evaluating smart growth
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Evaluating Smart GrowthState and Local Policy Outcomes
G r e G o r y K . I n G r a m a n d y u - H u n G H o n G
Policy Focus Report Lincoln Institute of Land Policy
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Evaluating Smart Growth:State and Local Policy Outcomes
Gregory K. Ingram and Yu-Hung Hong
Contents2 Executive Summary
4 Low-density Development and Smart Growth Policies
9 Growth Patterns and Trends
15 Natural Resources and Environmental Quality
19 Transportation
24 Affordable Housing
29 Fiscal Dimensions
33 Survey of Opinion Leaders
37 Conclusions and Recommendations
44 References
Polic Focs Report Series
The policy ocus report series is published by the Lincoln Institute o Land Policy to address
timely public policy issues relating to land use, land markets, and property taxation. Each report
is designed to bridge the gap between theory and practice by combining research ndings, case
studies, and contributions rom scholars in a variety o academic disciplines, and rom
proessional practitioners, local ocials, and citizens in diverse communities.
Abot the Athors
Gregory K. Ingram is president and CEO o the Lincoln Institute o Land Policy and cochair
o the Department o International Studies. Contact: [email protected]
Yu-Hung Hongis a ellow at the Lincoln Institute o Land Policy and a visiting assistant
proessor at Massachusetts Institute o Technology. Contact: [email protected]
Copyright 2009 by Lincoln Institute o Land Policy.
All rights reserved.
113 Brattle Street
Cambridge, MA 02138-3400, USA
Phone: 617-661-3016 x127 or 800-526-3873
Fax: 617-661-7235 or 800-526-3944
Email: [email protected]
Web: www.lincolninst.edu
ISBN 978-1-55844-193-4
Policy Focus Report/Code PF020
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 1
. . . . . . . . . . . . . . . .
About this ReportPolic Evalatio
Growth Patterns and Trends: Gerrit Kaap
and Rebecca Lewis, University o Maryland
Natural Resources and Environmental Quality:
Terr Moore and Beth Goodma, ECONorth-
west, Oregon
Transportation: Tim Chapi and Keith Ihlaeldt,
Florida State University
Aordable Housing: Start Meck, Rutgers
Center or Government Services, and Timoth
MacKio, Monmouth University, New Jersey
Fiscal Dimensions: Robert W. Brchell and
William R. Dolphi, Rutgers Center or Urban
Policy Research
Survey o Opinion Leaders and Regulatory
Analysis: Alla Wallis and Tom Clark,
University o Colorado Denver
State Case Stdies
Florida: Tim Chapi and Keith Ihlaeldt,Florida State University
Maryland: Gerrit Kaap and Rebecca Lewis,
University o Maryland
New Jersey: Start Meck, Rutgers Center
or Government Services
Oregon: Terr Moore and Beth Goodma,
ECONorthwest
Colorado: Alla Wallis, University o Colorado
Denver
Indiana: Eric D. Kell, Ball State University
Texas: Robert G. Paterso, Rachael Rawlis,
Frederick Steier, and Mig Zhag, University
o Texas at Austin
Virginia: Case Dawkis, Virginia Tech
The Lincoln Institute initiated a research project
in late 2006 to evaluate the eectiveness o smart
growth policies rom 1990 to as ar past 2000 as
data allowed. The analysis ocused on our states
with well-established statewide smart growth
programs (Florida, Maryland, New Jersey, and
Oregon) and our states (Colorado, Indiana, Texas,
and Virginia) that oered a range o other land
management approaches.
This report summarizes the ndings and recom-
mendations o the complete evaluation, which
is published in the 2009 Lincoln Institute book,
Smart Growth Policies: An Evaluation of Programs
and Outcomes, edited by Gregory K. Ingram,
Armando Carbonell, Yu-Hung Hong, and Anthony
Flint. This book is the source o the data and
statistics cited here unless otherwise noted.
The goal o the evaluation was to examine the
eectiveness o various policies in achieving ve
commonly identied smart growth objectives:
promotecompactdevelopment;
protectnaturalresourcesand
environmentalquality;
provideandpromoteavarietyof
transportationoptions;
supplyaffordablehousing;and
createnetpositivescalimpacts.
Using 52 indicators based on U.S. Census Bureau
data and other state and local datasets, several
research teams compared dierences in peror-
mance among the selected states and between
the groups o smart growth and other states.
Another team surveyed opinion leaders on their
perceptions about the ecacy o smart growth
programs, and other researchers prepared case
studies on the political, environmental, and regu-
latory conditions in the eight selected states.
Contributors
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2 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
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Executive Summary
This evaluation o the eectivenesso smart growth policies in the
United States ocused on our states
with well-established statewide smart
growth programs (Florida, Maryland, New
Jersey, and Oregon) and our other states
(Colorado, Indiana, Texas, and Virginia)
that demonstrate a range o other land man-
agement approaches (Ingram et al. 2009).
The evaluation was objectives-based and
examined the extent to which ve specic
smart growth objectives were achieved,based on measureable and comparable
perormance indicators primarily during
the decade rom 1990 to 2000:
promote compact development;
protect natural resources and environ-
mental quality;
provide and promote a variety o
transportation options;
supply aordable housing; and create net positive scal impacts.
No state did well on all perormance mea-
sures, although individual states succeeded
in one or more o their priority policy areas.
Maryland was successul in protecting
natural resources through its land preser-
vation programs and state unding or the
purchase o armland conservation ease-
ments. New Jersey policies that responded
to state supreme court decisions led to an
aordable housing approach that slowed
house price escalation and encouraged
rental and multiamily housing production.
Oregons commitment to establishing
urban growth boundaries was able to reduce
development on armland in the Willamette
Valley. The state also perormed well in
reducing trac congestion growth by
Portlad, Orego
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 3
. . . . . . . . . . . . . . . .
encouraging commuters to use transit and
by systematically planning or bicyclists and
pedestrians.
At the same time, some smart growthstates ailed to achieve objectives in policy
areas that were not given high priority
during the study period, such as providing
aordable housing in Oregon and Maryland,
and managing the spatial structure o urban
growth in Florida.
The message is clear: achieving smart
growth is possible, but states must remain
ocused on their key policy goals. No single
approach is right or all states. For example,
Colorado has no statewide smart growth
program, but it outperormed some states
with such policies by supporting local gov-
ernment actions to pursue eective land use
planning within a regional context. The more
successul states use a variety o regulatory
controls, market incentives, and institutional
policies to achieve their objectives.
RECOMMEnDAT IOnS
Program Structure and Transparency
Thedesignof smartgrowthprograms
and supporting regulations and incentives
should be guided by a vision o sustain-
able and desirable development outcomes.
Anytop-downorbottom-upsmart
growth policies must be coordinated at
the regional level to be able to achieve
their desired objectives.
Policymakersmustarticulatethemeans
o achieving smart growth objectives and
speciy implementation mechanisms,
rather than just declare objectives.
Functional Linkages for Policy Design
Thedesignof growthmanagementpolicies
should take account o interactions among
policies and coordination across relevant
agencies.
Smartgrowthpoliciesshouldmakeuse
o economic incentives, such as pricing
and tax policies, that have shown promise
in other countries. Smartgrowthprogramsneedtoconsider
the income distribution consequences
o their policies.
Sustainability and Monitoring
of Programs
Crediblecommitmentfromdifferent
levels o government is crucial or the
successul implementation o smart
growth programs.
Improvementsinmeasurementand
collection o data, particularly related to
environmental quality and public nance,
are needed to better monitor program
perormance.
More evidence is needed about the nature
o interactions among smart growth poli-
ciesparticularly those related to land use,
transportation, and housing aordability.
Clearer denition o perormance indi-
cators and measurement o their attain-
ment would acilitate the evaluation o
smart growth programs and contribute to
their technical and political sustainability.
Although this evaluation o smart growth
programs concentrates primarily on state-
wide perormance during the 1990s, the
ndings and recommendations will be use-
ul or ormulating growth management
policies in todays context o high energy
costs, historic housing market volatility, and
increasing pressures to reduce greenhouse
gas emissions. Many smart growth objectives
are precisely the outcomes posited to address
these current challenges acing state and
local policy makers.
