANT NON-SEASONAL HOUSING UNITS BY CATEGORY ....................26
3.9 TOTAL SINGLE FAMILY DETACHED HOUSING UNITS BY CATEGORY .................27
3.10 TOTAL NEW HOUSING UNITS BUILT BY CATEGORY ..........................................28
3.11 SCHOOL POPULATION BY CATEGORY ................................................................29
4 FISCAL IMPACT ANALYSIS IN MASSACHUSETTS......................................1
4.1 THE DHCD GROWTH IMPACT HANDBOOK ........................................................30
4.1.1 Population Estimate Model.......................................................................31
4.1.2 School Cost Models...................................................................................31
4.1.3 Municipal Cost Models.............................................................................31
4.1.4 Local Aid and Property Tax Models.........................................................32
4.1.5 Quality of Life Issues (QOL).....................................................................32
4.1.6 Marginal Cost Analysis.............................................................................33
4.1.7 Appendices................................................................................................33
4.2 THE EOEA MASSACHUSETTS FISCAL IMPACT TOOL (FIT)...............................33
4.3 CONCLUSIONS ....................................................................................................33
5 FISCAL IMPACT THEORY................................................................................34
5.1 PER CAPITA MULTIPLIER METHOD ....................................................................35
5.2 SERVICE STANDARD METHOD ...........................................................................35
5.3 CASE STUDY METHOD .......................................................................................36
5.4 COMPARABLE CITY METHOD.............................................................................36
5.5 POPULATION ESTIMATES AND SCHOOL COSTS ...................................................37
5.6 CONCLUSIONS ....................................................................................................37
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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6 TEST OF THE PRE-CAPITA FORECASTING METHOD.............................38
6.1 REAL VERSUS PREDICTED MUNICIPAL EXPENDITURES, 1990-2000...................38
6.2 ESTIMATION ERRORS BY COMMUNITY RANK AND TYPE ...................................40
6.3 YEAR-BY-YEAR EXPENDITURE CHANGES..........................................................41
6.4 CONCLUSIONS ON THE PER-CAPITA FORECASTING METHOD .............................43
7 POPULATION FORECASTING..........................................................................44
7.1 DIFFERENCES BETWEEN THE MODELS ...............................................................44
7.2 HOUSING UNITS BY VALUE ................................................................................45
7.3 AGGREGATION BIAS (OR THE ECOLOGICAL FALLACY)......................................46
7.4 CONCLUSIONS ....................................................................................................46
8 EXPENDITURE ANALYSIS................................................................................48
8.1 PER-CAPITA MUNICIPAL EXPENDITURES BY POPULATION GROWTH .................49
8.2 EXPENDITURES BY GROWTH RATE CATEGORY ..................................................49
8.3 MUNICIPAL EXPENDITURES BY KIND OF COMMUNITY (KOC) CODE .................50
8.4 CONCLUSIONS ON MUNICIPAL EXPENDITURE ANALYSIS ...................................51
8.5 TOTAL GENERAL FUND EXPENDITURE CHARTS.................................................53
8.6 TOTAL EXPENDITURES MINUS EDUCATION EXPENDITURE CHARTS ..................55
8.7 GENERAL GOVERNMENT EXPENDITURE CHARTS...............................................57
8.8 POLICE EXPENDITURE CHARTS ..........................................................................59
8.9 FIRE EXPENDITURE CHARTS ..............................................................................61
8.10 OTHER PUBLIC SAFETY EXPENDITURE CHARTS.................................................63
8.11 EDUCATION EXPENDITURE CHARTS...................................................................65
8.12 PUBLIC WORKS/HIGHWAY EXPENDITURE CHARTS ............................................67
8.13 OTHER PUBLIC WORKS EXPENDITURE CHARTS .................................................69
8.14 HEALTH & WELFARE EXPENDITURE CHARTS ....................................................71
8.15 CULTURE & RECREATION EXPENDITURE CHARTS .............................................73
8.16 DEBT SERVICE EXPENDITURE CHARTS ..............................................................75
8.17 FIXED COSTS EXPENDITURE CHARTS.................................................................77
8.18 INTERGOVERNMENTAL EXPENDITURE CHARTS..................................................79
8.19 “OTHER” EXPENDITURE CHARTS.......................................................................81
9 REVENUE ANALYSIS..........................................................................................83
9.1 MUNICIPAL REVENUE PER CAPITA ....................................................................83
9.2 MUNICIPAL REVENUE BY KIND OF COMMUNITY ...............................................85
9.3 CONCLUSIONS ON MUNICIPAL REVENUE ANALYSIS ..........................................85
9.4 PER CAPITA TOTAL REVENUE CHANGE 1990-2000...........................................87
9.5 PER CAPITA TAX LEVY CHANGE 1990-2000.....................................................89
9.6 PER CAPITA STATE AID CHANGE 1990-2000.....................................................91
9.7 PER CAPITA LOCAL RECEIPT CHANGE 1990-2000.............................................93
9.8 PER CAPITA “OTHER” REVENUE CHANGE .........................................................95
9.9 RELIANCE ON REVENUE TYPES ..........................................................................97
10 STATE AID ANALYSIS....................................................................................99
10.1 STATE AID TRENDS 1990-2002.........................................................................99
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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10.2 STATE AID PER CAPITA ...................................................................................100
10.3 STATE AID BY KIND OF COMMUNITY...............................................................101
10.4 CONCLUSIONS ON STATE AID ..........................................................................101
10.5 EDUCATION STATE AID CHARTS......................................................................102
10.6 NON-SCHOOL STATE AID CHARTS...................................................................104
11 SCHOOL COST ANALYSIS..........................................................................106
11.1 SCHOOL COST TRENDS 1990-2000..................................................................106
11.2 PER-PUPIL COSTS BY PUPIL GROWTH CATEGORY ...........................................107
11.3 PER-PUPIL STATE EDUCATION AID BY PUPIL GROWTH CATEGORY.................108
11.4 PER-PUPIL COSTS BY KIND OF COMMUNITY ....................................................109
11.5 CONCLUSIONS ON SCHOOL COSTS ...................................................................110
11.6 CHARTS FOR SCHOOL COSTS PER PUPIL BY PUPIL GROWTH CATEGORY.........111
11.7 CHARTS FOR SCHOOL COSTS PER PUPIL BY KIND OF COMMUNITY .................113
12 TAX COLLECTION ANALYSIS...................................................................115
12.1 TAX SYSTEM ANALYSIS 1990 – 2000..............................................................115
12.2 PER CAPITA LEVY CHANGE ANALYSIS, 1990 – 2000......................................116
12.3 PER PARCEL LEVY CHANGE ANALYSIS, 1990 – 2000.....................................116
12.4 PROPOSITION 2½ LEVY LIMITS AND LEVY CEILINGS, 1990 – 2002.................117
12.5 PROPOSITION 2½ OVERRIDE VOTES, 1990-2000.............................................118
12.6 MOODY’S BOND RATINGS, 1990-2000............................................................120
12.7 MUNICIPAL DEBT, 1990-2000.........................................................................121
12.8 CONCLUSIONS ..................................................................................................122
12.9 TAX LEVY CHANGE CHARTS ...........................................................................123
12.10 PER-CAPITA TAX LEVY CHANGE CHARTS ...................................................124
12.11 PER PARCEL TAX LEVY CHANGE CHARTS ...................................................126
12.12 PROPOSITION 2½ LEVY LIMITS AND LEVY CEILINGS CHARTS .....................128
12.13 MUNICIPAL DEBT CHARTS ...........................................................................130
13 REGIONAL ANALYSIS.................................................................................115
13.1 TOWN CATEGORIES BY BENCHMARKS REGION................................................132
13.2 CONCLUSIONS ..................................................................................................134
13.3 EXPENDITURE CHANGE BY BENCHMARKS REGION ..........................................135
13.4 REVENUE CHANGE BY BENCHMARKS REGION .................................................136
13.5 STATE AID BY BENCHMARKS REGION .............................................................136
13.6 SCHOOL COSTS BY BENCHMARKS REGION ......................................................137
13.7 TAX COLLECTION ISSUES BY BENCHMARKS REGION .......................................138
13.8 DEMOGRAPHICS BY BENCHMARKS REGION .....................................................139
14 OTHER ISSUES AFFECTING IMPACT ANALYSIS................................141
14.1 INDIRECT COSTS AND BENEFITS OF HOUSING DEVELOPMENT .........................141
14.1.1 Quality of Life.........................................................................................141
14.1.2 Economic Impact....................................................................................142
14.2 SECONDARY BENEFITS OF HOUSING DEVELOPMENT .......................................142
14.2.1 Population Stability................................................................................142
14.2.2 The Household as an Economic Engine.................................................143
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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14.2.3 The Household as a Civic and Social Resource.....................................143
14.3 COSTS AND BENEFITS ARE AN UNEQUAL BENEFIT AND BURDEN .....................144
14.4 MINIMIZING THE IMPACTS OF DEVELOPMENT USING “SMART GROWTH”........144
14.5 NORTHEAST LAND CONSUMPTION EXCEEDS POPULATION GROWTH ...............145
14.6 ROADS, HIGHWAYS AND TRANSPORTATION ....................................................146
14.7 SCHOOLS ..........................................................................................................146
14.8 UTILITIES .........................................................................................................147
14.9 PREVENTING SPRAWL THROUGH GOOD PLANNING ..........................................147
15 FINDINGS AND CONCLUSIONS.................................................................149
15.1 FINDINGS .........................................................................................................149
15.2 NEXT STEPS .....................................................................................................149
15.3 CONCLUSIONS ..................................................................................................150
Table of Figures
FIGURE 2.1 GROWTH IN THE TOTAL NUMBER OF HOUSING UNITS BY TOWN, 1990 TO 2000.....................14
FIGURE 2.2 PERCENTAGE GROWTH IN HOUSING UNITS BY TOWN, 1990 TO 2000......................................14
FIGURE 2.3 MIGRATION PATTERNS OF MASSACHUSETTS RESIDENTS ........................................................15
FIGURE 2.4 REVENUE PERCENTAGE BY TYPE, 1981-2001 (ADJUSTED FOR 2000).....................................17
FIGURE 2.5 REVENUE BY TYPE, 1981-2001 (IN MILLIONS), ADJUSTED FOR 2000.......................................17
FIGURE 2.6 GROWTH IN TOTAL REVENUES IN ADJUSTED DOLLARS BY TOWN, 1990 TO 2000...................19
FIGURE 2.7 PERCENTAGE GROWTH IN TOTAL REVENUES IN ADJUSTED DOLLARS BY TOWN, 1990 TO 2000
............................................................................................................................................................19
FIGURE 3.1 TOTAL POPULATION BY POPULATION GROWTH CATEGORY, 1990-2000..................................24
FIGURE 3.2 TOTAL POPULATION BY KIND OF COMMUNITY, 1990-2000.....................................................24
FIGURE 3.3 TOTAL OWNER- AND RENTER-OCCUPIED HOUSING UNITS BY POPULATION GROWTH
CATEGORY, 2000.................................................................................................................................25
FIGURE 3.4 TOTAL OWNER- AND RENTER-OCCUPIED HOUSING UNITS BY KIND OF COMMUNITY, 2000....25
FIGURE 3.5 TOTAL VACANT NON-SEASONAL HOUSING UNITS BY POPULATION GROWTH CATEGORY, 1990-
2000....................................................................................................................................................26
FIGURE 3.6 TOTAL VACANT NON-SEASONAL HOUSING UNITS BY KIND OF COMMUNITY, 1990-2000.......26
FIGURE 3.7 TOTAL SINGLE FAMILY DETACHED HOUSING UNITS BY POPULATION GROWTH CATEGORY,
2000....................................................................................................................................................27
FIGURE 3.8 TOTAL SINGLE FAMILY DETACHED HOUSING UNITS BY KIND OF COMMUNITY, 2000.............27
FIGURE 3.9 TOTAL NEW HOUSING UNITS IN THE PREVIOUS FIVE YEARS BY POPULATION GROWTH
CATEGORY, 1990-2000.......................................................................................................................28
FIGURE 3.10 TOTAL NEW HOUSING UNITS IN THE PREVIOUS FIVE YEARS BY KIND OF COMMUNITY, 1990-
2000....................................................................................................................................................28
FIGURE 3.11 TOTAL NET AVERAGE MEMBERSHIP OF PUPILS BY POPULATION GROWTH CATEGORY, 1990-
2000....................................................................................................................................................29
FIGURE 3.12 TOTAL NET AVERAGE MEMBERSHIP OF PUPILS BY KIND OF COMMUNITY, 1990-2000..........29
FIGURE 6.1 PERCENT DIFFERENCE BETWEEN ACTUAL AND PREDICTED GENERAL FUND EXPENDITURES,
1990-2000...........................................................................................................................................39
FIGURE 6.2 PERCENT DIFFERENCE BETWEEN ACTUAL AND PREDICTED EDUCATIONAL EXPENDITURES,
1990-2000...........................................................................................................................................39
FIGURE 6.3 PERCENT DIFFERENCE BETWEEN ACTUAL AND PREDICTED GENERAL FUND EXPENDITURES
MINUS EDUCATIONAL EXPENDITURES, 1990-2000.............................................................................40
FIGURE 6.4 SELECTED EXPENDITURES BY YEAR FOR DEDHAM, ADJUSTED FOR INFLATION ........................42
FIGURE 6.5 SELECTED EXPENDITURES BY YEAR FOR ROWLEY, ADJUSTED FOR INFLATION ........................42
FIGURE 8.1 POPULATION CHANGE VS. GENERAL FUND EXPENDITURE CHANGE IN ADJUSTED DOLLARS,
1990-2000...........................................................................................................................................49
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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FIGURE 8.2 PER CAPITA TOTAL GENERAL FUND EXPENDITURE CHANGE ...................................................53
FIGURE 8.3 MEDIAN PERCENT CHANGE IN PER CAPITA TOTAL GENERAL FUND EXPENDITURES BY
GROWTH CATEGORY, 1990-2000........................................................................................................53
FIGURE 8.4 MEDIAN PERCENT CHANGE IN PER CAPITA TOTAL GENERAL FUND EXPENDITURES BY KIND OF
COMMUNITY, 1990-2000.....................................................................................................................54
FIGURE 8.5 MEDIAN PERCENT CHANGE IN PER CAPITA TOTAL GENERAL FUND EXPENDITURES BY KIND OF
COMMUNITY AND GROWTH RATE, 1990-2000....................................................................................54
FIGURE 8.6 PER CAPITA TOTAL EXPENDITURE MINUS EDUCATION CHANGE .............................................55
FIGURE 8.7 MEDIAN PERCENT CHANGE IN PER CAPITA TOTAL EXPENDITURES MINUS EDUCATION BY
GROWTH CATEGORY, 1990-2000........................................................................................................55
FIGURE 8.8 MEDIAN PERCENT CHANGE IN PER CAPITA TOTAL EXPENDITURES MINUS EDUCATION BY KIND
OF COMMUNITY, 1990-2000................................................................................................................56
FIGURE 8.9 MEDIAN PERCENT CHANGE IN PER CAPITA TOTAL EXPENDITURES MINUS EDUCATION BY KIND
OF COMMUNITY AND GROWTH RATE, 1990-2000...............................................................................56
FIGURE 8.10 PER CAPITA GENERAL GOVERNMENT EXPENDITURE CHANGE................................................57
FIGURE 8.11 MEDIAN PERCENT CHANGE IN PER CAPITA GENERAL GOVERNMENT EXPENDITURES BY
GROWTH CATEGORY, 1990-2000........................................................................................................57
FIGURE 8.12 MEDIAN PERCENT CHANGE IN PER CAPITA GENERAL GOVERNMENT EXPENDITURES BY KIND
OF COMMUNITY, 1990-2000................................................................................................................58
FIGURE 8.13 PER CAPITA POLICE EXPENDITURE CHANGE ...........................................................................59
FIGURE 8.14 MEDIAN PERCENT CHANGE IN PER CAPITA POLICE EXPENDITURES BY GROWTH CATEGORY,
1990-2000...........................................................................................................................................59
FIGURE 8.15 MEDIAN PERCENT CHANGE IN PER CAPITA POLICE EXPENDITURES BY KIND OF COMMUNITY,
1990-2000...........................................................................................................................................60
FIGURE 8.16 PER CAPITA FIRE EXPENDITURE CHANGE ..............................................................................61
FIGURE 8.17 MEDIAN PERCENT CHANGE IN PER CAPITA FIRE EXPENDITURES BY GROWTH CATEGORY,
1990-2000...........................................................................................................................................61
FIGURE 8.18 MEDIAN PERCENT CHANGE IN PER CAPITA FIRE EXPENDITURES BY KIND OF COMMUNITY,
1990-2000...........................................................................................................................................62
FIGURE 8.19 PER CAPITA OTHER PUBLIC SAFETY EXPENDITURE CHANGE .................................................63
FIGURE 8.20 MEDIAN PERCENT CHANGE IN PER CAPITA OTHER PUBLIC SAFETY EXPENDITURES BY
GROWTH CATEGORY, 1990-2000........................................................................................................63
FIGURE 8.21 MEDIAN PERCENT CHANGE IN PER CAPITA OTHER PUBLIC SAFETY EXPENDITURES BY KIND
OF COMMUNITY, 1990-2000................................................................................................................64
FIGURE 8.22 PER CAPITA EDUCATION EXPENDITURE CHANGE..................................................................65
FIGURE 8.23 MEDIAN PERCENT CHANGE IN PER CAPITA EDUCATION EXPENDITURES BY GROWTH
CATEGORY, 1990-2000.......................................................................................................................65
FIGURE 8.24 MEDIAN PERCENT CHANGE IN PER CAPITA EDUCATION EXPENDITURES BY KIND OF
COMMUNITY, 1990-2000.....................................................................................................................66
FIGURE 8.25 PER CAPITA PUBLIC WORKS/HIGHWAY EXPENDITURE CHANGE ............................................67
FIGURE 8.26 MEDIAN PERCENT CHANGE IN PER CAPITA PUBLIC WORKS/HIGHWAY EXPENDITURES BY
GROWTH CATEGORY, 1990-2000........................................................................................................67
FIGURE 8.27 MEDIAN PERCENT CHANGE IN PER CAPITA PUBLIC WORKS/HIGHWAY EXPENDITURES BY
KIND OF COMMUNITY, 1990-2000.......................................................................................................68
FIGURE 8.28 PER CAPITA OTHER PUBLIC WORKS EXPENDITURE CHANGE .................................................69
FIGURE 8.29 MEDIAN PERCENT CHANGE IN PER CAPITA OTHER PUBLIC WORKS EXPENDITURES BY
GROWTH CATEGORY, 1990-2000........................................................................................................69
FIGURE 8.30 MEDIAN PERCENT CHANGE IN PER CAPITA OTHER PUBLIC WORKS EXPENDITURES BY KIND
OF COMMUNITY, 1990-2000................................................................................................................70
FIGURE 8.31 PER CAPITA HEALTH & WELFARE EXPENDITURE CHANGE ....................................................71
FIGURE 8.32 MEDIAN PERCENT CHANGE IN PER CAPITA HEALTH & WELFARE EXPENDITURES BY GROWTH
CATEGORY, 1990-2000.......................................................................................................................71
FIGURE 8.33 MEDIAN PERCENT CHANGE IN PER CAPITA HEALTH & WELFARE EXPENDITURES BY KIND OF
COMMUNITY, 1990-2000.....................................................................................................................72
FIGURE 8.34 PER CAPITA CULTURE & RECREATION EXPENDITURE CHANGE .............................................73
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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FIGURE 8.35 MEDIAN PERCENT CHANGE IN PER CAPITA CULTURE & RECREATION EXPENDITURES BY
GROWTH CATEGORY, 1990-2000........................................................................................................73
FIGURE 8.36 MEDIAN PERCENT CHANGE IN PER CAPITA CULTURE & RECREATION EXPENDITURES BY KIND
OF COMMUNITY, 1990-2000................................................................................................................74
FIGURE 8.37 PER CAPITA DEBT SERVICE EXPENDITURE CHANGE ..............................................................75
FIGURE 8.38 MEDIAN PERCENT CHANGE IN PER CAPITA DEBT SERVICE EXPENDITURES BY GROWTH
CATEGORY, 1990-2000.......................................................................................................................75
FIGURE 8.39 MEDIAN PERCENT CHANGE IN PER CAPITA DEBT SERVICE EXPENDITURES BY KIND OF
COMMUNITY, 1990-2000.....................................................................................................................76
FIGURE 8.40 PER CAPITA FIXED COSTS EXPENDITURE CHANGE .................................................................77
FIGURE 8.41 MEDIAN PERCENT CHANGE IN PER CAPITA FIXED COSTS EXPENDITURES BY GROWTH
CATEGORY, 1990-2000.......................................................................................................................77
FIGURE 8.42 MEDIAN PERCENT CHANGE IN PER CAPITA FIXED COSTS EXPENDITURES BY KIND OF
COMMUNITY, 1990-2000.....................................................................................................................78
FIGURE 8.43 PER CAPITA INTERGOVERNMENTAL EXPENDITURE CHANGE..................................................79
FIGURE 8.44 MEDIAN PERCENT CHANGE IN PER CAPITA INTERGOVERNMENTAL EXPENDITURES BY
GROWTH CATEGORY, 1990-2000........................................................................................................79
FIGURE 8.45 MEDIAN PERCENT CHANGE IN PER CAPITA INTERGOVERNMENTAL EXPENDITURES BY KIND OF
COMMUNITY, 1990-2000.....................................................................................................................80
FIGURE 8.46 PER CAPITA “OTHER” EXPENDITURE CHANGE .......................................................................81
FIGURE 8.47 MEDIAN PERCENT CHANGE IN PER CAPITA “OTHER” EXPENDITURES BY GROWTH CATEGORY,
1990-2000...........................................................................................................................................81
FIGURE 8.48 MEDIAN PERCENT CHANGE IN PER CAPITA “OTHER” EXPENDITURES BY KIND OF
COMMUNITY, 1990-2000.....................................................................................................................82
FIGURE 9.1 POPULATION CHANGE BY PER-CAPITA TOTAL REVENUE CHANGE 1990-2000........................84
FIGURE 9.2 PERCENT MEDIAN REVENUE GROWTH BY POPULATION GROWTH CATEGORY, 1990-2000......84
FIGURE 9.3 PER CAPITA TOTAL REVENUE CHANGE ....................................................................................87
FIGURE 9.4 MEDIAN PERCENT CHANGE IN PER CAPITA TOTAL REVENUES BY GROWTH CATEGORY, 1990-
2000....................................................................................................................................................87
FIGURE 9.5 MEDIAN PERCENT CHANGE IN PER CAPITA TOTAL REVENUES BY KIND OF COMMUNITY, 1990-
2000....................................................................................................................................................88
FIGURE 9.6 MEDIAN PERCENT CHANGE IN PER CAPITA TOTAL REVENUES BY KIND OF COMMUNITY AND
GROWTH RATE, 1990-2000.................................................................................................................88
FIGURE 9.7 PER CAPITA TAX LEVY REVENUE CHANGE ..............................................................................89
FIGURE 9.8 MEDIAN PERCENT CHANGE IN PER CAPITA TAX LEVY REVENUES BY GROWTH CATEGORY,
1990-2000...........................................................................................................................................89
FIGURE 9.9 MEDIAN PERCENT CHANGE IN PER CAPITA TAX LEVY REVENUES BY KIND OF COMMUNITY,
1990-2000...........................................................................................................................................90
FIGURE 9.10 PER CAPITA STATE AID REVENUE CHANGE ...........................................................................91
FIGURE 9.11 MEDIAN PERCENT CHANGE IN PER CAPITA STATE AID REVENUES BY GROWTH CATEGORY,
1990-2000...........................................................................................................................................91
FIGURE 9.12 MEDIAN PERCENT CHANGE IN PER CAPITA STATE AID REVENUES BY KIND OF COMMUNITY,
1990-2000...........................................................................................................................................92
FIGURE 9.13 PER CAPITA LOCAL RECEIPT REVENUE CHANGE ...................................................................93
FIGURE 9.14 MEDIAN PERCENT CHANGE IN PER CAPITA LOCAL RECEIPT REVENUES BY GROWTH
CATEGORY, 1990-2000.......................................................................................................................93
FIGURE 9.15 MEDIAN PERCENT CHANGE IN PER CAPITA LOCAL RECEIPT REVENUES BY KIND OF
COMMUNITY, 1990-2000.....................................................................................................................94
FIGURE 9.16 PER CAPITA “OTHER” REVENUE CHANGE ..............................................................................95
FIGURE 9.17 MEDIAN PERCENT CHANGE IN PER CAPITA “OTHER” REVENUES BY GROWTH CATEGORY,
1990-2000...........................................................................................................................................95
FIGURE 9.18 MEDIAN PERCENT CHANGE IN PER CAPITA “OTHER” REVENUES BY KIND OF COMMUNITY,
1990-2000...........................................................................................................................................96
FIGURE 9.19 MEDIAN RELIANCE ON REVENUE TYPE BY GROWTH CATEGORY, 2000.................................97
FIGURE 9.20 MEDIAN RELIANCE ON REVENUE TYPE BY KIND OF COMMUNITY, 2000...............................97
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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FIGURE 9.21 MEDIAN RELIANCE ON TAX LEVY REVENUE BY KIND OF COMMUNITY AND GROWTH
CATEGORY, 2000.................................................................................................................................98
FIGURE 10.1 EDUCATION AID BY TYPE (IN MILLIONS) ADJUSTED FOR INFLATION, 1990-2002..................99
FIGURE 10.2 NON-SCHOOL AID BY TYPE (IN MILLIONS) ADJUSTED FOR INFLATION, 1990-2002.............100
FIGURE 10.3 MEDIAN EDUCATION AID GROWTH PERCENT PER CAPITA BY GROWTH CATEGORY ...........102
FIGURE 10.4 MEDIAN EDUCATION AID GROWTH IN DOLLARS PER CAPITA BY GROWTH CATEGORY, 2000
..........................................................................................................................................................102
FIGURE 10.5 MEDIAN PERCENT EDUCATION AID GROWTH PER CAPITA BY KIND OF COMMUNITY .........103
FIGURE 10.6 MEDIAN DOLLAR EDUCATION AID GROWTH PER CAPITA BY KIND OF COMMUNITY, 2000.103
FIGURE 10.7 MEDIAN PERCENT NON-SCHOOL AID GROWTH PER CAPITA BY GROWTH CATEGORY ........104
FIGURE 10.8 MEDIAN NON-SCHOOL AID GROWTH IN DOLLARS PER CAPITA BY GROWTH CATEGORY, 2000
..........................................................................................................................................................104
FIGURE 10.9 MEDIAN PERCENT NON-SCHOOL AID GROWTH PER CAPITA BY KIND OF COMMUNITY ......105
FIGURE 10.10 MEDIAN DOLLAR NON-SCHOOL AID GROWTH PER CAPITA BY KIND OF COMMUNITY, 2000
..........................................................................................................................................................105
FIGURE 11.1................................................................................................................................................107
FIGURE 11.2 GROWTH IN PER-PUPIL EXPENDITURES 1990-2000 BY NET AVERAGE MEMBERSHIP GROWTH
QUINTILE (IN REAL DOLLARS)..........................................................................................................111
FIGURE 11.3 CHANGE IN STATE AID PER PUPIL, TOTAL COST PER PUPIL, AND NET AVERAGE MEMBERSHIP
OF PUPILS BY GROWTH IN NET AVERAGE MEMBERSHIP OF PUPILS, 1990-2000................................111
FIGURE 11.4 MEDIAN STATE AID AND COST PER PUPIL IN 2000...............................................................112
FIGURE 11.5 CHANGE IN STATE AID PER PUPIL, TOTAL COST PER PUPIL, AND NET AVERAGE MEMBERSHIP
OF PUPILS BY KIND OF COMMUNITY, 1990-2000..............................................................................113
FIGURE 11.6 MEDIAN STATE AID AND COST PER PUPIL IN 2000...............................................................113
FIGURE 11.7 MEDIAN COST PER PUPIL BY KIND OF COMMUNITY AND PUPIL GROWTH CATEGORY IN 2000
..........................................................................................................................................................114
FIGURE 11.8 MEDIAN STATE AID PER PUPIL BY KIND OF COMMUNITY AND PUPIL GROWTH CATEGORY IN
2000..................................................................................................................................................114
FIGURE 12.1 TAX LEVY CHANGE BY POPULATION GROWTH CATEGORY, 1990-2000..............................123
FIGURE 12.2 TAX LEVY CHANGE BY KIND OF COMMUNITY, 1990-2000..................................................123
FIGURE 12.3 PER CAPITA TAX LEVY CHANGE BY POPULATION GROWTH CATEGORY, 1990-2000...........124
FIGURE 12.4 PER CAPITA TAX LEVY CHANGE BY KIND OF COMMUNITY, 1990-2000..............................124
FIGURE 12.5 PER CAPITA TAX LEVY BY POPULATION GROWTH CATEGORY, FY2000.............................125
FIGURE 12.6 PER CAPITA TAX LEVY BY KIND OF COMMUNITY, 1990-2000.............................................125
FIGURE 12.7 PER PARCEL TAX LEVY CHANGE BY POPULATION GROWTH CATEGORY, 1990-2000..........126
FIGURE 12.8 PER PARCEL TAX LEVY CHANGE BY POPULATION GROWTH CATEGORY, 1990-2000..........126
FIGURE 12.9 MEDIAN OF AVERAGE PER PARCEL TAX LEVY BY POPULATION GROWTH CATEGORY, 2000
..........................................................................................................................................................127
FIGURE 12.10 MEDIAN OF AVERAGE PER PARCEL TAX LEVY BY POPULATION GROWTH CATEGORY, 2000
..........................................................................................................................................................127
FIGURE 12.11 LEVY LIMITS AS A PERCENT OF CURRENT LEVY BY POPULATION GROWTH CATEGORY,
1990-2000.........................................................................................................................................128
FIGURE 12.12 LEVY LIMITS AS A PERCENT OF CURRENT LEVY BY KIND OF COMMUNITY, 1990-2000....128
FIGURE 12.13 CURRENT LEVIES AS A PERCENT OF TOTAL ASSESSMENT BY POPULATION GROWTH
CATEGORY, 1990-2000.....................................................................................................................129
FIGURE 12.14 CURRENT LEVIES AS A PERCENT OF TOTAL ASSESSMENT BY KIND OF COMMUNITY, 1990-
2000..................................................................................................................................................129
FIGURE 12.15 MEDIAN PER CAPITA DEBT CHANGE BY POPULATION GROWTH CATEGORY, 1990-2000..130
FIGURE 12.16 MEDIAN PER CAPITA DEBT CHANGE BY KIND OF COMMUNITY, 1990-2000.....................130
FIGURE 12.17 MEDIAN PER CAPITA DEBT IN DOLLARS BY POPULATION GROWTH CATEGORY, 2000.....131
FIGURE 12.18 MEDIAN PER CAPITA DEBT IN DOLLARS BY KIND OF COMMUNITY, 2000.........................131
FIGURE 13.1 REGIONAL DEFINITIONS FOR MASSACHUSETTS (MASSACHUSETTS BENCHMARKS)..............133
FIGURE 13.2 MEDIAN EXPENDITURE CHANGE PER CAPITA BY BENCHMARKS REGION, 1990-2000..........135
FIGURE 13.3 MEDIAN REVENUE CHANGE PER CAPITA BY BENCHMARKS REGION, 1990-2000................136
FIGURE 13.4 MEDIAN STATE AID CHANGE BY BENCHMARKS REGION, 1990-2000..................................136
FIGURE 13.5 MEDIAN STATE AID PER CAPITA BY BENCHMARKS REGION, 2000......................................137
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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FIGURE 13.6 MEDIAN CHANGE IN INTEGRATED OPERATING COSTS PER PUPIL BY BENCHMARKS REGION,
1990-2000.........................................................................................................................................137
FIGURE 13.7 MEDIAN INTEGRATED OPERATING COSTS AND SCHOOL AID PER PUPIL BY BENCHMARKS
REGION, 2000....................................................................................................................................138
FIGURE 13.8 LEVY LIMITS AS PERCENT OF CURRENT LEVY BY BENCHMARKS REGION, 1990-2000........138
FIGURE 13.9 CURRENT LEVIES AS A PERCENT OF TOTAL ASSESSMENTS BY BENCHMARKS REGION, 1990-
2000..................................................................................................................................................139
FIGURE 13.10 TOTAL POPULATION BY BENCHMARKS REGION, 1990-2000..............................................139
FIGURE 13.11 TOTAL NON-SEASONAL VACANT HOUSING UNITS BY BENCHMARKS REGION, 1990-2000140
FIGURE 13.12 TOTAL HOUSING UNITS IN THE PREVIOUS FIVE YEARS BY BENCHMARKS REGION, 1990-
2000..................................................................................................................................................140
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
9 UMASS DONAHUE INSTITUTE
The Fiscal Impact of New Housing Development in Massachusetts:
A Review of Current Theory and Municipal Data
UMass Donahue Institute
Office of the President
University of Massachusetts
1 Introduction
There has been a great deal of news recently about the rising prices of housing in
Massachusetts. There have been 380 articles in the past year on housing in the Boston
Globe alone, according to Lexis-Nexis, and most of these discuss the lack of,
construction of, or need for affordable housing units.1 One of the reasons cited for the
increase in housing prices is the lack of construction of new housing units in all price
ranges.
