United States Department of Agriculture

Forest 

Service

Pacific Northwest 

Research Station

General Technical Report

PNW-GTR-905 April 

2015

Urban Green Space and Vibrant 

Communities: Exploring the Linkage 

in the Portland-Vancouver Area

Edward A. Stone, JunJie Wu, and Ralph Alig
Pacific Northwest Research Station

Web site http://www.fs.fed.us/pnw

Telephone (503) 808-2592

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FAX (503) 808-2130

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Mailing address Publications Distribution 

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P.O. Box 3890 

	 	 Portland, OR 97208-3890

Authors

Edward A. Stone is a graduate research assistant and JunJie Wu is Emery N. 

Castle Chair in resource and rural economics, Department of Applied Economics, 

Oregon State University, 213 Ballard Extension Hall, 2591 SW Campus Way, Corvallis, OR 97331. Ralph Alig is a research forester emeritus, U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station, Forestry Sciences 

Laboratory, 3200 SW Jefferson Way, Corvallis, OR 97331. 

This material is based upon work supported by USDA Forest Service Pacific Northwest Research Station under Agreement No. 11-JV-11261985-073. Any opinions, 

findings, and conclusions or recommendations expressed in this material are those 

of the author(s) and do not necessarily reflect the views of their home institutions or 

the U.S. Department of Agriculture.

Cover: View of Portland’s 5,100-acre Forest Park adjacent to the city’s downtown 

and industrial core.
Abstract

Stone, Edward A.; Wu, JunJie; Alig, Ralph. 2015. Urban green space and vibrant 

communities: exploring the linkage in the Portland-Vancouver area. Gen. Tech. 

Rep. PNW-GTR-905. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 43 p.

This report investigates the interactions between household location decisions and 

community characteristics, including green space. Household location decisions 

are a primary driver of land-use change, and collective location decisions affect 

community characteristics. At the same time, community characteristics affect 

location decisions. Neighborhoods or communities that have well-managed green 

space programs are more attractive to residents, a two-way interaction that tends 

to be self-reinforcing. Communities with high amenities and public services attract 

high-income residents, enhancing the tax base and the provision of amenities and 

services. This report surveys the literature investigating these interactions and 

explores several applicable empirical approaches for the Portland, Oregon, and 

Vancouver, Washington, metropolitan area.

The emergence of spatially explicit data and software facilitates the investigation of relationships between location choice and community characteristics. Using 

data from Portland, Oregon, and Vancouver, Washington, this report details several 

possible empirical approaches, including instrumental variables, reduced-form 

estimation, and treatment effects. The primary challenge for the researcher is the 

endogeneity of community characteristics. 

Keywords: Amenities, community characteristics, population change, residential location choices, urban green space.
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Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

Introduction

The purpose of this report is to survey the most significant developments in theory 

and analysis that explore interactions between household location decisions and 

urban green space; and, based on survey results, to explore several empirical 

approaches available for analysis of these interactions using data from Portland, 

Oregon, and Vancouver, Washington. 

The linkage between urban green space and healthy communities has long been 

recognized. Urban green space generates many economic and environmental benefits. It provides amenities and recreational areas, cools urban heat islands, moderates local climate, reduces energy costs, enhances property values, and increases 

municipal revenue through property tax assessments (e.g., Donovan and Butry 2010, 

2011; USDA FS 2014). It provides place for people to socialize and play, reducing 

stress and benefiting the health of urban dwellers (Ulrich 1993, 1984; USDA FS 

2011). Recent research (Donovan and Butry 2011) suggests that more canopy cover 

is associated with higher birth weights in Portland, Oregon. Urban green space 

also provides many environmental benefits and ecosystem services. Urban forests 

and trees filter air, water and sunlight; provide shelter to animals; and reduce the 

number of unhealthful ozone days that plague major cities in peak summer months 

(USDA FS 2014). As urban areas expand, the importance of the benefits that urban 

green space provides, as well as the challenges to their conservation, will increase 

(USDA FS 2014).

Conserving urban forests and other green space can influence the defining 

features of the urban landscape, such as the location and density of residential 

development and the overall size of the city. Because people find it more desirable 

to live near urban forests and other open space, preserving urban green space can 

change the relative desirability of different locations in an urban area. A community 

with a well-managed open space program can attract more high-income households, 

which may lead to a larger tax base and better public services in the community. 

Interactions between household location decisions and municipal profiles are 

two-directional. For example, suburbanization is often associated with income 

stratification and concentrated poverty, with fiscal and social implications (e.g., 

Mieszkowski and Mills 1993). It has been linked to congestion and obesity (e.g., 

Nechyba and Walsh 2004, Plantinga and Bernell 2007). Municipal profiles in turn 

affect household locational patterns. The expression “flight from blight” refers 

to falling incomes and deteriorating public safety and services, prompting highincome households to relocate from city centers to suburbs and thus contributing 

Urban green space 

generates many 

economic and 

environmental benefits. 