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4 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
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C H A P T E R 1
Low-Density Developmentand Smart Growth Policies
planning or compact urban growth and
transit-oriented development. These growth
management approaches have attracted
much public attention and research, but they
have received little systematic evaluation.
Several states have applied smart growth
policies or decades, and others are just
beginning to use them to address emerging
issues in the twenty-rst century.
An H IS TO R IC AL V IEW O F
GROWTH PATTERnS
Low-density development at the urban
ringe has been prevalent in the United
States since the 1940s. The average amount
o developed land per capita increased rom
0.32 acres in 1982 to 0.38 acres in 2002.
More important, the amount o newly devel-
Few public policy issues are as con-
tested as urban sprawl. Across the
United States, people are debating
the issue o low-density develop-
ment at the urban ringe as state and local
governments try to reconcile growing
demands or new housing and commercial
development with needs to protect open
space. The debate over sprawl oten pits
the public good against private sel-interest.
While most people agree that protecting
natural resources and open space is impor-
tant, many also value their property rights
and resist policies that may reduce the
value o their land holdings.
In response to this challenge, several U.S.
jurisdictions have implemented smart growth
principles since the early 1970s through
Baltimore, Marlad
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 5
. . . . . . . . . . . . . . . .
figure 1
Percet Growth i Persoal Icome Ieces Growth i Developed Ladad Poplatio, 19822002
oped land per added resident over this period
averaged about 0.6 acresnearly twice the
level o average land consumption. Growth
in population will increase overall landconsumption, and income growth can explain
much o the growth in area per person.
Figure 1 shows that between 1982 and
2002 the number o acres o developed
land increased by 46 percent, while the U.S.
population and personal income grew by 24
and 77 percent, respectively. This is consis-
tent with the nding that land consumption
will increase by about 30 percent i personal
income doubles. I the land area per person
remained constant, population growth
would be responsible or growth in land area
o 24 percent, leaving 22 percent o the 46
percent total area due to other actors. I
all o this additional 22 percent growth was
caused by the 77 percent increase in income,
each 10 percent increase in income would
have caused a 3 percent increase in land area
per person, a relationship similar to that
ound in estimates o the demand or lot
size (Glaeser, Kahn, and Rappaport 2008).
However, regional trends in population
density are certainly not uniorm. Figure 2
illustrates that the average density in the
Northeast was the highest among the our
regions in 1982, and more than twice that
in the Midwest and South. Between 1982and 2003, the incremental density was lower
than the average density in all regions, indi-
cating that all development was oriented
toward lower densities. The West experi-
enced rapid population growth, but man-
aged to keep its incremental density higher
than the other regions (see Fulton et al.
2001 or similar ndings).
TH E EV O L u T IO n O F S M AR T
G R O WTH PO L IC IES
In the ace o this decreasing density and
the spread o development at the ringe o
many urban areas, some states and localities
began to put policies in place to shape settle-
ment patterns. By the early 1990s, these
eorts came to be knows as smart growth
programs. What is now termed smart growth
has evolved rom a continuous process o
state land use policy development that has
coalesced around a set o objectives: pro-
mote compact development; protect natural
resources and environmental quality; create
Percent
1982 1987 1992 1997 2002
Personal
Income
Developed
Land
Population
80
100
120
140
160
180
200
Notes: Personal income is in
2005 dollars. Population and
developed land estimates do
not include Alaska.
Sources: U.S. Census Bureau
(1990c;2000c);U.S.Census
Bureau(1990d;2000d);U.S.
CensusBureau(2007);and
U.S. Department o Agriculture
(1982;1987a;1992;1997;
2003).
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figure 2
Average ad Icremetal Desities Var Across u.S. Regios,19822003
Sources:U.S.CensusBureau(2007);U.S.DepartmentofAgriculture(2003).
transportation options and walkable neigh-
borhoods; supply aordable housing;
generate net positive scal impacts; encour-
age community collaboration and acilitate
transparent and eective development
decision making processes (Smart Growth
Network 2009).
The smart growth movement can be
divided into our waves. The rst three waves
were dened by John DeGrove (1984; 1992;
2005). We propose a ourth wave to charac-
terize the emerging eatures o the smart
growth movement in the twenty-rst century
(Ingram at el. 2009). The rst wave com-
menced in the 1970s with the enactment o
growth management programs to advance
environmental protection in Caliornia,
Colorado, Florida, Hawaii, North Carolina,
Oregon, and Vermont. These programs
were based on the regulation and control
o land development either throughout the
state or within specially designated zones
(DeGrove 1984).
The second wave, rom the 1980s into the
early 1990s, marked a shit rom regulating
and controlling growth to planning that was
aimed at promoting economic growth andprotecting natural resources. The deployment
o inrastructure also became more impor-
tant as a land use planning tool. The second-
wave smart growth states included Florida,
Georgia, Maine, New Jersey, Rhode Island,
Vermont, and Washington (DeGrove 1992).
The third wave, beginning in the mid-
1990s, was distinguished by the addition o
positive incentives to infuence growth and
by more growth-accommodating policies.
Some states shited rom land use regulation,
urban growth boundaries, and requirements
or local comprehensive plans to urban re-
vitalization, zoning reorm, and better coor-
dination o state agencies and their growth
policies. More emphasis was also placed at
the local, metropolitan, and regional levels,
and less on the hegemony o statewide
programs. The third wave brought several
additional states into the movement, includ-
ingMaryland,Minnesota,Pennsylvania,
Tennessee, and Utah (DeGrove 2005).
A ourth wave o smart growth policies is
still emerging. The need to respond to climate
change, environmental challenges, and soar-
ing energy costs, along with a new emphasis
on investments in public inrastructure, have
bolstered the demands being placed on smart
growth initiatives. Because automotive travel
produces a large share o greenhouse gas
emisions, support is increasing or land use
policies that oster more compact develop-
ment patterns, transit use, and walking.
New regional approaches are likely to
encourage development proposals that ad-
here to a smart growth ramework. Market
orces are also encouraging more compact,
mixed-use development as households
attempt to limit their travel costs and
achieve other energy savings.
West
Midwest
South
Northeast
3.52 3.46 2.67
1.06
2.72
1.39
6.16
1.10
Average Density 1982
(Persons per acre)
Incremental Density 19822003
(Added population per acre of
newly developed land)
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 7
. . . . . . . . . . . . . . . .
An O PPO R Tu n E T IM E FO R
EVALuAT IOn
Smart growth programs in some states are
now in their ourth decade, but new environ-
mental objectives have raised the stakes on
their success. With many years o experience
behind us, and the likelihood that reliance
on growth management policies will grow in
the uture, this is an opportune moment to
evaluate how eective smart growth policies
and programs have been at achieving their
goals and objectives.
This evaluation is based on a comparison
between our states that had statewide smart
growth policies in place by 2000 (Florida,
Maryland, New Jersey, and Oregon) and
our other states that did not (Colorado,
Indiana, Texas, and Virginia). Some o these
latter states did acilitate local and regional
smart growth initiatives by enabling local
governments to promulgate local options,
while others did little or nothing. These
eight states constitute a purposive and not
a random sample as part o the research
methodology (box 1).
The analysis revealed that the treatment
varied greatly across the our smart growth
states, producing a range o outcomes that
overlap with some o those in the other
selected states. Outcomes and policies were
thus ound to be more continuous across
the eight states rather than dichotomous
between the two groups o states.
This evaluation addresses two key ques-
tions. First, does the presence o state-level
smart growth programs result in objectively
measurable improvement in perormance?
Second, to the extent that smart growth
programs are successul, what underlies this
success? Conversely, i they ail, what are
the causes o their shortcomings?