Virtually all new housing construction in Massachusetts is controlled and
regulated at the local level. Within the confines of state law, municipalities have the right
to adopt zoning and subdivision regulations as they see fit, and to issue or deny building
permits and subdivision certifications. Recently, there have been some criticisms of how
many municipalities in Massachusetts make these decisions, as they are seen as
supporting efforts to curb development instead of regulate it more effectively. The major
reasons used by municipalities to deny new construction within their borders are
environmental protection, historic preservation, traffic control, and fiscal impact. The
fiscal impacts of new development are an important factor for municipalities to consider,
but these impacts are seen to greatly affect the development of affordable housing, where
fiscal impact models are sometimes used as a basis for denying development rights based
on the costs of the development to municipal services, especially school systems.
Because of this, the UMass Donahue Institute (UMDI) was asked by the Citizen’s
Housing and Planning Association (CHAPA) to analyze the fiscal impact of housing on
municipalities, including examining the assumptions that lie behind the models relied
upon by many cities and towns. As the Per Capita Multiplier Method is the most
common method used for fiscal impact analysis,2 this report concentrates on examining
its accuracy and the underlying trends that would affect its ability to create reliable
forecasts. We examined the methods used to forecast population in newly-constructed
housing units and how well they work in practice. In addition, we examined trends in
municipal expenditures and revenues for trends in municipal finance that could affect
fiscal impact analysis. It is hoped that such an examination will make it easier for
municipalities and developers to understand the real impacts of development and will
1 According to a September 17, 2002 search for headlines containing “Housing” in the Lexis-Nexis
Academic Universe Database.
2 Burchell, R., D. Listokin and W. Dolphin, 1985. The New Practitioner’s Guide to Fiscal Impact
Analysis, New Jersey, Center for Urban Policy Research. P. 6.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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create a more robust model of the effects of development on municipalities within
Massachusetts.
1.1 Methods and Data Sources
After reviewing the available data and the various methods used to perform fiscal
impact analysis in communities across the nation, we chose the methods and data to
research our study topic that we felt were the most straightforward to understand and
relied on publicly available and accurate data.
UMDI generally concentrated on studying the time period between 1990 and
2000. We chose this time period because it fits with accurate population data released
from the decennial Census of Population by the U.S. Bureau of the Census. We also used
data from the Division of Local Services of the Massachusetts Department of Revenue
(DLS). Both of these data sources are publicly available and can easily be downloaded
from Internet web sites. Unfortunately, while Census data was available from 1980, not
all the DLS data was available that far in the past. We also used data from the
Massachusetts Department of Education whenever it was applicable. A chart of the data
sources and types is below.
Table 1.1 Data Source Table
Data Provider Data Title Data Type Time Period
U.S. Bureau of the
Census
Decennial Census Population Change
Housing Unit Type
and Change
1990 and 2000
Dept. of Revenue,
Division of Local
Services
Municipal Data Bank Municipal Revenues
by Source
Municipal
Expenditures by
Type
State Aid by Type
Property and Parcel
Tax Data
Other Various Data
1990 through
2000
Dept. of Education School District Data School Population
School Budget Data
1990 through
2000
• Inflation Adjustment: All dollar amounts were standardized to Year 2000
values using the Consumer Price Index from the Bureau of Labor Statistics. The
index for “all urban consumers” for the United States was used for adjustment
(U.S. All items, 1982-84=100 - CUUR0000SA0). Dollar amounts for years after
2000 were brought back to 2000 values for comparison purposes.
• Per capita values for municipal expenditures and revenues were calculated using
relatively accurate municipal population data from both the 1990 and 2000
decennial Census’ sample count data files (STF/SF3 data).
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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• Per pupil values were calculated by using data from the Department of Education
on Net Average Membership of Pupils, which apportions the number of pupils
sent to a public school system by municipality even if that municipality is part of
a regional school system.
• Population estimates were calculated using 1990 Public Use Microdata Sample
(PUMS) data from the 5 percent sample. Unfortunately, 2000 Census PUMS data
was not available in time to be used in this report
• Population Growth Ranks were calculated by assigning a quintile rank to each
municipality in Massachusetts based on their population growth from 1990 to
2000. These quintiles (or fifths) each contain 70 municipalities, except for the
third, which contains 71. The quintiles were automatically generated in SPSS.
• Kind of Community codes were created in 1985 for the Massachusetts
Department of Education. While they are now somewhat dated, they still describe
most towns fairly accurately and have become a coding system that is used by
various agencies in Massachusetts, and are therefore a standard that can be used
for comparison purposes.
All data was processed in either SPSS v.11.0 or Microsoft Excel 2000. All maps
were generated using Maptitude v.4.5 using data from the Massachusetts Geographic
Information Systems Agency (MassGIS) of the Executive Office of Environmental
Affairs.
1.2 Limitations of the Study
As in any research, this study has certain limitations brought on by the availability
of data and the types of analyses used.
• Because accurate population data was not available for years other than 1990 and
2000, the comparison gap for the Per Capita Multiplier Method analysis is 10
years. This may exaggerate differences or changes in per capita spending over a
smaller gap of one year. However, year-to-year comparisons using estimated
population data between 1990 and 2000 also showed differences in per capita
spending that were sometimes quite significant. This was partly attributable to
the fall in state aid and tax receipts after the recession of the early 1990’s and the
subsequent rebounding of both local and state revenues.
• Using graphical representations of median data points, as are used to represent
most trends in per capita expenditures and revenues, can mask the wide variation
in each data category. For clarity, we decided not to use error bars or other
graphical representations of the range of data points within each category. The
reader should always be aware, however, that the median is simply the middle
measurement of a wide range of data and is only used to illustrate trends.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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• We chose to separate municipalities into five different rankings (or quintiles)
based on their population growth rates from 1990 to 2000 to make analysis easier.
These quintiles consist of equal numbers of municipalities (70 in each, except for
the third which contains 71). This was done to make a grouping scheme that was
easy for the reader to understand. We believe that using five equal categories, or
“bins,” to contain data avoids the problem of “binning.” Binning occurs when the
size of the “bins” chosen for analysis are so large that they obscure relevant data.
Even so, some data is lost whenever any categorizations of this sort are made.
• Using 1990 Public Use Microdata Sample (PUMS) data misses any changes in
household composition that may have occurred from 1990 to 2000. Also, PUMS
data from the 5 percent sample is aggregated into regions called Public Use
Microdata Areas, or PUMAs, which must contain at least 100,000 persons. These
large areas do not allow town-by-town estimates of household populations that
could be used for forecasting purposes.
• This study did not examine capital expenditures (for durable items such as new
buildings, etc.). While these are an important part of municipal spending, we felt
that achieving a concise and clear answer on the changes over time in capital
expenditures would be difficult due to the details of public financing that we
would need to collect. Instead, we chose to use debt service payments as a proxy
for capital expenditure changes as the data was easily available and is directly
related to new capital expenditures.
• State aid for education is given directly to regional school systems and does not
appear in either the expenditures or revenues of the municipalities that belong to
them. Conversely, state aid for education does appear in the budgets of
municipalities that operate their own school systems. Therefore, comparing
education expenditures on a municipal level is difficult. While we could have
apportioned state aid to regional school systems to the member towns per pupil,
we felt that this would be an artificial solution. Instead, we have separated out the
effects of state aid for education in certain charts throughout the report so that
municipalities can be compared without education expenditures and aid included.
The reader should be aware, however, that the total amount of state education aid
disbursed to school systems is not included in this analysis.
• Due to the individual nature of cities and towns in Massachusetts, it is difficult to
compare them to each other. The decisions made in each municipality affect how
monies are collected and expended, and each municipality has different priorities
that stem from the different wishes and needs of its citizens. Therefore, any
comparison that tries to fit these various municipalities into simple categories will
miss these individual variations.
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2 Demographic and Municipal Revenue Trends
2.1 Demographic Changes 1990-2000:
With the recent release of the year 2000 decennial census, we now have a reliable
data source for performing recent comparisons over time. According to the census, the
population of the Commonwealth grew 5.5 percent between 1990 and 2000.3 This is
much less than the 13 percent growth seen for the nation overall. However, number of
households in Massachusetts increased almost 9 percent, compared to almost 15 percent
for the nation. The number of housing units lagged this growth, however. Massachusetts
saw an increase of 6 percent between 1990 and 2000, while the nation saw an increase of
slightly over 13 percent. To ensure that there are enough housing units to house all
newly-created households, these growth rates should be roughly equal.
Most of the growth in households has been housed in existing vacant units. In
1990, the vacancy rate for housing in Massachusetts was 10 percent in total, with a 1.7
percent owned vacancy rate and a 6.9 percent rented vacancy rate. In 2000, the vacancy
rate was 6.8 percent of all housing, with rental housing reporting a 3.5 percent vacancy
rate and owner-occupied housing reporting vacancy rates of less than one percent.4
As shows in figure 2.1, most of the growth in population and housing in
Massachusetts occurred within the 495 beltway. There was also a significant amount of
growth in the Springfield Metropolitan Statistical Area (MSA). Except for this area,
there was little numeric growth in the western part of Massachusetts. However, looking
at percentage growth rates shows a different story. Many of the highest percentage
growth rates occurred in small towns that would be considered “exurban”, or in regions
beyond suburbs that were often rural in nature. Even so, there were still high percentage
growth rates within the 495 beltway and in southeastern Massachusetts. Figure two
illustrates these trends. Measuring both numeric growth rates and percentage growth can
best find where the stresses of growth are being felt.
3 http://quickfacts.census.gov/qfd/states/25000.html
4 U.S. Census Bureau, 1990 and 2000 Decennial Censuses
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 2.1 Growth in the Total Number of Housing Units By Town, 1990 to 2000
Source: Decennial Census, 1990 and 2000, U.S. Census Bureau
Figure 2.2 Percentage Growth in Housing Units By Town, 1990 to 2000
Source: Decennial Census, 1990 and 2000, U.S. Census Bureau
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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2.2 Out-Migration from Massachusetts5
One of the trends observed over the last ten years is the out-migration of people
from Massachusetts to other states, most notably to other New England states. While
definitive data on the reasons for this movement is hard to obtain, many researchers
believe that people are moving partly due to the high cost of housing in Massachusetts.
A June 2002 article in Massachusetts Benchmarks analyzed migration patterns from 2000
through 2001 using data from the Internal Revenue Service (IRS).
The IRS tracks migration using tax return data. These data show that almost
184,000 people left the Commonwealth in 2000, including 45,000 to neighboring New
England states (see Figure Two). Nearly 139,000 moved out of New England entirely.6
Relocation patterns suggest that reasons for out-migration may include more affordable
housing, better job opportunities, and retirement.
Figure 2.3 Migration Patterns of Massachusetts Residents
Counties Where 1,000 persons or More Moved From 2000-01
Source: Internal Revenue Service, 2000-2001 County Migration Data
5 Portions of this section previously appeared in Massachusetts Benchmarks, June 2002 and are
used with permission.
6 During the same period, 166,000 people moved into the state.
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Major trends within New England include migration from the Boston Metro and
Northeast regions to Southern New Hampshire and Southern Maine, from the Southeast
region to Rhode Island, and from Central and Western Massachusetts to Northern
Connecticut. Of the top five destination states, two are immediate neighbors. The top 20
destinations include the other five New England states, with New Hampshire being the
most popular. Outside of New England, major destinations include many Florida
counties, Southern California, and New York City and its surrounding areas.
While we do not know at this point who has left the state or who is contemplating
doing so, previous migration research has been very consistent in finding that young,
higher skilled people are more likely to migrate. Domestic migrants (distinguished from
international migrants), generally in their twenties and thirties, have higher educational
and income characteristics than the overall population. As our economy triggers an
outflow of migrants due to labor market conditions, it is likely that we will be losing the
best-educated members of our young labor force.
As the Massachusetts workforce ages, the ability of regions to accommodate
younger workers and their families becomes an increasingly critical economic issue.
Throughout Massachusetts, high-tech as well as manufacturing businesses rely on
younger workers to fill the job ranks. Without a steady influx of new talent, these
industries face a declining labor force. Other fields, including teaching, nursing and
public safety all rely on young workers to balance attrition due to retirements. Regions
across the state are already experiencing serious shortages of nurses and teacher shortages
have, increasingly, become a concern.7 But in spite of the need to encourage young
workers to stay and work in Massachusetts, housing in many parts of the state is
unaffordable to younger workers and their families.
The ongoing challenge of workforce retention in Massachusetts and the critical role
the availability of affordable housing plays in meeting this challenge underscores the
importance of accurately estimating the costs and the benefits of housing development.
Developing accurate estimates, however, requires a thorough understanding of the fiscal
environment in which Massachusetts cities and towns operate. In the pages that follow
we examine historical trends in municipal revenues and expenditures in an effort to better
understand the fiscal context in which municipalities make their development decisions.
2.3 Municipal Revenue Trends 1981-2001
There were some significant changes in municipal finance trends between 1990 and
2000. The Massachusetts Education Reform Act (MERA) took effect in 1994 and
changed the way that schools are funded, many cities and towns saw significant growth
in population and tax base, and Proposition 2½, which became effective in 1982 (20
years before the writing of this report) continued to have a significant effect on revenues.
7 A Statement by David P. Driscoll, Massachusetts Commissioner of Education, On Teacher
Shortages, August 15, 2001. Massachusetts Department of Education, 2001 News Archive.
www.doe.mass.edu/news/archive01
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Figure 2.4 Revenue Percentage by Type, 1981-2001 (Adjusted for 2000)
0% 20% 40%60%80%100%
2001 2000 1999 1998 1997 1996 1995 1994
1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982
1981
Tax Levy State Aid Local Receipts All Other
Source: Division of Local Services, Dept. of Revenue
Figure 2.5 Revenue by Type, 1981-2001 (in millions), Adjusted for 2000
$- $2,000 $4,000 $6,000$8,000 $10,000$12,000$14,000 $16,000
2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981
Tax Levy State Aid Local Receipts All Other Source: Division of Local Services, Dept. of Revenue
Proposition 2½ Effective Date
Mass. Education Reform Act Effective Date
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Because of MERA and Proposition 2½, the Commonwealth stepped up the amount of aid
that it distributed to cities and towns, in part using funds from the successful state lottery.
Between 1981 (the year before prop 2 ½ was effective) and 2001, the mix of
revenue sources for municipalities has shifted many times. Revenues are divided into
four different sources by the Division of Local Services: Tax levies (collected from
property taxes), state aid, local revenues (such as vehicle excise taxes), and all other
sources. In 1981, the average percentage of tax levy revenues as compared to total
revenues was 59 percent. By 1988, this percentage had dropped to 46 percent, due
mostly to an increase in state aid from 20 percent in 1981 to 31 percent in 1998. By
1993, the reliance on property tax it had risen again to 53 percent of total revenues, and
state aid had decreased to 22 percent. By 2001, state aid had increased to 28 percent and
the tax levy had decreased again to 49 percent.
The situation is similar when viewing revenues in dollars, but there are some
important differences. For example, except for a decrease in property tax revenues
collected by municipalities from 1982 to 1984 to bring them into line with the
requirements of Proposition 2 ½, actual dollar amounts of property taxes collected have
increased in real dollars every year since then. Total municipal revenues have increased
38 percent in real dollars in the last 20 years, to $14.8 billion in 2001. There have been
waves in municipal revenue collections over time, with an overall loss of $816 million
from 1981 to 1982 due to Proposition 2 ½, a subsequent increase, fueled mostly by state
aid but also by increasing property tax collections up until 1989, and a revenue decrease
due to the collapse of the “Massachusetts Miracle.” Municipalities did not regain the total
1989 revenue level until 1995, and they have now far surpassed it, with an increase of
almost $2.5 billion in total yearly revenue recorded from 1995 to 2001. State aid saw its
lowest post-1990 dollar level in 992, with $2.5 billion reported as revenue by
municipalities, and its highest in 2001, with $4.1 billion reported (again, all revenues are
in year 2000 dollars). In all, total municipal revenues grew 16 percent from 1990 to
2000.
2.4 Geographic Patterns of Municipal Revenues, 1990 to 2000
The geographic patterns of change in municipal revenue track population changes
somewhat, but there are some differences. Looking at the change in total revenue in real
dollars by municipality, illustrated in figure three. definitely shows many of the same
trends seen in total population change illustrated in figure one. Most of the growth has
occurred in the Greater Boston region of the state, with a smaller bet definite pattern of
growth in the Springfield MSA. A closer look at the two maps shows that, while some
towns demonstrate similar trends in population and revenue growth, other towns can vary
widely. One example is North Adams, which posted negative population growth from
1990 to 2000 but added revenues at a high rate during that same time period. There are
similar discrepancies in the percentage growth maps, with some towns showing
population growth without much revenue growth or vice-versa.
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Figure 2.6 Growth in Total Revenues in Adjusted Dollars By Town, 1990 to 2000
091827
Miles
Dollar Change
-100 Million to 0
0 to 1 Million
1 Million to 5 Million
5 Million to 15 Million
15 Million to 65 Million
Source: Division of Local Services, Dept. of Revenue
Figure 2.7 Percentage Growth in Total Revenues in Adjusted Dollars By Town,
1990 to 2000
091827
Miles
Percent Change
-29 to 0 Percent
0 to 25 Percent
25 to 50 Percent
50 to 75 Percent
75 to 90 Percent
Source: Division of Local Services, Dept. of Revenue
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2.5 Demographic and Municipal Revenue Trends: Conclusions
While the population of Massachusetts has grown 5.5 percent between 1990 and
2000, the number of households grew almost 9 percent. This implies that household sizes
are getting smaller and that less households have dependents. Even though the number of
households grew almost 9 percent, the supply of housing units needed to house them
increased only 6 percent, decreasing Massachusetts’ supply of vacant housing stock.
While this is good news for unit owners and sellers, it means that there are less available
units for buyers or renters. Most new housing unit construction occurred in the eastern
portion of the Commonwealth, with a small but significant amount occurring in the
Springfield MSA.
Municipal revenue trends tend to follow population and housing construction
trends, but with deviations. Growth rates in municipal revenue do not exactly track
growth rates in population and housing units. The total revenues that all cities and towns
collected increased 16 percent from 1990 to 2000, but the mix of revenue types varies
from year to year due to state cutbacks during the recession of the early part of the
1990’s. By 2001, cities and towns were, on average, collecting 49 percent of their
revenues from real property taxes and 28 percent from state aid.
Geographic trends in revenue collection generally match those in housing unit
construction and population growth, but there are some differences. Some slow-growing
municipalities (like North Adams) showed revenue growth, and some faster growing
municipalities showed lagging revenue growth. The reasons for this will be examined
later in this report.
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3 Demographic Analysis and Municipal Rankings
3.1 Introduction
It is difficult to analyze 351 separate cities and towns, each with different
population mixes, governmental structures, and taxpayer priorities, without creating a
system that groups them together based on similarities, or that divides them based on
measurable differences. Because this is a difficult task, we chose to use two separate
systems for grouping municipalities: population growth rankings based on the growth
rate of each municipality’s population from 1990 to 2000, and the Kind of Community
coding developed by the Commonwealth of Massachusetts and used by various agencies
to describe municipal characteristics. Using these two different systems allowed us to
“triangulate” the findings for each categorizing scheme and obtain a clearer view of what
changes occurred in municipal finance between 1990 and 2000.
3.2 Ranking Municipalities by Population Growth
To make analysis easier, each of the 351 cities and towns were assigned a rank
from one (very low growth) to five (very high growth) based on their percentage
population growth rate. Each fifth, or quintile, rank contained 70 towns, except for the
third, which contained 71. The median growth rate was taken for each quintile. We used
these rankings to examine per capita growth rates of certain general fund expenditure
categories (including general government, police, fire, public utilities, fixed costs and
debt service), as well as total expenditures. The median growth rates of these categories
were compared to the median population growth rate for each population ranking to find
overall trends.
There was a great difference between the lowest and highest growth rates
calculated for each municipality. Table 6.2 shows the lowest, highest, average and
median growth rates for each category. Note that the total range of population growth for
municipalities in Massachusetts was from –51.5% to 71.1%. These towns are
aberrations, as Harvard reported the largest percent population decrease due to the
closing of Fort Devens and Aquinnah (Gay Head) reported the highest increase most
likely because of its small size (344 persons in 2000). These outliers do not substantially
affect the analysis.
Table 3.1 Population Growth Rates by Quintile Rank, 1990-2000
Growth Rank Very Low (1) Low (2) Medium (3) High (4) Very High (5)
Lowest -51.5% 1.07% 4.7% 9.8% 16.8%
Highest 1.05% 4.6% 9.7% 16.6% 71.1%
Average -4.2% 2.9% 6.9% 12.9% 27.8%
Median -2.8% 2.9% 6.4% 12.6% 25.1%
Source: 1990 and 2000 Decennial Census, U.S. Census Bureau; Author Calculations
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3.3 Ranking Municipalities by Kind of Community
However, ranking municipalities by growth rate may not be the most accurate
method for finding patterns, because each town has very different characteristics.
Another method for ranking towns has been devised for the Department of Education.
This method uses certain criteria to separate towns into seven different categories:
Urbanized Center (1); Economically Developed Suburb (2); Growth Community (3);
Residential Suburb (4); Rural Economic Center (5); Small Rural Community (6); and
Resort, Retirement, and Artistic Community (7). All 351 towns fall into one of these
codes, and there is a rough parity between the numbers of towns within each code.
DOR/DLS refers to this as the “Kind of Community,” or KOC code. While this coding
system may be somewhat dated, as it was created in the mid-1980’s, our analysis showed
to our satisfaction that it was still basically sound.
Looking at growth rates within each KOC code begins to make the picture clearer.
The number of municipalities that fall within each community type and growth ranking
are listed in table 3.2. Note that Chelsea is only municipality rated as “very high”
population growth in the “Urban Center” community type, and it is not possible to draw
conclusions based on only one data measurement.
Table 3.2 Number of Municipalities Within Categories
Growth
Rate
Urbanized
Center
Economically
Developed
Suburb
Growth
Community
Residential
Suburb
Rural
Economic
Center
Small Rural
Community
Resort,
Retirement,
and Artistic
Very Low 17 15 5 4 15 4 10
Low 14 17 6 7 19 5 2
Medium 10 10 5 12 21 8 5
High 3 9 15 18 3 14 8
Very High 1 8 15 12 3 15 16
Total 45 59 46 53 61 46 41
Source: Division of Local Services, Dept. of Revenue; Author Calculations
3.4 Demographic Analysis of the Categories
To better explain the different categories used in this report to aggregate
municipalities for analysis, we have chosen some key demographic indicators that can be
used to understand better each population growth category and kind of community. The
following figures compare general population in 1990 and 2000, the total number of
housing units, the number of vacant housing units, single family detached housing units
to all housing units in 2000, new housing units built in the five years previous to the
decennial Census (1985 to March 1990 vs. 1995 to March 2000), and the net average
membership of pupils in 1990 and 2000. We present this selected information to help the
reader understand the demographics of each category used in the previous analysis.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
23 UMASS DONAHUE INSTITUTE
3.5 Conclusions
Most of the following figures are self-explanatory. Generally, population grew in
all categories used in this analysis except for the “very low” growth cities and towns,
which saw an decline in their aggregated population (see figures 3.1 and 3.2). The largest
number of people lived in “low” growth cities and towns, while the largest populations
by kind of community were in Urban Centers and Economically Developed Suburbs.
Not surprisingly, the largest number of housing units were also in these
categories. Viewing rented vs. owned units shows that the vast majority of those are in
Urban Centers, which are the only kind of community where the number of rented
housing units exceeds owned housing units (see figure 3.3). There are surprisingly few
renter occupied units in certain community types, such as Small Rural communities and
Resort, Retirement, and Artistic communities.
Figures 3.5 and 3.6 show the decline in vacant non-seasonal housing units
between 1990 and 2000 in all categories of analysis, especially in “low” growth
municipalities and in Urban Centers. Note that while this analysis excludes seasonal
housing units, the vast majority of all vacant units in many types of communities
(especially Resort, Retirement, and Artistic) were for seasonal use.
Another interesting housing statistic is the number of single family detached units,
which are lowest as a proportion of all units in “very low” and “low” growth
municipalities and highest in the “very high” growth municipalities (see figure 3.7). Not
surprisingly, Urban Centers have the smallest proportion of this housing type (see figure
3.8), while suburban and rural community types have high proportions of single family
detached housing.
There was a very large drop in the number of housing units built in a five year
period before each Census was taken. Comparing the period of 1985 to March 1990 with
1995 to March 2000 shows that all categories of analysis showed declines, especially
lower growth rate municipalities (see figure 3.9) and Urban Centers (see figure 3.10).
Finally, school populations have increased in all categories of analysis, but the
“low” growth municipalities still educate the largest number of students, as do Urban
Centers and Economically Developed Suburbs (see figures 3.11 and 3.12).
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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3.6 Total Population by Category
Figure 3.1 Total Population by Population Growth Category, 1990-2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
To
t
a
l
P
o
p
u
l
a
t
i
o
n
2,500,000
2,000,000
1,500,000
1,000,000
500,000
0
1990
2000
Source: Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 3.2 Total Population by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Total Population
3,000,0002,000,0001,000,0000
1990
2000
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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3.7 Total Housing Units by Category
Figure 3.3 Total Owner- and Renter-Occupied Housing Units by Population
Growth Category, 2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
To
t
a
l
H
o
u
s
i
n
g
U
n
i
t
s
(
0
0
0
'
s
)
500
400
300
200
100
0
Owner
Occupied
Renter
Occupied
Source: Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 3.4 Total Owner- and Renter-Occupied Housing Units by Kind of
Community, 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Total Housing Units (000's)
7006005004003002001000
Owner
Occupied
Renter
Occupied
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
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3.8 Total Vacant Non-Seasonal Housing Units by Category
Figure 3.5 Total Vacant Non-Seasonal Housing Units by Population Growth
Category, 1990-2000
Growth Category 1990-2000
Very HighHighMediumLowVery LowTo
t
a
l
N
o
n
-
S
e
a
s
o
n
a
l
V
a
c
a
n
t
H
o
u
s
i
n
g
U
n
i
t
s
(
0
0
0
'
s
)
60
50
40
30
20
10
0
1990
2000
Source: Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 3.6 Total Vacant Non-Seasonal Housing Units by Kind of Community,
1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Total Non-Seasonal Vacant Housing Units (000's)
80706050403020100
1990
2000
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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3.9 Total Single Family Detached Housing Units by Category
Figure 3.7 Total Single Family Detached Housing Units by Population Growth
Category, 2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
To
t
a
l
H
o
u
s
i
n
g
U
n
i
t
s
1,000
800
600
400
200
0
Total
Single Family
Detached
Source: Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 3.8 Total Single Family Detached Housing Units by Kind of Community,
2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Total Housing Units (000's)
1,2501,0007505002500
Total
Single Family
Detached
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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3.10 Total New Housing Units Built by Category
Figure 3.9 Total New Housing Units in the Previous Five Years by Population
Growth Category, 1990-2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
To
t
a
l
H
o
u
s
i
n
g
U
n
i
t
s
B
u
i
l
t
(
0
0
0
'
s
)
60
50
40
30
20
10
0
1985 to
March 1990
1995 to
March 2000
Source: Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 3.10 Total New Housing Units in the Previous Five Years by Kind of
Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Total Housing Units Built (000's)
80706050403020100
1985 to
March 1990
1995 to
March 2000
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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3.11 School Population by Category
Figure 3.11 Total Net Average Membership of Pupils by Population Growth
Category, 1990-2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
To
t
a
l
N
e
t
A
v
e
r
a
g
e
M
e
m
b
e
r
h
i
p
o
f
P
u
p
i
l
s
350,000
300,000
250,000
200,000
150,000
100,000
50,000
0
1990
2000
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 3.12 Total Net Average Membership of Pupils by Kind of Community,
1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Total Net Average Membership of Pupils
400,000300,000200,000100,0000
1990
2000
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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4 Fiscal Impact Analysis in Massachusetts
The Division of Community Services (now the Division of Municipal
Development) at the Department of Housing and Community Development (DHCD)
created in the past a document entitled the “Growth Impact Handbook.” This document
examines methods for calculating the fiscal impact of growth on municipalities, and is
intended to help cities and towns understand and plan for the effects of development.
Unfortunately, this handbook has not been updated in several years. In April 2002, the
Executive Office of Environmental Affairs (EOEA), as a part of their Community
Preservation Initiative, created a Fiscal Impact Tool (FIT) based in part on the old DHCD
manual. This tool is a computer program that contains a great deal of information about
town financing, as well as data from models used to estimate the population and other
impacts of new development. We examined these resources to discover what fiscal
impact theories and data sources were used, so that we could then examine those theories
and data.
4.1 The DHCD Growth Impact Handbook
DHCD collected information to allow cities and towns to predict a variety of
fiscal effects of new development on municipal services and published it in one reference
book. Data are available to assist municipalities in estimating school and municipal
operating and capital costs, property tax revenues, and local aid impacts. Some of this
information is taken directly from state formulas for local aid, property tax rates, and
other factors that are relatively easy to calculate accurately. Others, however, are not
easy (or are impossible) to calculate, and therefore rely on models to create estimates of
future impacts.
The DHCD handbook lists various methods for assessing the fiscal impact of
development. These methods are listed in table one. The most common approaches are
the first two in the chart, cost averaging and marginal costs (DHCD). While cost
averaging is much easier than marginal cost analysis, it is much less accurate. Cost
averaging is often based on a per capita calculation of municipal costs.
Table 4.1 Methods for Assessing Municipal Fiscal Impacts
Method Comment
Cost Averaging Usually a Per Capita multiplier
Marginal Cost Analysis More realistic, but more difficult
Service Standard Based on staff needs and costs for new development
Comparable City Looks at similar, larger communities
Proportional Valuation Assigns costs based on proportional share of local real estate
valuations
Employee Anticipation Based on anticipated needs of new employees
Source: Growth Impact Handbook, DHCD, p.12-13.