It provides amenities 

and recreational areas, 

cools urban heat 

islands, moderates 

local climate, reduces 

energy costs, enhances 

property values, and 

increases municipal 

revenues.
2

General technical report pnw-gtr-905

to suburbanization and sprawl. Conversely, high-income communities may enact 

zoning and tax regimes that affect land-use patterns by attracting new residents or 

restricting the pattern of development. 

In many cases—and in many economic models (e.g., Wu 2006)—the interaction between household location choices and municipal profiles is self-reinforcing. 

“Flight from blight” further diminishes central city incomes and tax revenues, leading to deteriorating public services and safety and thus more flight. High-income 

suburbs with better public services attract more high-income households. Other 

urban-development phenomena may also be self-reinforcing, including gentrification and urban revitalization.

Literature Review

Household preferences and collective location decisions determine land use patterns and neighborhood characteristics. Two primary bodies of economic literature 

attempt to explain historical development patterns through the lens of household 

locational choice. The urban economics literature, in particular the monocentric 

city model with early incarnations by Alonso (1964), Mills (1967), and Muth (1969), 

explains changes in urban land use patterns in terms of rising incomes, falling 

commuting costs, and newer housing on the periphery. In contrast, the local public 

finance approach explains development patterns in terms of preferences for alternative bundles of local taxes and public goods and services. This body of literature 

expands on Tiebout’s (1956) household sorting model. 

Although urban economics models capture the primary drivers of the historical 

development pattern, they do not account for other factors that influence household locational choice within a metropolitan area, including amenities and public 

finances (Nechyba and Walsh 2004). Local public finance models include these 

factors and better explain why many households moving to the suburbs prefer to 

form homogenous groups, but they are typically aspatial. Below we first review 

the literature on household location decisions and then focus on the interactions 

between location decisions and municipal profiles. 

Household Location Decisions

Suburbanization has been a dominant trend in aggregate household location decisions and urban spatial development in the modern era. The classic monocentric 

city model offers important insights into this phenomenon. In this model (Alonso 

1964, Mills 1967, Muth 1969), all employment lies within the central business 

district (CBD), households are differentiated by income, and the key difference 

between alternative household locations is distance to the CBD. Because housing 
3

Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

close to the employment center is relatively expensive, households face a tradeoff 

between commuting time and housing price. Those who choose to live farther away 

incur higher commuting costs but face lower housing prices and can thus afford to 

consume more housing. The primary driver behind suburbanization and modern 

urban spatial development has been falling commuting cost owing to the proliferation of the automobile and the development of highway systems. Simple CBD 

models account for this driver and correctly predict expanding urban footprints in 

the face of decreasing transportation costs. However, simple CBD models do not 

account for a number of other relevant factors—including alternative transportation 

modes, locational amenities, and age of the housing stock—nor do they predict 

multicentric metropolitan areas and various historical development patterns that we 

observe. A number of researchers have relaxed assumptions and generalized CBD 

models to address these concerns. 

LeRoy and Sonstelie (1983) incorporated two alternative transport modes—one 

fast and one slow—and demonstrated that when more affluent households are better able to afford the faster mode, they will tend to suburbanize more rapidly than 

others. They argued that this was the case early on with the automobile. However, 

as the cost of the faster mode falls (the vast majority of American households can 

now afford to commute by car), more affluent households lose this comparative 

advantage for suburbanizing. In fact, because wages—and thus opportunity cost 

of time—are higher for high-earners, LeRoy and Sonstelie (1983) predicted gentrification by more affluent households as commuting costs fall and less affluent 

households suburbanize. According to this model, when high- and low-income 

households use the same transport mode, high-income households will tend to 

locate in the city center.

Brueckner et al. (1999) added natural and historical amenities to explain 

alternative income distributions across different cities. They observed the stark 

difference between most American cities, where high-income households tend to 

live in the suburbs, and many European cities, where the wealthy occupy the central 

city.1

 Their model explains these differences in terms of differing levels of natural 

and historical amenities across cities. As with classic CBD models, the rich are 

pulled to the suburbs by their preference for more housing, which is available more

1

 The simple CBD model is consistent both with higher income households locating in 

the center (the ratio of commuting cost per mile to housing consumption increases with 

income) and with higher income households locating in the suburbs (opposite). However, 

it seems implausible that the behavior of this ratio across countries differs enough to fully 

explain differences in spatial income distribution. See Brueckner et al. (1999) for a more 

complete discussion.

There is a stark 

difference between 

most American cities, 

where high-income 

households tend to 

live in the suburbs, and 

many European cities, 

where the wealthy 

occupy the central city.
4

General technical report pnw-gtr-905

cheaply on the periphery; simultaneously, they are pulled into the center by the high 

time-cost of commuting. However, this model also allows for heterogeneous levels 

of natural and historical amenities between the center and the suburbs. When the 

central city has high levels of amenities, like Paris, these constitute an additional 

attraction for the wealthy. On balance, the time-cost effect and the amenity effect 

outweigh the housing price effect, and the wealthy locate in the center. When the 

central city has low or even negative amenity value, such as in Detroit, the housing 

price effect dominates, and the wealthy locate in the suburbs. A key assumption of 

this model is that preferences for amenities rise with income.