Florida
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8 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
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Box 1
Research Methodolog
The evaluation measured the achievement o ve smart growth objectives in each o the
eight states. The analytic methods used varied according to the data available, ranging
rom descriptive statistics to xed-eect regression models (Ingram et al. 2009, 1020). The
ocus was on changes in perormance indicators over time, given that current levels o many
measures (e.g., population density) refect the cumulative eects o past policies, technolo-
gies, and relative prices. The eects o recent policies are likely to be observed only in cur-
rent changes in perormance indicators. It is also likely that some smart growth objectives
reinorce each other, and others are antagonistic.
To make comparisons across states over multiyear periods, the evaluation developed a set
o perormance indicators that were dened consistently over time and available or all states.
These indicators relied heavily on data rom the U.S. Census Bureau and other nationally
collected datasets uniormly available at the state and county levels. The analysis generally
starts in 1990 and continues as ar past 2000 as data allow, but ocuses primarily on the
decade o the 1990s. In addition to the perormance indicators, the evaluation also analyzed
how opinion leaders perceive the eectiveness o smart growth programs, and how imple-
mentation has changed over time.
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 9
. . . . . . . . . . . . . . . .
C H A P T E R 2
Growth Patterns and Trends
Amajor objective o smart growth
policies is to alter the spatial dis-
tribution o population and employ-
ment, principally by increasing the
density and intensity o development, pro-
moting compactness, and slowing the sprawl-
ing o development to rural and undeveloped
areas. Four measures o growth patterns are
used to assess relative changes in spatial struc-
ture in the eight states: land use patterns,
spatial concentration, urbanization, and
centralization (Ingram et al. 2009, 2245).
A baseline review o the size and growth
o the eight case study states in 2000 shows
that they range widely in population (rom
more than 20 million in Texas to 3.4 million
in Oregon); size (rom 265,000 square miles
in Texas to 7,500 square miles in New Jersey);
and population density (rom 1,115 persons
per square mile in New Jersey to 35 in
Oregon). Their population and employment
growth rates over time vary much less, how-
ever. As gure 3 shows, on average, popula-
tion increased more slowly in the smart
growth states (15.9 percent) than in the other
selected states (19.4 percent) rom 1990 to
2000, as did employment (22.5 percent
versus 24.8 percent) rom 1994 to 2004.
LAnD uSE PATTERnS
Land uses vary considerably across the
states. For example, about hal o state area
is rangeland in Texas, cropland in Indiana,
orestland in Virginia, and ederal land in
Oregon. The area o developed land, which
is most relevant to smart growth policy,
increased in each o the eight states in every
ve-year interval rom 1982 through 1997.
Fort Worth, Texas
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. . . . . . . . . . . . . . . . . .
The largest proportional increase in devel-
oped land occurred in Florida, ollowed by
Virginia; the smallest increase was in Indi-
ana. The average proportional increase was26 percent or the our smart growth states
and 21 percent or the our other states.
Because the states dier so widely in land
use and growth, these proportional increases
can be made comparable by relating the
increase in each states developed land area
to the increase in population over similar
periods. Figure 4 shows the ratio (marginal
land consumption) as the increase in square
miles o developed land per 1,000 new resi-
dents. While the average or smart growth
states is lower than or the other selected
states, the best perormersOregon and
Coloradoare rom the two dierent
groups (box 2).
SPAT IAL COnCEnTRAT IOn
The distribution o population over space
can be measured by the Gini coecient,
which is an index o inequality based on the
Lorenz curve measuring how evenly a vari-
able is spread. When activities are uniormly
figure 3
Emplomet Growth Geerall Exceeded Poplatio Growth
Note: Employment counts are derived rom the U.S. Census Bureaus Zip Code Business Patterns
in 1994 and 2004, with 1994 the earliest year or which data were available. Counts do not in-
clude government workers, arm workers, or part-time or sel -employed persons.Sources:U.S.CensusBureau(1990b;1996;2000b;2006).
figure 4
Developed Lad Geerall Icreased Less i Smart Growth States tha i Other Selected States
Florida Maryland New Jersey Oregon Colorado Indiana Texas Virginia
Smart Growth States
Other Selected States
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
MarginalLandConsumption
PercentCha
ngeinEmployment,1994
2004
40
35
30
25
20
15
10
5
0
0 5 10 15 20 25 30 35 40
Percent Change in Population, 19902000
Florida
Maryland
New JerseyOregon
Colorado
Indiana
Texas
Virginia
Smart Growth State Average
Other Selected State Average
Notes: Averages are 0.61 or the smar t growth states and 0.71 or the other selected states. Growth in developed land is measured rom 1987 to 1997 in square
miles. Population growth is measured rom 1990 to 2000. Marginal land consumption is square miles per 1000 additional residents.
Source: U.S. Department o Agriculture (2000).
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 11
. . . . . . . . . . . . . . . .
distributed, the Gini coecient is zero; when
activity is concentrated in one place, it is one.
Decreases in the spatial Gini coecient over
time thereore indicate that the development
pattern is becoming more dispersed. SpatialGini coecients vary rom 0.25 to 0.90 across
all U.S. states, and are usually high in states
with only one large city and low in states
with no large cities and dispersed populations.
Here the concern is more about the change
in concentration over time than its level,
which refects the legacy o the past more
than the eects o current policies. Increased
concentration (higher Gini coecients) would
generally be consistent with smart growth
policies.Figure 5 shows statewide Gini coecients
calculated rom census tract data or popula-
tion and rom zip code data or employment
over ten-year intervals. Oregon is the only
state where population concentration in-
creased and employment concentration did
not decrease. While employment was typi-
cally more concentrated than population,
its concentration declined more than that
o population over the decade. The average
reduction in Gini coecients or the smart
growth states was greater than or the other
states, or both population (-.007 versus-.002) and employment (-.021 versus -.011).
This outcome is generally counter to smart
growth objectives. While these dierences
have similar patterns or most states, they
are not statistically signicant across states.
Gini coecients or population and
employment also were calculated or each
large metropolitan area (with population
over one million) in the eight case study
states. The results are similar to the state-
wide outcomes, but with larger Gini co-ecients. Again, the average reduction in
Gini coecients in the smart growth states
was greater than that in the other selected
states or both population (-.019 versus
-.017) and employment (-.044 versus -.028).
Echoingthestateresults,Portlandwasthe
only metropolitan area where population
concentration did not decrease.
figure 5
Poplatio ad Emplomet i Most States Became Less Cocetrated
Sources:U.S.CensusBureau(1990b;1996;2000b;2006);GeoLytics(2002).
0.5
0.6
0.7
0.8
0.9
1.0
Florida Maryland New Jersey Oregon Colorado Indiana Texas Virginia
Other Selected StatesSmart Growth States
1990 Population 2000 Population 2004 Employment1994 Employment
GiniCoefcient
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Box 2
urba Growth Maagemet i Orego ad Colorado
F
rom 1982 to 1997, Oregon and Colorado distin-
guished themselves by having smaller increases inmarginal land consumption than any o the other case
study states. Oregon experienced a decline in the amount
o developed land per capita over the 1990s, and in Colo-
rado 88 percent o the population lived in only 5 percent
o the census tracts during the same period.
Oregon has one o the rst and best-known statewide
smart growth programs in the country (Ingram et al.
2009, 188198). The 1960s development boom in the
state consumed productive armland and raised ears
about pollution, deteriorating quality o lie, and loss o
the states economic base. In response, Oregon passed
the 1973 Senate Bills 100 and 101 that emphasized
the need to protect its agricultural and orestry lands
by establishing an urban growth boundary (UGB).
The UGB identies and separates land that can be urban-
ized rom land that must remain rural, and encourages
compact development. Oregons urbanization goal re-
quires all cities to dene, adopt, and plan development
within growth boundaries that provide enough land to
accommodate projected residential and employment
growth over 20 years. Some communities also identiy urban
reserved areas intended to accommodate growth over a longertime horizon.
The state also provides incentives in the orm o grants and
technical assistance to jurisdictions undertaking planning unc-
tions. In the 1980s, the state and ederal governments provid-
ed $24 million in planning grants to local governments, or
nearly 63 percent o the planning budget during that period
(Rohse 1987). Between 1997 and 2007, these planning
grants declined to $12 million. Oregon also deers taxes on
armland and orestland. A recent study estimates the total
amount o taxes deerred between 1974 and 2004 at more
than $4.8 billion (Richmond 2007).