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4.1.1 Population Estimate Model
To forecast school population, the most important (and expensive) component of
growth, DHCD used a model created by Dr. Robert Burchell of Rutgers University in the
mid-1980’s. This model, described in the 1985 book “The New Practitioner’s Guide to
Fiscal Impact Analysis,” is derived from data collected by the U.S. Census Bureau. It
estimates the number of persons living in certain types and sizes of housing. These
estimates are meant to be applied to the number of housing units to be constructed by
type, and an overall estimate of the additional population that can be expected is
generated for a new development.
4.1.2 School Cost Models
After the additional school-aged population emanating from new development is
estimated, the cost of educating that population needs to be calculated. DHCD
recommends that operating and capital costs be estimated separately. To estimate school
operating costs, DHCD recommends using the per-capita cost averaging method. After
subtracting the estimated number of children who will attend private school (from 7 to
12% of all children, or the reported percentage from the Dept. of Education for the
community), the average school cost per pupil, either from the DoEd or calculated from
the school operating budget, is multiplied by the estimated new public school students.
DHCD recommends a different process for estimating school capital costs. This
is a real cost estimating method for new school construction that takes the
Commonwealth’s share of the cost (through the School Building Assistance Program)
into account. One area where estimates can be used is in the school construction cost if
the municipality does not have an exact cost for the construction. These estimates are
based on DoEd School Building Assistance Bureau numbers, and they use one set of cost
estimates for the entire Commonwealth.
4.1.3 Municipal Cost Models
The DHCD manual offers a great deal of information on using the per-capita
averaging method for estimating municipal operating costs. A detailed worksheet that
contains categories for all major budget areas, and these areas match the standard budget
categories from the Division of Local Services of the Massachusetts Department of
Revenue. The calculation formula is simply the current cost of a service divided by the
number of current residents, then multiplied by estimated new residents.
Estimating new municipal capital costs is carried out in a similar fashion as
estimating school capital costs. The type and size of the new or upgraded capital asset is
estimated using various formulas, and the cost is estimated in a similar fashion.
Construction costs for buildings are estimated using data from R. S. Means, a publisher
that prints the “Means Assemblies Cost Data” publication for estimating building costs.
This data can be adjusted for different regions of the Commonwealth to create a more
accurate picture of construction costs.
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4.1.4 Local Aid and Property Tax Models
Calculating changes to Chapter 70 education aid and lottery distributions is
complex and ever-changing, and the DHCD document does not go into it in detail.
However, a simple method for estimating whether Chapter 70 school aid will increase,
created by John Mullin, is included. Essentially, if the increase in the number of school
aged children is higher than the increase in total property value, Chapter 70 aid will
increase. If the reverse is true, it will decrease. As for lottery aid, disbursements depend
on the amount of money collected by the lottery, the change in property valuation
compared to the state, and change in population. Unlike local aid, calculating changes in
property tax revenues is straightforward. The value of new property is known, as is the
amount of money taken in by local impact fees (if applicable).
4.1.5 Quality of Life Issues (QOL)
DHCD included a discussion on quality of life issues that could be taken into
account when doing an analysis. To measure quality of life, DHCD uses the examples of
a Planners Advisory Service (PAS) memo from August 1996 (Defining and Measuring
Quality of Life), the community report card compiled on itself by the City of
Jacksonville, Florida, the Oregon Benchmarks Program, and the criteria used by the
Places Rated Almanac.
The PAS memo from 1996 was written by Michelle Gregory and published in the
August 1, 1996 issue of the Planner’s Advisory Service Memo. The abstract from the
APA web site states that “Quality of life assessment involves identifying the elements of
living that a community wants to preserve, enhance, or achieve. This Memo discusses the
types of indicators used in quality of life evaluations, including the Places Rated Almanac
and the Lomax Index.”8
The method developed by the City of Jacksonville used many elements to compile
its “community report card,” including education levels, economic information, public
safety data, measures of water and air quality, health statistics, the social environment
(racial harmony, individual opportunity, etc.), government policies and leadership, and
cultural and recreational resources. Oregon went even farther than Jacksonville, creating
a state measure of QOL called the Oregon Benchmarks Program that uses 259 different
standards. Finally, the national-level Places Rated Almanac uses indicators on cost of
living, employment, housing, transportation, education, health care, crime, culture,
recreation, and climate to come up with its QOL index. Unfortunately, there is no real
information on applying this information to a cost-benefit analysis available.
8 Planner’s Advisory Service, American Planning Association, http://www.planning.org.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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4.1.6 Marginal Cost Analysis
The DHCD manual also included a discussion on using the marginal cost method
of fiscal impact analysis. However, the discussion is biased towards the expensive side
of marginal cost analysis, which occurs when the capacity of a service is exceeded and
new capacity has to be developed. Some discussion of issues that are related to “smart
growth” strategies is also included, but due to the age of this manual, there are no “smart
growth” methods or information resources available.
4.1.7 Appendices
The report ends with a large section of appendices that contain multiple types of
information, including school size recommendations, school reimbursement percentages
by town, estimates of per-capita expenditures and employment by type per town size for
Massachusetts, trip generation rates for certain land uses, water use estimates and water
system costs, and road construction costs. This is a very useful section, even though
much of the information contained within it is only briefly discussed in the text.
4.2 The EOEA Massachusetts Fiscal Impact Tool (FIT)
In 2002, the Executive Office of Environmental Affairs (EOEA) has created a
computer model for forecasting growth impacts that is partly based on information from
the DHCD Handbook, called the Massachusetts Fiscal Impact Tool (FIT). It is a custom
computer program that runs on Windows machines. It has a database containing much of
the information presented in the DHCD report appendices as well as complete municipal
finance data and “cherry sheet” (local aid) data. Analyses can be generated for
residential or commercial development. The concept is that, after going through all of the
screens and inputting all of the relevant information, a realistic estimate of the direct
fiscal impact will be created and exported as a Microsoft Excel spreadsheet.
The MA-FIT model comes pre-programmed with municipal expenditure and
population data to allow use of the per-capita average cost analysis method. It also
allows actual data to be entered into the program for users who want to perform a
marginal cost analysis. It is also possible to use a mixture of methods depending upon
the availability of data to the user. While it is not really possible to estimate future
lottery, chapter 70, and other state aid programs, the program does predict future aid
impacts using the per capita multiplier method.
4.3 Conclusions
While both the DHCD handbook and the EOEA FIT tool are useful to those who
want to calculate the fiscal impacts of new development, they are based on the standard
impact methods developed years ago, most notably the Per Capita Multiplier Method.
Therefore, their accuracy is reliant on the accuracy of the underlying models. These
models are discussed in the next chapter.
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5 Fiscal Impact Theory
There are many different models for predicting fiscal impacts, and the most
commonly-used are laid out in the book The New Practitioner’s Guide to Fiscal Impact
Analysis by Robert Burchell, David Listokin and William Dolphin and published by the
Center for Urban Policy Research at Rutgers University.9 This is considered one of the
important works on the subject and Professor Burchell is nationally-recognized as a
leading expert in the fiscal impact analysis field.
The New Practitioner’s Guide to Fiscal Impact Analysis identifies six different
methods for conducting fiscal impact analysis. There are two basic approaches to
municipal cost analysis: average costing and marginal costing. Average costs are simply
per-unit costs, whether the unit is a person, a household, or some other measure. In
impact analysis, the new number of units (often people) is multiplied by the average cost
per unit for a particular service and added to the existing budget. This is one of the most
common methods for estimating fiscal impacts.10 Marginal cost analysis uses an analysis
of the current capacity and infrastructure of a community to discover whether certain
types of new development will rely on existing capacities or will “push” certain services
over a “threshold” that will require new, expensive capital investments.11
Table 5.1 Comparison of Average Costing vs. Marginal Costing Methods12
Advantages Disadvantages
Average
Costing
Easier data gathering;
In the long term, estimates of growth
impact similar to Marginal Costing
Does not consider existing excess or
deficient capacity that might exist for
particular services or the possibility that a
new development might fall at the
threshold level, calling for major new
capital construction to accommodate
increased growth
Marginal
Costing
Takes potential deficiencies into
account;
Careful analysis of existing
demand/supply relationships for local
governmental and school services;
In the long term, estimates of growth
impact similar to Average Costing
Getting the data takes more time and
effort
Analysis can be more complex and
require more input from different
departments or people
The three fiscal impact analysis techniques based on the average costing approach
are the Per Capita Multiplier method, the Service Standard method, and the Proportional
Evaluation method. Of these, the first two methods are used for residential land uses and
the third is used for non-residential land uses. The three marginal costing techniques are
9 Burchell, R., Listokin, D., and Dolphin, R. The New Practitioner’s Guide to Fiscal Impact
Analysis. New Brunswick, NJ, Center for Urban Policy Research 1985.
10 Burchell, R., Listokin, D., and Dolphin, R. The New Practitioner’s Guide to Fiscal Impact
Analysis. New Brunswick, NJ, Center for Urban Policy Research 1985, p 6.
11 Ibid., p.6.
12 Ibid., pp 6-38.
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the Case Study method, the Comparable City method, and the Employment Anticipation
method. Of these, only the case study method is applicable for both residential and
nonresidential land uses, while the others are useful for one or the other, respectively.13
Because this report focuses on the fiscal impact of housing development, we will only
review forecasting methods that are useful for residential development.
5.1 Per Capita Multiplier Method
The per-capita multiplier method is one of the most common methods used to
forecast the fiscal impacts of residential development. It is very simple to use, as all that
is required is a population forecast and current budget numbers. This method takes the
cost of a municipal service and divides it by the current population to calculate the per
capita cost of the service. The new population forecast that would result from the new
development is multiplied by the per-capita value and added to the current budget,
creating the municipal expenditure forecast. A similar process is used to estimate new
taxes, and the results are compared to each other to discover if the new development will
have a positive or negative impact on development.
According to Burchell, there are certain assumptions built into this model. Over
the long term, it is assumed that current average operating costs per capita and per student
are the best estimates of future operating costs caused by growth. In addition, current
local levels and types of city services are the most accurate indicators of future service
levels, as it is assumed that they will continue on the same scale in the future. In
addition, the current composition of the population and the population contributing to
future costs are assumed to be similar enough to cause average costs to be correct, and
current expenditures by various city departments are assumed to stay constant and can be
used to estimate how future expenditures will be allocated.14
Because of the assumptions built into this model, it will not work accurately if
there are changes in levels of city service per capita, population composition, or
municipal cost structures. This approach to estimating the fiscal impacts of residential
development is widely used in Massachusetts. A test of this model using actual
municipal expenditure data revealed that when the predicted fiscal impacts generated by
this model are compared to the actual financial experiences of Massachusetts cities and
towns, it is evident that, for most municipalities in Massachusetts, the predictive validity
of the per-capita model is quite limited. An examination of how this model works in
Massachusetts is presented in the next chapter.
5.2 Service Standard Method
This is another average costing method that uses data from the U.S. Census of
Governments on employment levels and the capacity of facilities for similarly-sized and
located municipalities and school districts. The Service Standard method calculates the
needed number of new employees by city service type to serve the proposed new
13 Ibid., pp 6-38.
14 Burchell, R., Listokin, D., and Dolphin, R. The New Practitioner’s Guide to Fiscal Impact
Analysis. New Brunswick, NJ, Center for Urban Policy Research 1985, pp 9-10.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
36 UMASS DONAHUE INSTITUTE
development. This analysis calculates expenditure per employee for each type of service,
as well as the annual capital expenditure for that service. The annual capital expenditure
is derived from data collected in the Census of Governments.
The assumptions in this method are similar to the per capita method above, that
there will be no change in current service levels over time. In addition, This method
assumes that similarly-sized and located cities will have similar expenditures and
employment patterns. It has the advantage of being very detailed and relatively simple to
use. Its disadvantage is that calculating fiscal impacts by using cost multipliers derived
from aggregated data is likely to over- or underestimate actual expenditures for individual
cities or towns.15
5.3 Case Study Method
This is also known generally as the marginal cost method, although there is more
than one way of measuring marginal costs. It relies on collecting a large amount of
information specific to the city or town being analyzed. It’s main focus is to discover
which services possess excess capacity that can be inexpensively used to deal with new
population, and which services are at capacity and require new capital investments to
serve additional development. The excess capacity or needed capital investments are
factored into the cost estimates for new employment or other non-capital outlays for
servicing the new population and the result is the fiscal impact of the new development.
Like the previous methods, this is based on certain assumptions. It assumes that
each municipality has a different mix of service capacities which would have a
significant affect on the ability to accurately forecast growth, that calculating excess
capacity of need for new capital outlays is the most accurate way of calculating future
costs, that actual local service levels differ from national averages and are a better
measure of future service levels, and that local decision-makers are the most familiar with
the needs and capacities of municipal services.
Unlike the averaging methods, the case study method requires a great deal of
research to discover actual local costs and capital needs. It also requires input from many
local decision-makers and department heads. However, it is considered to be the most
accurate method for forecasting the costs associated with new development.16 As the FIT
computer model allows users to use the case study model in their analyses, we feel that
this should be the recommended method for using this tool.
5.4 Comparable City Method
The Comparable City method is a type of marginal costing method that includes
some aspects of averaging methods. The averaging comes from the creation of
multipliers calculated from the U.S. Census of Governments. The multipliers are based
on growth rates and community size. The method projects increases or decreases in
15 Ibid, pp. 22-23.
16 Burchell, R., Listokin, D., and Dolphin, R. The New Practitioner’s Guide to Fiscal Impact
Analysis. New Brunswick, NJ, Center for Urban Policy Research 1985, pp. 15-16.
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37 UMASS DONAHUE INSTITUTE
future gross expenditures for the five basic municipal services (general government,
public safety, public works, health and welfare, recreation and culture) and is based on
the assumption that municipalities of similar size and with similar growth rates will have
similar changes in their municipal costs by category.
Unlike the case study method above, the reliance on average multipliers limits
accuracy. If local costs for services are different than the average multipliers would
suggest, there may be under- or overestimation of costs Therefore, this method is only
somewhat better than pure cost averaging methods.17
5.5 Population Estimates and School Costs
One of the major factors affecting municipal cost forecasting is population
change. It is difficult to know how many people are going to be attracted by new
development, and what mix of people that population will contain. To forecast new
population, Burchell et al. created a model based on U.S. Census data from the 1980
Census of Population. It estimates the number of persons living in certain types and sizes
of housing by two categories: overall new population and school-aged children. This
model is discussed in detail later in this report.
The data on school-aged children created from the population forecasting model
Burchell and his colleagues created is then used to estimate school operating costs. The
generally-accepted method for doing this is to use the per-capita cost averaging method.
After subtracting the estimated number of children who will attend private school, the
average school cost per pupil is multiplied by the estimated number of new public school
students.
5.6 Conclusions
The many different methods for fiscal impact analysis can be reduced to two basic
types: averaging and marginal. While the averaging method has the potential to be
inaccurate, it is much more likely to be used in practice. This can lead to serious over-
estimation or underestimation of future costs. A much more accurate method, according
to the literature, is to use marginal costing methods such as the Case Study method.
While marginal costing requires a more extensive data collection and analysis process to
create a prediction of future costs, it is also much more likely to be accurate.
17 Ibid, pp.23-24.
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38 UMASS DONAHUE INSTITUTE
6 Test of the Pre-Capita Forecasting Method
One of the major forecasting methods used to infer the effect of new development
is the per-capita method. In this method, costs of municipal services are averaged across
the population and any additional members of the population are expected to increase
costs in that proportion. For example, if a town of 1000 persons spends $100,000 on
police serves, the per-capita cost of this service is calculated to be $100 per person. If 10
new people move into the town, this forecasting method would predict that the police
budget would become $101,000. This method is often used because it is easy to apply
and simple to understand. Unlike the marginal cost method, it requires little research into
the state of services in a municipality and the actual effects that growth could have.
UMDI decided to test the per-capita forecasting method to determine whether it was a
valid tool for estimating the impacts of development on a municipality. We tested in the
simplest manner possible: by analyzing real changes over time and comparing them to
projected changes using the per-capita method.
6.1 Real versus Predicted Municipal Expenditures, 1990-2000
The simplest test of the per-capita model is to choose two points in time with
reliable data and compare the earlier point to the later point using the model. We have
chosen to compare 1990 and 2000, as accurate Census data is available for these years.
We calculated the per-capita expenditures in 1990 for certain budget categories as
reported by each municipality to the Division of Local Services of the Dept. of Revenue
(DLS), calculated the population difference between 1990 and 2000, multiplied the per-
capita 1990 calculation by the population change, and adjusted the 1990 dollar figures for
inflation to create an estimated year 2000 budget figure. We then simply compared the
actual year 2000 budget figures to the estimated year 2000 figure and measured the
difference.
Chart one graphs the difference between the general fund expenditures that would
be expected using the per-capita forecasting method and the actual expenditures. Results
were sorted from lowest (a –86% difference) to highest (a +52% difference). A negative
result means that the actual general fund expenditures were lower than the projected
expenditures using the per-capita method of impact analysis. The sorted results imply that
there is a trend of the per-capita method underestimating spending. In fact, 79, or 23% of
all municipalities showed inflation-adjusted increases in their expenditures that were
below what would be predicted by the model. The vast majority showed increases that
were higher than would be expected, many much higher. The median estimation error
was 9% of expenditures, and 63 towns showed under-estimations of 20% or more. Since
the MA-FIT model from EOEA allows the program user to use this method, it is possible
for the results of an analysis to be based on estimates that either over- or underestimate
future impacts.
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Figure 6.1 Percent Difference Between Actual and Predicted General Fund
Expenditures, 1990-2000
-100%
-80%
-60%
-40%
-20%
0%
20%
40%
60%
Sorted Observations
Pe
r
c
e
n
t
D
i
f
f
e
r
e
n
c
e
Source: U.S. Census Bureau, 1990 and 2000 Decennial Census
Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Examining education expenditures showed similar trends. Because of the effects
of the Massachusetts Education Reform Act (MERA), over 80% of all municipalities
spent more on education than would have been predicted by the per-capita model. Some
spent much more. Table one shows the ten municipalities with the most divergence from
the per-capita model’s expected results. Of the bottom 5 municipalities (the ones where
the cost was most overestimated) three are special cases, as all of their education costs in
2000 were paid directly by the Commonwealth to the school system, which means that
the municipality did not record the expenditure on their yearly balance sheet.
Figure 6.2 Percent Difference Between Actual and Predicted Educational
Expenditures, 1990-2000
-60%
-40%
-20%
0%
20%
40%
60%
80%
Sorted Observations
Pe
r
c
e
n
t
D
i
f
f
e
r
e
n
c
e
Source: U.S. Census Bureau, 1990 and 2000 Decennial Census
Division of Local Services, Mass. Dept. of Revenue
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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A caveat for this section is that not all education costs are reflected in the
education line item in a municipalities’ reported expenditures. For example, maintenance
on playing fields used by a school system may actually be funded through the parks and
recreation department, and certain items such as pension plans may also appear in other
areas of a municipal budget. More impotantly, some smaller towns that belong to
regional school districts will not have state aid appear in their town education budget, as
that aid goes to the school system. The reader is directed to chapter 11, which is
dedicated to examining educational funding, for a more detaile ddiscussion of education
funding measurement and issues.
To help control for the effect that MERA may be having on municipal finance, we
removed education expenditures identified by municipalities from the general fund
expenditure total and plotted them in chart three. This graph shows the data in a way that
would be expected for an averaging prediction method, as the curve crosses almost
exactly at the center point of the graph. Even so, the further away from the average a
town is, the more it is over- or under-estimated..
Figure 6.3 Percent Difference Between Actual and Predicted General Fund
Expenditures Minus Educational Expenditures, 1990-2000
-100%
-75%
-50%
-25%
0%
25%
50%
75%
Sorted Observations
Pe
r
c
e
n
t
D
i
f
f
e
r
e
n
c
e
Source: U.S. Census Bureau, 1990 and 2000 Decennial Census
Division of Local Services, Mass. Dept. of Revenue, 1990-2000
6.2 Estimation Errors by Community Rank and Type
The above analysis raises the question of which municipalities are overestimated,
which are underestimated, and which have the least or most estimation error. To remove
the potentially misleading effect of education expenditures, which are generally estimated
using data on the number of school children and not on the total population, this question
is examined using non-education expenditures only.
Tables 6.1 and 6.2 show the number of municiplaities in each of five categories
based on the amount of overestimation or underestimation of real non-school costs the
model predicted, based on population change from 1990 to 2000. The data show that
almost 19 percent of all municipalities had real budgets in 2000 that were more than 20
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
41 UMASS DONAHUE INSTITUTE
percent under their predicted costs based on the Per Capita Multiplier Method model and
using 1990 real budget information. In addition, over 14 percent had real budgets in 2000
that were more than 20 percent higher than the predicted budget based on 1990 data and
the model. The municiplaities are separated into their growth ranks (Table 6.1) and their
kinds of community (Table 6.2).
Table 6.1 Real Budget Versus Estimation Error of Municipal Budgets by
Population Growth Rank, 1990-2000 (with Percentages by Rank)
Growth Rate Under 20%
Under 5%
to 20%
Under 5% to
Over 4%
Over 5% to
Over 20% 21% and Over Total
Very Low 9 (12.9%) 7 (10.0%) 13 (18.6%) 21 (30.0%) 20 (28.6%) 70 (100%)
Low 15 (21.4%) 10 (14.3%)13 (18.6%) 24 (34.3%) 8 (11.4%) 70 (100%)
Medium 9 (12.7%) 19 (26.8%)20 (28.2%) 18 (25.4%) 5 (7.0%) 71 (100%)
High 13 (18.6%) 16 (22.9%)10 (14.3%) 20 (28.6%) 11 (15.7%) 70 (100%)
Very High 20 (28.6%) 14 (20.0%)12 (17.1%) 17 (24.3%) 7 (10.0%) 70 (100%)
Mass. 66 (18.8%) 66 (18.8%)68 (19.4%) 100 (28.5%)51 (14.5%) 351 (100%)
Source: U.S. Census Bureau, 1990 and 2000 Decennial Census
Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Table 6.2 Real Budget Versus Estimation Error of Municipal Budgets by Kind of
Community (with Percentages by Community Type)
Kind of
Community Under 20%
Under 5%
to 20%
Under 5% to
Over 4%
Over 5% to
Over 20%
21% and
Over Total
Urban Center 7 (15.6%) 10 (22.2%)11 (24.4%) 15 (33.3%) 2 (4.4%) 45 (100%)
Econ Dev Suburb 10 (16.9%) 16 (27.1%)12 (20.3%) 16 (27.1%) 5 (8.5%) 59 (100%)
Growth Comm 4 (8.7%) 11 (23.9%)9 (19.6%) 17 (37.0%) 5 (10.9%) 46 (100%)
Res Suburb 5 (9.4%) 6 (11.3%) 11 (20.8%) 20 (37.7%) 11 (20.8%) 53 (100%)
Rural Econ Ctr 15 (24.6%) 12 (19.7%)10 (16.4%) 11 (18.0%) 13 (21.3%) 61 (100%)
Small Rural 15 (32.6%) 7 (15.2%) 9 (19.6%) 10 (21.7%) 5 (10.9%) 46 (100%)
Resort Retirement 10 (24.4%) 4 (9.8%) 6 (14.6%) 11 (26.8%) 10 (24.4%) 41 (100%)
Massachusetts 66 (18.8%) 66 (18.8%)68 (19.4%) 100 (28.5%)51 (14.5%) 351 (100%)
Source: U.S. Census Bureau, 1990 and 2000 Decennial Census
Division of Local Services, Mass. Dept. of Revenue, 1990-2000
6.3 Year-by-Year Expenditure Changes
While accurate population data is not available on a year-by-year basis, it is
possible to track the yearly changes in expenditures for different expenditure types and
compare this to the population growth rate. Doing this on a municipal level shows that
changes in expenditures on a yearly basis have little to do with population change alone,
but are likely to be dependent on many factors.
Figures 6.4 and 6.5 show expenditures for different budget categories for two
different municipalities, Dedham and Rowley. Dedham is an economically developed
suburb (KOC 2) in the Boston Metro Benchmarks region whose population growth from
1990 to 2000 ranked in the “very low” category (Rank 1), and whose public school pupil
growth ranked in the “low” category (Rank 2). Dedham actually had a negative
population growth rate (-1.34 percent) from 1990 to 2000. Rowley is a growth
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
42 UMASS DONAHUE INSTITUTE
community (KOC 3) in the Northeast Benchmarks region whose population growth rate
and public school pupil growth ranked in the “very high” category (Rank 5). Rowley’s
population growth for the decade was 23.5 percent.
Figure 6.4 Selected Expenditures by Year for Dedham, Adjusted for Inflation
$0
$1,000,000
$2,000,000
$3,000,000
$4,000,000
$5,000,000
$6,000,000
$7,000,000
19901991199219931994199519961997199819992000
Year
Do
l
l
a
r
s
20,000
20,500
21,000
21,500
22,000
22,500
23,000
23,500
24,000
24,500
25,000
Po
p
u
l
a
t
i
o
n
General Government Police Fire Fixed Costs Population Linear (Population)
`
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Figure 6.5 Selected Expenditures by Year for Rowley, Adjusted for Inflation
$0
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
$700,000
$800,000
19901991199219931994199519961997199819992000
Year
D
o
l
l
a
r
s
4,000
4,200
4,400
4,600
4,800
5,000
5,200
5,400
5,600
Po
p
u
l
a
t
i
o
n
General Government Police Fire Fixed Costs Population Linear (Population) Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
As Figure 6.4 shows, Dedham had a slight decline in population with a relatively
steady expenditure amount for each of the four selected expenditure categories. In fact,
there were yearly variations in all of the selected categories, and while population went
down, by the end of the decase each category rose in real dollars between 3 percent and
33 percent. Compare this behavior to Rowley, which had much more year-to-year
volatility, whith the end of the decade seeing expenditure changes from –22 percent to
110 percent for different categories.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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6.4 Conclusions on the Per-Capita Forecasting Method
If the per-capita method of measuring the fiscal impact of additional population
on municipal budgets were accurate, we would expect to see the results of a test such as
this to show much less variation, or at least to show that the average result follows the
model. Unfortunately, the data we have generated show neither. While we are only
comparing two points in time (1990 and 2000), we are comparing these two points for all
351 cities and towns in the Commonwealth. The consistency of the results implies that
the method is faulty. While we chose not to publish all of the graphs that we generated in
this analysis, it should be noted that we analyzed many different line items (general
government, police, fire, debt service, and fixed costs) and saw similar results.
In addition, there is inconsistency within town budgets themselves when using
this test. A single town may show vastly different results among the selected budgetary
line items that were analyzed, with overall expenditures showing a 20% overestimation,
fire costs showing a 30% overestimation, education costs showing a 5% overestimation,
etc. In other words, this method does not even work consistently within a single
municipal budget. This is because changes in expenditures over time are affected by
many different internal and external pressures, only one of which is population change.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
44 UMASS DONAHUE INSTITUTE
7 Population Forecasting
One of the criticisms of fiscal impact forecasting tools relate to population
projection techniques. Many people rely on the tables created by Burchell et. al from
1980 Census data and published in The New Practitioner’s Guide to Fiscal Impact
Analysis. Unfortunately, this population projection data is now very out of date and is
not specific to Massachusetts, which may mean that it is affected by different population
patterns in the other New England states. The New Practitioner’s Guide’s calculations
for household composition in the New England states are contained in Table A.1 in
Appendix A.
There are three potential problems with using Burchell’s model today. First, the
data used to create the model is now 22 years old. Second, the larger geographic region
that it covers means that Massachusetts-specific trends may be missed. Third, the lack of
detail on the number of bedrooms in certain housing types may mask the population
differences seen in practice in larger units. As the proper type of Census 2000 data is not
yet available, UMDI used data from the 1990 Decennial Census of Population and
Housing for Massachusetts to create a more localized and up-to-date estimate.18
This estimate was developed by first calculating the average total population and
the average population of school aged children (defined as children between the ages of 5
and 17) of each census-defined type of housing.. Categories of housing types were pre-
defined by the Census dataset as being single family detached, single family attached,
various sized multi-family buildings (from 2 to “50 or more” units), mobile homes, and
“other.” The number of bedrooms recorded ranges from none to “5 or more.” For the
purpose of this analysis, the various multifamily building sizes were broken out
somewhat differently than the data in Table A.1. We separated apartments using Census
categories into 2-4 units buildings, 5-9 unit buildings, 10-19 unit buildings, 20-49 unit
buildings, and 50 or more unit buildings instead of combining apartments into “garden”
and “high-rise” categories. (These categories were collapsed into 2-4 unit buildings and
5+ unit buildings for regional comparisons due to small sample sizes.)
7.1 Differences Between the Models
A comparison between the New Practitioner’s Guide tables and the tables for
newly-constructed housing units (Table A.4) in Massachusetts reveals some important
differences. For example, the 1980 New England PUMS data predicts that there will be
2.417 persons and 0.243 school-aged persons in each 2-bedroom single-family detached
house, while the 1990 Massachusetts PUMS data predicts 2.325 persons and 0.248
18 The Bureau of the Census creates the Public Use Microdata Sample, or PUMS, from census
questionnaires. A percentage of answered “long form” questionnaires (either one or five percent) are
selected from the total for a state and aggregated by Public Use Microdata Areas (PUMAs). A PUMA
must contain at least a certain amount of people (either 400,000 for one percent or 100,000 for five percent
sample as a way of protecting the confidentiality. Approximate sample sizes for Massachusetts are 122
thousand households for the five-percent sample and 25 thousand for the one-percent sample.
http://www.census.gov/geo/puma/puma2000.html
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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school-aged persons in this type of house. The relative accuracy of this estimate did not
hold up when larger houses were examined.
For a four-bedroom single family detached home, the New Practitioner’s model
predicts 4.141 persons and 1.470 school-aged children, while the Census data for
Massachusetts predicts 3.578 persons and 0.817 school-aged children For a development
of 100 four-bedroom homes, the New Practitioner’s model would over-estimate the new
population by 56 persons, a relevant number when using per-capita fiscal impact
forecasting methods. For school-aged children, the model over-estimates 65 children.
For 100 three-bedroom homes, the over-estimation would be 41 persons and 28 school-
aged children, while for 100 2-bedroom homes there would only be a 6 person
overestimation and a 1 child underestimation.
In addition, an analysis of regional Census data indicates that the Rutgers model
overestimates the number of new residents and school-age children that accompany new
residential development even more in certain regions of the Commonwealth. For
example, in the Berkshires region of the state a 100 unit three-bedroom single-family
detached development would have, on average, 196 persons (1.9555 persons per unit), or
a predicted difference of 59 persons. However, the more recent 1990 Census data for
Massachusetts also shows that, for units in multi-unit buildings, the Rutgers model
underestimates both general population and school-aged children.
7.2 Housing Units by Value
While the New Practitioner’s model selected housing units constructed from
1975 to 1980 to create a population forecasting model, UMDI decided to analyze both
new units and households who recently moved into any housing unit. We did this
because moving into a new housing unit is usually caused by a “life cycle change” for a
household (new children, retirement, etc), and it may not matter if the housing unit is
newly constructed or not. According to the 1990 PUMS manual, a recent mover is a
household who has moved within the 5 years before the Census was taken.19 Therefore,
in 1990 the questionnaire asked where the residents lived in 1985. We further subdivided
recent movers into owners and renters and sorted them by housing value to see if there
were any important differences. These results of these analyses can be found in tables
A.4 and A.5 in Appendix A.