Wu (2006) incorporated amenities in a different fashion. Distinguishing 

between exogenous amenities (natural and historical features) and endogenous 

amenities (e.g., local public services), this study incorporated exogenous amenities 

in a modified CBD model. Alternative locations within the city differ in terms of 

the distance to the employment center and the level of local amenities. In contrast to 

the model presented by Brueckner et al. (1999), spatially heterogeneous amenities 

in this model attract households to various suburbs. With this spatial heterogeneity 

in amenities, households may be willing to pay more for a nice location than for 

a short commute; thus, housing prices may not fall uniformly as we move away 

from the center. At a given distance from the center, higher income households will 

choose locations with better amenities. This model is consistent with noncontiguous 

development patterns and non-distance-based patterns of income segregation. Wu 

(2006) included a model incorporating endogenous amenities as well, discussed 

below with local public finance models.

Brueckner and Rosenthal (2009) posited that the age of housing stock is an 

important determinant of the location of high- and low-income households. The 

resulting model is consistent with both suburbanization and gentrification. In 

addition to short commutes and low housing prices, high-income households 

prefer newer housing. Commuting concerns pull households inward; housing price 

concerns pull them outward. The location of new or newly remodeled housing 

determines the direction of the housing-age effect. As a city grows, new housing is 

always available on the periphery. Some new housing is also available in the interior—more so during periods of rapid redevelopment. If new housing is abundant in 

the interior city, it exerts an additional pull, causing some high-income households 

to locate in the center. Holding housing age constant, this model predicts a negative 

relationship between income and distance—the rich prefer to live in the center. 

Contrast this with the traditional CBD model, in which suburbanization by the rich 

implies a positive relationship between income and distance.
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Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

Central business district models, including those discussed above, assume 

monocentricity—that is, all firms (and thus all employment) locate in the CBD. 

Whereas household location is determined endogenously within the model, firm 

location is given exogenously. Ogawa and Fujita (1980) and Fujita and Ogawa 

(1982) relaxed this assumption and explored the conditions under which a nonmonocentric city is the equilibrium urban spatial configuration. In addition to 

commuting cost, these models include a transaction cost parameter that measures 

the benefits of spatial clustering for firms. When transaction costs are high relative 

to marginal commuting costs, the incentive for firms to cluster outweighs the incentive for households to locate close to work. A monocentric city is the equilibrium 

spatial arrangement. Higher relative marginal commuting costs give rise to multiple 

dispersed employment centers, as households have increasingly strong incentives to 

minimize commuting distance. In the extreme case in which firms do not benefit 

from spatial proximity, the equilibrium spatial arrangement is a fully mixed city 

with firms and residences dispersed throughout. 

Interactions Between Location Decisions and Municipal Profiles

Local public finance models offer an alternative lens through which to examine 

household locational choice. Even broadened to include amenities, housing age, 

and transit considerations, CBD models do not fully capture the role of community 

characteristics. Dating back to Tiebout (1956), local public finance models endogenize the provision of public services. In other words, these models account for the 

interaction between household locational choice and the levels of local taxes and 

public services. Households choose a location based in part upon their preferences 

for various bundles of local taxes and public services at the community scale. They 

“vote with their feet.” Simultaneously, households influence the level of taxes and 

services in a community through the representative process and through their tax 

contributions and impacts on neighbors. A brief discussion of the link between 

household locational choice and community characteristics follows.

A household chooses a home based on income/wealth, owner preferences, home 

characteristics, and community characteristics. Based on their finances, families 

choose a preferred option from available house-community combinations. The role 

of community characteristics in this process is clear: families like nice, safe neighborhoods and good school districts. The link between household location decisions 

and community characteristics is more involved. Relevant community characteristics include tax rates and the levels of amenities and public services. Some 

community characteristics, primarily amenities, are truly unaffected by household 

location decision. Consider natural features, such as hills, lakes, or rivers, or a 
6

General technical report pnw-gtr-905

well-established man-made attraction, like a historical site or a bustling commercial 

district. These types of sites exist prior to any location decisions and will persist 

regardless of those decisions. Other community characteristics are endogenous; 

they are affected by household location decisions.

Collective location decisions—and the preferences and characteristics of 

the resulting population—affect these endogenous community characteristics in 

three ways: voting, local public finance externalities, and peer externalities. First, 

community residents vote for their preferred bundle of taxes and services. As the 

voter base changes owing to household relocations, the results of these votes may 

change. Voting determines the local tax rate directly, but relocation decisions can 

alter the size of the tax base, affecting local public service provision indirectly. 