Colorados approach to managing urban growth in the early
1970s was to enable local governments to engage in planning
and to implement land use controls, but not to have a mandat-
ed statewide growth control program (Ingram et al. 2009, 200
208). The Colorado Constitution and Local Government Land
Use Control and Enabling Act granted counties and municipali-
ties the authority to plan and regulate land use. In principle,
the Land Use Commission and the Department o Local Aairs
could override local planning permission or land development
Portlad, Orego
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 13
. . . . . . . . . . . . . . . .
in preserved areas. In practice, however, the state
role has been to assist, not direct, local govern-
ments to identiy and adopt guidelines or matters
o state interest.
During the 1990s, the Great Outdoors Colorado
Trust Fund was established to receive hal o the
proceeds o the Colorado Lottery. A portion o the
trusts unds can be awarded or open space plan-
ning grants. Consistent with general citizen sup-
port or preserving open space, Colorado ranked
rst in the nation between 1999 and 2004 in the
dollar value o voter-approved open space bonds
and tax measures (Trust or Public Land 2005).
Colorado also provides several examples o volun-
tary regional collaborations designed to manage
growth. The establishment o the Denver Region
Council o Governments Metro Vision 2020 is
most noteworthy. It includes the Mile High Com-
pact and has created a voluntary growth boundary
covering the six-county area along the Front Rangeo the Rocky Mountains where more than 60
percent o the states population resides. Other
elements o Vision 2020 call or reinorcing spines
o development along transit corridors. Passage
o the $4.7 million FasTracks bond initiative in
2004 also provided signicant support or the
implementation o Vision 2020.
uRBAnIZAT IOn
Smart growth programs seek to encourage
inll development in urbanized areas and
reduce the spread o development to adjoin-ing rural areas. To assess perormance on
these objectives, population growth was
classied by three locations: areas denoted
as urban in 1990; those newly urban between
1990 and 2000; and those rural in both 1990
and 2000. The smart growth states had a
larger share o new residents settle in urban
and newly urbanized areas, and a smaller
share in rural areas (gure 6).
Oregon had the highest share o popula-
tion growth in already urbanized areas at 49percent; New Jersey was second at 45 percent;
and Colorado was third at 38 percent. Indi-
ana had the lowest share at 6 percent. Using
this same classication or large metropolitan
areas, the results were similar to those at the
statelevel.Portlandhadthehighestshare
o additional residents settle in already
urbanized areas in the 1990s (59 percent),
while MiamiFt. Lauderdale had the
second highest (54 percent).
C En TR AL IZ AT IO n
To measure changes in the centralization
o population and employment over time,
each large metropolitan area in the eight
figure 6
Smart Growth States Kept More PoplatioGrowth i urbaized Areas
Sources:U.S.CensusBureau(1990b;2000b);GeoLytics(2002).
0
10
20
30
40
50
Urban in 1990 Urbanized
after 1990
Percentof1990
200
0
Population
Growth
Rural in 1990
and 2000
Smart Growth States
Other Selected States
ver, Colorado
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14 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
case study states was divided into concentric
rings with radii o 5, 10, 20, and 30 miles.
The shares o population and employment
located within each ring were calculated at
the beginning and end o a ten-year period.
As might be expected, employment was more
centralized than population. The shares
o population and employment within the5-, 10-, and 20-mile rings decreased or re-
mained the same in all metropolitan areas.
Employment decentralized more than popu-
lation. Figure 7 shows that there was less
decentralization in the metropolitan areas
o the smart growth states, a result consis-
tent with the analysis o urbanization above.
S u M M AR y
Overall, the changes in growth patterns in
the smart growth states show some consis-tency with smart growth objectives. In these
figure 7
Metropolita Area Poplatio ad Emplomet Decetralized Lessi Smart Growth States
Sources:U.S.CensusBureau(1996;2006).
our states, marginal land consumption per
new resident was lower, the share o new
population locating in urban areas was
higher, and population and employment
decentralization was lower than in the our
other selected states. Smart growth states
also added a smaller share o new popula-
tion in rural areas. At the same time,however, the concentration o population
and employment declined more in the smart
growth states than in the other states.
When ranked in terms o overall peror-
mance, the top three states were Oregon,
Colorado, and New Jersey, with Florida
ranked eighth. Oregon perormed well
across most measures including land use,
urbanization, and concentration. Colorados
strong showing indicates that smart growth
outcomes can be attained without a man-datory statewide smart growth policy.
0
1
2
3
4
5
6
7
8
9
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 15
. . . . . . . . . . . . . . . .
C H A P T E R 3
Natural Resourcesand Environmental Quality
While smart growth policies are
intended to improve the envi-
ronment and protect natural
resources, the strength o the
linkages between specic programs and objec-
tives varies. Smart growth policies oten
relate directly to land use, including land
conservation, but only indirectly to air and
water quality through impacts on transpor-
tation and development patterns.
Data are reasonably accessible or landuse and land conservation measures that
are consistent over time and across states,
but it was impossible to obtain comparable
data or air and water quality. As a result,
all natural resource and environmental
perormance measures in this evaluation
pertain to the use and conservation o
land (Ingram et al. 2009, 4657).
LAnD COnSERVAT IOn
While all o the case study states support
land trusts and related conservation ease-
ments, state policies dier in many details.
Maryland and Oregon have programs to
protect open space and environmentally
sensitive land and to preserve agricultural
land. Maryland provides state unding to
purchase conservation easements on arm-
land. New Jersey preserves agricultural lands
by purchasing development rights, and pro-tects environmentally sensitive lands through
regional planning. Florida has purchased
over 2.5 million acres o environmentally
sensitive land, but has yet to und its pro-
gram or protecting agricultural lands.
Among the other selected states, Colo-
rado oers state tax credits or private con-
servation easements, purchases conservation
Virgiia
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16 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
land, and uses lottery proceeds to und park
and conservation programs. Indiana has a
modest land trust program unded rom sales
o anity license plates. Texas has threeprograms aimed at the preservation o orest-
land, one o which involves state purchase
o conservation easements. Virginia has
no statewide program, but acilitates local
eorts to adopt conservation policies.
R ES O u R C E L An D
AnD FARMLAnD
The analysis relied on two comprehensive
datasets available at ve-year intervals. The
rst, the National Resource Inventory, pro-
vides inormation on several land use cate-
gories, ve o which were aggregated into a
single measure termed resource land: crop-
land, pastureland, rangeland, orestland,
and conservation reserve program land. The
second dataset, available rom the National
Agricultural Statistics Service, collects inor-
mation on arm acreage through a census
o arms. Changes in the amounts o resource
land and armland were related to the
change in population in each o the eight
state cases.
figure 8
Smart Growth States Lost Less Lad per new Residettha Other Selected States
Sources:U.S.DepartmentofAgriculture(1987b;2000;2002).
As gure 8 indicates, the smart growth
states experienced smaller losses per new
resident in both land categories. Maryland
lost the least amount o resource land pernew resident (0.38 acres), while Indiana
(0.90 acres) and Oregon (0.88 acres) lost the
most. Virginia lost the least amount o arm-
land per new resident (0.04 acres), while
Colorado (2.36 acres), Indiana (1.63 acres),
and Oregon (0.89 acres) lost the most.
Oregons loss is surprising given the states
goal o protecting armland. Further anal-
ysis revealed, however, that the loss was
primarily in the sparsely populated eastern
part o the state. The densely settled Willa-
metteValleyregionaroundPortlandactu -
ally increased the amount o armland
per added resident by 0.05 acres.
L An D TR u S TS An D
COnSERVAT IOn PROGRAMS
All eight case study states support the place-
ment o private land in land trusts and o
armland in conservation programs. During
the two overlapping 15-year periods shown
in gure 9, smart growth states perormed
less well than the other selected states, but
the within-group perormance varied widely.
In 2005, New Jersey (3.6 percent) and Mary-
land (2.5 percent) had the highest percent-
ages o their areas in conservation easements
held by land trusts, while Oregon (0.1 per-
cent) and Florida (0.2 percent) had among
the lowest.