Comparing tables A.4 and A.5 to the Rutgers data contained in Table A.1 shows
less of a divergence from the New Practitioner’s model, but also some differences. Most
important is that there seems to be less school-aged children in more expensive homes,
whether those homes are owned or rented. This pattern is evident across all types of
housing units. Generally, detached structures such as single-family homes and mobile
homes show equal or lesser average populations using Massachusetts-only data, but
attached and multi-unit homes tend to show greater average populations. In all, the tables
contained in Appendix A outline the average population by housing unit type, tenure and
value calculated from 1990 PUMS data. These data confirm what housing specialists that
19 1990 PUMS Manual, U.S. Bureau of the Census
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
46 UMASS DONAHUE INSTITUTE
we queried suspected: that there is a definite increase in the number of school-aged
children in units of three bedrooms or larger, and there are, on average, more school-aged
children in housing units of lesser value than there are in housing units of greater value.
7.3 Aggregation Bias (or the Ecological Fallacy)
Aggregation bias is caused by a mismatch in the scale of data available for
analysis and the geographic area or population being studied.20 It is sometimes called the
“ecological fallacy,” which refers to drawing erroneous conclusions from ecological
inferences about individual behavior based on aggregate data.21 Whenever data drawn
from a large geographic area is used to predict the behavior, demographics, or outcomes
of a small geographic area, the results are subject to aggregation bias.
Aggregation bias in estimations can be minimized by using care in developing
those estimations. Two researchers who were trying to create a constant-quality price
index (CQI) for multi-family housing found that apparent inconsistencies in the accepted
methods for creating these indeces were explained by examining the available data and
basing the index on the data that created the best analysis, which turned out to be the
square footage of the multifamily unit.22 Other research has examined using statistical
methods to factor out errors using statistical models. However, those models and
methods are beyond the scope of this paper.
Generally, any researcher using a household population model based on
aggregated data over a wide geogaphic area to predict the population of a few households
in a specific municipality opens themselves up to two different forms of aggregation bias.
First, the assumption that households living in a certain form of housing in a particular
town have the same composition as all households in that housing type in a large
geographic area ignores spatial differences in household composition. Second, assuming
that the composition of one specific household can be predicted by the average
population of all hosueholds ignores the effect of non-random factors on the population
forecasting model’s assumptions. In other words, the messy reality of families and
households has a way of undermining the best-laid forecasting models of social scientists.
7.4 Conclusions
The problems with creating any population forecasting method are that it uses
average data to forecast specific outcomes and it assumes that patterns that occurred in
the past will continue to occur in the future. Unfortunately, both of these problems can
introduce inaccuracy into the forecast. Average data for a group of states, one state, or
even a part of a state will miss important trends that may be occurring on a municipal
20 Smith, T. 2001. Aggregation Bias in Maximum Likelihood Estimation of Spatial Autoregressive
Processes, Philadelphia, Dept. of Systems Engineering, University of Philadelphia, p. 2.
21 Freedman, D. 1999. Ecological Inference and the Ecological Fallacy, Technical Report No. 549,
Berkeley, Dept. of Statistics, University of California, p. 1.
22 Guttery, R, and C. F. Sirmans. 1998. Aggregation Bias in Price Indices for Multi-Family Rental
Properties, Journal of Real Estate Research, v.15, n.3, p. 323.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
47 UMASS DONAHUE INSTITUTE
level. Also, household composition has been changing in Massachusetts, with single
family households and smaller households becoming more prevalent.23
There are additional issues with the geographic specificity of these models. For
example, the Rutgers model used data for the entire New England region to create
population forecasting models for use in individual cities and towns, and our models use
Massachusetts-specific or regional models for the same purpose. But even the models
that we created have discrepancies when compared to each other. Cutting the data in
different ways, whether by housing value, Benchmarks region, rented or owned, etc. has
an effect on the outcome of the prediction. Data available from the decennial Census
does not contain municipal level information that can be used to create population
forecasts except for a small amount of cities whose populations are above 100,000 and
therefore become their own Public Use Microdata Area (PUMA). It is possible to
perform a case study of similar housing types in the same or similar municipalities in the
same manner that case studies are performed, but there is no guarantee that even these
projections will be correct.
Even with models that concentrate on a smaller geographic area, there is still the
real and unavoidable effect of aggregation bias, which can lead the users of the model
into false conclusions. Population forecasts can be used to estimate future population,
but the results will always be just estimates, and the smaller the scale of the estimate, the
more likely it is to be wrong.
23 UMass Donahue Institute. 1998. A Profile of Housing in Massachusetts, Amherst, MA,
University of Massachusetts, p. 6.
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8 Expenditure Analysis
To examine further if and how fiscal impact analysis models can predict change,
UMDI examined the expenditure data collected by the Division of Local Services (DLS)
from 1990 through 2000 and compared it to the population growth rate as reported by the
U.S. Census Bureau. Municipalities were ranked by growth rate in population, and were
separated into one of seven “kinds of communities” using a classification scheme
developed for the Division of Local Services. Ranking the municipalities allows trends
to be discovered that may be lost if all 351 municipalities are analyzed separately. Note
that the data used in this chapter is expenditure data, meaning it tracks the amount of
money spent on various budget categories that is collected from any source, whether it is
property taxes, state aid, federal grants, etc..
Originally we thought a more accurate analysis would result from creating a
median yearly growth rate instead of simply comparing 1990 to 2000. This would cancel
out any special budgetary circumstances that may have occurred in either 1990 or 2000
that may have been outside the normal parameters of a municipalities “average” budget.
However, due to the massive decrease in municipal budgets from 1990 to 1992 and the
slow but steady climb back to and past “Massachusetts Miracle” levels, nearly all
municipalities posted a negative median yearly budget growth between 1990 and 2000.
We felt that, on the whole, comparing 1990 to 2000 would create the most accurate
assessment as both years were at the height of decade-long booms that were in the
process of bursting.
Table 8.1 Total Massachusetts Municipal Expenditures by Type, 1990-2000
Massachusetts 1990
Rank
1990 2000
Rank
2000
Change
90-00
Rank
90-00
Population 6,016,425 6,349,097 5.5%
Population 5-17 940,711 1,102,796 17.2%
Education $4,575,975,992 1 $5,852,557,097 1 27.9% 1
Fixed Costs $1,311,831,981 2 $1,336,861,696 2 1.9% 7
Police $825,661,636 3 $962,392,976 3 16.6% 3
Debt Service $695,236,789 4 $819,181,584 4 17.8% 2
Fire $687,021,548 5 $724,011,117 5 5.4% 6
General Government $607,580,273 6 $618,006,690 6 1.7% 8
Other Public Works $567,853,174 7 $451,986,887 7 -20.4% 11
Public Works Highways $444,004,014 9 $397,226,053 8 -10.5% 10
Inter- Governmental $305,021,637 10 $332,775,214 9 9.1% 5
Culture & Recreation $262,091,095 11 $294,393,297 10 12.3% 4
Health & Welfare $464,113,310 8 $204,215,954 11 -56.0% 13
Other Public Safety $189,788,929 12 $173,810,090 12 -8.4% 9
Other Expenditures $84,946,445 13 $48,983,257 13 -42.3% 12
General Fund Total $11,021,126,842 $12,216,401,912 10.8%
Source: U.S. Census Bureau, Decennial Census 1990 and 2000
Division of Local Services, Mass. Dept. of Revenue, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
49 UMASS DONAHUE INSTITUTE
Overall, expenditures in Massachusetts increased 10.8 percent between 1990 and
2000, while population increased only 5.5 percent, as shown in table 8.1. In 1990 and
2000, the largest single municipal expenditure item was education, which increased
almost 28 percent and rose from 41.5 to 47.9 percent of all reported municipal
expenditures. Second were fixed costs, which increased only 2 percent. The major
expenditure loser from 1990 to 2000 were health and welfare expenditures, which
decreased 56 percent and fell from 8th place in expenditures in 1990 to 11th in 2000. This
section will examine the relationships between certain of the above expenditure
categories and growth rates in population, parcels, and also by kind of community.
8.1 Per-Capita Municipal Expenditures by Population Growth
It is difficult to discern a pattern that could be used to explain the relationship
between population change and municipal expenditure change. Figure 8.1 is a simple
plot of population change versus per-capita expenditure change from 1990 to 2000,
where each point represents one of the 351 cities and towns in Massachusetts. It shows
that some municipalities had high population growth with negative per-capita expenditure
growth, some had negative population growth with a high per-capita expenditure growth.
Figure 8.1 Population Change vs. General Fund Expenditure Change in Adjusted
Dollars, 1990-2000
-60%
-40%
-20%
0%
20%
40%
60%
80%
-60% -40% -20% 0%20%40%60%80% 100% 120%
General Fund Change
Po
p
u
l
a
t
i
o
n
C
h
a
n
g
e
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
8.2 Expenditures by Growth Rate Category
As figure 8.2 below shows, when the median growth rate for each municipality
growth category is charted, a negative correlation between population growth and per-
capita total general fund expenditure increases can be seen. Except for the towns in the
“high growth” quartile, the decrease in growth rate for total expenditures and general
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
50 UMASS DONAHUE INSTITUTE
fund expenditures for each growth category seems quite linear. While there is a great
deal of variation between the highest and lowest per-capita expenditure growth rate for
each category, the general trend implies that growth may be the best way to keep per-
capita costs under control. Table 6.3 shows the per-capita expenditure growth rates for
each growth category. Removing education expenditures from the total shows a slightly
different pattern, one where certain growth categories actually showed decreases in per-
capita expenditures for non-education line items (see figure 6.6.2).
Table 8.2 Per-Capita Total General Fund Expenditure Growth Rates by
Population Growth Quintile Rank, 1990-2000
Growth Rank Very Low (1) Low (2) Medium (3) High (4) Very High (5)
Lowest -46.2% -43.7% -30.2% -41.6% -36.4%
Highest 107.0% 52.0% 55.8% 60.8% 60.8%
Average 18.1% 11.5% 8.8% 9.62% 5.5%
Median 17.3% 10.2% 8.6% 10.6% 5.9%
Source: Division of Local Services, Dept. of Revenue; Author Calculations
We examined each budget line items, such as education, police, fire, public
works, etc, to see if there were similar correlations by population growth. The charts for
each line item can be found in sections 8.7 through 8.19. As the high growth rate in total
municipal education expenditures suggests, median per-capita education spending per
quintile is much higher than general fund expenditure growth. Figure 8.23 shows that the
highest median growth rate in per-capita education spending was found in the “medium”
growth rate towns. While these towns showed a median population growth of 6.4
percent, their median education expenditure growth was over 24 percent. Except for this
spike, there is a slight general downward trend in per-capita cost increases that goes
along with that seen in most other general fund line-item expenditures.
While most expenditure line items have shown median increases in all population
growth rate categories, some expenditure types have shown declines. The largest per-
capita decline in spending was seen in public works and highway spending (see figures
8.25 and 8.26). Health and welfare per-capita expenditures also bucked the trend,
showing no discernable pattern in changes by growth rate, as did culture and recreation
and debt service. Generally, we have seen that most per-capita expenditures do change
over time, and more often than not the change is an increase in spending. Overall
expenditure patterns for aggregate general fund spending do imply that growth can help
to hold down these per capita cost increases.
8.3 Municipal Expenditures by Kind of Community (KOC) Code
The above analyses suggest that there is a negative correlation between growth
and increases in municipal expenditures per capita. Figure 8.4 examines the same data as
before, except that all municipalities are categorized by their KOC code. They show a
different picture, one that is less obvious. Essentially, the community type with the
lowest overall growth in per-capita expenditures were the Small Rural Communities
(KOC 5), which also had a rather high population increase. Those with the highest
growth were the Residential Suburbs (KOC 4) and Urbanized Centers (KOC 1), two very
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
51 UMASS DONAHUE INSTITUTE
different types of communities. Interestingly, while KOC types 2 and 3 showed very
similar expenditure growth patterns, KOC 2 communities (Economically Developed
Suburbs) had the third lowest median population growth, while KOC 3 (Growth
Communities) had a the second highest median population growth rate.
Looking at growth rates within each KOC code begins to make the picture clearer.
While the patterns are not completely consistent throughout the community types, there is
an overall pattern of median per-capita costs decreasing as growth rates increase in each
type of community. Note that Chelsea is only municipality rated as “very high”
population growth in the “Urban Center” community type, and it is not possible to draw
conclusions based on only one data measurement. Note that growth rate breakouts by
kind of community are only shown for total expenditures and total expenditures minus
education (sections 8.5 and 8.6).
8.4 Conclusions on Municipal Expenditure Analysis
Analyzing municipal expenditures is difficult. While there are general rules about
which types of expenditures fall under each expenditure category, there can be some
variation between municipalities in their exact categorization. For example, certain costs
may be categorized as expenditures in one town and therefore show up in expenditure
data, while other towns may classify the same cost as a capital expense which is not
tallied in expenditure data. In addition, certain costs associated with education, such as
pensions or playing fields, may actually be tallied under other line items in the
expenditure data, such as fixed costs or recreation. Therefore, analyzing this data can
only give an approximate picture of the fiscal realities of municipalities.
The wide variation in per-capita general fund expenditure growth rates illustrates
the vast differences between each municipality in Massachusetts. The scatterplot of
expenditure growth versus population growth (figure 8.1) shows that towns with the same
level of population growth can have widely differing per-capita expenditure growth rates.
Even so, aggregating these expenditure growth rates into categories based on the
population growth rate of each municipality begins to show patterns that imply that
higher-growth municipalities have less per-capita increases in expenditures than lower-
growth ones.
Analyzing the various budget categories by growth rate ranking showed similar
growth trends. Of note is the profoundly negative growth in “public works/highway” and
“other public works” per-capita expenditures from 1990 to 2000. Contrasted with the
increase in education spending, this implies that education expenditures may be
cannibalizing monies that may have been used for other purposes. Of interest is the lack
of any pattern seen in some of the line items, such as “fixed costs” and “debt service”
expenditures. While these seem to have increased significantly for some municipalities,
they have not done so for all.
Looking at per-capita expenditure growth by community type also illustrates
some interesting trends. Certain community types (urbanized centers (1), economically
developed suburbs (2), residential suburbs (4), and rural economic centers (5)) show
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
52 UMASS DONAHUE INSTITUTE
median per-capita expenditures growth rates that are higher than median population
growth rates, while the other three community types show the opposite. Looking at the
growth rates of each town within the seven community types shows a general but
imperfect pattern of lower-growth towns having greater increases in median per-capita
expenditures than higher-growth towns.
Overall, there is a trend of per-capita expenditures increasing less with higher
population growth rates. This implies that growth helps keep per-capita costs down, but
there may be other forces at work as well. Older municipalities may possess older
infrastructure that is more expensive to maintain, or there may be more poverty in certain
types of cities and towns that requires more services. Even so, the pattern is intriguing.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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8.5 Total General Fund Expenditure Charts
Figure 8.2 Per Capita Total General Fund Expenditure Change
Total Expenditure Change 1990-2000
-100
-50
0
50
100
150
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 8.3 Median Percent Change in Per Capita Total General Fund
Expenditures by Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Total
Expenditures
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 8.4 Median Percent Change in Per Capita Total General Fund
Expenditures by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
151050
Total
Expenditures
Population
Change
Figure 8.5 Median Percent Change in Per Capita Total General Fund
Expenditures by Kind of Community and Growth Rate, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Total Expenditures
302520151050-5
Growth Rate
Very Low
Low
Medium
High
Very High
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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8.6 Total Expenditures Minus Education Expenditure Charts
Figure 8.6 Per Capita Total Expenditure Minus Education Change
Total Expenditures Minus Education Change 1990-2000
-100
-50
0
50
100
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Figure 8.7 Median Percent Change in Per Capita Total Expenditures Minus
Education by Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
20
10
0
-10
Total Minus
Education
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 8.8 Median Percent Change in Per Capita Total Expenditures Minus
Education by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
151050-5
Total Minus
Education
Population
Change
Figure 8.9 Median Percent Change in Per Capita Total Expenditures Minus
Education by Kind of Community and Growth Rate, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Total Exp. Minus Education
3020100-10-20-30
Growth Rate
Very Low
Low
Medium
High
Very High
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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8.7 General Government Expenditure Charts
Figure 8.10 Per Capita General Government Expenditure Change
General Government Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
e
n
t
C
h
a
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g
e
Figure 8.11 Median Percent Change in Per Capita General Government
Expenditures by Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
20
10
0
-10
General
Government
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 8.12 Median Percent Change in Per Capita General Government
Expenditures by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Population Change
151050-5
General
Government
Population
Change
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
59 UMASS DONAHUE INSTITUTE
8.8 Police Expenditure Charts
Figure 8.13 Per Capita Police Expenditure Change
Police Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Figure 8.14 Median Percent Change in Per Capita Police Expenditures by Growth
Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Police
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
60 UMASS DONAHUE INSTITUTE
Figure 8.15 Median Percent Change in Per Capita Police Expenditures by Kind of
Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
151050
Police
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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8.9 Fire Expenditure Charts
Figure 8.16 Per Capita Fire Expenditure Change
Fire Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Figure 8.17 Median Percent Change in Per Capita Fire Expenditures by Growth
Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
20
10
0
-10
Fire
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
62 UMASS DONAHUE INSTITUTE
Figure 8.18 Median Percent Change in Per Capita Fire Expenditures by Kind of
Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
2520151050
Fire
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
63 UMASS DONAHUE INSTITUTE
8.10 Other Public Safety Expenditure Charts
Figure 8.19 Per Capita Other Public Safety Expenditure Change
Other Public Safety Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
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n
t
C
h
a
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g
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Figure 8.20 Median Percent Change in Per Capita Other Public Safety
Expenditures by Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
20
10
0
-10
Other Public
Safety
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
64 UMASS DONAHUE INSTITUTE
Figure 8.21 Median Percent Change in Per Capita Other Public Safety
Expenditures by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Population Change
151050
Other Public
Safety
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
65 UMASS DONAHUE INSTITUTE
8.11 Education Expenditure Charts
Figure 8.22 Per Capita Education Expenditure Change
Education Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Figure 8.23 Median Percent Change in Per Capita Education Expenditures by
Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Education
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
66 UMASS DONAHUE INSTITUTE
Figure 8.24 Median Percent Change in Per Capita Education Expenditures by
Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
35302520151050
Education
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
67 UMASS DONAHUE INSTITUTE
8.12 Public Works/Highway Expenditure Charts
Figure 8.25 Per Capita Public Works/Highway Expenditure Change
Public Works/Highway Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
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n
t
C
h
a
n
g
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Figure 8.26 Median Percent Change in Per Capita Public Works/Highway
Expenditures by Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
20
10
0
-10
-20
Public Works/
Highway
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
68 UMASS DONAHUE INSTITUTE
Figure 8.27 Median Percent Change in Per Capita Public Works/Highway
Expenditures by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
151050-5-10-15-20
Public Works/
Highway
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
69 UMASS DONAHUE INSTITUTE
8.13 Other Public Works Expenditure Charts
Figure 8.28 Per Capita Other Public Works Expenditure Change
Other Public Works Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
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n
t
C
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a
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g
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Figure 8.29 Median Percent Change in Per Capita Other Public Works
Expenditures by Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
20
10
0
-10
-20
-30
-40
-50
Other Public
Works
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 8.30 Median Percent Change in Per Capita Other Public Works
Expenditures by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
20100-10-20-30-40-50-60
Other Public
Works
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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8.14 Health & Welfare Expenditure Charts
Figure 8.31 Per Capita Health & Welfare Expenditure Change
Health & Welfare Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
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C
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a
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Figure 8.32 Median Percent Change in Per Capita Health & Welfare Expenditures
by Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
-10
Health &
Welfare
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 8.33 Median Percent Change in Per Capita Health & Welfare Expenditures
by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
20151050-5-10
Health &
Welfare
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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8.15 Culture & Recreation Expenditure Charts
Figure 8.34 Per Capita Culture & Recreation Expenditure Change
Culture & Recreation Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
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C
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a
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Figure 8.35 Median Percent Change in Per Capita Culture & Recreation
Expenditures by Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Culture &
Recreation
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 8.36 Median Percent Change in Per Capita Culture & Recreation
Expenditures by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
302520151050
Culture &
Recreation
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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8.16 Debt Service Expenditure Charts
Figure 8.37 Per Capita Debt Service Expenditure Change
Debt Service Expenditure Change 1990-2000
-250
0
250
500
750
1,000
Sorted Observations
Pe
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c
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C
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a
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g
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Figure 8.38 Median Percent Change in Per Capita Debt Service Expenditures by
Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
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n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Debt Service
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
76 UMASS DONAHUE INSTITUTE
Figure 8.39 Median Percent Change in Per Capita Debt Service Expenditures by
Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
1501209060300-30
Debt
Service
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
77 UMASS DONAHUE INSTITUTE
8.17 Fixed Costs Expenditure Charts
Figure 8.40 Per Capita Fixed Costs Expenditure Change
Fixed Costs Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Figure 8.41 Median Percent Change in Per Capita Fixed Costs Expenditures by
Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Fixed Costs
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
78 UMASS DONAHUE INSTITUTE
Figure 8.42 Median Percent Change in Per Capita Fixed Costs Expenditures by
Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Population Change
151050-5-10-15-20
Fixed
Costs
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
79 UMASS DONAHUE INSTITUTE
8.18 Intergovernmental Expenditure Charts
Figure 8.43 Per Capita Intergovernmental Expenditure Change
Intergovernmental Expenditure Change 1990-2000
-100
-50
0
50
100
150
200
250
300
350
400
450
500
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Figure 8.44 Median Percent Change in Per Capita Intergovernmental
Expenditures by Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Intergovernmental
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
80 UMASS DONAHUE INSTITUTE
Figure 8.45 Median Percent Change in Per Capita Intergovernmental
Expenditures by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
403020100-10
Intergovern-
mental
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
81 UMASS DONAHUE INSTITUTE
8.19 “Other” Expenditure Charts
Figure 8.46 Per Capita “Other” Expenditure Change
Other Expenditure Change 1990-2000
-150
-100
-50
0
50
100
150
200
250
300
350
400
450
500
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Figure 8.47 Median Percent Change in Per Capita “Other” Expenditures by
Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
20
10
0
-10
-20
-30
Other
Expenditures
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
82 UMASS DONAHUE INSTITUTE
Figure 8.48 Median Percent Change in Per Capita “Other” Expenditures by Kind
of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
20100-10-20-30-40
Other
Expenditures
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
C-83 UMASS DONAHUE INSTITUTE
9 Revenue Analysis
Unlike expenditure data, which can exclude monies used for certain purposes
such as capital projects, revenue data tracks all of the monies that a municipality has
available to it for any purpose. The Division of Local Services (DLS) of the
Massachusetts Department of Revenue separates municipal revenues into four different
categories: “tax levies,” “state aid,” “local receipts,” and “all other.” 24 “Tax levies”
consist of property taxes only, “state aid” contains all types of aid from all sources, “local
receipts” consists mainly of excise taxes and fees, and “all other” includes miscellaneous
items such as earnings on investments. We also examined the different types of state aid
that went to cities and towns to look for patterns in aid disbursement.
9.1 Municipal Revenue Per Capita
As with municipal expenditures, it is difficult to see the relationship between
population change and per-capita revenue change (see figure 9.1). Some towns saw an
increase in their population and a decrease in their per-capita revenues between 1990 and
2000, and some saw the opposite. As figure 9.2 shows, municipally-collected revenues
increase as the population increases. However, this increase is not linear. The rate of
total revenue change (indicated by the green bar) would be expected to decrease as the
population change rate decreases, but does not. In fact, the lowest growing quintile of
towns, which actually posted a negative median growth rate, has about the same revenue
increase percentage as the next-highest quintile (16.8% vs. 17.3%). When looking at the
data using per-capita expenditures, they show that per-capita revenues collected by
municipalities increased most in the slowest-growing cities and towns and least in the
highest-growing (See figure 9.8).
Figure 9.8 shows that the lowest growth municipalities had the highest percentage
increase in total revenues per capita. Figures 9.9 shows that the tax levy change pattern is
very similar to the total revenue pattern of growth, which is not surprising as tax levies
make up most of the revenues collected by cities and towns. The state aid change pattern
is interesting, as it shows that state aid has been growing for “high” growth towns at a
slightly higher rate than for “very high” growth towns and at a much higher rate than
“low” or “medium” growth municipalities. This is likely to be due to school aid levels,
which will be discussed later in the report. Local receipts also show an interesting
pattern, with large increases in “very low” and “low” growth municipalities, and small
increases in the higher population growth categories. These cities and towns may be
looking for new sources of income as they have no growth to drive their revenues, or they
may have more local receipt income sources.
24 http://www.dls.state.ma.us/databank.htm
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
C-84 UMASS DONAHUE INSTITUTE
Figure 9.1 Population Change by Per-Capita Total Revenue Change 1990-2000
Population Change by Total Revenue Change
-60
-40
-20
0
20
40
60
80
-50 0 50 100 150
Revenue Change
Po
p
u
l
a
t
i
o
n
C
h
a
n
g
e
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 9.2 Percent Median Revenue Growth By Population Growth Category,
1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Pe
r
c
e
n
t
G
r
o
w
t
h
45
40
35
30
25
20
15
10
5
0
-5
Tax Levy
State Aid
Local Receipts
Total Receipts
Population
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
An important fact to note is, even though most of the five growth categories show
that a large percentage increase has occurred in state aid, the dollar figures show that this
revenue source is much less important to cities and towns than property taxes. Figure
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
C-85 UMASS DONAHUE INSTITUTE
9.19 illustrates the Year 2000 reliance on different revenue sources for each growth
category. The median share of tax levy revenue to total revenue for all growth levels of
municipalities is over 50 percent, and it increases across population growth levels. In
other words, higher population growth towns are more reliant on property taxes than
lower growth ones are.
9.2 Municipal Revenue by Kind of Community
Per-capita revenue changes also vary among the different kinds of communities.
Figure 9.6 shows that the highest median percentage change in total revenues was found
in the residential suburb class of community (KOC 4). The lowest was in the
economically developed suburbs (KOC 2). Interestingly, the second and third highest
median increases were in Rural Economic Centers (KOC 5) and Urban Centers (KOC 1),
even though these two kinds of communities posted the lowest population growth rates.
Residential suburbs also had the largest per-capita median tax levy percentage
increase, but only the fourth-largest increase in state aid. The largest single revenue type
change has been in the urban centers, where per-capita median state aid has increased
over 25 percent. The changes in local receipt revenue seen in the previous section can
also be seen here, with Urban Centers and Economically Developed Suburbs (KOC 2)
showing large per-capita median increases and all other community types showing more
moderate increases. Interestingly, the Resort, Retirement, and Artistic Communities
(KOC 7) actually how a decrease in per-capita local receipt revenue. Separating out per
capita total revenue change by kind of community and growth rate shows that the same
general pattern of higher growth rate municipalities having less increase in per-capita
revenues can be seen by community type, with Chelsea, as the only Urban Center in the
“very high” growth category, being the one outlier.
Again, percentage changes do not directly reveal how important a revenue type is
to a municipality. Urban Centers are by far the least reliant on tax levy revenue and
Residential Suburbs are the most reliant. Urban Centers are also much more reliant on
state aid than any other community type. Looking at community type by growth rate
shows that reliance on tax levy revenue is fairly consistent across population growth
categories.
9.3 Conclusions on Municipal Revenue Analysis
While overall revenues have increased in real dollars between 1990 and 2000, it is
important to note that there was a large retrenchment in state aid after the collapse of the
“Massachusetts Miracle” that took years for municipalities to recover from (See chapter
one). The across-the-board increase in revenues from tax levies is very interesting, as we
would expect Proposition 2½ to have a greater effect on the municipalities’ ability to
collect property taxes. This is likely due to the increase in value of real estate over time,
especially in the metro Boston area.
While high-growth municipalities posted higher percentage gains in collected
revenues, their per-capita gains are much less than those in the low-growth categories,
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
C-86 UMASS DONAHUE INSTITUTE
especially in the area of state aid. This implies either that these municipalities need less
money for operating expenses and therefore collect less taxes or that they have less
ability to collect taxes. This question is tested in a later section of the report.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
C-87 UMASS DONAHUE INSTITUTE
9.4 Per Capita Total Revenue Change 1990-2000
Figure 9.3 Per Capita Total Revenue Change
Total Revenue Change 1990-2000
-50
-25
0
25
50
75
100
125
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 9.4 Median Percent Change in Per Capita Total Revenues by Growth
Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Total
Revenue
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
C-88 UMASS DONAHUE INSTITUTE
Figure 9.5 Median Percent Change in Per Capita Total Revenues by Kind of
Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
20151050
Total
Revenue
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 9.6 Median Percent Change in Per Capita Total Revenues by Kind of
Community and Growth Rate, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
50403020100-10
Growth Rate
Very Low
Low
Medium
High
Very High
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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9.5 Per Capita Tax Levy Change 1990-2000
Figure 9.7 Per Capita Tax Levy Revenue Change
Total Tax Levy Change 1990-2000
-100
-50
0
50
100
150
200
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 9.8 Median Percent Change in Per Capita Tax Levy Revenues by Growth
Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Tax Levy
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
C-90 UMASS DONAHUE INSTITUTE
Figure 9.9 Median Percent Change in Per Capita Tax Levy Revenues by Kind of
Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
2520151050
Tax Levy
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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9.6 Per Capita State Aid Change 1990-2000
Figure 9.10 Per Capita State Aid Revenue Change
Total State Aid Change 1990-2000
-150
-100
-50
0
50
100
150
200
250
300
350
400
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 9.11 Median Percent Change in Per Capita State Aid Revenues by Growth
Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
State Aid
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 9.12 Median Percent Change in Per Capita State Aid Revenues by Kind of
Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
302520151050
State Aid
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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9.7 Per Capita Local Receipt Change 1990-2000
Figure 9.13 Per Capita Local Receipt Revenue Change
Total Local Receipt Revenue Change 1990-2000
-100
-50
0
50
100
150
200
250
300
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 9.14 Median Percent Change in Per Capita Local Receipt Revenues by
Growth Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Local
Receipts
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
C-94 UMASS DONAHUE INSTITUTE
Figure 9.15 Median Percent Change in Per Capita Local Receipt Revenues by
Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
2520151050-5
Local
Receipts
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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9.8 Per Capita “Other” Revenue Change
Figure 9.16 Per Capita “Other” Revenue Change
"Other" Revenue Change 1990-2000
-150
-100
-50
0
50
100
150
200
250
300
Sorted Observations
Pe
r
c
e
n
t
C
h
a
n
g
e
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 9.17 Median Percent Change in Per Capita “Other” Revenues by Growth
Category, 1990-2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
-10
-15
Other
Revenue
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
C-96 UMASS DONAHUE INSTITUTE
Figure 9.18 Median Percent Change in Per Capita “Other” Revenues by Kind of
Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
403020100-10-20-30
Other
Revenue
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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9.9 Reliance on Revenue Types
Figure 9.19 Median Reliance on Revenue Type by Growth Category, 2000
Growth Rate 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
70
60
50
40
30
20
10
0
Tax Levy
State Aid
Local Receipts
Other
Source: Division of Local Services, Mass. Dept. of Revenue, 2000
Figure 9.20 Median Reliance on Revenue Type by Kind of Community, 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent
706050403020100
Tax Levy
State Aid
Local Receipts
Other
Source: Division of Local Services, Mass. Dept. of Revenue, 2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 9.21 Median Reliance on Tax Levy Revenue by Kind of Community and
Growth Category, 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent of All Revenue
80706050403020100
Growth Rate
Very Low
Low
Medium
High
Very High
Source: Division of Local Services, Mass. Dept. of Revenue, 2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
99 UMASS DONAHUE INSTITUTE
10 State Aid Analysis
State aid is an important component of municipal revenues. Certain types of
communities are very reliant on this aid, especially Urban Centers. The Division of
Local Services keeps records on many different aid programs, many of which are
education-related. However, since the majority of state aid seems to fall into only three
categories, UMDI has aggregated state aid into education and non-education expenditures
for this analysis, and also analyzed trends in lottery and “additional assistance” aid.