If high-income households move out of central cities and into suburbs in classic “flight-from-blight,” we would expect an erosion of the city tax base and a 

strengthening of the suburban tax base, leading to deteriorating public services in 

the center and enhanced services in the suburbs. This is the local public finance 

externality. Collective location decisions that shift income distributions affect the 

ability of jurisdictions to provide services. Finally, peer externalities also affect the 

level or quality of service independently of finances. Consider public education, for 

example. Funding affects school quality, and wealthier school districts tend to be 

better-funded—the local public finance externality. Highly involved parents may 

also affect school quality. So, two comparably funded districts with different levels 

of parental engagement might expect different results. Peer externalities are present 

when the level of the public service provided depends on the characteristics of the 

population being served as well as the level of funding. Interestingly, peer externalities may preclude the possibility of leveling the playing field by increasing funding 

to lagging communities. Desire to take advantage of perceived peer externalities 

may influence location decision and has been put forth as an explanation for the 

formation of homogenous suburbs (Nechyba and Walsh 2004).

Of course, some community characteristics defy identification as purely endogenous or exogenous. The presence of a previously existing park or open space is 

exogenous. However, the quality of experience in the park may be endogenous and 

subject to change owing to voting, local public finance externalities, peer externalities, and spatial context. The community could vote to cut or boost maintenance 

funding. A weakening tax base could force maintenance reductions via local public 

finance externalities. Citizen use levels and participation in volunteer maintenance 

could affect quality of experience—examples of peer externality. Finally, spatial 

context matters; a well-maintained park in a high-income neighborhood provides 

amenities to local residents and increases values of nearby properties, while an 

A well-maintained 

park in a high-income 

neighborhood 

provides amenities 

and increases nearby 

property values, while 

an undermaintained 

park that serves as a 

focal point for crime 

is much less valuable 

to local residents and 

could be viewed as a 

disamenity.
7

Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

undermaintained park that serves as a focal point for criminal behavior is much less 

valuable to local residents and could potentially be viewed as a disamenity (Anderson and West 2006, Troy and Grove 2008). 

By incorporating interaction between community characteristics and household 

location decisions, local public finance models go beyond their CBD counterparts. 

Following Tiebout’s seminal 1956 paper, other researchers expanded on Tiebout’s 

general equilibrium model. Ellickson (1971) derived the single-crossing property, a 

necessary condition for equilibrium characterized by income stratification. Epple et 

al. (1984) incorporated housing markets. Epple and Sieg (1999) developed a general 

method for estimating equilibrium models of local jurisdictions. Although these 

studies generate strong predictions of characteristics of communities in equilibrium—including income stratification across communities or, more generally, 

income stratification across communities by preference—they ignored location.

Wu (2006) incorporated distance and exogenous amenities in a hybrid of CBD 

and local public finance models. The first model from this paper, mentioned above, 

simply adds exogenous amenities to a CBD model. A second model, however, 

includes both exogenously determined amenities and endogenously determined 

taxes and public services. This model predicts income stratification by amenity 

level for a given distance from the city center.

In addition to urban economics and local public finance, papers from several 

economic sub-genres informed our investigation of the link between land use and 

municipal profiles. Hedonic home pricing offers insight into the preferences driving household location choice, which in turn drives land-use change. Oates (1969) 

introduced hedonic modeling to test Tiebout’s hypothesis, and a wide range of 

empirical studies use hedonics to estimate the value of community characteristics 

(both positive and negative) as capitalized in home sale prices (e.g., Anderson and 

West 2006, Bowes and Ihlanfeldt 2001, Cohen and Coughlin 2008, Troy and Grove 

2008).

One clear message emerges from this literature: when one is estimating the 

effect of amenities (or disamenities) on home prices, spatial context matters. For 

example, Cohen and Coughlin (2008) found that the effect of proximity to an 

airport varies with distance. If you are too close, airport noise drives down home 

prices; if sufficiently far away to mitigate noise, proximity to the airport drives up 

home prices. Troy and Grove (2008) found that although some parks are amenities 

and exert a positive effect on home prices, parks in high crime areas may be seen as 

a disamenity or liability, and thus proximity to them may be correlated with lower 

home prices. In this case, negative spatial context renders the willingness to pay 

for proximity to a park negative. Other studies have found that the amenity value 
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General technical report pnw-gtr-905

of open space varies widely with distance from the city center (Geoghegan et al. 

1997), whether the site is permanently designated as open space (Irwin and Bockstael 2001), the type and proximity of open space (e.g., Anderson and West 2006, 

Smith et al 2002), and income and age structure of the neighborhood (Anderson 

and West 2006), to name a few. Recent research indicates that the presence of an 

additional bird species increases home prices by approximately $32,000 (Farmer 

et al. 2013). Similarly, Donovan and Butry (2010) found that the presence of street 

trees increases home sales price and reduces time on market. Investigating how 

households value particular community characteristics—and how those values 

vary depending on context—enhances our understanding of household location 

decisions.

Related research documents the positive effects of urban trees on home rental 

prices (Donovan and Butry 2011), crime (Donovan and Prestemon 2012), and health 

(Donovan et al. 2011). The Green Cities Research Alliance, a collaborative effort 

between the Forest Service and various research entities in the Pacific Northwest, is 

a good source for emerging research on the interaction between urban areas and the 

environment, including the studies listed above.