In terms o armland in conservation
programs, Colorado (5.6 percent) perormed
best in 2002, ollowed by Maryland and
Oregon (both at 2.8 percent). The share o
area in land trusts is thus a poor predictor
o the share o armland in conservation pro-
grams, except in Maryland where both are
reasonably high.0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
Resource Land 1982-1997 Farmland 19872002
Acres
LostperNewR
esident
Smart Growth States Other Selected States
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 17
. . . . . . . . . . . . . . . .
STATE PARKL AnD
A nal indicator related to natural resources
is the amount o land devoted to state parks.
Acres o parkland per 1,000 persons, the
level o service measure used by the National
RecreationandParkAssociation,haschanged
over time in the two groups o states. Asgure 10 shows, the smart growth states were
slightly ahead o the other selected states
on this indicator in 1990, but the dierence
between the two groups was negligible by
2006. In that year, Colorado had the most
parkland per 1,000 persons (42.5 acres),
ollowed by New Jersey (38.5 acres). Virginia
had the lowest service level (8.1 acres) and
Indiana the next lowest (10.3 acres).
S u M M AR yThe evidence on natural resource and
environmental quality measures is mixed,
with neither group o states clearly outper-
orming the other in terms o protecting
undeveloped areas. At the individual state
figure 9
Smart Growth States Protected Smaller Shares o Their Area i Lad Trstsad Farmlad Coservatio Programs
Notes: Land trust data excludes land owned by The Nature Conservancy. Farmland data includes land enrolled in
Conservation Reserve and Wetlands Reserve programs.
Sources:LandTrustAlliance(n.d.;2005);U.S.DepartmentofAgriculture(1987b;2002).
figure 10
Smart Growth States Lost Their Lead i State ParkladAter the 1990s
Source: National Association o State Park Directors (n.d.).
level, Maryland had the highest averageranking across all measures (box 3), with
New Jersey and Colorado tied or second.
Indiana had the lowest average ranking.
Colorado again perormed well despite its
lack o a statewide smart growth program.
1990 2005 1987 2002
State Area in Land Trusts Farmland Area in Conservation
PercentArea
3.00
2.50
2.00
1.50
1.00
0.50
0.00
Smart Growth States
Other Selected States
1990 2006
Acres
per1000
Persons
0.00
5.00
10.00
15.00
20.00
25.00
Smart Growth States
Other Selected States
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18 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
Box 3
Lad Coservatio Programs i Marlad
M
aryland is the gatekeeper to the largest and
most productive estuary in the United States, the
Chesapeake Bay. Because most o Maryland lies within
the watershed, the health o the bay has been a major
driver o the states land use and environmental policies
or many years.
This evaluation revealed that Maryland had the largest
percentage increase in acres o armland enrolled in land
preservation programs among the eight selected states
(Ingram et al. 2009, 166175). About 2.5 percent o
Marylands land was privately conserved in 2005, the
second highest level among the selected states. In addi-
tion, Maryland preserved 343,000 acresabout 5 per-
cent o its total land areathrough state and county
transer o development rights and purchase o devel-
opment rights programs (Lynch et al. 2007).
Beginning in the 1960s, the Maryland General Assembly
and various governors proposed and enacted a series o
land use laws designed to protect the environment. These
laws were intended to help the state acquire parkland,
protect orests and wetlands, reduce soil erosion, preserve
armland, and regulate storm water runo. Much o the
ocus turned to the Chesapeake Bay ollowing passage
o the Chesapeake Bay Agreement in 1983.
The emphasis on land use grew in the 1990s, beginning
with the Economic Growth, Resource Protection, and Plan-
ning Act o 1992 (the Growth Act) and the Smart Growth
Areas Act o 1997, which took an inside/outside approach
in an eort to direct growth to Priority Funding Areas
(PFAs) while preserving undeveloped areas through the
Rural Legacy Program.
The centerpiece o the Smart Growth Areas Act attempted
to infuence development decisions by restricting growth-
related state spending to specic areas. County govern-
ments were required to designate certain areas as PFAs,
whichincludedallincorporatedmunicipalities;heavily
developed areas inside the circumerential highways
around Baltimore and the Maryland suburbs o Washing-
ton,DC;andotherareasmeetingspecicstatecriteria.
The counties were required to map their PFAs and submit
their plans to the Maryland Department o Planning (MDP)
or review and comment. The goal was to use the power
o the state budget as an incentive or smarter growth.
State programs were geared either to support develop-
ment within the PFAs or to protect undeveloped land
outside them.
Chesapeake Ba, Marlad
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 19
. . . . . . . . . . . . . . . .
C H A P T E R 4
Transportation
Smart growth proponents view trans-
portation as a major determinant
o land use patterns and an impor-
tant maniestation o the success
o smart growth policies. They argue that
expanding transport options, altering trans-
port pricing, and ostering pedestrian-riendly
settings yield less single-occupant car travel,
less congestion growth, and more trips by
transit, biking, and walking. These patternsare associated with more compact, mixed-
use, and dense urban orms. This evaluation
thereore looked at perormance indicators
related to mode choice and trac conges-
tion to assess how they are associated with
smart growth programs, which refect dierent
policy approaches (Ingram et al. 2009, 5875).
Transportation data that are dened con-
sistently across states are reasonably available,
including census data that record commute
mode and travel time. Data rom the Texas
Transport Institute, which estimates annual
delay per peak-period traveler and a peak-
period travel time index, have been used to
examine levels and changes in congestion.
Census data are available every 10 years
or all states and municipalities, and the
Texas Transport Institute data are availableannually or 85 U.S. metropolitan areas. Con-
gestion data were used or cities with popula-
tions o one to three million, and seven o
the eight case study states (all but New Jersey)
had at least one such metropolitan area with
congestion data. Vehicle miles traveled
(VMT) was one explanatory variable used
to analyze the change in congestion.
Dallas, Texas
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20 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
C O M M u TE M O D ES
Data on mode choice or work trips were
tabulated or cities and counties with an
average density o at least 50 persons persquare mile. Counties were sorted into three
density categoriesvery high (more than
500 persons per square mile), high (101 to
500), and medium (50 to 100)to control
or the level o urbanization.
Figure 11 shows that the transit share o
work trips varied greatly with population
density in both the smart growth and other
selected states. The transit share o work
trips across the country during the 1990s
was 6 percent in very high density counties,1 percent in high density counties, and 0.5
percent in medium density counties.
Work trip transit shares in all our smart
growth states exceeded U.S. averages, as
did those in Colorado. In addition, average
work trip transit shares increased rom 1990
to 2000 in the smart growth states in all
density categories, but generally declined in
the other selected states except Colorado.
Given that smart growth programs
typically provide bike lanes, bike racks,
sidewalks, and priced parking, they should
figure 11
Work Trip Trasit Shares Started Higher ad Rose i Smart Growth States
Sources:U.S.CensusBureau(1990e;2000e).
increase the share o bike/walk commutes
or at least retard its decline. But as gure
12 indicates, while the bike/walk share was
generally higher in the smart growth states,its share declined over time and was essen-
tially unrelated to population density. The
exception to this pattern is Oregon, where
the bike/walk share increased rom 1990
to 2000 by more than 10 percentlikely
refecting the state requirement that local
governments produce bike and pedestrian
plans as part o their transportation plans
(box 4).
C O n G ES T IO n
The relationship between the orm o
development and trac congestion is much
debated. Some analysts believe that dense,
compact development promotes transit use
and shorter automobile trips, while others
contend that decentralization reduces the
distances rom home to work and spreads
car travel more widely over existing trans-
port capacity. Since smart growth programs
typically seek to reduce congestion, assessing
the change in travel delays over time is
essential.
1990 2000 1990 2000 1990 2000
Very High Density Counties High Density Counties Medium Density Counties
Smart Growth States
Other Selected States
9
8
7
6
5
4
3
2
1
0
Percent
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 21
. . . . . . . . . . . . . . . .