While we realize that there are cities and towns that use non-school aid for education
purposes, the data available from the DLS does not track how general aid like additional
assistance is used by municipalities. Even so, the trends in state aid changes over time
show a distinct shift in focus from general support to education-specific support.
10.1 State Aid Trends 1990-2002
The general trend for state aid disbursement to municipalities can be seen in
figures 2.4 and 2.5 in Chapter 2. Overall, aid decreased as the economy contracted
during the early 1990’s, and greatly increased through the mid- to late-1990s as the
economy improved. However, the mix of aid types has changed during the last 12 years
Figure 10.1 Education Aid by Type (in Millions) Adjusted for Inflation, 1990-2002
$0 $500 $1,000$1,500$2,000$2,500$3,000
FY90
FY91
FY92
FY93
FY94
FY95
FY96
FY97
FY98
FY99
FY00
FY01
FY02
Chapter 70* Total School Transportation School Construction Retired Teachers' Pensions
Tuition of State Wards Racial Equality School Lunch Other Education
Source: Division of Local Services, Dept. of Revenue, FY 1990 through 2002
Aid intended specifically for school systems shrank at about the same rate as aid
for other services in the early 1990’s, and then grew much faster than non-school aid after
that. Figure 10.1 shows the trend of school aid disbursements by type from 1990 to
2002. In 1990, about 50 percent of all aid going to cities and towns in aggregate was for
education, while by 2002 about 70 percent was for education. Note that “Chapter 70”
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
100 UMASS DONAHUE INSTITUTE
school aid previous to 1994 was interpolated by the DLS by aggregating the different aid
types available at that time into one “Chapter 70” equivalent number. Note also that
Chapter 70 aid is the largest single type of school aid, and that it has increased in real
dollars from about $1.3 billion in 1990 to almost $2.6 billion in 2002 (all dollars are 2000
equivalents for compatibility purposes).
Figure 10.2 below shows that non-school aid decreased greatly after 1991 and has
remained below its previous levels. Note the increased reliance on lottery monies and
decreased reliance on the so-called “additional assistance” over time. All dollars are
adjusted for inflation to 2000 levels.
Figure 10.2 Non-School Aid by Type (in Millions) Adjusted for Inflation, 1990-
2002
$0 $200 $400 $600$800$1,000$1,200$1,400$1,600 $1,800
FY90
FY91
FY92
FY93
FY94
FY95
FY96
FY97
FY98
FY99
FY00
FY01
FY02
Lottery Additional Assistance Highway Fund Local Share of Racing
Regional Public Libraries Police Career Incentive Urban Renewal Veterans' Benefits
Exemptions: Vets, Blind, Exemptions: Elderly State Owned Public Libraries
Other General
G Source: Division of Local Services, Dept. of Revenue, FY 1990 through 2002
10.2 State Aid Per Capita
Because of the large number of aid types versus the large proportion of aid dollars
emanating from only three programs (Chapter 70 school aid, Lottery disbursements and
Additional Assistance), we decided to examine state aid by aggregating it into school aid
and non-school aid by town. In addition, we also examined Lottery and Additional
Assistance aid change over time.
When looking at cities and towns by their growth rate, the data showed that
higher growth municipalities are generally receiving higher percentage increases per
capita in state aid (see figure 10.3). However, the actual median per-capita aid in dollars
for 2000 show that “low” growth rate municipalities receive the most aid (see figure
10.4). For non-school aid, the changes were quite different. The trend for lottery
disbursements showed that lower-growth towns received the largest per-capita percent
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
101 UMASS DONAHUE INSTITUTE
increase and the largest number of dollars per-capita in 2000 (see figures 10.7 and 10.8).
However, the profound negative change in additional assistance for all growth categories
pushes the median total non-school aid change into negative territory. Median aid in
dollars for 2000 shows the limited effect that additional assistance has on municipalities,
and how only the lowest growth towns obtain any appreciable aid from this source.
Interestingly, the pattern in dollars for lottery and total non-school aid also favors lower
growth towns.
10.3 State Aid by Kind of Community
When we examined the data by the kind of community, it showed similar patterns
as were seen in growth category analysis, with some exceptions. Figure 10.5 shows that
per-capita education aid increased significantly in four community types, less in Growth
Communities (KOC 3), Rural Economic Centers (KOC 5), and actually declined in Small
Rural Communities (KOC 6). The explanation for that decline lies in the fact that many
of these communities belong to regional school districts, which usually get their aid
directly from the State. However, while many community types saw percentage
increases, Urban Centers (KOC 1) received by far the highest amount of per-capita state
education aid in 2000 (see figure 10.6). Like the growth category analysis above, the
community type analysis shows an increase in lottery aid and a substantial decrease in
additional assistance per capita for all community types, leading to a general median
decrease per capita in non-school aid for every community type except Growth
Communities and Small Rural Communities. Again, median per capita dollar amounts of
non-school aid show that urban center still receive the highest share (see figure 10.10).
Note also the reliance on lottery aid as a portion of all aid dollars by Rural Economic
Centers (KOC 5).
10.4 Conclusions on State Aid
Over the decade from 1990 to 2000, the nature of state aid shifted to education aid
and away from aid for general government activities. By 2000 about 70 percent of all
municipal aid was earmarked for education purposes. Additional Assistance aid saw the
largest decrease in both percent and dollar terms. While education aid generally showed
high percentage increases, Urban Centers still receive the largest share per-capita in
dollar terms.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
102 UMASS DONAHUE INSTITUTE
10.5 Education State Aid Charts
Figure 10.3 Median Education Aid Growth Percent Per Capita By Growth
Category
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
80
70
60
50
40
30
20
10
0
-10
Total School
Aid
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 10.4 Median Education Aid Growth in Dollars Per Capita By Growth
Category, 2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
C
a
p
i
t
a
S
c
h
o
o
l
A
i
d
(
$
)
$300
$250
$200
$150
$100
$50
$0
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
103 UMASS DONAHUE INSTITUTE
Figure 10.5 Median Percent Education Aid Growth Per Capita By Kind of
Community
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
100806040200-20-40
Total School
Aid
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 10.6 Median Dollar Education Aid Growth Per Capita By Kind of
Community, 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Per Capita School Aid ($)
$700$600$500$400$300$200$100$0
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
104 UMASS DONAHUE INSTITUTE
10.6 Non-School State Aid Charts
Figure 10.7 Median Percent Non-School Aid Growth Per Capita By Growth
Category
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
75
50
25
0
-25
-50
-75
-100
Lottery
Additional
Assistance
Total Non-
School Aid
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 10.8 Median Non-School Aid Growth in Dollars Per Capita By Growth
Category, 2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
A
i
d
P
e
r
C
a
p
i
t
a
(
$
)
$200
$175
$150
$125
$100
$75
$50
$25
$0
Lottery
Additional
Assistance
Total Non-
School Aid
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
105 UMASS DONAHUE INSTITUTE
Figure 10.9 Median Percent Non-School Aid Growth Per Capita By Kind of
Community
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
7550250-25-50-75-100
Lottery
Additional
Assistance
Total Non-
School Aid
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 10.10 Median Dollar Non-School Aid Growth Per Capita By Kind of
Community, 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Per Capita Aid ($)
$250$200$150$100$50$0
Lottery
Additional
Assistance
Total Non-
School Aid
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
106 UMASS DONAHUE INSTITUTE
11 School Cost Analysis
Education is the largest single municipal expense for all cities and towns in
Massachusetts. In 2000, the combined total spending on school costs for all
municipalities in Massachusetts was almost $5.9 billion, or 48% of the $12.2 billion
expended that year. The lowest percentage expenditures were seen by towns that belong
to regional school districts or certain cities where state aid flowed directly to the district
(places like Heath and Springfield had no recorded education expenditures), while some
towns saw school costs up to 78% of total municipal expenditures, such as Hancock
(78.5%) and Belchertown (72.4%). While most of these expenditure figures include state
aid, they still show the importance of education costs to school funding. In addition, the
total number of pupils in Massachusetts school systems rose 18.7 percent from 1990 to
2000, according to data from the Massachusetts Department of Education.. The
following analysis uses data from the Division of Local Services of the Massachusetts
Dept. of Revenue along with data from the Mass. Dept. of Education to analyze changes
in education expenditures from 1990 to 2000. Because the first time period is before the
1994 start date of the Massachusetts Education Reform Act (MERA), this analysis
captures some of the changes brought about by that legislation.
11.1 School Cost Trends 1990-2000
This section uses different data than previous analyses. It uses net average
membership of pupils in school and the integrated operating cost for each municipality in
Massachusetts. The net average membership of pupils is a statistic created by the
Massachusetts Department of Education that calculates the “average enrollment of local
residents, pupils in regional school districts, and those being tuitioned to out-of-town
schools averaged across the school year.”25 In other words, it is a measure of the number
of pupils that each municipality sends to a school system, whether that system is regional
or covers only one town. The integrated operating cost includes “a community's share of
regional school district spending as well as that from its own local schools. This
approach accounts for spending outside the school budget that benefits schools, such as
insurance and pupil support services.”26
Figure 11.1 shows a simple scatterplot of the change in net average membership
of pupils in school versus the change in integrated operating cost for each municipality in
Massachusetts. This simple measure shows that, while there was a very general
relationship between increasing school populations and increasing school costs between
25 Division of Local Services, Massachusetts Dept. of Revenue, Municipal Data Bank, Schv8601.xls.
Non-residents are not counted in the Net Average Membership Pupils figure.
26 Division of Local Services, Massachusetts Dept. of Revenue, Municipal Data Bank, Schv8601.xls.
Integrated Operating Cost also includes EEO grant spending but does not include other non-general fund
expenditures.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
107 UMASS DONAHUE INSTITUTE
1990 and 2000, it is not as linear as one would expect. Much of this has to do with the
Massachusetts Education Reform Act, or MERA.
Figure 11.1
School Cost Change by Municipality, 1990-2000
50
0
50
100
150
100 50 0 50 100 150
Percent Change in Integrated School Costs
Pe
r
c
e
n
t
C
h
a
n
g
e
i
n
A
v
g
M
e
m
b
e
r
s
h
i
p
Source: Division of Local Services, Mass. Dept. of Revenue, 1990 and 2000
Mass. Dept. of Education, 1990 and 2000
11.2 Per-Pupil Costs by Pupil Growth Category
One of the important factors that affects forecasting future education costs is the
change in per-pupil expenditures. Generally, future education budgets are estimated
using a per-pupil cost multiplier, with the assumption that these costs are consistent from
year to year. Looking at the change in cost-per-pupil (calculated by dividing the
integrated operating cost by the net average membership of pupils for each municipality)
shows that there have been changes in the per-pupil expenditure rate over time for almost
all municipalities. From 1990 to 2000, the median municipal per-pupil expenditure rose
almost 16 percent for the state.
If net average membership growth rates are separated out into quintiles (in a
similar fashion as population growth rates were in the previous chapters) then patterns in
cost changes start to appear. Table 9.1 shows the growth rates in net average
membership of pupils from 1990 to 2000 for each quintile and for the state as a whole.
As before, each quintile contains 70 towns except for the third, which contains 71.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
108 UMASS DONAHUE INSTITUTE
Table 11.1 Growth Rate by Quintile in Net Average Membership of Pupils
Change Very Low (1) Low (2) Medium (3)High (4) Very High (5) Total
Median -2.1 9.2 17.5 26.1 46.4 17.5
Mean -5.4 9.1 17.2 26.2 50.5 19.5
Minimum -54.4 4.5 13.4 21.2 31.9 -54.4
Maximum 3.9 13.3 21.1 31.6 133.3 133.3
Source: Mass. Dept. of Education
Table 11.2 shows the growth rate in the cost per pupil by pupil growth quintile
from 1990 to 2000, adjusted for inflation. These numbers show that, on the whole, costs
per pupil have risen regardless of the rate in pupil growth, but these costs have risen less
in high-pupil-growth rate towns than in low growth rate towns. The median data is
illustrated in figure 11.2 below. Note that the population growth rates shown here are
different than the ones used in previous chapters, as they are based on the quintiles used
to represent growth in net average membership of pupils.
Table 11.2 Growth Rate by Quintile in Cost Per Pupil in Real Dollars
Change Very Low (1) Low (2)Medium (3)High (4)Very High (5) Total
Median 25 18.1 14.2 16.8 2 15.6
Mean 28 22 15.1 15.8 6.9 17.6
Minimum -5.4 -20.9 -7.5 -8 -46.1 -46.1
Maximum 82.8 115.6 53 50.8 45.3 115.6
Source: Mass. Dept. of Education
11.3 Per-Pupil State Education Aid by Pupil Growth Category
Per-pupil expenditures are not the whole story, however. Municipalities receive a
significant amount of education aid from the State that helps to defray the cost of
providing education services to residents. According to the Mass. Department of
Education, financing schools is seen as a local responsibility, but ensuring fairness across
communities is seen as a State responsibility. Fairness is achieved through the funding
structure set up in the Massachusetts Education Reform Act of 1993, or MERA. The
primary goals of MERA were to set a minimum spending level for each school district,
calculate what the various municipalities could afford to pay, and use state funds to make
up the difference.27 It was originally intended to run from 1993 through 2000, but was
extended after that time to continue providing State funds to local schools using the same
aid formula.
Table 11.2 shows the median growth rate in the cost per pupil by pupil growth
category from 1990 to 2000, adjusted for inflation. These numbers show that, on the
whole, median costs per pupil have risen regardless of the rate of pupil growth, but these
costs have risen less in high-pupil-growth rate towns. Note that the population growth
27 http://finance1.doe.mass.edu/chapter70/formula98.html
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
109 UMASS DONAHUE INSTITUTE
rates shown here are different than the ones used in previous chapters, as they are based
on the growth in net average membership of pupils instead of the population as a whole.
When looking at school aid per capita, it is more useful to examine median aid per
pupil, as that more directly measures the effect of state aid on education costs. Like
education costs, median aid per pupil has gone up from 1990 to 2000, but not consistently
across pupil growth categories. Figure 10.2 in Chapter 10 shows the changes in state aid
for public K-12 education from FY1990 through FY2002. The pattern of yearly change
it shows is similar to the pattern seen in Figure 2.5, Revenues by Type, 1981-2001, in
Chapter 2. There was a tapering off of aid for education in the early 1990’s that was
recovered from by 1995, and a general increase since that time.
The largest portion of state education aid is “Chapter 70” aid, which is distributed
through a complex formula that was created in 1993 under the Massachusetts Education
Reform Act. Note that the aid system in 1990, although not called “Chapter 70” at the
time, was interpolated by the Division of Local Services from the different types of aid
available at that time to create a comparable statistic to post-1993 data.
Figure 11.6 shows the median change in aid disbursement from 1990 to 2000 by
growth in net average membership of pupils. Not surprisingly, municipalities with high
growth rates showed the highest increases in state education aid, as well as other types of
aid. However, when aid is broken down into dollars per pupil, the picture changes
somewhat. While aid as a whole has gone up significantly since 1990, median per-pupil
aid expenditures have actually decreased for most pupil growth rate categories. Looking
at median dollar costs per pupil in 2000 shows a slight decrease in cost per pupil for
higher pupil growth categories, and a bell-shaped curve of median aid per pupil, with the
“medium” growth rate communities posting the largest per-pupil aid expenditure. This
is partly because simple growth in pupils is not what causes more state aid to be
disbursed to a municipality.
11.4 Per-Pupil Costs by Kind of Community
The results seen in the previous section do not explain the changes in state aid
distribution that have occurred since 1990, because growth in the student population is
not the only factor that would affect aid distributions. Changes are better illustrated by
looking at the kind of community, as this can work as a proxy for the most important
factor in education aid, which is a municipality’s ability to pay.
Figures 11.7 and 11.8 illustrate percentage change over time and the current
median cost per student and state aid per student by kind of community. As these figures
show, urban centers (KOC 1) posted the second largest median percentage increase in aid
per pupil after growth communities (KOC 3) and receive the highest median dollar
amount of state aid per pupil. Interestingly, the median cost per pupil for each type of
community hovers around $7,000 except for the resort, retirement, and artistic
communities (KOC 7), which just break $8,000. Note that the statistics for many rural
and small towns do not reflect aid that is received directly by multi-town school districts.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
110 UMASS DONAHUE INSTITUTE
Looking at the data on dollar expenditures per pupil for total cost and for state aid
by both community type and growth rate show a general consistency in per-pupil costs
across the board, with small rural communities posting lower median expenditures due to
their state aid mostly flowing towards regional school districts. For state aid, Urban
Centers of all pupil growth categories show much higher median state aid numbers
(generally from over $4,000 to over $5,000 per pupil), with only “low” growth rate
Economically Developed Suburbs, “medium” growth rate Rural Economic Centers and
“high” growth rate Growth Communities receiving over a median $2,000 in aid per pupil.
11.5 Conclusions on School Costs
Education costs are the single most important expenditure a city or town makes.
Both school costs and the number of pupils in Massachusetts’ schools went up from 1990
to 2000. The total amount of integrated operating costs statewide rose 34.9 percent in
that time, while the total net average membership of pupils rose only 18.7 percent.
However, part of this increase is due to increasing school aid under the 1993 Education
Reform Act.
When looking at school costs and school aid per student, some surprising findings
become apparent. While all growth classifications of municipalities had per-pupil
increases in total costs, these costs increased less in higher pupil growth categories. In
this, per-pupil expenditures follow the trend in other expenditures. This makes sense
because of the large share of total municipal expenditures covered by education costs.
In addition, certain types of municipalities showed median changes in per-capita state aid
that were negative. This trend was explained by using community types to classify the
data, which showed that Urban Centers were getting most of the benefit of state aid.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
111 UMASS DONAHUE INSTITUTE
11.6 Charts for School Costs Per Pupil By Pupil Growth Category
Figure 11.2 Growth in Per-Pupil Expenditures 1990-2000 by Net Average
Membership Growth Quintile (in Real Dollars)
Growth in Net Average Membership 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
50
40
30
20
10
0
-10
Cost Per Pupil
Pupils
Population
Source: Massachusetts Dept. of Education 1990-2000
Division of Local Services, Mass. Dept. of Revenue 1990-2000
Figure 11.3 Change in State Aid Per Pupil, Total Cost Per Pupil, And Net Average
Membership of Pupils by Growth in Net Average Membership of Pupils, 1990-2000
Growth in Net Average Membership 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
50
45
40
35
30
25
20
15
10
5
0
-5
-10
-15
Aid Per Pupil
Cost Per Pupil
Pupils
Source: Division of Local Services, Dept. of Revenue, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
112 UMASS DONAHUE INSTITUTE
Figure 11.4 Median State Aid and Cost Per Pupil in 2000
Growth in Net Average Membership 1990-2000
54321
Me
d
i
a
n
D
o
l
l
a
r
C
o
s
t
$8000
$7000
$6000
$5000
$4000
$3000
$2000
$1000
$0
School Aid
Per Pupil
Cost Per Pupil
Source: Division of Local Services, Dept. of Revenue, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
113 UMASS DONAHUE INSTITUTE
11.7 Charts for School Costs Per Pupil By Kind of Community
Figure 11.5 Change in State Aid Per Pupil, Total Cost Per Pupil, and Net Average
Membership of Pupils by Kind Of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
3020100-10-20-30
School Aid
Cost Per Pupil
Pupils
Source: Division of Local Services, Dept. of Revenue, 1990-2000
Figure 11.6 Median State Aid and Cost Per Pupil in 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median in Dollars
$9,000$6,000$3,000$0
Aid Per Pupil
Cost Per Pupil
Source: Division of Local Services, Dept. of Revenue, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
114 UMASS DONAHUE INSTITUTE
Figure 11.7 Median Cost Per Pupil by Kind of Community and Pupil Growth
Category in 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Cost Per Pupil
$10000$8000$6000$4000$2000$0
Very Low
Low
Medium
High
Very High
Source: Division of Local Services, Dept. of Revenue, 1990-2000
Figure 11.8 Median State Aid Per Pupil by Kind of Community and Pupil Growth
Category in 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median School Aid Per Pupil
$6000$4000$2000$0
Very Low
Low
Medium
High
Very High
Source: Division of Local Services, Dept. of Revenue, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
F-115 UMASS DONAHUE INSTITUTE
12 Tax Collection Analysis
To see how cities and towns are dealing with their changing revenue picture,
UMDI analyzed the adoption of split property tax systems which tax residential parcels at
a lower rate than commercial and industrial parcels. This taxing scheme helps
municipalities reduce costs on residents by shifting more of the burned on industrial and
commercial land. We also examined trends in tax levy changes per capita and per parcel,
as well as motor vehicle excise taxes, to see how change over time has affected property
tax collection. In addition, we examined levy limits and levy ceilings mandated by
Proposition 2½, bond ratings, and municipal debt levels over time, all to paint a more
complete picture of what happened in Massachusetts’ cities and towns between 1990 and
2000.
12.1 Tax System Analysis 1990 – 2000
One of the ways that cities and towns can collect more property taxes without
adding an extra tax burden to residential households is to adopt a differential tax rate, or a
two-tiered tax system. In this approach, residential property is taxed at one rate and other
types of property (commercial, industrial, etc.) are taxed at another, higher rate. This
allows more of the tax burden to be shifted onto non-residential property.
Even though many communities in Massachusetts have experienced financial
pressures in recent years, there has been little change in the number of municipalities that
use two-tiered taxing systems. In fact, there has been a slight decrease in the number of
these communities. In 1990, there were 109 communities that used different tax rates for
different types of property. This number decreased to 102 by 2000, and had not changed
in 2002. Of these, most are either Urban Centers (KOC 1) or Economically Developed
Suburbs (KOC 2). Further, the fastest-growing municipalities are the least likely to use
two-tiered taxing systems.
Table 12.1: Two-Tiered Tax Systems by Kind of Community
Kind of Community TieredTotalPercent
Urban Center 38 45 84%
Econ. Dev. Suburb 40 59 68%
Growth Community 7 46 15%
Res. Suburb 3 53 6%
Rural Econ Ctr 9 61 15%
Small Rural Comm. 1 46 2%
Resort, Retirement 4 41 10%
Total 102 351 29%
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Table 12.2: Two-Tiered Tax Systems by Growth Rate, 1990-2000
Growth RateTieredTotalPercent
Very Low 27 70 39%
Low 34 70 49%
Medium 21 71 30%
High 10 70 14%
Very High 10 70 14%
Total 102 351 29%
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
12.2 Per Capita Levy Change Analysis, 1990 – 2000
Tax levies from real property are the single most important revenue stream for
most cities and towns in Massachusetts. We analyzed the components of municipal tax
levies to track change over time and to see how much tax levy revenue was collected in
2000, both by community growth rate and kind of community. Generally, tax levy
revenue is collected mostly from residential real property, and it is this property that has
risen the most in value over time.
As we would expect, higher growth rate towns posted higher increases in total tax
levy. Interestingly, the total tax levy change for all growth categories grew slower than
the residential tax levy change, implying that other types of real property declined in
value over the period of 1990-2000. Also interesting is that, even though “very low”
growth towns posted a median population loss in that period, they show a fairly
significant increase in total tax levy in inflation-adjusted dollars.Total tax levies by kind
of community grew in a similar pattern to population change.
Per capita growth rates show a different picture. Median total tax levy growth per
capita was highest in the “very low” growth rate towns and lowest in the “very high”
growth rate towns, while higher population growth towns generally showed a greater
median percentage divergence in residential property levies. Per capita patterns by kind
of community are similar to the overall trend.
In dollar terms, the median per capita total tax levy ranged from about $1,000 to
about $1,.200 in 2000, with the highest amounts seen in the “very low” growth and “very
high” growth municipalities. All other types of levy revenue were quite low for each
population growth category. Median per capita levy revenues showed much more
variation by kind of community, with Urban Centers (KOC 1) collecting the smallest
amount per capita (less than $800) and Resort, Retirement and Artistic communities
(KOC 7) collecting the highest amount (almost $1600).
12.3 Per Parcel Levy Change Analysis, 1990 – 2000
Looking at tax levy revenue per parcel gives an entirely different picture. The
median of the average tax levy per parcel for residential parcels has changed significantly
in all growth categories, but was especially pronounced in “high” and “very high”
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
F-117 UMASS DONAHUE INSTITUTE
population growth municipalities, which recorded over 15 percent growth rates in real
dollar value. The median of average commercial property tax levies also climbed more
in these towns, posting about a 15 percent increase in value from 1990 to 2000, and less
in lower growth cities and towns. Of interest is the negative median change in average
industrial property tax levies. The median value of this levy type decreases more the
higher the growth rate category.
This trend can also bee seen in the different community types, with a consistent
decrease in median average industrial property tax levy in all community types except
Small Rural Communities (KOC 6). Interestingly, median average commercial property
tax levies in Urban Centers (KOC 1), Growth Communities (KOC 3), and Small Rural
Communities (KOC 6) increased at a higher rate than residential properties. Residential
Suburbs (KOC 4) and Resort, Retirement, and Artistic Communities (KOC 7) posted the
highest median residential parcel tax levy growth rates (20%).
Although the percentage changes imply that there are sharp differences in the
amount of money collected per parcel by community type, this is not really the case.
Looked at by population growth category, the median average per parcel tax levy for
2000 hovered around $2,200 to $2,500 per parcel for all growth categories. The real
differences were in commercial and industrial parcels, with “low” growth municipalities
posting the highest median average tax levies for each type of parcel, and “very high”
growth municipalities posting the lowest.
A similar trend can be seen in median average tax levies per parcel by kind of
community. Except for Economically Developed Suburbs (KOC 2) and Residential
Suburbs (KOC 4), the median average tax levy hovers around $2,000 to $2500.
Economically Developed Suburbs have median tax levies of about $3,000 per parcel, and
Residential Suburbs have median values of about $4,000.The real difference is in
commercial and industrial parcels, where Economically Developed Suburbs have by far
the highest median average tax levy per parcel and Urban Centers have the second
highest.
12.4 Proposition 2½ Levy Limits and Levy Ceilings, 1990 – 2002
Proposition 2½ was enacted in 1980 and regulates both the amount that a
municipality in Massachusetts can change its tax levy every year and the maximum
percentage of total real and personal property value that that tax levy can be28. As the
name suggests, both of these amounts are 2½ percent. We tracked the change in excess
levy capacity and in percentage of current total levies to the levy ceiling from 1990 to
2000 to see if there were any identifiable trends. We found that, while communities had
more excess capacity in 2000 than in 1990, they were closer to their levy ceilings in
2000.
28 Division of Local Services, Undated. Levy Limits: A Primer on Proposition 2½. Boston,
Massachusetts Department of Revenue p.2.
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A levy limit is the total amount that a community is allowed to increase its tax
rates every year. It is based on the previous year’s limit plus whatever increases are
allowed by law, such as new growth allowances and override votes.29 The excess levy
limit is the amount that a community could have added to their tax levy but did not, for
whatever reason. The median excess levy as a percent of the total levy limit has increased
for all categories of population growth level from 1990 to 2000. This means that the
median town or towns in each category were not “bumping up” against the mandated
levy limits as much in 2000 as they were in 1990. While the 2000 percentages are not
high (they range from about 0.45 to 0.8 percent), they are higher than the 1990 median of
less than 0.1 percent for each growth category. Interestingly, Economically Developed
Suburbs, Growth Communities, and Residential Suburbs all have significantly lower
excess percentages than other community types. Resort, Retirement, and Artistic
Communities had the highest excess capacity in 2000 at a median of about 2.75 percent.
A levy ceiling is the theoretical maximum amount that a community can levy as
taxes on property. It is calculated by taking 2.5 percent of the “total full and fair cash
value of all taxable real and personal property” in a community.30 It changes when the
value of this property changes, either by new property being added or property values
changing, so it is always being re-adjusted. The closer the total tax assessment of a
community is to 2.5 percent of total property value, the less room a community has to
raise taxes. At some point, a community may “bump into” the levy ceiling an have no
more room to increase revenues short of a revaluation of property or addition of new
property. When we analyzed the percentage of current tax levy of total municipal
property assessment for each municipality, we found that the median for each category
was much closer to 2.5 percent in 2000 than it was in 1990. Interestingly, while all
growth categories of towns were in the 1.2 to 1.6 percent range of tax levy to levy ceiling,
all kinds of communities but two were in this range. The exceptions were Urban Centers,
which were higher at almost 1.7 percent of total assessment, and Resort, Retirement and
Artistic Communities, which were much lower with a less than one percent median
percentage of total assessments.
12.5 Proposition 2½ Override Votes, 1990-2000
One indicator of municipal financial stress is the number and type of override
votes attempted in a fiscal year. Essentially, an override vote represents an instance
where a municipality spent up to its levy limit in that fiscal year and needed to ask the
voters for permission to spend over that limit for certain budget items. There have been
significant changes in the number of override votes requested by municipal governments,
which have declined drastically between 1990 and 2000.
In 1990, there were 442 override votes held by 131 separate municipalities. Of
these, only 40 percent, or 178, were passed. While most of these 131 municipalities
requested only one override vote, 58 of them held more than one. The town with the
most override vote requests in Fiscal Year 1990 was Chatham, with 31 separate override
29 Ibid., p.4.
30 Ibid., p.4.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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requests, 12 of which were successful. By 2000, the number of override votes had
declined significantly to 56, held by 29 separate municipalities. Of these, Westminster
held the largest amount of override votes, with six separate votes (two of which were
successful). Overall, 27 of the 56 override votes in 2000, or 48 percent, were successful
Table 12.3 Override Votes by Population Growth Rank, 1990
Growth
Rank
Culture
Recreation
Employee
Benefits Funds
General
Operating
Health and
Human
Services
Public
Safety
Public
Works Schools Total
Very Low 8 1 1 30 4 8 6 11 69
Low 1 27 1 9 6 2 46
Medium 1 31 4 2 11 49
High 5 1 1 56 6 12 21 22 124
Very High 15 2 3 65 3 22 30 14 154
Total 29 5 5 209 14 55 65 60 442
Table 12.4 Override Votes by Population Growth Rank, 2000
Growth Rank Culture Recreation General OperatingPublic SafetyPublic WorksSchools Total
Very Low 1 3 4
Low 1 4 1 1 7
Medium 1 4 2 7
High 2 5 2 1 11 21
Very High 2 3 2 3 7 17
Total 6 17 4 5 24 56
Tables 12.3 and 12.4 above show the override vote requests by population growth
category and type of override funding. They show that in both 1990 and 2000, the most
votes were requested in higher growth rate municipalities. Interestingly, the most
requested funding type in 1990 was for general government and general operating
expenses, while in 2000 it was school funding.
Table 12.5 Override Votes by Kind of Community, 1990
Kind of Community
Culture
Recreation
Employee
Benefits Funds
General
Operating
Health
Human
Services
Public
Safety
Public
Works Schools
Urban Center 4
Econ. Dev. Suburb 10 2 4
Growth Community 10 41 2 13 13 13
Residential Suburb 4 1 1 72 2 9 11 16
Rural Economic Ctr 22 8 5 5
Small Rural Comm. 1 26 4 9 11
Resort, Retirement 14 4 4 34 10 19 27 11
Total 29 5 5 209 14 55 65 60
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Table 12.6 Override Votes by Kind of Community, 2000
Kind of Community
Culture
Recreation
General
Operating
Public
Safety
Public
Works Schools
Urban Center 1
Economically Developed Suburb 1 4
Growth Community 2 8 2 8
Residential Suburb 2 4 4 7
Rural Economic Center 1 3
Small Rural Community 1 2
Resort, Retirement, and Artistic 1 4 1
Total 6 17 4 5 24
When viewed by the kind of community, override votes in 1990 were most
prevalent in the Resort, Retirement, and Artistic, Residential Suburb, and Growth
Community categories. By 2000, the Resort Retirement and Artistic communities had
vastly decreased their override vote requests, but the other two community types still
accounted for the majority of override votes.