Beyond hedonics, a number of papers have focused on urban sprawl. The term 

sprawl has negative connotations and is often cited as an example of a land-use pattern with negative social implications. Nechyba and Walsh (2004) provided a comprehensive review of the literature on sprawl. They argued that, despite its negative 

reputation, sprawl occurs because individual households are happier with the larger 

homes and lots that it offers. However, they did identify four costs: road congestion, 

vehicle pollution, loss of open space, and unequal service and public good provision 

across metropolitan areas resulting from self-segregation and associated pockets of 

affluence and poverty. Lopez (2004) and Plantinga and Bernell (2007) investigated 

the link between obesity and urban sprawl. These papers and most of the related 

literature focused on concrete impacts of sprawl: weight, emissions, and income 

distribution. Brueckner and Largey (2008) notably departed from this trend and 

focused on sprawl and the reduction of social interaction. They investigated the 

premise that low-density living reduces social interaction to the detriment of society 

as a whole.

A well-developed economics literature exists on household locational choice. 

Although less well studied, a number of papers focus on the role of race. For 

example, Bayer and McMillan (2005) developed a sorting model that includes race 

and conducted simulations to illustrate the effect of race. They found that racial 

sorting contributes to the racial disparity in neighborhood amenity consumption, 

though that sorting could be the result of discrimination or simply a preference to 
9

Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

live among like households. Ihlanfeldt and Sjoquist (2000) provided a review of 

related studies. This literature sheds light on our race results, described in detail 

below. The economic processes and implications involved will be the focus of 

future research.

In the sections that follow, we apply lessons from the above literature to investigate a case study of household locational choice. First, we explore the data sources 

and processing methods. Then, we discuss appropriate econometric models and 

estimation challenges.

Data and Methods

Our study area was the Portland, Oregon, and Vancouver, Washington, metropolitan area—specifically Clackamas, Multnomah, and Washington Counties in 

Oregon, and Clark County in Washington (see app.). Combined, the four counties 

have an area of 3,727 square miles and a 2000 population of 1.79 million, which 

grew to 2.07 million in 2010 (15.5 percent decennial growth). The study area also 

included 41 incorporated cities and towns, mostly clustered around Portland. We are 

interested in explaining the variation in population change within this metropolitan 

area. 

Data for the area were obtained from several sources, most importantly local 

governments and the U.S. Census. For the three counties in Oregon, the elected 

regional government, Metro, maintains the Regional Land Information System 

(RLIS), a high-quality geographic information system (GIS) database with a variety 

of data layers. RLIS layers used included administrative boundaries, zoning, water 

features, parks and open space, and tax lots, which included parcel-level data on 

land-use and on home characteristics (for residential properties). We obtained 

historical RLIS data for the year 2000. The Clark County, Washington, assessor’s 

office also offers similarly detailed GIS data, which we obtained for the year 2000. 

Though similar, these datasets are not identical, and substantial care was taken 

to ensure consistency in the data across states. With inconsistencies resolved, we 

merged the relevant GIS layers, so that for each layer, we had a single shapefile 

(map) for the entire study area. For some layers, like zoning and tax lots, the data 

we needed were already associated with the maps. For other layers, like school 

districts and census geographies, we could map these geographies, but few data 

were associated with the maps. We had to obtain data from other sources and join it 

to our GIS layers.

We obtained population and demographic data from the 2000 and 2010 decennial censuses. Data on population, race/ethnicity and age were available at the 

census block level. Data on education, income, housing characteristics, and more 

Clackamas, Multnomah, 

and Washington 

Counties in Oregon, 

and Clark County in 

Washington have an 

area of 3,727 square 

miles and a 2000 

population of 1.79 

million, which grew 

to 2.07 million in 2010 

(15.5 percent decennial 

growth).
10

General technical report pnw-gtr-905

were available at the block group level. To give an idea of scale, in 2000 our study 

area contained 34,178 census blocks and 1,160 block groups. We also obtained 

redistricting data from the 2010 census, which included population and race/ethnicity data. Additional data sources included county governments for property tax levy 

rates, state departments of education for school quality data, and the USDA Forest 

Service for national forest maps. 

With underlying data in place, the next step was choosing units of observation. 

Existing geographies tend to be problematic. Using counties would provide only 

four observations and ignore variation in population change and local municipal 

profiles within the counties. Using cities drops unincorporated areas and again 

ignores variation, particularly in the largest city, Portland. U.S. Census geographies, 

including census blocks and census block groups, are much smaller than counties and cities and so can capture variation within cities and counties. However, a 

considerable proportion of census geographies shift boundaries over time, leading 

to consistency problems when measuring population change. Furthermore, the size 

of census geographies varies widely, as census blocks and block groups are drawn 

to have roughly equal populations. Thus rural census blocks with low population 

density are much larger than densely populated urban census blocks. Finally, census 

geography boundaries are not random, but tend to follow evident development patterns and form homogenous units. While these make sense as cohesive units within 

a city or county, nonrandom boundaries can lead to endogeneity issues and biased 

estimates (Banzhaf and Walsh 2008). 