Figure 13 shows average annual increases
in peak-period hours o delay, including
data or Florida and Maryland beore and
ater their respective programs were initi-
ated. Oregons start date preceded the avail-
able data, and no data were available or New
Jersey. The results are mixed. The average
annual increase in congestion in the smart
growth states (1.73 hours) exceeds that in
the other selected states (1.31 hours). Yet
the initiation o smart growth programs in
Florida and Maryland reduced the annual
increase in trac delays, thus providing
some evidence o program success.
The results rom a sample o six smart
growth states indicate that smart growth
figure 12
The Bike/Walk Share Geerall Started Higher i Smart Growth States,bt Declied Drig the 1990s
Sources:U.S.CensusBureau(1990e;2000e)
figure 13
Aal Icreases i Trafc Delas i Smart Growth States Declied AterSmart Growth Programs Were Itrodced
Source: Ingram et al. (2009, 65).
Percent
1990 2000 1990 2000 1990 2000
Very High Density Counties High Density Counties Medium Density Counties
Smart Growth States
Other Selected States
6
5
4
3
2
1
0
AnnualIncrease
in
Hou
rs
ofDelay,
1982
2003
2.50
2.00
1.50
1.00
0.50
0.00
Other Selected StatesSmart Growth States
Florida
Before
Florida
After
Maryland
Before
Maryland
After
Oregon Colorado Indiana Texas Virginia
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22 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
Box 4
Trasportatio Plaig i Orego
O
regons integration o transpor tation planning with
land use management is among the most advanced
in the country (Ingram et al. 2009, 188198). The states
transportation plans must be based on an inventory o
local needs and consider all modes (cars, reight, bicycle,
pedestrian, public transit). In 1993 the state created the
Oregon Transportation and Growth Management Program
(TGM), a partnership between the Oregon Department
o Land Conservation and Development and the Oregon
Department o Transportation. One o the primary goals
o TGM is to make walking, biking, and using transit sae
and convenient. The program also aims to enhance the
states road system as a whole and to improve the abilityo commercial enterprises to move goods and services
along the highways (Oregon Transportation and Growth
Management Program 2009).
TGMs unding rom the Federal Highway Administration
enables the state to leverage its own investment with ed-
eral dollars. TGM recognizes that scattered development
without good connections between local destinations in-
creases the need or driving and that well-planned devel-
opment with good street and walkway connections im-
proves transportation options (such as walking and
biking) and can reduce automobile usage.
Thus TGM promotes planning concepts including mixed-
use,compactdevelopment;goodconnectivitybetween
localdestinations;revitalizingdowntownandmainstreets
wheregoodtransportationoptionsarealreadyavailable;
transit-orienteddevelopment;andbicycleandpedestrian
networks. From 2007 to 2009, TGM granted $3.8 million
in nancial and technical assistance to 60 local transpor-
tation projects throughout Oregon.
TGM also sponsors community workshops, lecture series,
and other events to improve public understanding o land
use and transportation planning
concepts. Through its Quick
Response and Transportation
System Plan Assessments pro-
grams, TGM oers direct design
assistance and assessmentso transportation needs to
communities. These unctions
aid local governments in their
search or grants to carry out
necessary projects. Its Code
Assistance program helps local
governments revise zoning
and development codes.
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 23
. . . . . . . . . . . . . . . .
programs have a statistically signicant and
behaviorally meaningul eect on conges-
tion. The travel delay regression indicates
that smart growth programs reduced theannual increase in delay by 2.2 hours or
every year that the program was in place.
For example, i the travel delay had been
increasing by 4.0 hours per year, smart
growth programs would reduce that number
to 1.8 hours per year.
An attempt was also made to relate the
congestion reduction rom smart growth
programs to three underlying causal actors
increased population density, increased
transit ridership, and changes in VMT. Taken
together, these variables were ound to explain
no more than 20 percent o the eect o
state smart growth programs on congestion.
SuMMARy
Analysis o the transportation indicators,
especially work-trip transit ridership and
changes in congestion, provides reasonablystrong evidence that smart growth programs
are associated with desirable outcomes.
While the evidence on bike/walk commutes
was less compelling than that on transit,
smart growth states had somewhat higher
shares o work trips by these modes.
When perormance across the three
major indicators was aggregated or each
state, Oregon ranked at the top. That state
did very well in transit and bike/walk com-
mutes, and was the top smart growth statein terms o congestion. Indiana and Texas
were at the bottom o the overall rankings.
It is noteworthy that the our states that
perormed best in the rankings on trans-
portation (Oregon, Virginia, Colorado,
and New Jersey) also perormed best on
growth patterns and trends.
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24 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
C H A P T E R 5
Aordable Housing
While improving the aordabil-
ity o housing is a common
goal o smart growth programs,
the emphasis placed on this
objective varies across the case study states.
New Jersey has the strongest state-level
program, which stems rom theMt. Laurel I
and IIstate supreme court decisions requiring
municipalities to provide realistic opportuni-
ties or low- and moderate-income housingon a regional air-share basis.
Florida requires that local plans include a
housing element. Oregon has a requirement
or provision o needed housing, while
Maryland has no specic state-level housing
mandate. None o the other selected states
had a state-level aordable housing require-
ment during the 1990s, although Virginia
added such a requirement in 2003 (Ingram
et al. 2009, 7687).
HOuSInG VALuES
The rst measure used to assess housing
aordability was the change in median
housing values rom 1990 to 2000. Figure
14 shows that median values rose in all eight
states, with the largest percentage increase
(118 percent) in Oregon. Although New Jer-sey posted the smallest percentage increase,
it had the highest median housing value o
the eight case study states in both 1990 and
2000. Colorado had the second highest
median value in 2000, ollowed by Oregon
and Maryland. Housing in the smart growth
states was clearly more expensive than in
the other selected states.
Bolder, Co
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 25
. . . . . . . . . . . . . . . .
Even so, the average increase in median
prices was lower in the smart growth states
(31 percent) than in the other selected states
(58 percent). As a result, the dierence inmedian house values between the two
groups shrank rom $34,000 in 1990 to
$25,000 in 2000. The source o the price
increases in the smart growth states could
have been on the demand side (greater amen-
ities), the supply side (higher regulatory
costs), or both.
MuLTIFAMILy AnD
REnTAL HOuS InG
States that successully promote aordable
housing are likely to produce more multi-amily and rental units. As gure 15 shows,
all eight case study states added varied
shares o rental units during the 1990s.
Multiamily units made up a larger average
share o new housing in the smart growth
states (21 percent) than in the other selected
states (13 percent). Smart growth states were
figure 14
Percetage Icreases i Media Hose Vale Varied Widel Amog the Case Std States
Note: Includes all owner-occupied units.
Sources:U.S.CensusBureau(1990a,tableH061A;2000a,tableH85).
figure 15
Smart Growth States Added a Larger Share o Mltiamil uits Drig the 1990s
Sources:U.S.CensusBureau(1990a;2000a).
1990 2000
22%
24%
4%
118%
94%
73%
32%
31% 42%
Florida Maryland Jersey Oregon Colorado Indiana Texas Virginia U.S.
MedianHouseValue
$180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
Smart growth state average increase = 31 percent. Other selected state average increase = 58 percent. Average
Rental Multifamily
PercentofUnits
Ad
ded
35
30
25
20
15
10
5
0Florida Mar yland Jersey Oregon Colorado Indiana Texas Virginia
Other Selected StatesSmart Growth States
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26 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
Box 5
The Cocil o Aordable Hosig i new Jerse
The Council on Aordable Housing (COAH) is a key
planning agency that administers the New Jersey Fair
Housing Act (Ingram et al. 2009, 177187). The enact-
ment o this legislation was a response to a series o
state supreme court decisions, known as the Mt. Laurel
cases, which dealt with aordable housing and exclusion-
ary zoning. In the rst case, Southern Burlington County
NAACP v. Township of Mount Laurel, 67 N.J. 151, the court
ruled in 1975 that developing municipalities have a con-
stitutional obligation to provide a realistic opportunity or
the construction o low- and moderate-income housing.