12.6 Moody’s Bond Ratings, 1990-2000
A good measure of fiscal health is a community’s bond rating. We have analyzed
the Moody’s bond ratings for each community in 1990 and in 2000 and tracked the
changes over time. Overall, most communities in Massachusetts have either retained the
same bond rating or have improved their bond rating over that time period. In addition,
in FY2000 the vast majority of municipalities in Massachusetts carried favorable bond
ratings.
For communities that had bond rating from Moody’s in both 1990 and 2000, we
found that there was a general increase in bond ratings across the board in all community
types and population growth rate categories (see tables F.7 and F.8). We also found that
the vast majority of communities carried A or above ratings in Massachusetts in 2000
(see tables 12.7 and 12.8). Note that we excluded the “2” and “3” sub-categories of bond
rating from our analysis as they were adopted after 1990.31
Table 12.7 Change in Municipal Bond Ratings by Population Growth Rate For
Municipalities Rated in Both 1990 and 2000
Growth Rate LoweredNo Change Raised
Very Low 7.5% 77.5% 15.0%
Low 14.0%55.8% 30.2%
Medium 4.9% 56.1% 39.0%
High 0.0% 64.9% 35.1%
Very High 2.9% 62.9% 34.3%
Total 6.1% 63.3% 30.6%
31 Division of Local Services, Department of Revenue.
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Table 12.8 Change in Municipal Bond Ratings by Kind of Community For
Municipalities Rated in Both 1990 and 2000
Kind of Community LoweredNo ChangeRaised
Urban Center 12.8%59.0% 28.2%
Econ Dev Suburb 6.1% 55.1% 38.8%
Growth Comm 3.4% 79.3% 17.2%
Res Suburb 2.7% 45.9% 51.4%
Rural Econ Ctr 8.3% 79.2% 12.5%
Small Rural 0.0% 71.4% 28.6%
Resort 0.0% 90.9% 9.1%
Total 6.1% 63.3% 30.6%
Table 12.9 Bond Ratings by Population Growth Rate, 2000
Growth Category AaaAa1Aa A1 A BaaBaa1
Very Low 6.5%2.2%21.7%13.0%41.3%6.5%8.7%
Low 5.5%1.8%20.0%9.1% 50.9%5.5%7.3%
Medium 3.8%3.8%17.3%23.1%42.3%0.0%9.6%
High 4.4%4.4%15.6%28.9%40.0%6.7%0.0%
Very High 0.0%1.9%18.9%26.4%49.1%0.0%3.8%
Total 4.0% 2.8% 18.7%19.9% 45.0% 3.6% 6.0%
Table 12.10 Bond Ratings by Kind of Community, 2000
Kind of Community Aaa Aa1Aa A1 A Baa Baa1
Urban Center 2.2% 0.0%4.4%15.6%48.9%13.3% 15.6%
Econ Dev Suburb 12.3%5.3%40.4%19.3%21.1%0.0% 1.8%
Growth Comm 0.0% 0.0%12.8%17.9%66.7%0.0% 2.6%
Res Suburb 4.3% 8.7%32.6%34.8%13.0%4.3% 2.2%
Rural Econ Ctr 0.0% 0.0%0.0%5.7% 80.0%2.9% 11.4%
Small Rural 0.0% 0.0%0.0%16.7%83.3%0.0% 0.0%
Resort 0.0% 0.0%11.8%29.4%52.9%0.0% 5.9%
Total 4.0% 2.8%18.7%19.9%45.0%3.6% 6.0%
12.7 Municipal Debt, 1990-2000
Municipal debt has increased from 1990 to 2000. The total outstanding debt for
all municipalities in Massachusetts increased from $3.7 billion in 1990 to $7.7 billion in
2000 in constant dollars. As these numbers imply, total outstanding debt per capita has
increased as well. We found some interesting patterns in debt per capita across town
growth categories and kinds of community.
Looking at median per capita debt change by population growth category shows
us that median debt has changed inconsistently. However, it is notable that the median
per capita debt change for “very high” growth communities was the smallest by far of all
categories (less than 40 percent), while the median debt change for “high” growth
communities was the highest (over 90 percent). When looking at per capita debt change
by kind of community, a different pattern emerges. Urban Centers (KOC 1) had the
highest median per capita change of almost 200 percent from 1990 to 2000. Residential
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Suburbs (KOC 4) had the second-highest change of over 150 percent. The next highest
median changes were Economically Developed Suburbs and Rural Economic Centers,
both tied at slightly over 50 percent. Small Rural Communities (KOC 6) and Resort,
Retirement, and Artistic Communities (KOC 7) both had negative changes in median per-
capita debt.
Looking at the actual dollar amount per capita shows that the change patterns
mask one reality., which is that “Very High” growth municipalities have the highest
median per-capita debt, while “very low” growth municipalities have the lowest. By
community type, Urban Centers have the highest median per capita debt in 2000 (almost
$1500), which was not true in 1990.
12.8 Conclusions
This section examines many different aspects of tax policy and fiscal health in
Massachusetts’ communities. The overall conclusion that we draw is that, overall,
Massachusetts’ cities and towns were in better fiscal shape in 2000 than they were in
1990. The slowdown of override votes, improvement in bond ratings, and increase in
excess levy capacity all point to municipalities that were able to operate within the
normal revenues that they collected in 2000. This is partly due to the improvement in
residential tax values from 1990 to 2000 both raised levy limits and ceilings, allowing for
more tax growth.
The dark clouds on the horizon are that municipalities are getting closer to the
mandated 2.5 percent ceiling of property taxes to property values, and that municipal debt
has increased significantly. Any downturn in property values could have a significant
negative effect on municipal finances as levy ceilings lower and debt becomes harder to
service.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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12.9 Tax Levy Change Charts
Figure 12.1 Tax Levy Change by Population Growth Category, 1990-2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
50
40
30
20
10
0
-10
Residential
Total Levy
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 12.2 Tax Levy Change by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Cet
Small Rural
Resort
Median Percent Change
403020100
Residential
Levy
Total Levy
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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12.10 Per-Capita Tax Levy Change Charts
Figure 12.3 Per Capita Tax Levy Change by Population Growth Category, 1990-
2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
30
25
20
15
10
5
0
-5
Residential
Levy
Total Levy
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 12.4 Per Capita Tax Levy Change by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
2520151050
Residential
Levy
Total Levy
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 12.5 Per Capita Tax Levy by Population Growth Category, FY2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
D
o
l
l
a
r
L
e
v
y
$1400
$1200
$1000
$800
$600
$400
$200
$0
Residential
Commercial
Industrial
Personal
Total Levy
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 12.6 Per Capita Tax Levy by Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Crt
Small Rural
Resort
Median Dollar Levy
$1600$1200$800$400$0
Residential
Commercial
Industrial
Personal
Total Levy
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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12.11 Per Parcel Tax Levy Change Charts
Figure 12.7 Per Parcel Tax Levy Change by Population Growth Category, 1990-
2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
20
15
10
5
0
-5
-10
Residential
Commercial
Industrial
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 12.8 Per Parcel Tax Levy Change by Population Growth Category, 1990-
2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
2520151050-5-10-15
Residential
Commercial
Industrial
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 12.9 Median of Average Per Parcel Tax Levy by Population Growth
Category, 2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
L
e
v
y
P
e
r
P
a
r
c
e
l
$10,000
$8,000
$6,000
$4,000
$2,000
$0
Residential
Commercial
Industrial
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 12.10 Median of Average Per Parcel Tax Levy by Population Growth
Category, 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Levy Per Parcel
$20,000$15,000$10,000$5,000$0
Residential
Commercial
Industrial
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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12.12 Proposition 2½ Levy Limits and Levy Ceilings Charts
Figure 12.11 Levy Limits as a Percent of Current Levy By Population Growth
Category, 1990-2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
E
x
c
e
s
s
a
s
%
o
f
L
e
v
y
L
i
m
i
t
1.0
.8
.6
.4
.2
0.0
Excess
in 1990
Excess
in 2002
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 12.12 Levy Limits as a Percent of Current Levy By Kind of Community,
1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Excess as % of Levy Limit
3.02.52.01.51.0.50.0
Excess
in 1990
Excess
in 2002
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 12.13 Current Levies as a Percent of Total Assessment By Population
Growth Category, 1990-2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
T
a
x
L
e
v
y
a
s
%
o
f
T
o
t
a
l
A
s
s
e
s
s
m
e
n
t
1.6
1.4
1.2
1.0
.8
.6
.4
.2
0.0
1990
2002
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 12.14 Current Levies as a Percent of Total Assessment By Kind of
Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Tax Levy as % of Assessment
1.81.61.41.21.0.8.6.4.20.0
1990
2002
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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12.13 Municipal Debt Charts
Figure 12.15 Median Per Capita Debt Change By Population Growth Category,
1990-2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
c
e
n
t
C
h
a
n
g
e
100
90
80
70
60
50
40
30
20
10
0
-10
Total Debt
Population
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 12.16 Median Per Capita Debt Change By Kind of Community, 1990-2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Percent Change
200150100500-50
Total Debt
Population
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Figure 12.17 Median Per Capita Debt in Dollars By Population Growth Category,
2000
Growth Category 1990-2000
Very HighHighMediumLowVery Low
Me
d
i
a
n
P
e
r
C
a
p
i
t
a
D
e
b
t
$1,200
$1,000
$800
$600
$400
$200
$0
1990
2000
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 12.18 Median Per Capita Debt in Dollars By Kind of Community, 2000
Urban Center
Econ Dev Suburb
Growth Comm
Res Suburb
Rural Econ Ctr
Small Rural
Resort
Median Per Capita Debt
$1,500$1,000$500$0
1990
2000
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
132 UMASS DONAHUE INSTITUTE
13 Regional Analysis
To give the reader an idea about the differences in municipal finance patterns
across regions, we have prepared an analysis based on regional definitions used in
Massachusetts Benchmarks, a publication of the University of Massachusetts and the
Federal Reserve Bank of Boston. Benchmarks separates the Commonwealth into seven
different regions: The Berkshires, Boston Metro, Cape and Islands, Central, Northeast,
Pioneer Valley, and Southeast. We chose important findings from previous report
sections and analyzed them to give a more complete picture of the changes in finances
and demographics from 1990 to 2000.
13.1 Town Categories by Benchmarks Region
Massachusetts Benchmarks, a publication of the University of Massachusetts and
the Federal Reserve Bank of Boston, separates the Commonwealth into seven distinct
regions for the purposes of analysis. These regions were created to reflect political,
social, and economic realities within the Commonwealth. A map of these regions is in
Figure 13.1.
Separating the two different categories of municipalities used in the previous
analyses, population growth category from 1990 to 2000 and kind of community, shows
some of the differences between each region. Table 13.1 shows the number of towns in
each population growth rate category by Benchmarks region. It shows that each region
has had different growth patterns between 1990 and 2000. In the Berkshires, for
example, over half of the communities showed “very low” growth from 1990 to 2000,
while in the Cape and Islands over two-thirds showed “very high” growth in that period.
The kinds of community in each Benchmarks region also differ substantially. In
the Boston Metro region, the vast majority of the cities and towns fall into three
categories. In fact, that region contains 31 percent of the Urban Centers, 38 percent of
the Residential Suburbs, and 61 percent of the Economically Developed Suburbs in the
Commonwealth. The nature of the Southeast region is implied by the fact that 46 percent
of all Growth Communities are located there, while both the Berkshires and the Cape and
Islands regions contain the majority of all Resort, Retirement, and Artistic communities
(see table 13.2).
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
133 UMASS DONAHUE INSTITUTE
Figure 13.1 Regional Definitions for Massachusetts (Massachusetts Benchmarks)
Source: Massachusetts Benchmarks Project.
Table 13.1 Population Growth Categories by Benchmarks Region
Growth Category Berkshire
Boston
Metro
Cape and
Islands Central Northeast
Pioneer
Valley Southeast
Very Low 18 20 3 8 2 14 5
Low 2 16 14 10 20 8
Medium 2 14 1 19 11 11 13
High 5 15 3 9 8 18 12
Very High 5 10 16 12 11 6 10
Total 32 75 23 62 42 69 48
Source: Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000; Author Calculations
Table 13.2 Kinds of Community by Benchmarks Region
Kind of Community Berkshire
Boston
Metro
Cape and
Islands Central Northeast
Pioneer
Valley Southeast
Urban Center 3 14 1 8 6 7 6
Econ Dev Suburb 36 6 12 2 3
Growth Comm 1 1 6 5 4 8 21
Res Suburb 1 20 1 8 11 6 6
Rural Econ Ctr 6 3 22 3 19 8
Small Rural 9 12 3 18 4
Resort 12 1 15 1 3 9
Total 32 75 23 62 42 69 48
Source: Division of Local Services, Mass. Dept. of Revenue; Author Calculations
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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13.2 Conclusions
Most of the conclusions relative to this section have been made in previous
chapters of the report. However, there are some region-specific findings that are of
interest. The loose pattern of high population growth helping to hold down per-capita
expenditure increases can be seen between the different regions, but it is not as prevalent
as when it is seen in population growth rate categories. Even so, the Cape and Islands
region saw the largest decrease in per-capita costs and the highest population growth,
while lower population growth regions like the Berkshires, Boston Metro, and Pioneer
Valley saw increases in per-capita expenditures minus education costs. The exception
was the Southeast region, which had both robust population growth and increases in per-
capita expenditures.
Revenues also changed by region, but there was little relationship between
median population growth and median revenue growth. The Cape and Islands had the
highest median population growth and the highest median state aid change per capita, but
had a low median tax levy increase and a median local receipt decrease per capita
between 1990 and 2000. The Central region, which had mid-level population growth
overall, had a relatively high increase in all three charted revenue sources per capita, and
higher median tax levy increases per capita than the Southeast region, which had a higher
population change.
State aid has changed in some surprising ways by region. The Boston Metro and
Northeast regions recorded a decrease in median non-school aid per capita, likely due to
the lessening of additional assistance aid to the Urban Centers there. These regions also
saw large per-capita increases in school aid, as did the Cape and Islands region. The
Central region had the lowest median per capita school aid change, along with a positive
change in non-school aid. Although the Central region contains Worcester, the second
largest city in Massachusetts, it also contains many small towns whose aid increases
would skew the median change results. In dollar terms, the Southeast region received the
highest median amount of school aid per capita in 2000.
Median school cost changes by pupil show that there is little relationship between
pupil population growth and cost per pupil growth across regions. In the Northeast, the
median cost per pupil rose relatively little (about 7 percent), while the median pupil
population rose almost 25 percent. Conversely, the Pioneer Valley region had a cost per
pupil increase of about 27 percent and a pupil population increase of only about 9
percent. The Cape and islands, the highest growth rate region, had both a high cost per
pupil increase and a high pupil population increase. Median dollar values per pupil were
fairly consistent between regions, except for the Cape and Islands where they were
significantly more.
The most surprising finding in the regional analysis was the large difference
between the median excess levy limit in the Berkshires region and in all other regions.
As shown in figure 13.8, the median 10.3 percent excess is more than 7 times greater than
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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the next highest median excess percentage of levy limits in the Cape and Islands region.
The Southeast, Northeast and Boston Metro regions all had significantly lower median
excess percentages than other regions, but all regions had a larger excess percentage in
2000 than in 1990. Figure 13.9 shows that all regions also were closer to their levy
ceilings in 2000 than they were in 1990, although there were variations in the median tax
levy as a percentage of total property assessments. The Cape and Islands regions
recorded the lowest percentage, much lower than other regions.
Demographically, the Boston Metro region has by far the largest population and
the largest number of vacant housing units. However, the Boston Metro region also saw
the largest decrease in vacant housing units from 1990 to 2000. This can partly be
explained by the difference in new housing unit construction between 1985-1990 and
1995-2000, which shows a large drop-off in new units constructed in all regions of the
Commonwealth.
13.3 Expenditure Change by Benchmarks Region
Figure 13.2 Median Expenditure Change Per Capita by Benchmarks Region, 1990-
2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Median Per Capita Change
2520151050-5-10
Total
Expenditures
Total Minus
Education
Population
Change
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
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13.4 Revenue Change by Benchmarks Region
Figure 13.3 Median Revenue Change Per Capita by Benchmarks Region, 1990-
2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Median Per Capita Revenue Change
302520151050-5-10
Tax Levy
State Aid
Local
Receipts
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
13.5 State Aid by Benchmarks Region
Figure 13.4 Median State Aid Change by Benchmarks Region, 1990-2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Median Percent Change
100806040200-20-40
Non-School
Aid
School Aid
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
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Figure 13.5 Median State Aid Per Capita by Benchmarks Region, 2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Median Dollars Per Capita, 2000
$400$300$200$100$0
Non-School
Aid
School Aid
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
13.6 School Costs by Benchmarks Region
Figure 13.6 Median Change in Integrated Operating Costs Per Pupil by
Benchmarks Region, 1990-2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Median Percent Change
302520151050
Cost Per
Pupil
Number of
Pupils
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
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Figure 13.7 Median Integrated Operating Costs and School Aid Per Pupil by
Benchmarks Region, 2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Median Dollars Per Student
$10,000$7,500$5,000$2,500$0
Cost Per
Pupil
School Aid
Per Pupil
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
13.7 Tax Collection Issues by Benchmarks Region
Figure 13.8 Levy Limits as Percent of Current Levy by Benchmarks Region, 1990-
2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Median Excess as % of Levy Limit
1.501.251.00.75.50.250.00
Excess Levy
1990
Excess Levy
2002
10.3%
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
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Figure 13.9 Current Levies as a Percent of Total Assessments by Benchmarks
Region, 1990-2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Median Tax Levy as % of Assessment
1.81.61.41.21.0.8.6.4.20.0
1990
2002
Source: Division of Local Services, Mass. Dept. of Revenue, 1990-2000
13.8 Demographics by Benchmarks Region
Figure 13.10 Total Population by Benchmarks Region, 1990-2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Total Population (000's)
3,0002,5002,0001,5001,0005000
1990
2000
Source: Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
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Figure 13.11 Total Non-Seasonal Vacant Housing Units by Benchmarks Region,
1990-2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Total Non-Seasonal Vacant Housing Units (000's)
6050403020100
1990
2000
Source: Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
Figure 13.12 Total Housing Units in the Previous Five Years by Benchmarks
Region, 1990-2000
Berkshire
Boston Metro
Cape and Islands
Central
Northeast
Pioneer Valley
Southeast
Total Housing Units Built (000's)
6050403020100
1985 to
March 1990
1995 to
March 2000
Source: Decennial Census (SF3), U.S. Bureau of the Census, 1990-2000
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14 Other Issues Affecting Impact Analysis
14.1 Indirect Costs and Benefits of Housing Development
While this report has focused primarily on evaluating models that estimate direct
costs and benefits incurred by municipalities, we should consider the limitations any
fiscal impact model suffers from—that is, the ability to estimate the indirect costs and
benefits of a proposed project. Cost-benefit analysis models only incorporate direct,
quantifiable impacts, those which can be measured in economic and financial terms, in
the analysis. However, communities experience a variety of indirect and long-term
economic costs and benefits created by new households. In addition, they may enjoy
many qualitative or immeasurable impacts as a result of a proposed project, but cost-
benefit analysis is unable to include these factors.
To improve on a traditional cost-benefit assessment, analysts should supplement
their hard numbers with 1) qualitative assessments of immeasurable effects and 2)
quantitative assessments of the indirect economic impacts resulting from housing
development.
14.1.1 Quality of Life
Evaluations of potential development projects should consider the changes in
quality of life which may result from a proposed project. Those considering quality-of-
life issues may have to address environmental effects (such as a possible increases in air
or noise pollution), traffic congestion, historical preservation, aesthetics, social
environment, and public safety, to name a few. Some of these effects can be estimated
quantitatively—traffic, for instance—but even so, putting a dollar value on them is very
difficult. An incremental change in traffic flow may not be noticeable to one person,
while another may find it especially disagreeable and disruptive to his or her life. Some
analysts measure environmental damage by estimating the cost of clean-up, as well as
costs incurred by the local community, such as health and time lost at work, but most
costs and benefits aren’t nearly so readily quantifiable in economic terms. These indirect
costs and benefits tend to be economically non-quantifiable because of their inherent
subjectivity. What one may consider invigorating and exciting another may find hectic
and stressful. As a result, it’s difficult to place a value on indirect benefits and costs for
the community at large.
The best way to get a sense of the quality of life within a community is to ask
local residents what they like and dislike and what they want to see in their community
20 years from now. Only by going to the people can planners and other development
decision-makers ascertain a community’s priorities.
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14.1.2 Economic Impact
The most commonly employed techniques of fiscal impact analysis often fails to
consider the secondary or indirect economic benefits of residential development.
When a project is particularly costly or large-scale, municipalities have been
known to hire consultants to conduct a regional economic impact analysis which
estimates the multiplier effect—how much a project will promote the infusion of money
into the local economy generally, creating more businesses and jobs and thus generating
more tax revenue. For example, cities considering the construction of a new sports
stadium have often relied on economic impact studies to assess how not just the team but
the local economy will benefit. Such studies tend to be done to assess the impacts of
large-scale commercial or industrial projects. But the systematic failure to consider the
indirect or secondary economic benefits of housing growth is a major limitation of
conventional approaches to the analysis of the fiscal impact of housing.
14.2 Secondary Benefits of Housing Development
According to development literature, there are a variety of beneficial indirect
impacts of new housing development within a community and region: research has
clearly demonstrated that in most regions housing has the potential of becoming an
engine of economic growth because of its high yield on invested resources, a high
multiplier effect, and a host of beneficial forward and backward linkages in the
economy.32 Some of the most important economic benefits are discussed in the sections
below.
14.2.1 Population Stability
It is increasingly clear that a limited supply of affordable housing is limiting
population growth in many communities in the Commonwealth. Due to the high cost of
housing, households are being forced in increasing numbers to look outside of the Boston
Metropolitan region for housing opportunities. A large number of the households that
left Massachusetts were from counties in the metropolitan areas (Middlesex, Suffolk and
Essex counties) and many relocated to New Hampshire where housing is more
affordable. Impacts are being felt at the state level: currently, total out-migration of
households exceeds total in-migration of households.33
As the median age in many towns increases, this phenomenon has potentially
serious implications for local tax base stability. Population attrition in communities
occurs in an ongoing way due to death, lower birth rates and out-migration. Without an
age-diverse influx of households, the population will age and decline. Along with this
32 Nordberg, Rainer. Alleviating Poverty Through Housing Development. In Global Overview,
2000, Vol. 6, No. 4. The United Nations Centre for Human Settlements (Habitat).
http://216.239.51.100/search?q=cache:aFD8ROWE_rAC:www.unhabitat.org/HD/hdv6n4/alleviating_pover
ty.htm+multiplier+effect+%2B+housing+development&hl=en&ie=UTF-8
33 Street Signs. Massachusetts Benchmarks. Summer 2002, Volume five, issue three, p. 21.
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progression, public investments in infrastructure, public services and capital
improvements are also likely to decline. In contrast, new and varied housing
development within communities will enhance the ability of communities and regions to
maintain steady-state, age-diverse population levels. This in turn will helps to ensure
fiscal health and government stability at the local level.
14.2.2 The Household as an Economic Engine
Another indirect benefit of new housing development comes as a result of the
buying-power of new households. Through its expenditures for household goods and
services, a household represents a powerful engine for local and regional economic
development. Purchasing by local households contributes to the health of the region’s
commercial economy which, in turn, supports the community directly through the
commercial tax base and indirectly through state sales taxes.
Although it is common for communities to focus on the costs of supporting
households with children, it is important to note that these households have the most
purchasing power of all types of households with which to contribute to local and
regional commerce. According to the most recent consumer expenditure survey by the
Bureau of Labor Statistics,34 the highest annual household expenditures are made by
husband and wife households with children between the ages of newborn and 17. These
husband/wife households with children spend 30% more in average annual expenditures
than husband and wife households without children ($57,178 versus $43,946) and 107%
more than single persons and other consumer units. The majority of purchases for this
household type are for housing (32 percent), transportation (20 percent), and food (13
percent). All three types of expenditures have the potential to significantly enrich local
and the regional economy.
14.2.3 The Household as a Civic and Social Resource
Another important contribution made by local households comes through public
service and other volunteer activities. Massachusetts communities rely in innumerable
ways on the activities of residents. Volunteer activities not only enrich the civic and
social realms but they also result in tremendous cost savings for communities. In the
majority of communities in the state, volunteers staff the town council, the planning
board, the school board, the board of health and other critical governing bodies.
Recent studies illustrate that households of different ages volunteer in different
ways. In fact, younger households of childbearing and rearing ages (particularly between
the ages of 31 and 41) contribute very significantly. A poll done by the American
Association of Retired Persons (AARP)shows that the 31-41 year old age-group is the
primary force behind PTA, PTO, and other school organizations. Thirty-five percent of
this group is active in school-related activities, versus 8.4 percent for adults between 50
34 The Consumer Expenditure Survey (CEX). Bureau of Labor Statistics.
http://www.bls.gov/cex/home.htm
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and 70 or 2.4 percent for adults over 70.35 Respondents between the ages of 31 and 49
are also far more likely to be active in professional and trade organizations (34 percent as
compared to 25 percent or less for other age groups). The poll also shows that this age
group is also one of the most likely age ranges to be active in environmental causes and
neighborhood groups.
Clearly, the value of these households should not be underestimated and should
be considered in local development decisions. The secondary benefits – both economic
and social - of having younger families within a community are significant. Given this
fact, it important that communities balance their fiscal-impact measures with a
recognition of the secondary short and long-term benefits provided by an age-diverse
population.
14.3 Costs and Benefits are an Unequal Benefit and Burden
While the majority of the costs of housing development seem to fall on municipal
budgets in the form of services and education expenditures, the benefits of development
are more diffuse. Income taxes and sales taxes are collected directly by the
Commonwealth, as are gas taxes and many fees. Municipalities only get to collect
property taxes and excise fees, and perhaps some one-time impact fees for new
development. Even though the monetary benefits of even the most inexpensive housing
are likely to be overwhelmingly positive, most of these benefits do not directly find their
way into municipal budgets.
14.4 Minimizing the Impacts of Development using “Smart Growth”
According to a recent article in Commonwealth Magazine, a large number of
Massachusetts communities are attempting to minimize population growth through large-
lot zoning. A year 2000 study of sixteen Massachusetts communities showed that new
construction was allowed at only half the density of existing residential districts.36 But
this strategy to limit population growth – mainly to prevent school cost increases -
suggests that communities are ignorant of another factor with a critical impact on the
local budget: the density and location of new development. The truth is, low density,
dispersed development – otherwise known as sprawl - costs towns dearly.
A definition of sprawl:
An inefficient, scattered, auto dependent pattern of development that creates artificial
geographic barriers between normal daily activities, wastes natural resources and taxes,
underutilizes existing infrastructure in cities and other built up areas, broadens the geographic
and psychological distance between different classes and races and stunts long term, quality
economic growth.37
35 America’s Social Fabric – Joining the club(s). AARP Research Center. December 1997.
http://research.aarp.org/general/civic_inv_toc.html
36 Michael Jonas, Anti-family values. Commonwealth Magazine, Spring 2002, page 4.
37 The High Price of Urban Decay and Suburban Sprawl in Rhode Island, Grow Smart Rhode Island
web site, 2002. http://www.growsmartri.com/sprawl.html
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14.5 Northeast Land Consumption Exceeds Population Growth
Nationally, land consumption is exceeding population growth by a great deal and
related costs to communities are higher than ever. According to recent research, this
pattern is particularly evident in the Midwest and Northeast.38 In these regions,
population movement tends to be from dense urban centers to lower-density suburban
and rural places. The Massachusetts track record is particularly troubling. Despite its
ranking as 47th in the nation in per capita issuance of new housing permits since 1992,
Massachusetts ranked fifth in the nation for loss of land to development over the course
of the 1990’s.39
Through the process of dispersed development, previously undeveloped areas are
built up, resulting in a wide-variety of cost impacts on the local community. The
economic impacts are numerous, ranging from capital costs for transportation and public
works infrastructure to ongoing operating costs to providing public services. In many
cases, neither developers nor new residents adequately foot the bill adequately for
development-related costs.40 According to the Sierra Club, sprawl is, in fact, financed by
taxpayers through local, state and federal subsidies: “These range from the obvious to the
obscure and include big projects-like the billions we spend on new roads as well as
smaller ones-like the tax-breaks that encourage businesses to move to the edge of
town.”41
Low-density development is particularly costly to communities in the following
areas: roads and highways; schools; utilities and public works; fire, police and EMS
services. Other local costs come in the form of adverse impacts on the environment as
well as quality of life in the community. Local environmental impacts created by
dispersed, low-density development include fragmented open space and wildlife habitat;
loss of working farmland and forestland; air pollution, decline in water quality due to
increased urban runoff; erosion; and noise. Personal costs to residents include increased
auto dependency leading to decreased discretionary time; increased commuting times and
costs; traffic accidents; and psychic costs related to the loss of sense of place, declines in
social interaction; loss of open space and recreational space, and as well as the loss of
cultural and historic character in a community.
The following paragraphs examine line items in local budgets that are most
impacted by low density, dispersed development.
38 A Complex Relationship: Population Growth and Suburban Sprawl, The Sierra Club, 2002.
http://www.sierraclub.org/sprawl/population.asp
39 Jonas, ibid., page 4.
40 The Practice of Local Government Planning, p. 391.
41 Sierra Club website citing “The Cost of Sprawl,” Maine State Planning Office, May 1997, p. 9.:
http://www.sierraclub.org/sprawl/report00/police.asp
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14.6 Roads, Highways and Transportation
Roads and highways allow dispersed development to take place and, in turn,
increase the need for driving within communities. Initial and maintenance costs for road
and highway infrastructure are steep with cost impacts are felt at the local, state and
federal levels. Subsidies for new roads and highways come from the state and federal
government, but development, upkeep and maintenance of local roads is funded locally.
Low-density, dispersed development requires the highest number of highway and
road miles to serve it. A recent study in Rhode Island showed that, as of 1995, rural
towns had three times as many miles of local roads per 1,000 housing units as urban
communities. 42 According to calculations cited in the Rhode Island study, compact
development as opposed to sprawl development would save 43% of projected local road
construction costs over a twenty-year period. Standard planning estimates suggest that
per unit capital costs vary widely with houses on one acre lots between 107 and 118
percent higher than per unit costs for houses in cluster developments or townhouse
apartments.43 Finally, in addition to the initial capital outlays for sprawling road
networks, communities must also bear the burden of ongoing road maintenance,
including seasonal plowing.
The operating costs of fire, police and EMS service also increase in proportion to
the number of road miles in a community. Serving new dispersed development is more
time-consuming and costly than serving locations in existing developed locations in
town. According to a recent study by the Maine State Planning Office, even small towns
face large cost increases to serve housing in outlying areas. According to their look at
Kennebunk, Maine, new development 25 minutes outside of town created the need for
another police patrol and the cruiser and officers needed for the patrol will cost the town
an additional $175,000 per year. 44
14.7 Schools
According to the Sierra Club, sprawl often forces communities to build new
schools on the outskirts of town, while neglecting existing schools within the town’s
developed areas.45 The impacts of this type of development pattern can be seen in the
case of the state of Maine, which in a recent period in which it lost 27,000 students, it
simultaneously spent $727 million on new school construction.46
According to recent planning guidebooks, per-dwelling capital costs for schools
are 18 percent higher for housing units in large-lot, dispersed development (1 dwelling
42 H.C. Planning Consultants, Inc. and Planimetrics, LLP. The Costs of Suburban Sprawl and Urban
Decay in Rhode Island, December, 1999, p. 7.