We avoided problems associated with existing geographies by adopting an 

approach suggested by Banzhaf and Walsh (2008). Specifically, a grid of 2-mile 

squares was laid over a map of the metro area, then a circle 2 miles in diameter 

was drawn centered in each square. We dropped circles that were not completely 

within our four counties, leaving a sample of 844 circular observations. We did not 

consider these to represent communities in any social or political sense. Rather, this 

method constituted an effective sampling methodology, which allowed us to take 

advantage of high-resolution spatial data.

Of course, a grid of circles 2 miles in diameter is not the only option. Alternative diameters, shifting the grid incrementally, and random locations as opposed 

to a grid are possible. Using hexagons or squares rather than circles would provide 

full coverage of the study area. Comparing alternative units is a good strategy for 

testing the sensitivity of parameter estimates. Parameters which are highly sensitive 

to the spatial sampling unit should be viewed with skepticism.

With units selected, we used GIS software and data to quantify variables 

measuring characteristics for each observation. This procedure differs depending 
11

Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

on the data in question. Some GIS layers covered the entire study area, like census 

geographies, tax lots, zoning, and school districts. These layers each contained one 

or more potential explanatory variables. For each layer, a GIS script aggregated the 

variables of interest from the underlying geometry to our circular observations. 

Specifically, the script computed an area-weighted sum for each variable. The 

number of elements in this weighted sum varied with the data layer in question. For 

example, school districts tend to be large, so most circles fell into one or only a few 

school districts. On the other hand, tax lots are much smaller. In dense residential 

areas within the city, some circles may contain thousands of tax lots. The number 

of elements in the weighted sum also varies in space for the same data layer. For 

instance, census block groups are small in densely populated, central areas and big 

in less populated areas far from the city center. So, some more remote circles fall 

entirely within one block group while in the center a single circle intersected dozens 

of block groups.

For GIS layers that contain only points, such as bus stops, the appropriate 

measure might be the number of bus stops in a circular observation. Access to parks 

is another example. We could measure access in a variety of ways: park acreage 

within the circle, distance to the nearest park, and number or acreage of parks 

within some distance, to name a few. Wu and Plantinga (2003) emphasized distance 

as a superior measure of open space access. Distances from each observation to the 

city center and the nearest parks and open spaces were also computed.

The final step in preparing our data was building interaction terms. The 

hedonics literature reveals variations in amenity values depending on many factors 

including income, proximity, age, urban density, and crime, to name a few. Interaction terms allow the researcher to model differential effects. For example, a park 

located in a low-income, high-crime neighborhood may not be valued as highly as 

a park in a high-income, low-crime neighborhood. A model including interaction 

terms between crime or income and park proximity might pick up this differential 

effect, whereas an alternative specification would not. Anderson and West (2006) 

provided a good discussion and a hedonic model with several classes of open space 

and multiple interaction terms. The interaction terms included in table 1 represent a 

small subset of the possible interaction terms.

Table 1 defines our variables. Note that only our dependent variables and those 

explanatory variables included in our model appear, although we did consider and 

test a number of alternative specifications and alternative variables. Population 

changes for the entire population and by race are located above the line in table 

1. Population change is our primary dependent-variable candidate, as it is a proxy 

for household locational choice, particularly with uniform circular observations. 
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General technical report pnw-gtr-905

Table 1—Variable definitions

Variable Definition

ch_pop Change in population 2000–2010

ch_aspac Change in Asian/Pacific Islander population 2000–2010

ch_black Change in black population 2000–2010

ch_white Change in white population 2000–2010

ch_latino Change in Latino population 2000–2010

d_ctr Distance to city center (miles)

d_ctr_sq Distance to city center squared

tax00 2000 property tax levy (dollars per $1,000 assessed value)

sch_qual School quality index

s_prk_18 Interaction term: share of area in parks/open space × share of population < 18

s_prk_64 Interaction term: share of area in parks/open space × share of population 65+

dprk_18 Interaction term: distance to nearest parks/open space × share of population < 18

dprk_64 Interaction term: distance to nearest parks/open space × share of population 65+

shr_for Share of area in forests

shr_agr Share of area in agriculture

shr_nf Share of area in national forest

dist_nf Distance to nearest national forest boundary (miles)

wat_shr Share of area in major waterways and lakes

value Mean assessed value of single-family residences ($)

lotsz Mean lot size for single-family residences (acres)

r_shr_bl Share of population black only

r_shr_ap Share of population Asian or Pacific Islander only

r_shr_om Share of population Native American only, other minority only, or two or more 

races

r_shr_la Share of population Latino

pov_shr Share of population in poverty

urbp_shr Share of population classified as urban

shr_zcom Share of area zoned commercial

shr_zind Share of area zoned industrial

shr_zmfr Share of area zoned multi-family residence

shr_zsfr Share of area zoned single-family residence

shr_clar Share of area in Clark County, Washington

shr_wash Share of area in Washington County, Oregon

shr_mult Share of area in Multnomah County, Oregon
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Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

Because all observations are identically sized and shaped, modeling population 

change is equivalent to modeling change in population density. We included population change by race as well, because preliminary ordinary least squares analysis 

(results omitted here due to sheer volume) indicated that race is a persistently 

significant factor affecting household location choice. Our raw school data was 

obtained from the state departments of education. Because the test scores vary by 

state, we constructed an index. In each school district, school quality was defined 

as the district score over the state average, so the values are bounded below by zero 

and centered around one. This is just one of several alternative indices we explored. 