In the second case, Mt. Laurel II (92 N.J. 158) in 1983,
the court held that all municipalities should share the
obligation to provide the opportunity or the development
o aordable housing, and provided specic judicial guide-
lines or municipalities to ollow, so as to ulll their con-
stitutional obligation. Municipalities that enacted zoning
had to provide realistic opportunities to meet their air share
o low- and moderate-income housing in their regions.
Under the Fair Housing Act, COAHs responsibilities in-
cludedeninghousingregions;estimatingmoderateand
low-incomehousingneeds;settingcriteriaandguidelines
or municipalities to determine and address their air
sharenumbers;andreviewingandapprovinghous-
ing elements/air-share plans and regional contribution
agreements or municipalities. Once its housing element
and air share plans are approved, the municipality has
a degree o protection rom Mt. Laureltype lawsuits.
During the 1990s, the council used an allocation ormula
to establish goals or all municipalities in the state. As
o 2008, COAH had begun round three and moved to a
growth share ormula that bases the aordable housing
determination or a municipality on the actual growth o
market-rate units and nonresidential development.
Crabr, new Jerse
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 27
. . . . . . . . . . . . . . . .
figure 16
Hosig Cost Brdes Were Geerall Higher i the Smart GrowthStates, Especiall or Reters
Note: Cost-burdened owners and renters are dened as those paying 30 percent or more
o income or housing.
Source:U.S.CensusBureau(1990a;2000a).
figure 17
The Share o Cost-brdeed Owers Rose i the Smart GrowthStates i the 1990s
Note: Cost-burdened owners and renters are dened as those paying 30 percent or more
o income or housing.
Source:U.S.CensusBureau(1990a;2000a).
also less likely to add housing in rural
areas. New Jersey had the highest shares
o both rental and multiamily units o
the eight case study states (box 5), whileMaryland had the lowest rental share
and Colorado had the lowest multi-
amily share.
H O u S In G C O S T B u R D En
Aordability is determined by both
housing prices and household incomes.
The housing cost burden is the percent
o income spent on housing, commonly
measured by the ratio o median house
prices and rents to median household
income. Figure 16 indicates that renter
and owner housing cost burdens were
slightly higher in the smart growth states
than in the other selected states. The
share o income spent on housing changed
little rom 1989 to 1999, except that
owners in smart growth states saw their
cost burdens edge up rom 22 percent
to 23 percent.
In both groups o states, the housing
cost burden or renters was consistently
higher than or owners. The renter bur-
den ell slightly in all states except Oregon
(up 5.5 percent) and Colorado (up 1.1
percent). The owner cost burden rose
the most in Indiana (15.6 percent) and
Oregon (13.7 percent), and decreased
the most in Texas (-3.8 percent) and
Virginia (-2.3 percent).
A generally accepted standard o
aordability is that housing costs should
be less than 30 percent o household
income. Accordingly, a specic indicator
o aordability (or its lack) is the share
o households whose housing cost bur-
den exceeds 30 percent o income. As
shown in gure 17, the share o cost-
burdened owners rose between 1989
and 1999 in both groups o states, while
the share o cost-burdened renters ell or was
unchanged.
The shares o both cost-burdened owners
and renters were higher in the smart growth
states than in the other selected states. Oregon
posted the largest increase in the share o
cost-burdened owners (5.8 percent), ollowed
by Maryland (3.8 percent). Texas was the only
30
25
20
15
10
5
0
HousingCostas
PercentofIncome
1989 1999 1989 1999
Owners Renters
Smart Growth States Other Selected States
40
35
30
25
20
15
10
5
0PercentofCost-burd
ened
Households
1989 1999 1989 1999
Owners Renters
Smart Growth States
Other Selected States
l i i
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28 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
state to show a decline. The share o cost-
burdened renters increased only in Oregon
(2.5 percent) and Maryland (0.1 percent),
and ell the most in Texas (-3.9 percent).Note that these changes in housing cost
burdens do not account or any osetting
cost reductions, such as or transportation,
that may be associated with smart growth
programs.
Statistical regressions were used to anal-
yze the determinants o the change in the
shares o cost-burdened owners and renters
across the case study states. Regressions that
hypothesized a uniorm eect rom smart
growth programs ound a statistically signi-
cant relationship. Smart growth programs
were associated with increased shares o
cost-burdened households.
Additional regressions that allowed each
state to have an independent eect ound
that the shares o cost-burdened renters and
owners increased the most in Oregon and
the least in Texas. But New Jersey and
Floridasmart growth states that require
aordable housing elements in local plans
perormed better than Oregon and Mary-
land or owners, and better than Oregon,
Maryland, Virginia, and Colorado or
renters.
SuMMARy
These results indicate that smart growth
programs that lack an aordable housing
element have been associated with increasesin housing cost burdens, especially or owners.
While smart growth states experienced a
smaller increase in median housing prices
and added a greater share o multiamily
units in the 1990s, they also had higher shares
o cost-burdened owners and renters than
the other selected states.
Regression results indicate that the
smart growth states had greater increases in
the share o cost-burdened owners than o
renters. New Jersey, with its court-mandatedaordable housing requirement, was rst
overall in a composite ranking across all
housing aordability indicators. Oregon
ranked last, having experienced the largest
increases in housing values, housing cost
burdens, and shares o cost-burdened
households.
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 29
. . . . . . . . . . . . . . . .
C H A P T E R 6
Fiscal Dimensions
Smart growth programs seek to
concentrate economic activity in
areas that are already developed
and to control growth in undevel-
oped and rural areas. Fiscal revenues and
expenditures have a role to play in these
eorts. Some central questions are the extent
to which revenues rom developed areas are
sucient to pay or expenditures, and how
the balance o revenues and expenditures
compares between smart growth and
other states.
To examine these issues, counties in the
eight case study states were separated into
two groups according to their population
densitiesrural/undeveloped, and urban/
suburban or otherwise developed (Ingram et
al. 2009, 88115). The densities that dened
the two categories varied with the average
population density o each state. The anal-
ysis looked at ten variables related to econo-
mic development or which comparable data
were available or the decades rom 1980
to 1990 and rom 1990 to 2000: population
growth and density, households, employment,
personal income, retail sales, tax base, hous-
ing values, multiamily units, and journey-
to-work travel times.
The shares o incremental growth in
each variable that occurred in each states
urban/suburban and rural/undeveloped
counties were calculated or both decades.
The proportion in the 1980s was then sub-
tracted rom that in the 1990s to provide
a simple summary statistic measuring the
change in the distribution o economic acti-
vity. For example, i rural counties received
8 percent o a states population growth in
Idiaapolis, Idiaa
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30 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
figure 18
Smart Growth States Had Less Growth i Rral/udeveloped Areas i the 1990stha i the 1980s
Source:U.S.CensusBureau(1980;1990c;2000c);WoodsandPooleEconomics,Inc.(2005).
the 1980s and 13 percent in the 1990s, the
net increment or rural counties would be
5 percent.
Figure 18 shows how much the shares ostatewide growth in rural/undeveloped coun-
ties changed rom the 1980s to the 1990s on
eight o the ten variables. The only activity
where that share decreased was multiamily
housing in the smart growth states. In all
other cases, the rural/undeveloped share o
growth was equal to or larger in the 1990s
than in the 1980s. It is striking that the rural
growth rate or all activities in the smart
growth states was less than or equal to that
in the other selected states. This indicatesthat smart growth states were more success-
ul in ostering density in urban/suburban
areas and in moderating the growth o devel-
opment in rural/undeveloped areas.
The analysis o scal impacts was based
on public nance data drawn rom the U.S.
Census o Governments. However, the 2002
census had two serious data problems that
made it necessary to impute values or many
missing variables beore expenditures and
revenues could be analyzed. First, a signi-
cant amount o inormation or New Jerseyand Texas was missing, because local juris-
dictions did not report it. Second, some
expenditures and revenues are not counted,
especially i they are nonrecurring or in the
orm o intrajurisdictional transers (table 1).