43 The Practice of Local Government Planning, p.32.
44 Sierra Club website citing “The Cost of Sprawl,” Maine State Planning Office, May 1997, p. 9.:
http://www.sierraclub.org/sprawl/report00/police.asp
45 Sierra Club website: http://www.sierraclub.org/sprawl/report00/schools.asp
46 "The Cost of Sprawl," Maine State Planning Office, May 1997, p. 8. As cited on the Sierra Club
website: http://www.sierraclub.org/sprawl/report00/schools.asp
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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per acre) than for houses in compact developments.47 In addition to costs related to new
school construction, school systems built in sprawling towns incur higher transportation
costs related to student busing.
14.8 Utilities
In most cases, local taxpayers pay the cost of hooking up a new development to
water and sewer lines. The location as well as the density of a development affects these
initial capital costs as well as ongoing maintenance costs: the more dispersed the housing
the higher the cost to the town. In terms of per-dwelling capital costs for water, sewer
and other utilities, large lot, dispersed housing (one dwelling per acre) costs 187 percent
more to serve than moderate-density subdivisions (five dwellings per acre) ($25,187
versus $8,781), and between 191 percent and 305 percent more than compact forms of
development like cluster housing, town houses and garden apartments.
Water consumption rates also vary with the type of development. According to
standard planning estimates, low-density development consumes more due to lawn
irrigation and water line leakage. The higher number of linear feet of water lines required
to serve low-density development increases its exposure to leaks.48
14.9 Preventing Sprawl through Good Planning
According to the Sierra Club, sprawl in parts of the Midwest and Northeast is
largely a product of poor land-use planning, irresponsible development and the migration
of people out of the cities and into the suburbs. In these communities, poor planning and
lack of regional cooperation play larger roles than net population growth than driving
sprawl.49
In spite of these trends, a variety of tools are available to communities to help
them plan in ways that provide housing and valuable tax resources while decreasing local
sprawl. One useful tool is called build out analysis. Build out analysis is useful for
assessing existing zoning patterns and other regulatory conditions within a community.
A build out analysis quantifies the potential development impacts allowed by current
conditions on a variety of levels including: infrastructure, service needs and
demographics. In many cases, a build out analysis shows that existing master plans
(including zoning) are inadequate to prevent disastrous and costly consequences of
sprawl in a community.
A variety of build out analysis tools are available through the Massachusetts
Executive Office of Environmental Affairs (EOEA) website:
http://commpres.env.state.ma.us/. A GIS-based build out analysis like those done by
EOEA, provides a way for a community to identify limitations in current zoning patterns
47 The Practice of Local Government Planning, p.392.
48 The Practice of Local Government Planning, p.391.
49 New Research on Population, Suburban Sprawl and Smart Growth. Sierra Club website:
http://www.sierraclub.org/sprawl/whitepaper.asp
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and plan changes to the master plan to shape more cost-effective and environmentally
sound development in the future.
Other information helpful to communities includes a range of Smart Growth
Techniques including traditional neighborhood design (TND); conservation subdivision
design; transfer of development rights (TDR);50 the establishment of growth boundaries;
pedestrian friendly and transit-oriented design; development impact fees; redevelopment
of blighted areas and prevention of development in floodplains and coastal areas.51
Additional information on Smart Growth methods available to communities can be found
in the U.S. Environmental Protection Agency’s Smart Growth Policy database.52
50 Information about these Smart Growth techniques can be found on the EOEA website at
http://commpres.env.state.ma.us/content/cptools.asp
51 Information about these and other Smart Growth techniques can be found on the Sierra Club
website at: http://www.sierraclub.org/sprawl/factsheet.asp#Solutions
52 The EPA’s Smart Growth Policy Database is available online at:
http://cfpub.epa.gov/sgpdb/browse.cfm
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15 Findings and Conclusions
15.1 Findings
Our first major finding is that population forecasing is an inaccurate science. It is
affected by aggregation bias and is very dependent on the factors that are empohasized in
the creation of the model. While it is important to have a model that gives planners and
municipal officials some idea of the expected population that will occupy newly-built
housing units, noone should expect that these models will give the user results that are
reliable.
We also found that using average cost data to predict new municipal expenditures
to support new residents are not reliable. While about 19 percent of all cities and towns
had budget forecasts that were within a reasonable margin or error using the Per Capita
Multiplier Method, the rest did not, and one-third were severely over- or underestimated.
We cannot recommend using averaging models to predict future municipal costs, and
instead recommend that marginal costing methods, specifically the Case Study method,
be used to predict the fiscal impacts of new development. This includes projecting
school costs, which are also not reliably estimated using average per-pupil data.
Another interesting finding was that costs for many municipalities are increasing
regardless of population growth, or the lack of it. After adjusting for inflation we found a
significant number of municipalities whose costs increased substantially over the last
decade. There is something else occurring in municipal finance that affects the cost of
services, and that factor or factors seem to be more relevant than simple population
change. In addition, population growth seems to be negatively correlated with increases
in per capita municipal spending. However, more research needs to be done in this area to
make conclusions on the reasons for this observation.
The mix of state aid given to communities has changed over time. State aid for
non-education purposes has decreased over time, especially in the “additional aid”
categoy. State aid is becoming an important component of education funding for poorer
communities and urban centers, but we found that per-pupil amounts are not increasing or
are decreasing for many middle-and upper income communities.
We found that the fiscal health of municipalities generally imprved between 1990
and 2000. One of the reasons for this is that property tax revenues have increased over
time in many parts of the state, expecially in the Boston Metro area. The significant
increase in the value of land and housing units over the last decade has had a positive
effect on municipal finance.
15.2 Next Steps
The findings in this report suggest that there are some additional studies that
could be undertaken to clarify some perceived trends and create better methods for
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
150 UMASS DONAHUE INSTITUTE
municipal fiscal impact analysis. First, it may be possible to create better population
forecasting models using data from the decennial Census. Detailed Public Use Microdata
Sample (PUMS) data from the 2000 Census will be availabe in the near future. This data
could eb used to make population forecasts based on Public Use Microdata Areas
(PUMAs). Projecting population in all typs of housing units may not be possible for all
PUMA areas, but aggregation bias could possibly be lessened with carefully created
forecasts for as small a region as possible.
Also, it would be useful to examine further the relationship between growth and
municpal cost increases, and to examine cost increases in general. Undertanding the
mechanism or machanisms that cause many municipality's per-capita costs to rise faster
than inflation may help control that trend.
Finally, municipalities would be greatly assisted by the creation of a
straightforward method for performing marginal cost analysis (Case Study Method) for
their communities. The Fiscal Impact Tool (FIT) from the Executive Office of
Environmental Affairs would be a good starting point, as it alreasy has the ability to
accept specific input on needs for new personnel, equipment, or capital investments and
to create projections based on that data. The information collected for the past editions of
the Growth Impact Handbook from the Department of Housing and Community
Development is also helpful for creating case studies. What is needed is a method for
easing the use of this type of analysis, which is somethimes complex and always requires
a great deal of data.
15.3 Conclusions
Our analysis indicates that, for many Massachusetts communities, population
growth associated with new housing is not inevitably followed by increased demand for
services and higher municipal costs. Many of our fastest growing communities
experienced the slowest growth in per capita tax burden during the 1990s. In fact, there
seems to be little correlation between increases in per-capita costs and increases in
population, and it seems that municipal services are generally increasing in cost
regardless of growth. This strongly suggests that the standard models relied upon by
cities and town to estimate the fiscal impact of development may be systematically
overestimating these costs in many communities. Given the shortage of affordable
housing throughout Massachusetts and that these estimates are frequently used as the
basis for decision-making by local development agencies, it is clear that the methods
communities use to estimate the costs of development must be reconsidered.
Specifically, it is evident that the population forecasting model commonly relied
upon by many people to calculate the population impact of new housing does not fit well
with the current reality of Massachusetts. It regularly overestimates the population of
single-family detached housing, the most common type of new housing in Massachusetts,
and underestimates other housing types. Consequently, development decision-makers and
other users of fiscal impact models that rely on these population estimates, including the
EOEA’s Fiscal Impact Tool, may be making decisions based on outdated assumptions
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
151 UMASS DONAHUE INSTITUTE
about the size of households and the numbers of school-age children that follow the
development of housing in Massachusetts.
The fiscal landscape for Massachusetts is difficult to decipher, as the
Massachusetts Education Reform Act and Proposition 2½ make growth-driven outcomes
hard to distinguish from policy-driven outcomes. Even so, it seems that it is hard to make
the argument that growth automatically costs towns more money. Our analysis seems to
show that it is easier to claim that growth saves money by slowing down per-capita
increases in costs. However, our data may also suggest that growth squeezes municipal
budgets and makes certain mandated expenditure areas, such as education, take
precedence over others, such as public safety.
A much more accurate method for forecasting the fiscal impact of housing
development is the marginal cost method, although this method is more difficult to use
and requires much more information than the per-capita method. Even so, because all
municipalities have different priorities, histories, population mixes, and expenses, the
only reliable way to forecast the effect of growth on a city or town is to analyze the
specific data available for that specific town. Given the critical social need for and
economic importance of housing development in Massachusetts, it is clear that a more
accurate understanding of the true fiscal impacts of housing development is well worth
the extra effort.
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
152 UMASS DONAHUE INSTITUTE
Appendix A: Detailed Population Tables
To see if there are household composition differences in different parts of
Massachusetts, UMDI aggregated the various PUMA areas into the seven Massachusetts
Benchmarks region that the Institute has created for regional analysis within the state.
Figure 3.1 is a map of these regions.
Figure A.1: Regional Definitions for Massachusetts (Massachusetts Benchmarks)
Source: Massachusetts Benchmarks Project, map data from MassGIS.
This appendix contains extra tables that describe regional differences and
differences between owner-occupied and renter-occupied housing units. The tables are:
• Table A.1: Population Forecasting by Housing Type and Bedrooms For the New
England States for Housing built 1975 to 1980 (1980 Census Bureau Data) from
The New Practitioner’s Guide to Fiscal Impact Analysis
• Table A.2: Mean Population by Housing Unit Type and Region, 1990
• Table A.3: Mean School-Aged Children by Housing Unit Type and Region, 1990
• Table A.4: Mean Population and School-Aged Children (SAC) by Newly-
Constructed Housing Units, 1990
• Table A.5: Mean Population and School-Aged Children (SAC) by Recent
Movers in Owner-Occupied Housing Units by Unit Value, 1990
• Table A.6: Mean Population and School-Aged Children (SAC) by Recent
Movers in Rental Housing Units by Monthly Rent, 1990
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
153 UMASS DONAHUE INSTITUTE
Table A.1: Population Forecasting by Housing Type and Bedrooms
For the New England States for Housing built 1975 to 1980
(1980 Census Bureau Data)
from The New Practitioner’s Guide to Fiscal Impact Analysis
Type of House (A) Bedrooms
(B) Total People Per
House
(C) School Age Children
Per House
Single Family 2 2.417 0.243
3 3.345 0.793
4 4.141 1.470
5+ 4.853 2.052
Blended (All BRs)3.325 0.840
Townhouse 1 1.491 0.053
2 2.098 0.147
3+ 3.000 0.676
Blended (All BRs)2.355 0.348
Duplex, Triplex, 1 1.398 0.020
Quadplex 2 2.326 0.288
3+ 3.430 0.824
Blended (All BRs)2.350 0.356
Garden Apartments 1 1.295 0.007
2 2.142 0.203
3+ 3.074 0.883
Blended (All BRs)1.768 0.155
High Rise Studio 1.067 0.000
1 1.221 0.003
2+ 1.956 0.066
Blended (All BRs)1.376 0.022
Mobile Home 1 1.560 0.000
2 2.127 0.167
3+ 3.444 0.917
Blended (All BRs)2.505 0.398
Source: Burchell, et. al, The New Practitioner’s Guide to Fiscal Impact Analysis, pp. 64-65
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Table A.2: Mean Population by Housing Unit Type and Region, 1990
Housing Type Bedrooms Berkshire
Cape &
Islands Central
Greater
Boston Northeast
Pioneer
Valley Southeast
State
Avg.
Single family No BRs 1.0000 1.2114 1.57651.9063 1.6667 1.4667 1.1745 1.5318
detached 1 BR 1.6560 1.4702 1.64271.7149 1.7794 1.6729 1.7105 1.6770
2 BRs 1.9555 1.9730 2.12142.1302 2.1661 2.1211 2.1529 2.1166
3 BRs 2.7559 2.5406 2.93072.8665 2.9389 2.8559 3.0481 2.8985
4 BRs 3.0767 3.1564 3.40273.3938 3.5197 3.3428 3.5667 3.4241
5+ BRs 3.6543 3.1445 3.60693.8460 4.0793 3.8126 4.0236 3.8470
Blended
Single family No BRs NA 1.0000 NA 2.2074 1.0000 1.4167 1.0000 1.5298
attached 1 BR 1.0000 1.6519 1.79371.9433 1.5712 1.9507 1.7422 1.8239
2 BRs 2.2308 1.7524 2.24212.1469 2.2194 2.1985 2.2291 2.1782
3 BRs 2.7760 2.9286 3.11543.0286 2.7580 3.3150 3.1841 3.0365
4 BRs 3.4286 2.5171 3.81323.2431 3.4192 4.1677 4.0763 3.4651
5+ BRs NA 3.0200 3.81254.0053 4.2692 5.2390 4.0000 4.0867
Blended
Apt in 2- to 4- No BRs 1.3852 1.0659 1.31281.4389 1.3892 1.2169 1.1577 1.3679
flat bldg 1 BR 1.3823 1.3984 1.44981.6594 1.5831 1.5079 1.4604 1.5744
2 BRs 2.1370 1.9291 2.24692.2861 2.3308 2.2845 2.2980 2.2810
3 BRs 2.8624 2.8076 3.05553.1280 3.3267 3.1126 3.1276 3.1351
4 BRs 2.7103 2.3426 3.38183.4031 3.5338 3.2991 3.7673 3.4168
5+ BRs 3.2821 1.5082 3.02583.6069 3.7589 3.3865 4.2063 3.5785
Blended
Apt in 5+-flat No BRs 1.0563 1.1932 1.16141.1942 1.2266 1.1604 1.0554 1.1812
bldg 1 BR 1.1621 1.1228 1.31691.3770 1.3402 1.3374 1.3189 1.3503
2 BRs 1.8824 1.8210 2.32802.0910 2.2652 2.3542 2.2286 2.1759
3 BRs 3.0596 3.1049 3.78223.2884 3.4522 3.4458 3.5732 3.4010
4 BRs 4.2091 NA 3.70454.0638 5.7182 5.4588 3.9762 4.2325
5+ BRs 2.0000 NA 2.40004.0546 2.8913 3.4109 1.0000 3.4645
Blended
Mobile home No BRs 1.0000 NA 2.0000NA NA 1.0000 NA 1.6379
1 BR 1.1812 1.0000 1.36331.3763 1.5150 1.3322 1.5559 1.4093
2 BRs 1.8743 1.5575 1.84631.6270 1.8164 1.7777 1.7072 1.7601
3 BRs 2.3392 1.9091 2.84562.5920 3.1302 2.3983 2.4043 2.5835
4 BRs 4.0000 1.4706 6.3500NA 4.0588 3.4182 4.0000 3.6895
5+ BRs 6.0000 NA NA 3.0000 NA 8.0000 NA 5.2800
Blended
Source: Public Use Microdata Sample, 1990 Decennial Census and Author Calculations
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Table A.3: Mean School-Aged Children by Housing Unit Type and Region, 1990
Housing Type Bedrooms Berkshire
Cape &
Islands Central
Greater
Boston Northeast
Pioneer
Valley Southeast
State
Avg.
Single family No BRs 0.0000 0.0000 0.21180.1161 0.2000 0.0000 0.0000 0.0856
detached 1 BR 0.0000 0.0287 0.06790.1470 0.0686 0.1204 0.0741 0.0863
2 BRs 0.1510 0.1649 0.18190.1624 0.1958 0.1760 0.2025 0.1797
3 BRs 0.5081 0.4219 0.57340.4362 0.5042 0.5167 0.5904 0.5062
4 BRs 0.6691 0.7097 0.79920.6880 0.7872 0.7457 0.8391 0.7517
5+ BRs 1.1051 0.7411 0.75610.7779 1.0169 0.8693 1.0042 0.8606
Blended
Single family No BRs NA 0.0000 NA 0.3185 0.0000 0.0000 0.0000 0.1280
attached 1 BR 0.0000 0.0000 0.24310.1524 0.0997 0.4008 0.1250 0.1846
2 BRs 0.4000 0.0588 0.25750.2050 0.2119 0.3056 0.2521 0.2254
3 BRs 0.5226 0.4471 0.80420.5452 0.5647 0.8161 0.6954 0.6163
4 BRs 0.5857 0.4375 0.87130.5388 0.6967 1.2597 1.5085 0.7422
5+ BRs NA 0.3000 0.71880.5673 1.3357 2.2767 1.3025 0.7909
Blended
Apt in 2- to 4- No BRs 0.0000 0.0000 0.00000.0578 0.0942 0.1465 0.0000 0.0596
flat bldg 1 BR 0.0296 0.0299 0.05380.0901 0.1164 0.1043 0.0544 0.0834
2 BRs 0.2678 0.2330 0.27920.2647 0.3512 0.3210 0.3151 0.2890
3 BRs 0.5893 0.6177 0.69310.6213 0.8524 0.8025 0.7504 0.6987
4 BRs 0.5321 0.4781 0.85880.5876 0.8530 0.8257 0.9701 0.7021
5+ BRs 0.4768 0.0000 0.40040.4996 0.6519 0.5910 1.0375 0.5651
Blended
Apt in 5+-flat No BRs 0.0188 0.0000 0.01300.0144 0.0286 0.0044 0.0000 0.0136
bldg 1 BR 0.0408 0.0079 0.03760.0395 0.0533 0.0554 0.0335 0.0414
2 BRs 0.1930 0.1584 0.35140.2239 0.2953 0.3850 0.2978 0.2703
3 BRs 0.7747 0.9551 1.26030.7692 1.0715 1.2158 1.2234 0.9612
4 BRs 1.6273 NA 1.09461.0666 2.1318 2.3960 1.0929 1.2649
5+ BRs 0.0000 NA 0.20001.1006 0.0000 0.6589 0.0000 0.7599
Blended
Mobile home No BRs 0.0000 NA 0.0000NA NA 0.0000 NA 0.0000
1 BR 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0365 0.0116
2 BRs 0.1020 0.0000 0.11930.1172 0.1477 0.0765 0.0442 0.0829
3 BRs 0.2862 0.4546 0.54360.3520 0.6880 0.3167 0.2814 0.4008
4 BRs 0.0000 0.0000 2.7000NA 0.7059 0.7636 1.3333 0.9315
5+ BRs 0.0000 NA NA 2.0000 NA 5.0000 NA 3.1400
Blended
Source: Public Use Microdata Sample, 1990 Decennial Census and Author Calculations
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Table A.4: Mean Population and School-Aged Children (SAC) by
Newly-Constructed Housing Units, 1990
(Units constructed from January 1989 to March 1990)
Unit Type Bedrooms Pop SAC Unit Type BedroomsPop SAC
Single family
detached No BRs 1.28670.0000
Unit in 10-19-
flat bldg No BRs 1.2005 0.0267
1 BR 1.75530.1961 1 BR 1.5901 0.0851
2 BRs 2.32460.2484 2 BRs 2.3867 0.2415
3 BRs 2.93920.5055 3 BRs 3.7609 0.9890
4 BRs 3.57760.8174 4 BRs 5.2847 1.3358
5+ BRs 4.05960.9347 5+ BRs 3.7582 1.2637
Single family
attached No BRs 2.00000.0000
Unit in 20-49-
flat bldg No BRs 1.2671 0.0093
1 BR 1.97370.1733 1 BR 1.5287 0.0401
2 BRs 2.26780.2451 2 BRs 2.3325 0.2258
3 BRs 3.29290.8048 3 BRs 3.3137 0.6118
4 BRs 3.72971.0674 4 BRs 4.0000 0.0000
5+ BRs 4.09610.7046 5+ BRs 8.0000 2.0000
Unit in 2- to 4-flat
bldg No BRs 1.37050.0417
Unit in 50+-
flat bldg No BRs 1.1452 0.0050
1 BR 1.72870.0965 1 BR 1.3659 0.0110
2 BRs 2.43140.3168 2 BRs 2.1004 0.1506
3 BRs 3.46890.8579 3 BRs 3.3494 0.5791
4 BRs 4.08580.9816 4 BRs 2.4179 0.0000
5+ BRs 4.36700.7488 5+ BRs 1.0000 0.0000
Unit in 5-9-flat bldg No BRs 1.34580.0364 Mobile home1 BR 1.2732 0.0000
1 BR 1.55160.0967 2 BRs 1.9736 0.1844
2 BRs 2.51050.3959 3 BRs 2.7534 0.4959
3 BRs 3.79181.1324 4 BRs 4.0000 1.0000
4 BRs 4.15091.2727
5+ BRs 5.00002.0000
Source: Public Use Microdata Sample, 1990 Decennial Census and Author Calculations
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Table A.5: Mean Population and School-Aged Children (SAC) by
Recent Movers in Owner-Occupied Housing Units by Unit Value, 1990
Value in 1990 Dollars
Less than
$100,000
$100,000 to
$149,999
$150,000 to
$199,999
$200,000 to
$299,999 $300,000+
Unit Type Bedrooms Pop SAC Pop SAC Pop SAC Pop SAC Pop SAC
Single family 1 BR 2.0039 0.26681.79740.04442.0509 0.23231.7391 0 1.7619 0
detached 2 BRs 2.3790 0.32502.39590.21742.3044 0.18662.3513 0.2209 2.3118 0.1939
3 BRs 3.2144 0.75383.17680.61153.1083 0.53783.0447 0.5519 2.9670 0.5388
4 BRs 3.9509 1.07073.67790.89043.7279 0.93693.6768 0.9260 3.6213 0.9658
5+ BRs 4.9550 1.51054.79691.27114.2145 1.09104.3613 1.2077 4.1065 1.1482
All BRs 2.9127 0.60173.04190.54713.1590 0.58883.3499 0.7295 3.5035 0.8664
Single family 1 BR 1.4882 0 1.66290 1.8684 0 1.7910 0
Attached 2 BRs 2.2729 0.19452.20240.14821.9271 0.09401.8009 0.0567 1.8735 0
3 BRs 2.8359 0.37392.89330.46992.6271 0.40472.8613 0.2446 2.4148 0.1875
4 BRs 5.6000 3.60003.19000.67692.9755 0.45343.3803 0.8635 2.4150 0.4459
5+ BRs 4 0 5 1 4.4294 0.60594.1233 1.4110 3.4538 0.6988
All BRs 2.3554 0.27722.39510.24192.3403 0.25002.4914 0.2687 2.4330 0.2722
Unit in 2 to 4 1 BR 1.7214 0.24111.70980.10161.8665 0.21341.9269 0.0598 1.70640
unit bldg 2 BRs 1.9231 0.15032.19640.27502.3260 0.19882.2357 0.1977 2.2937 0.0769
3 BRs 3.4974 1.04573.26830.70933.2526 0.64573.1893 0.5203 2.5200 0.2184
4 BRs 3.8861 1.10553.14810.53583.5514 0.70653.0661 0.5225 3.3238 0.7910
5+ BRs 2.8013 0.55633.98150.83493.6210 0.71373.3585 0.4234 3.7300 0.4037
All BRs 2.4694 0.48072.63240.43942.7744 0.42172.7301 0.3542 2.6282 0.2281
Unit in 5 to 9 1 BR 1.2423 0 1.38320 1.5132 0 1.8092 0.0658 1.46430.2500
unit bldg 2 BRs 1.8731 0.07341.82370.08741.7684 0.06151.5935 0.0327 2.1795 0.1319
3 BRs 3 0 2.48840.20472.7209 0.10081.5783 0.1807 1.7125 0
4 BRs 1 0 3.84311.80393.4091 0.40916 1
All BRs 1.6673 0.04841.81570.10491.9026 0.06611.7890 0.0883 1.9448 0.1023
Unit in 10 to 1 BR 1.3192 0.01471.16060 1.6295 0.08371 0
19 unit bldg 2 BRs 1.9902 0.08051.74790.12351.6797 0 1.8516 0 2.1171 0.1024
3 BRs 4.1885 0.97542.31580.40792.0349 0.20932 0 2.2321 0
All BRs 1.8114 0.09101.60550.09791.6991 0.04221.8053 0 2.2671 0.1217
Unit in 20 to 1 BR 1.3055 0.03561.34720 1.0625 0 1.1714 0
49 unit bldg 2 BRs 1.9395 0.08621.79010.02851.7939 0.05571.8151 0.0679 2.0800 0
3 BRs 2.58590.70712.6342 0.36591.4468 0 2.3922 0
All BRs 1.6226 0.05961.69030.03991.6009 0.05021.6115 0.0432 2.1705 0
Unit in 50+ 1 BR 1.3606 0 1.22610 1.2279 0 1.1694 0 1 0
unit bldg 2 BRs 1.7826 0.04351.76520.04181.5531 0.02661.7264 0 1.7976 0
3 BRs 5 2 1.9438 0.23602.2583 0 1.7466 0
4 BRs 3 0 2.3684 0
All BRs 1.4531 0.01061.54820.03941.4477 0.02861.7254 0 1.7410 0
Mobile Home 1 BR 1.5648 0.03201.52940 3 0 2 0
2 BRs 1.8247 0.08661.65470.05831 0
3 BRs 3.0088 0.53072.53090.3580
4 BRs 4.5306 1.26532 0 3.5000 1.50004 2
All BRs 1.9057 0.12231.94470.15252.7313 0.80603 1
Source: Public Use Microdata Sample, 1990 Decennial Census and Author Calculations
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
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Table A.6: Mean Population and School-Aged Children (SAC) by
Recent Movers in Rental Housing Units by Monthly Rent, 1990
Rent in 1990 Dollars $0 to $350 $350 to $499 $500 to $599 $600 $749 $750 & Over
Unit Type Bedrooms Pop SAC Pop SAC Pop SAC Pop SAC Pop SAC
Single family 1 BR 1.5153 0.04041.52290.01031.64230.15941.7311 0.0257 2.1516 0.6210
Detached 2 BRs 2.2760 0.34852.26000.31552.61410.33612.6219 0.4066 2.6903 0.3406
3 BRs 3.0503 0.82132.95720.57223.12180.78603.3539 0.7985 3.3429 0.7565
4 BRs 3.5361 1.22063.31200.73794.02081.47073.5660 1.0720 3.9332 0.9168
5+ BRs 3.2760 1.06772.62610.22173.40000.67833.7514 1.5593 4.8479 1.0062
All BRs 2.5675 0.57062.39750.35492.77260.56082.9700 0.6213 3.4647 0.7382
Single family 1 BR 2.1548 0.37721.60830.11271.52040.09741.6552 0 2.8497 0.3105
Attached 2 BRs 2.6857 0.64712.79570.55712.59350.41352.4624 0.3059 2.3776 0.3129
3 BRs 3.7438 1.50824.06241.32733.40171.01553.5719 1.0940 3.4441 0.8050
4 BRs 5.8662 2.59165.88822.57243.62860.69054.3377 0.9868 3.1330 0.4388
5+ BRs 1.84000 7 2 5.1509 2.9528 5.5042 0.2185
All BRs 3.1424 1.01893.09950.80882.63860.51022.8939 0.6078 2.8710 0.4914
Unit in 2 to 4 1 BR 1.4190 0.09281.55360.08131.73100.08911.9067 0.0894 2.1190 0.1947
unit bldg 2 BRs 2.3364 0.37212.40640.34542.54060.39802.5288 0.3287 2.3079 0.2070
3 BRs 3.3628 0.94513.36900.92933.58451.03033.7911 1.0699 3.3314 0.6468
4 BRs 4.4203 1.66224.25831.40814.28351.72254.5834 1.2937 4.1179 0.6455
5+ BRs 4.8005 1.70483.53910.81072.76000.58675.6815 1.4569 4.6936 0.3694
All BRs 2.3463 0.45802.33060.38742.63870.50742.8054 0.5044 2.8736 0.4066
Unit in 5 to 9 1 BR 1.3168 0.07261.57290.09911.58420.07951.6335 0.1129 1.6707 0.0141
unit bldg 2 BRs 2.5355 0.56122.72760.57272.65000.49212.4212 0.2858 2.2129 0.1359
3 BRs 3.8343 1.58553.88751.37044.02801.40043.6996 1.1408 3.2901 0.4309
4 BRs 5.0211 2.39054.88392.14193.55561.18525.5882 2.8824 4.5039 0.5465
All BRs 2.0974 0.46772.20030.39002.28770.39772.2123 0.3043 2.3163 0.1687
Unit in 10 to 1 BR 1.3025 0.07751.57010.11441.48440.06291.5795 0.0708 1.5845 0.0460
19 unit bldg 2 BRs 2.4425 0.60532.63750.38092.46050.27072.3685 0.2986 2.2461 0.1450
3 BRs 3.9329 1.55123.95091.31173.61011.34173.4982 0.8107 3.2008 0.4740
4 BRs 5.9844 3.3385 5.1496 1.2756
All BRs 1.8986 0.40031.90490.23731.94520.19001.9995 0.2010 2.2590 0.1776
Unit in 20 to 1 BR 1.1850 0.02691.39440.05081.50300.04331.5914 0.0456 1.6255 0.0748
49 unit bldg 2 BRs 2.1732 0.32472.26080.28662.16790.24582.4325 0.3144 2.2228 0.1829
3 BRs 3.0049 0.80644.33651.92524 2.18183.2995 0.7754 3.1256 0.3562
All BRs 1.4138 0.10751.62810.12681.66050.10611.9357 0.1610 2.1295 0.1630
Unit in 50+ 1 BR 1.1241 0.00411.32420.00611.42030.03151.5432 0.0102 1.4688 0.0150
unit bldg 2 BRs 2.0380 0.16552.27970.27972.16780.25692.0705 0.2365 2.1418 0.1282
3 BRs 3.2459 1.18035.14672.10674 1.14293.0862 0.6322 3.2428 0.4526
4 BRs 7.1923 4.5962 2.4681 0
All BRs 1.2280 0.03961.48580.07881.59900.09511.6494 0.0757 1.8937 0.0959
Mobile home 1 BR 1.2422 0 1.36890
2 BRs 2.2340 0.21512.61960.36231.26580 2.6400 0.8720 5.0625 1.6250
3 BRs 1.8696 0 2.67350.27895 1 2 0
All BRs 1.9087 0.13702.22260.22421.40240.03662.5594 0.7622 5.0625 1.6250
Source: Public Use Microdata Sample, 1990 Decennial Census and Author Calculations
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
159 UMASS DONAHUE INSTITUTE
Appendix B: Towns by Classification
Municipality Kind of Community
Population
Growth Rank
Pupil Growth
Rank
Benchmarks
Region
Pop Ch.