A number of variables measure access to parks, open space, and natural amenities. 

Table 2 provides descriptive statistics for both our circular observations and 

the study area. For our circles, note that the reported means are unweighted. Most 

means were taken across all 844 observations, but several omitted circles with 

zero population to avoid misleading results. Also, several means for the study area 

differed greatly from the means for our observations. This is not a problem. Rather 

it is an artifact of averaging spatially aggregated data. Consider lot size. For the 

study area mean, every residential property gets equal weight in the average. For 

the mean across our circular observations, we first calculated an average lot size for 

each circle, then averaged those values. This process in essence gave large lots more 

weight, as it took fewer of them to fill a circle.

In many cases, these data are easiest to grasp visually in maps. A wide range 

of maps of both the underlying data and our 2-mile constructed observations are 

available in the appendix.

Estimation Strategies

The previous section explored the process of data collection, construction of units 

of observation, and quantification of variables. Although specific to the Portland, 

Oregon, and Vancouver, Washington, metropolitan area, this process is applicable 

to other regions and research questions. With data in place, the challenge is to 

specify an appropriate model to identify the causal relationships between population change and various aspects of local municipal profile. Several estimation 

strategies are available to the researcher investigating household locational choice 

and municipal profiles. 

Structural modeling—

The appropriate estimation strategy depends upon the precise research question. 

In all cases, an appropriate estimation strategy must account for the fact that some 

variables measuring local municipal profiles are exogenous, while others are endogenous. Examples of exogenous variables include natural features, parks and open 
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Table 2—Descriptive statistics for circular observations and study area

Circular observations Study area

Variable Mean

Standard 

error Minimum Maximum

Total change 

(percent)

ch_pop 267.2472 800.7952 -809.25 9,206.78 276,942 (15.5)

ch_aspac 40.76532 185.8258 -637.18 2,903.57 42,710 (46.3)

ch_black 11.58536 173.1598 -3,937.08 1,107.66 11,926 (23)

ch_white 155.2676 562.1191 -902.65 7,689.029 158,699 (10.5)

ch_latino 87.36041 282.9555 -247.76 2,367.86 92,378 (69.8)

Mean

d_ctr 25.45369 12.61727 0 56.3561

d_ctr_sq 806.8972 704.0584 0 3,176.01

tax00 11.13986 2.053088 8.0753 19.9163 14.024

sch_qual 1.044137 0.152553 0.7525 1.5309 1.0483

s_prk_18 0.007082 0.019543 0 0.269582

s_prk_64 0.002814 0.007962 0 0.11405

dprk_18 0.256128 0.524254 0 5.378

dprk_64 0.09081 0.204462 0 1.972563

shr_for 0.485297 0.410855 0 1 0.4951

shr_agr 0.123329 0.233305 0 0.970826 0.1156

shr_nf 0.257048 0.42955 0 1 0.4631

dist_nf 13.58053 15.10727 0 57.9847

wat_shr 0.015436 0.062493 0 0.586447 0.0193

valuea 197,135 92,900.79 0 1,351,806 182,131

lotsz a 1.810813 8.303067 0 170.17 0.6266

r_shr_bl a 0.013191 0.03188 0 0.347283 0.0283

r_shr_ap a 0.022905 0.028702 0 0.198945 0.0516

r_shr_om a 0.051311 0.059695 0 1 0.0795

r_shr_la a 0.049786 0.053631 0 0.406468 0.074

pov_shr 0.092086 0.059771 0 0.284563 0.0949

urbp_shr 0.237555 0.379167 0 1 0.9023

shr_zcom 0.007725 0.027783 0 0.22584 0.00763

shr_zind 0.022456 0.086001 0 0.944787 0.02123

shr_zmfr 0.011151 0.035964 0 0.287574 0.0102

shr_zsfr 0.07519 0.18448 0 0.94893 0.0694

shr_clar 0.174574 0.376461 0 1 0.176

shr_wash 0.186951 0.387035 0 1 0.1948

shr_mult 0.128591 0.325416 0 1 0.125

a

 Observations with zero population dropped from mean and standard deviation calculations (n = 195).
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Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

space, and historical development patterns. Examples of endogenous variables include median household income, school quality, and property tax rate. The quantity 

of urban trees in a given area and the extent of canopy cover are additional examples of endogenous variables, as trees are cut and planted during the residential development (or redevelopment) process. In the absence of endogeneity, the researcher 

could simply regress population change on variables quantifying municipal profile. 