Figure 19 shows the ratios o the respec-
tive growth o expenditures and revenues
(in constant dollars) in the 1990s and in the
1980s. A ratio over 1.0 indicates that growth
accelerated; that is, its absolute magnitude
in the 1990s exceeded that in the 1980s. Thisgure reveals three trends: (1) both expendi-
tures and revenues grew aster in rural areas
than in urban/suburban areas; (2) revenues
grew aster than expenditures in all areas;
and (3) smart growth states had lower growth
in both expenditures and revenues than the
other selected states. The aster growth in
rural areas refects the increase in develop-
Smart Growth States
Other Selected States
Average
Population
Households
Employment
Income
Retail Sales
Tax Base
House Value
MultifamilyUnits
-4 -2 0 2 4 6 8 10 12 14 16
Percentage Point Change in the Rural Share of Growth in the 1990s Relative to the 1980s.
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 31
. . . . . . . . . . . . . . . .
TaBle 1
Expeditres ad Revees i the Evalatio
Expeditres Revees
Iclded Exclded Iclded Exclded
Local public inrastructure
and services investments
Transers between the same
or dierent levels o government
Property, sales, income ranchise,
lodging, uel, and other taxes
Impact ees or capital
improvements
Salaries/wages and other
expenditures
Public services provided
by community associations
Fees collected rom inspections,
building permits, and ordinance lings
Debt service to support
large-scale capital project
Educational expenditures in
noneducational budgets o selected
municipalities and counties
Trac and parking nes
Purchases o computers,
oce urniture, and regular
vehicles
Municipal subsidies to charter schools Service charges or contracted solid
waste removal, animal control, and
special assessments or improvements
New components o expendituressuch as start-up computer costs
Intergovernmental transers
New spending due to the addition o
new government divisions or annexation
ment as documented in gure 18, while
the other patterns may refect prudent scal
management in the smart growth states.
Fiscal impact, a common metric used to
evaluate scal perormance, is the ratio o
figure 19
Aggregate Expeditres ad Revees Icreased Less i Smart Growth Statestha i Other Selected States
Source:U.S.CensusBureau(1982;1992;2002).
the change in revenues to the change in
expenditures. When this ratio is greater than
1.0, revenues are growing aster than expen-
ditures. The analysis indicates that the smart
growth states had a more avorable scal
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Urban/Suburban Rural/Undeveloped Urban/Suburban Rural/Undeveloped
Expenditures Revenues
Ratio
of1990s
Growth
to
1980s
Gr
owth
Smart Growth States
Other Selected States
Source: Ingram et al. (2009, 104105).
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32 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
balance (1.2) in the 1990s than the other
selected states (1.0) in urban/suburban areas.
What explains this outcome? Analysis o
the change in tax bases and tax rates revealsthat the smart growth states increased taxes
somewhat more than the other selected
states to strengthen their scal positions.
S u M M AR y
Overall, smart growth states did better than
the other selected states in controlling the
growth o economic development activities
in rural areas, and in achieving a avorable
balance o incremental revenues over incre-
mental expenditures. It is noteworthy that
the more avorable scal balances in the
smart growth states result rom larger tax
increases. This suggests that these states aregenerally more supportive o the public
sector than the other selected states, in terms
o both the regulatory structure underlying
smart growth programs and the provision o
nancial resources to the public sector. New
Jersey perormed best overall on measures
o the distribution o growth and scal
impact, ollowed by Florida and Maryland.
The lowest ranking states were Indiana
and Virginia.
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 33
. . . . . . . . . . . . . . . .
This evaluation also included a sur-
vey o 117 state and local opinion
leadersabout 15 in each o the
eight case study states. The ques-
tionnaire covered the period rom 2000 to
2007 and addressed ve major topics related
to smart growth programs: eectiveness in
achieving goals; eectiveness o sanctions
and incentives; public participation; costs o
regulatory compliance; and the governmentsrole in guiding land use decisions. The survey
was careul to dierentiate between state- and
local-level eorts and activities because sev-
eral o the other selected states enable local
governments to apply smart growth policies
(Ingram et al. 2009, 116133).
Respondents in the smart growth states
were two to three times more likely to believe
C H A P T E R 7
Survey of Opinion Leaders
that the costs o smart growth policies and
the time required to complete the review
process had become a lot higher than
respondents in the other selected states.
Opinion leaders generally had similar views
about the role o government in smart growth
policies, except that those rom the other
selected states were more likely to believe
that state governments should deer to
local governments on such issues.Responses on the eectiveness o achiev-
ing goals, o public participation, and o
sanctions are summarized in gure 20. Opin-
ion leaders elt that smart growth states had
been more eective than the other selected
states at the state level, but that the other
selected states had been nearly as eective
at the local level. These views rearm
Baltimore, Marlad
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34 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
figure 20
Srve Respodets Viewed State-level Programs i the Other Selected States asLeast Eective
Source: Ingram et al. (2009, 118125).
figure 21
Perceptios o State-level Eectiveess i Achievig Smart Growth Goals VarMore tha Those o Local-level Eectiveess Across All States
Source: Ingram et al. (2009, 119).
Smart Growth States, State Level
Smart Growth States, Local Level
Other States, State Level
Other States, Local Level
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Achieving Goals Public Participation Effective Sanctions
Average
Score
(5=highest)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
AverageScore(5=MostEffective)
Florida Maryland New Jersey Oregon Colorado Indiana Texas Virginia
State Level
Local Level
Other Selected StatesSmart Growth States
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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 35
. . . . . . . . . . . . . . . .
earlier ndings in this report that states with-
out statewide programs, such as Colorado,
have succeeded in achieving smart growth
objectives through locally implementedpolicies.
This point is reinorced in gure 21, which
shows by state the opinion leaders views o
the eectiveness o state and local govern-
ments in achieving smart growth objectives.
The lack o a statewide smart growth prog-
ram apparently contributes to increased
local activism in the other selected states
that may exceed the perceived eectiveness
o programs in smart growth states.
The strength o state regulatory regimes
was assessed as a composite o ve attributes:
state requirements or local planning; state
specication o the size o communities that
must plan; and state requirements or inter-
nal consistency, vertical consistency, and hori-
zontal consistency. The Wharton Residential
Land Use Regulatory Index was used to
measure the strength o local housing devel-
opment regulation (Gyourko, Saiz, and
Summers 2006).
Figure 22 shows that both state and local
regulations are strong in the our smart growth
states. Colorado, which does well on many
smart growth perormance indicators, also
has relatively strong local regulations, sug-
gesting that i they are reasonably consistent
within a state they can produce smart growth
outcomes similar to those in states with
strong state-level regulation (box 6).
In addition, whether a state is subject to
Dillons Rule (a legal doctrine holding that
localities have only those powers specically
delegated by state law) has little relation to
the presence o strong state or local regula-
tions. For example, Maryland is a Dillons
Rule state and Oregon is not, yet both have
strong state and local regulations (Richard-
son and Gough 2003).
figure 22
Smart Growth States Have Stroger State ad LocalResidetial Reglatios
Note: SD=Standard deviation.
Source: Ingram et al. (2009, 146).
IN TX VA
CO
OR
MDFL
NJ
MEAN+1.0 SD
High
Strong 20
Weak 0
10
Average Score=12
-1.0 SDLow
SCORE
i i
i
State Smart Growth
Requirements
Wharton
Residential Land
Use Regulatory
Index
Smart Growth States Other Selected States
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36 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y
. . . . . . . . . . . . . . . . . .
Box 6
Local Govermet Plaig Roles i Colorado
Colorado currently has no statewide mandated growth
management program. Instead, the states approach
has been largely to create a toolbox o planning powers
that local governments can adopt. The ew mandatory
requirements oten refect ederal requirements devolved
by the state to local or regional jurisdictions. State agen-
cies oer airly modest technical support or planning
(Ingram et al. 2009, 200208).
Although there is no mandatory state planning require-
ment, there are many locally initiated regulations. Under
Colorado Revised Statutes, counties and municipalitieswith a certain population level or growth rate must pre-
pare and adopt a master plan. The Department o Local
Aairs annually calculates which local governments meet
certain growth thresholds, and receives plans or advi