90-00 %
Abington Rural Economic Center (5) Medium (3) Low (2) Southeast 5.70
Acton Economically Developed Suburb (2) High (4) Very High (5)Boston Metro 13.76
Acushnet Rural Economic Center (5) Medium (3) Medium (3) Southeast 6.35
Adams Rural Economic Center (5) Very Low (1) Medium (3) Berkshire -6.73
Agawam Growth Community (3) Low (2) Low (2) Pioneer Valley 3.00
Alford Resort, Retirement, Artistic (7) Very Low (1) Low (2) Berkshire -4.55
Amesbury Rural Economic Center (5) Medium (3) Medium (3) Northeast 9.69
Amherst Growth Community (3) Very Low (1) Medium (3) Pioneer Valley -1.00
Andover Economically Developed Suburb (2) Medium (3) Medium (3) Northeast 7.19
Arlington Economically Developed Suburb (2) Very Low (1) Very Low (1) Boston Metro -5.02
Ashburnham Small Rural Community (6) Low (2) Medium (3) Central 2.08
Ashby Small Rural Community (6) Medium (3) Medium (3) Central 4.71
Ashfield Resort, Retirement, Artistic (7) Medium (3) Medium (3) Pioneer Valley 4.96
Ashland Economically Developed Suburb (2) Very High (5)Very High (5)Boston Metro 21.61
Athol Rural Economic Center (5) Very Low (1) Low (2) Central -1.33
Attleboro Urban Center (1) Medium (3) High (4) Southeast 9.60
Auburn Economically Developed Suburb (2) Medium (3) Medium (3) Central 5.97
Avon Economically Developed Suburb (2) Very Low (1) Very Low (1) Southeast -2.52
Ayer Urban Center (1) Medium (3) Very Low (1) Central 6.05
Barnstable Growth Community (3) Very High (5)High (4) Cape&Islands 16.78
Barre Rural Economic Center (5) High (4) High (4) Central 12.47
Becket Small Rural Community (6) Very High (5)Very Low (1) Berkshire 18.50
Bedford Economically Developed Suburb (2) Very Low (1) High (4) Boston Metro -3.09
Belchertown Growth Community (3) Very High (5)High (4) Pioneer Valley 22.58
Bellingham Growth Community (3) Low (2) High (4) Boston Metro 2.94
Belmont Economically Developed Suburb (2) Very Low (1) Medium (3) Boston Metro -2.13
Berkley Small Rural Community (6) Very High (5)Very High (5)Southeast 35.69
Berlin Residential Suburb (4) Low (2) Medium (3) Central 3.79
Bernardston Rural Economic Center (5) Medium (3) Low (2) Pioneer Valley 5.22
Beverly Economically Developed Suburb (2) Low (2) Low (2) Northeast 4.36
Billerica Economically Developed Suburb (2) Low (2) Medium (3) Northeast 3.65
Blackstone Rural Economic Center (5) Medium (3) High (4) Central 9.73
Blandford Small Rural Community (6) Low (2) Very Low (1) Pioneer Valley 2.27
Bolton Residential Suburb (4) Very High (5)Very High (5)Boston Metro 32.35
Boston Urban Center (1) Low (2) Medium (3) Boston Metro 2.59
Bourne Growth Community (3) High (4) Very Low (1) Cape&Islands 16.54
Boxborough Residential Suburb (4) Very High (5)Very High (5)Boston Metro 45.62
Boxford Residential Suburb (4) Very High (5)Very High (5)Northeast 26.41
Boylston Residential Suburb (4) High (4) Very High (5)Central 13.96
Braintree Economically Developed Suburb (2) Very Low (1) Low (2) Boston Metro -0.02
Brewster Resort, Retirement, Artistic (7) Very High (5)High (4) Cape&Islands 19.60
Bridgewater Growth Community (3) Very High (5)High (4) Southeast 18.52
Brimfield Small Rural Community (6) High (4) Medium (3) Pioneer Valley 11.26
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
160 UMASS DONAHUE INSTITUTE
Municipality Kind of Community
Population
Growth Rank
Pupil Growth
Rank
Benchmarks
Region
Pop Ch.
90-00 %
Brockton Urban Center (1) Low (2) Medium (3) Southeast 1.63
Brookfield Rural Economic Center (5) Low (2) High (4) Central 2.80
Brookline Economically Developed Suburb (2) Low (2) Low (2) Boston Metro 4.37
Buckland Rural Economic Center (5) Low (2) Low (2) Pioneer Valley 3.27
Burlington Economically Developed Suburb (2) Very Low (1) Very Low (1) Boston Metro -1.83
Cambridge Urban Center (1) Medium (3) Very Low (1) Boston Metro 5.80
Canton Economically Developed Suburb (2) High (4) Medium (3) Boston Metro 12.12
Carlisle Residential Suburb (4) Medium (3) Very High (5)Boston Metro 8.86
Carver Growth Community (3) Medium (3) Low (2) Southeast 5.41
Charlemont Small Rural Community (6) Medium (3) Very Low (1) Pioneer Valley 8.73
Charlton Small Rural Community (6) Very High (5)High (4) Central 17.62
Chatham Resort, Retirement, Artistic (7) Very Low (1) Very Low (1) Cape&Islands 0.70
Chelmsford Economically Developed Suburb (2) Low (2) Low (2) Northeast 4.55
Chelsea Urban Center (1) Very High (5)Very High (5)Boston Metro 22.19
Cheshire Rural Economic Center (5) Very Low (1) Very Low (1) Berkshire -2.24
Chester Rural Economic Center (5) Low (2) Medium (3) Pioneer Valley 2.19
Chesterfield Small Rural Community (6) High (4) Very Low (1) Pioneer Valley 14.60
Chicopee Urban Center (1) Very Low (1) High (4) Pioneer Valley -3.49
Chilmark Resort, Retirement, Artistic (7) Very High (5)Very High (5)Cape&Islands 29.69
Clarksburg Rural Economic Center (5) Very Low (1) Very Low (1) Berkshire -3.38
Clinton Urban Center (1) Low (2) Medium (3) Central 1.61
Cohasset Residential Suburb (4) Low (2) Medium (3) Boston Metro 2.63
Colrain Rural Economic Center (5) Low (2) Very Low (1) Pioneer Valley 3.19
Concord Economically Developed Suburb (2) Very Low (1) Medium (3) Boston Metro -0.49
Conway Resort, Retirement, Artistic (7) Very High (5)Very High (5)Pioneer Valley 18.31
Cummington Resort, Retirement, Artistic (7) Very High (5)Very Low (1) Pioneer Valley 24.59
Dalton Rural Economic Center (5) Very Low (1) Low (2) Berkshire -3.68
Danvers Economically Developed Suburb (2) Low (2) High (4) Northeast 4.29
Dartmouth Growth Community (3) High (4) Low (2) Southeast 12.56
Dedham Economically Developed Suburb (2) Very Low (1) Low (2) Boston Metro -1.34
Deerfield Rural Economic Center (5) Very Low (1) Low (2) Pioneer Valley -5.34
Dennis Resort, Retirement, Artistic (7) High (4) Low (2) Cape&Islands 15.21
Dighton Rural Economic Center (5) Medium (3) High (4) Southeast 9.66
Douglas Small Rural Community (6) Very High (5)Very High (5)Central 29.55
Dover Residential Suburb (4) High (4) Very High (5)Boston Metro 13.08
Dracut Growth Community (3) High (4) High (4) Northeast 11.60
Dudley Rural Economic Center (5) Medium (3) High (4) Central 5.20
Dunstable Residential Suburb (4) Very High (5)Very High (5)Northeast 26.52
Duxbury Residential Suburb (4) Low (2) Low (2) Southeast 2.54
East Bridgewater Growth Community (3) Very High (5)Medium (3) Southeast 16.84
East Brookfield Rural Economic Center (5) Low (2) Very Low (1) Central 3.15
East Longmeadow Economically Developed Suburb (2) Medium (3) Medium (3) Pioneer Valley 5.48
Eastham Resort, Retirement, Artistic (7) Very High (5)High (4) Cape&Islands 22.21
Easthampton Rural Economic Center (5) Low (2) Very Low (1) Pioneer Valley 2.94
Easton Residential Suburb (4) High (4) Medium (3) Southeast 12.58
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
161 UMASS DONAHUE INSTITUTE
Municipality Kind of Community
Population
Growth Rank
Pupil Growth
Rank
Benchmarks
Region
Pop Ch.
90-00 %
Edgartown Resort, Retirement, Artistic (7) Very High (5)High (4) Cape&Islands 23.42
Egremont Resort, Retirement, Artistic (7) Medium (3) Very Low (1) Berkshire 9.44
Erving Rural Economic Center (5) Medium (3) Very Low (1) Pioneer Valley 6.92
Essex Resort, Retirement, Artistic (7) Very Low (1) High (4) Northeast 0.21
Everett Urban Center (1) Medium (3) Very High (5)Boston Metro 6.54
Fairhaven Urban Center (1) Very Low (1) Low (2) Southeast 0.17
Fall River Urban Center (1) Very Low (1) Very Low (1) Southeast -0.83
Falmouth Growth Community (3) Very High (5)Medium (3) Cape&Islands 16.81
Fitchburg Urban Center (1) Very Low (1) Medium (3) Central -5.08
Florida Small Rural Community (6) Very Low (1) Medium (3) Berkshire -8.89
Foxborough Economically Developed Suburb (2) High (4) High (4) Boston Metro 10.99
Framingham Economically Developed Suburb (2) Low (2) Medium (3) Boston Metro 2.96
Franklin Economically Developed Suburb (2) Very High (5)Very High (5)Boston Metro 33.79
Freetown Growth Community (3) Very Low (1) Very Low (1) Southeast -0.59
Gardner Urban Center (1) Low (2) Medium (3) Central 3.20
Gay Head Resort, Retirement, Artistic (7) Very High (5)Very High (5)Cape&Islands 71.14
Georgetown Residential Suburb (4) High (4) High (4) Northeast 15.55
Gill Rural Economic Center (5) Very Low (1) Low (2) Pioneer Valley -13.90
Gloucester Urban Center (1) Medium (3) Medium (3) Northeast 5.42
Goshen Small Rural Community (6) High (4) Low (2) Pioneer Valley 10.96
Gosnold Resort, Retirement, Artistic (7) Very Low (1) Very High (5)Cape&Islands -12.24
Grafton Economically Developed Suburb (2) High (4) Medium (3) Central 14.26
Granby Growth Community (3) High (4) Medium (3) Pioneer Valley 10.19
Granville Small Rural Community (6) Medium (3) Low (2) Pioneer Valley 8.41
Great Barrington Urban Center (1) Very Low (1) Very Low (1) Berkshire -2.56
Greenfield Urban Center (1) Very Low (1) Very Low (1) Pioneer Valley -2.67
Groton Residential Suburb (4) Very High (5)Very High (5)Central 27.11
Groveland Residential Suburb (4) High (4) Very High (5)Northeast 15.80
Hadley Resort, Retirement, Artistic (7) High (4) High (4) Pioneer Valley 13.28
Halifax Small Rural Community (6) High (4) Medium (3) Southeast 14.92
Hamilton Residential Suburb (4) High (4) High (4) Northeast 14.22
Hampden Residential Suburb (4) High (4) Very High (5)Pioneer Valley 9.81
Hancock Small Rural Community (6) High (4) Low (2) Berkshire 14.81
Hanover Residential Suburb (4) High (4) Medium (3) Southeast 10.51
Hanson Growth Community (3) Medium (3) Low (2) Southeast 5.17
Hardwick Rural Economic Center (5) High (4) High (4) Central 9.94
Harvard Growth Community (3) Very Low (1) Very High (5)Central -51.49
Harwich Growth Community (3) Very High (5)High (4) Cape&Islands 20.55
Hatfield Rural Economic Center (5) Low (2) Very Low (1) Pioneer Valley 2.04
Haverhill Urban Center (1) High (4) High (4) Northeast 14.69
Hawley Resort, Retirement, Artistic (7) Medium (3) Very High (5)Pioneer Valley 5.99
Heath Small Rural Community (6) High (4) Low (2) Pioneer Valley 12.43
Hingham Residential Suburb (4) Very Low (1) Low (2) Boston Metro 0.31
Hinsdale Rural Economic Center (5) Very Low (1) High (4) Berkshire -4.44
Holbrook Economically Developed Suburb (2) Very Low (1) Very Low (1) Boston Metro -2.32
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
162 UMASS DONAHUE INSTITUTE
Municipality Kind of Community
Population
Growth Rank
Pupil Growth
Rank
Benchmarks
Region
Pop Ch.
90-00 %
Holden Residential Suburb (4) Medium (3) Medium (3) Central 6.79
Holland Small Rural Community (6) High (4) Low (2) Pioneer Valley 10.16
Holliston Residential Suburb (4) Medium (3) Medium (3) Boston Metro 6.77
Holyoke Urban Center (1) Very Low (1) Very Low (1) Pioneer Valley -8.85
Hopedale Rural Economic Center (5) Low (2) Low (2) Central 4.25
Hopkinton Residential Suburb (4) Very High (5)Very High (5)Boston Metro 45.21
Hubbardston Small Rural Community (6) Very High (5)Very High (5)Central 39.76
Hudson Economically Developed Suburb (2) Medium (3) Very Low (1) Boston Metro 5.11
Hull Rural Economic Center (5) Medium (3) Medium (3) Boston Metro 5.58
Huntington Small Rural Community (6) Medium (3) High (4) Pioneer Valley 9.41
Ipswich Resort, Retirement, Artistic (7) Medium (3) High (4) Northeast 9.38
Kingston Growth Community (3) Very High (5)Very High (5)Southeast 30.24
Lakeville Small Rural Community (6) Very High (5)High (4) Southeast 26.15
Lancaster Growth Community (3) High (4) High (4) Central 10.79
Lanesborough Small Rural Community (6) Very Low (1) Very Low (1) Berkshire -1.39
Lawrence Urban Center (1) Low (2) High (4) Northeast 2.62
Lee Rural Economic Center (5) Low (2) Low (2) Berkshire 2.33
Leicester Rural Economic Center (5) Low (2) Medium (3) Central 2.75
Lenox Growth Community (3) Very Low (1) Low (2) Berkshire 0.16
Leominster Urban Center (1) Medium (3) Very High (5)Central 8.28
Leverett Resort, Retirement, Artistic (7) Very Low (1) Very Low (1) Pioneer Valley -6.83
Lexington Economically Developed Suburb (2) Medium (3) Very High (5)Boston Metro 4.77
Leyden Small Rural Community (6) High (4) Medium (3) Pioneer Valley 16.62
Lincoln Residential Suburb (4) Medium (3) Very High (5)Boston Metro 5.09
Littleton Economically Developed Suburb (2) High (4) Very High (5)Boston Metro 16.07
Longmeadow Residential Suburb (4) Low (2) Low (2) Pioneer Valley 1.07
Lowell Urban Center (1) Low (2) Medium (3) Northeast 1.67
Ludlow Growth Community (3) High (4) Low (2) Pioneer Valley 12.69
Lunenburg Growth Community (3) Low (2) Medium (3) Central 3.12
Lynn Urban Center (1) Medium (3) High (4) Boston Metro 9.61
Lynnfield Residential Suburb (4) Low (2) Low (2) Northeast 4.46
Malden Urban Center (1) Low (2) Low (2) Boston Metro 4.56
Manchester Residential Suburb (4) Very Low (1) High (4) Northeast -1.10
Mansfield Growth Community (3) Very High (5)Very High (5)Southeast 35.28
Marblehead Economically Developed Suburb (2) Low (2) Medium (3) Northeast 2.03
Marion Growth Community (3) High (4) High (4) Southeast 13.95
Marlborough Economically Developed Suburb (2) High (4) High (4) Boston Metro 13.96
Marshfield Residential Suburb (4) High (4) Medium (3) Southeast 12.97
Mashpee Growth Community (3) Very High (5)Very High (5)Cape&Islands 64.21
Mattapoisett Growth Community (3) Medium (3) Medium (3) Southeast 7.15
Maynard Urban Center (1) Very Low (1) Low (2) Boston Metro 1.05
Medfield Residential Suburb (4) High (4) Very High (5)Boston Metro 16.54
Medford Urban Center (1) Very Low (1) Low (2) Boston Metro -2.86
Medway Residential Suburb (4) Very High (5)Very High (5)Boston Metro 25.34
Melrose Economically Developed Suburb (2) Very Low (1) Very Low (1) Boston Metro -3.61
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
163 UMASS DONAHUE INSTITUTE
Municipality Kind of Community
Population
Growth Rank
Pupil Growth
Rank
Benchmarks
Region
Pop Ch.
90-00 %
Mendon Residential Suburb (4) Very High (5)Very High (5)Central 31.82
Merrimac Rural Economic Center (5) Very High (5)Very High (5)Northeast 18.82
Methuen Urban Center (1) Medium (3) High (4) Northeast 9.50
Middleborough Rural Economic Center (5) High (4) Low (2) Southeast 11.61
Middlefield Small Rural Community (6) Very High (5)High (4) Pioneer Valley 38.27
Middleton Economically Developed Suburb (2) Very High (5)Very High (5)Northeast 57.37
Milford Urban Center (1) Medium (3) Low (2) Boston Metro 5.70
Millbury Rural Economic Center (5) Low (2) Medium (3) Central 4.55
Millis Residential Suburb (4) Low (2) Medium (3) Boston Metro 3.80
Millville Rural Economic Center (5) Very High (5)Very High (5)Central 21.82
Milton Economically Developed Suburb (2) Low (2) High (4) Boston Metro 1.31
Monroe Rural Economic Center (5) Very Low (1) Very High (5)Pioneer Valley -19.13
Monson Rural Economic Center (5) Medium (3) Medium (3) Pioneer Valley 7.50
Montague Rural Economic Center (5) Low (2) Very Low (1) Pioneer Valley 2.08
Monterey Resort, Retirement, Artistic (7) High (4) Very Low (1) Berkshire 16.02
Montgomery Residential Suburb (4) Very Low (1) Very Low (1) Pioneer Valley -13.83
Mount Washington Small Rural Community (6) Very Low (1) Low (2) Berkshire -3.70
Nahant Resort, Retirement, Artistic (7) Very Low (1) Low (2) Boston Metro -5.12
Nantucket Resort, Retirement, Artistic (7) Very High (5)Very High (5)Cape&Islands 58.35
Natick Economically Developed Suburb (2) Medium (3) Medium (3) Boston Metro 5.44
Needham Economically Developed Suburb (2) Medium (3) Medium (3) Boston Metro 4.91
New Ashford Resort, Retirement, Artistic (7) Very High (5)High (4) Berkshire 28.65
New Bedford Urban Center (1) Very Low (1) Low (2) Southeast -6.16
New Braintree Rural Economic Center (5) Medium (3) Low (2) Central 5.22
New Marlborough Resort, Retirement, Artistic (7) Very High (5)Low (2) Berkshire 20.48
New Salem Small Rural Community (6) High (4) Low (2) Pioneer Valley 15.84
Newbury Small Rural Community (6) Very High (5)Medium (3) Northeast 19.46
Newburyport Rural Economic Center (5) Medium (3) Very Low (1) Northeast 5.34
Newton Economically Developed Suburb (2) Low (2) High (4) Boston Metro 1.51
Norfolk Residential Suburb (4) High (4) Very High (5)Boston Metro 12.97
North Adams Urban Center (1) Very Low (1) Very Low (1) Berkshire -12.60
North Andover Economically Developed Suburb (2) Very High (5)High (4) Northeast 19.35
North Attleborough Rural Economic Center (5) Medium (3) High (4) Southeast 8.41
North Brookfield Rural Economic Center (5) Very Low (1) Very Low (1) Central -0.53
North Reading Residential Suburb (4) High (4) High (4) Northeast 15.29
Northampton Urban Center (1) Very Low (1) Very Low (1) Pioneer Valley -1.06
Northborough Economically Developed Suburb (2) Very High (5)High (4) Central 17.47
Northbridge Rural Economic Center (5) Very Low (1) High (4) Central -1.41
Northfield Rural Economic Center (5) Low (2) Very High (5)Pioneer Valley 3.98
Norton Growth Community (3) Very High (5)Very High (5)Southeast 26.44
Norwell Residential Suburb (4) Medium (3) Medium (3) Southeast 5.24
Norwood Economically Developed Suburb (2) Very Low (1) Very Low (1) Boston Metro -0.39
Oak Bluffs Resort, Retirement, Artistic (7) Very High (5)Very High (5)Cape&Islands 32.42
Oakham Small Rural Community (6) High (4) Very High (5)Central 11.31
Orange Rural Economic Center (5) Low (2) Low (2) Pioneer Valley 2.82
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
164 UMASS DONAHUE INSTITUTE
Municipality Kind of Community
Population
Growth Rank
Pupil Growth
Rank
Benchmarks
Region
Pop Ch.
90-00 %
Orleans Resort, Retirement, Artistic (7) Medium (3) Low (2) Cape&Islands 8.62
Otis Small Rural Community (6) Very High (5)Very Low (1) Berkshire 27.21
Oxford Rural Economic Center (5) Medium (3) Very Low (1) Central 6.07
Palmer Rural Economic Center (5) Low (2) High (4) Pioneer Valley 3.68
Paxton Residential Suburb (4) Medium (3) Medium (3) Central 8.38
Peabody Economically Developed Suburb (2) Low (2) Medium (3) Northeast 1.83
Pelham Residential Suburb (4) Low (2) Low (2) Pioneer Valley 2.18
Pembroke Growth Community (3) High (4) Medium (3) Southeast 16.38
Pepperell Small Rural Community (6) High (4) High (4) Northeast 10.34
Peru Small Rural Community (6) Medium (3) High (4) Berkshire 5.39
Petersham Small Rural Community (6) Low (2) Very Low (1) Central 4.33
Phillipston Small Rural Community (6) Medium (3) High (4) Central 9.16
Pittsfield Urban Center (1) Very Low (1) Very Low (1) Berkshire -5.82
Plainfield Small Rural Community (6) Low (2) Very Low (1) Pioneer Valley 3.15
Plainville Growth Community (3) High (4) High (4) Southeast 11.82
Plymouth Growth Community (3) High (4) Medium (3) Southeast 13.36
Plympton Growth Community (3) High (4) Low (2) Southeast 10.61
Princeton Residential Suburb (4) Medium (3) High (4) Central 5.14
Provincetown Urban Center (1) Very Low (1) Very Low (1) Cape&Islands -3.65
Quincy Urban Center (1) Low (2) High (4) Boston Metro 3.58
Randolph Economically Developed Suburb (2) Low (2) Medium (3) Boston Metro 2.89
Raynham Growth Community (3) Very High (5)Low (2) Southeast 18.97
Reading Economically Developed Suburb (2) Medium (3) Medium (3) Northeast 5.19
Rehoboth Small Rural Community (6) Very High (5)Medium (3) Southeast 17.51
Revere Urban Center (1) High (4) Medium (3) Boston Metro 10.51
Richmond Residential Suburb (4) Very Low (1) Very Low (1) Berkshire -4.35
Rochester Growth Community (3) Very High (5)Low (2) Southeast 16.83
Rockland Rural Economic Center (5) Medium (3) Low (2) Southeast 9.59
Rockport Resort, Retirement, Artistic (7) Low (2) High (4) Northeast 3.81
Rowe Resort, Retirement, Artistic (7) Very Low (1) Very Low (1) Pioneer Valley -7.14
Rowley Growth Community (3) Very High (5)Very High (5)Northeast 23.54
Royalston Small Rural Community (6) Medium (3) Very Low (1) Central 9.33
Russell Rural Economic Center (5) Low (2) Very Low (1) Pioneer Valley 3.95
Rutland Small Rural Community (6) Very High (5)Very High (5)Central 28.71
Salem Urban Center (1) Medium (3) High (4) Northeast 6.08
Salisbury Growth Community (3) High (4) Very Low (1) Northeast 13.73
Sandisfield Resort, Retirement, Artistic (7) Very High (5)Very Low (1) Berkshire 23.54
Sandwich Residential Suburb (4) Very High (5)Very High (5)Cape&Islands 30.00
Saugus Economically Developed Suburb (2) Low (2) Very Low (1) Boston Metro 2.07
Savoy Small Rural Community (6) High (4) Low (2) Berkshire 11.20
Scituate Residential Suburb (4) Medium (3) Medium (3) Southeast 6.42
Seekonk Growth Community (3) Low (2) Very Low (1) Southeast 2.91
Sharon Residential Suburb (4) High (4) High (4) Boston Metro 12.19
Sheffield Resort, Retirement, Artistic (7) High (4) High (4) Berkshire 14.60
Shelburne Rural Economic Center (5) Low (2) Very Low (1) Pioneer Valley 2.29
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
165 UMASS DONAHUE INSTITUTE
Municipality Kind of Community
Population
Growth Rank
Pupil Growth
Rank
Benchmarks
Region
Pop Ch.
90-00 %
Sherborn Residential Suburb (4) Medium (3) Very High (5)Boston Metro 5.29
Shirley Rural Economic Center (5) Low (2) High (4) Central 4.17
Shrewsbury Economically Developed Suburb (2) Very High (5)Very High (5)Central 31.04
Shutesbury Resort, Retirement, Artistic (7) High (4) High (4) Pioneer Valley 15.95
Somerset Economically Developed Suburb (2) Low (2) Very Low (1) Southeast 3.28
Somerville Urban Center (1) Low (2) Medium (3) Boston Metro 1.66
South Hadley Economically Developed Suburb (2) Low (2) Medium (3) Pioneer Valley 3.06
Southampton Small Rural Community (6) Very High (5)High (4) Pioneer Valley 20.30
Southborough Residential Suburb (4) Very High (5)Very High (5)Boston Metro 32.48
Southbridge Urban Center (1) Very Low (1) Very Low (1) Central -3.38
Southwick Growth Community (3) High (4) High (4) Pioneer Valley 15.23
Spencer Rural Economic Center (5) Very Low (1) Very Low (1) Central 0.40
Springfield Urban Center (1) Very Low (1) High (4) Pioneer Valley -3.12
Sterling Residential Suburb (4) High (4) Low (2) Central 11.97
Stockbridge Resort, Retirement, Artistic (7) Very Low (1) Very Low (1) Berkshire -5.48
Stoneham Economically Developed Suburb (2) Very Low (1) Low (2) Boston Metro 0.07
Stoughton Economically Developed Suburb (2) Low (2) Low (2) Southeast 1.39
Stow Residential Suburb (4) High (4) Medium (3) Boston Metro 10.77
Sturbridge Growth Community (3) Very Low (1) Low (2) Central 0.80
Sudbury Residential Suburb (4) Very High (5)Very High (5)Boston Metro 17.29
Sunderland Growth Community (3) High (4) Low (2) Pioneer Valley 11.12
Sutton Small Rural Community (6) Very High (5)Very High (5)Central 20.90
Swampscott Economically Developed Suburb (2) Medium (3) Medium (3) Boston Metro 5.58
Swansea Growth Community (3) Low (2) Very Low (1) Southeast 3.18
Taunton Urban Center (1) High (4) High (4) Southeast 12.33
Templeton Rural Economic Center (5) Medium (3) Low (2) Central 5.61
Tewksbury Economically Developed Suburb (2) Medium (3) Medium (3) Northeast 5.81
Tisbury Resort, Retirement, Artistic (7) Very High (5)High (4) Cape&Islands 20.35
Tolland Small Rural Community (6) Very High (5)Very High (5)Pioneer Valley 47.40
Topsfield Residential Suburb (4) Medium (3) Very High (5)Northeast 6.73
Townsend Small Rural Community (6) Medium (3) Medium (3) Central 8.26
Truro Resort, Retirement, Artistic (7) Very High (5)Very High (5)Cape&Islands 32.68
Tyngsborough Growth Community (3) Very High (5)Very High (5)Northeast 28.22
Tyringham Resort, Retirement, Artistic (7) Very Low (1) Very Low (1) Berkshire -5.15
Upton Resort, Retirement, Artistic (7) Very High (5)Very High (5)Central 20.63
Uxbridge Rural Economic Center (5) Medium (3) Very High (5)Central 7.11
Wakefield Economically Developed Suburb (2) Very Low (1) Low (2) Boston Metro -0.08
Wales Small Rural Community (6) High (4) Very Low (1) Pioneer Valley 10.92
Walpole Economically Developed Suburb (2) High (4) Very High (5)Boston Metro 12.86
Waltham Urban Center (1) Low (2) Very Low (1) Boston Metro 2.33
Ware Rural Economic Center (5) Very Low (1) Low (2) Pioneer Valley -1.03
Wareham Growth Community (3) Medium (3) High (4) Southeast 5.74
Warren Rural Economic Center (5) Medium (3) Medium (3) Central 7.64
Warwick Small Rural Community (6) Low (2) Very Low (1) Pioneer Valley 1.35
Washington Small Rural Community (6) Very Low (1) Very Low (1) Berkshire -11.54
THE FISCAL IMPACT OF NEW HOUSING DEVELOPMENT IN MASSACHUSETTS: A CRITICAL ANALYSIS
166 UMASS DONAHUE INSTITUTE
Municipality Kind of Community
Population
Growth Rank
Pupil Growth
Rank
Benchmarks
Region
Pop Ch.
90-00 %
Watertown Urban Center (1) Very Low (1) Low (2) Boston Metro -0.90
Wayland Residential Suburb (4) High (4) Very High (5)Boston Metro 10.33
Webster Urban Center (1) Low (2) Low (2) Central 1.35
Wellesley Economically Developed Suburb (2) Very Low (1) Very High (5)Boston Metro -0.01
Wellfleet Resort, Retirement, Artistic (7) High (4) Low (2) Cape&Islands 10.27
Wendell Growth Community (3) Medium (3) Very Low (1) Pioneer Valley 9.68
Wenham Residential Suburb (4) Medium (3) High (4) Northeast 5.41
West Boylston Economically Developed Suburb (2) High (4) Very High (5)Central 13.16
West Bridgewater Rural Economic Center (5) Low (2) Very Low (1) Southeast 3.83
West Brookfield Rural Economic Center (5) Medium (3) Medium (3) Central 7.70
West Newbury Small Rural Community (6) Very High (5)High (4) Northeast 21.28
West Springfield Urban Center (1) Low (2) Medium (3) Pioneer Valley 1.31
West Stockbridge Resort, Retirement, Artistic (7) Very Low (1) Very Low (1) Berkshire -4.52
West Tisbury Resort, Retirement, Artistic (7) Very High (5)Very High (5)Cape&Islands 44.78
Westborough Economically Developed Suburb (2) Very High (5)Very High (5)Central 27.34
Westfield Urban Center (1) Low (2) Low (2) Pioneer Valley 4.43
Westford Residential Suburb (4) Very High (5)Very High (5)Northeast 26.61
Westhampton Residential Suburb (4) High (4) Low (2) Pioneer Valley 10.63
Westminster Growth Community (3) High (4) Medium (3) Central 11.57
Weston Residential Suburb (4) High (4) Very High (5)Boston Metro 12.44
Westport Growth Community (3) Low (2) Low (2) Southeast 2.39
Westwood Economically Developed Suburb (2) High (4) Very High (5)Boston Metro 12.42
Weymouth Economically Developed Suburb (2) Very Low (1) Very Low (1) Boston Metro -0.14
Whately Resort, Retirement, Artistic (7) High (4) Medium (3) Pioneer Valley 14.40
Whitman Rural Economic Center (5) Medium (3) Very Low (1) Southeast 4.85
Wilbraham Residential Suburb (4) Medium (3) High (4) Pioneer Valley 6.63
Williamsburg Rural Economic Center (5) Very Low (1) Very Low (1) Pioneer Valley -3.50
Williamstown Resort, Retirement, Artistic (7) Low (2) Low (2) Berkshire 2.48
Wilmington Economically Developed Suburb (2) Very High (5)Very High (5)Northeast 21.03
Winchendon Rural Economic Center (5) Medium (3) High (4) Central 9.15
Winchester Economically Developed Suburb (2) Low (2) Low (2) Boston Metro 2.68
Windsor Resort, Retirement, Artistic (7) High (4) Low (2) Berkshire 13.64
Winthrop Rural Economic Center (5) Very Low (1) Low (2) Boston Metro 0.97
Woburn Economically Developed Suburb (2) Low (2) Low (2) Boston Metro 3.66
Worcester Urban Center (1) Low (2) High (4) Central 1.70
Worthington Small Rural Community (6) High (4) Very Low (1) Pioneer Valley 9.86
Wrentham Rural Economic Center (5) Very High (5)Very High (5)Boston Metro 17.19
Yarmouth Growth Community (3) Very High (5)Medium (3) Cape&Islands 17.16