Owing to endogeneity, this simple approach would yield biased estimates. Because 

some of the explanatory variables are affected by the dependent variable and thus 

correlated with the error term, the model becomes a system of simultaneous equations. In structural form, model looks like:

Y = Xnβn+Xs

β+εy, (1)

 

i i ii i XY X Z s y nn z x =+ + + γ γ γε , i=1, 2, ..., n (2)

where Y is the population change vector, Xn is exogenous municipal profile variables such as elevation and total water area, 1 2 ( , ,..., ) n X XX X s ss s = is the endogenous municipal profile variables such as school quality and acreage of green space, 

i Z is a vector of variables that affect endogenous profile variable i, but do not 

affect household location choices directly, the β’s and γ’s are the respective coefficients, and the ε’s are the error terms. The structural model can be estimated in 

different ways, depending on the availability of appropriate instrumental variables 

and data.

If variables i Z can be identified for each endogenous profile variable, and 

data on i Z are available, then 1 2 ( , ,..., ) n ZZ Z can serve as a set of instrumental 

variables because they are correlated with the endogenous explanatory variable and 

uncorrelated with the error term (i.e., causally unrelated to the dependent variable). 

In this case, the structural model can be estimated using second or third stage least 

squares or partial or full information maximum likelihood estimation methods. For 

example, using two-stage least squares, first regress each endogenous variable in Xs

on all exogenous variables in the model 1 2 ( , ,..., ) n ZZ Z and Xn) and obtain fitted 

values, XXXX

�ssss. Then replace endogenous variables with fitted values in (1) in the second 

stage regression.

(3)

Estimates derived from instrumental variables and two-stage least squares are 

only as reliable as the instruments. If the chosen instruments are correlated with 

the error term, the bias problems encountered in the structural form remain unresolved. If the chosen instruments are poor (weakly correlated with the endogenous 

variables they are replacing), the result is poor fitted values with little variation 

YYYY = XXXXnnnnββββnnnn + XXXX

�ssssββββssss + εεεεyyyy
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generated in the first stage. For this case study, appropriate instruments would need 

to be correlated with endogenous amenities, uncorrelated with the error term, and 

not included in the set of explanatory exogenous amenities. It can be challenging to 

identify such variables.

Reduced-form estimation— 

When appropriate instruments are not available, researchers may resort to 

estimating the reduced form of the structural model to uncover useful information about the effect of amenities on location choices. Solving for Y and 

1 2 ( , ,..., ) n X XX X s ss s = , one can derive each of these endogenous variables as a 

function of 1 2 ( , ,..., ) n ZZ Z and Xn. These reduced-form equations can then be 

estimated using an appropriate method. The related literature strongly suggests 

that exogenous natural amenities influence development patterns, and these development patterns in turn affect the level of endogenous social amenities (e.g., Wu 

2006). Thus exogenous variables can be used to explain endogenous variables. The 

reduced form approach has a major drawback: estimation does not identify the 

structural coefficients found in equation 1. So, while estimating the reduced form 

in this case sheds light on how natural amenities affect the level of social amenities, 

it does not reveal the effects of various elements of municipal profile on population 

change.

The model specification in equation 1 potentially includes multiple endogenous 

covariates, e.g., tax rate, school quality, and park access. This case study has a 

broad research question. How do elements of municipal profile affect population 

change? By narrowing the research question to focus on a single endogenous 

covariate, asking instead how property tax rate affects population change, a number 

of other estimation strategies from the treatment effects literature become available. 

Ordinary least squares (OLS) estimation of treatment effects is typically biased 

because treatment effectiveness depends on factors that determine whether an 

observation gets treated. In a medical context, the effectiveness of medical intervention depends on the characteristics of the patient. At the same time, the characteristics of the patient determine whether the patient receives the treatment. Although 

a full discussion of treatment effects is beyond the scope of this paper, a number of 

measures developed for nonexperimental settings in the medical field are increasingly being adopted by economists.

Treatment effects— 

Two relevant estimation strategies from this literature are propensity score matching and difference-in-difference estimators, both described below in the context of 
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Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

property taxes. Propensity score matching could be used to evaluate the effect of 

differential tax rates on population growth. Propensity score matching estimates the 

treatment effect by systematically comparing pairs of observations from the treatment and non-treatment groups that are otherwise alike. This method first estimates 

a model predicting likelihood of treatment and pairs observations for comparison 

based on the resulting fitted values. For property taxes, this would involve identifying low- and high-tax observations, regressing tax rate on variables thought to 

influence tax rate (e.g., income distribution, demographics), and calculating predicted tax rates using the estimated coefficients. Each low-tax observation is paired 

with the high-tax observation with the closest predicted tax rate. By controlling 

across a number of relevant covariates, propensity score matching improves the 

likelihood that observed differences in population change are in fact the result of 

different tax rates. It should be mentioned that propensity score matching methods 

are most commonly applied to two groups of observations that differ in terms of 

a discrete treatment. For our tax example, we would need to define a dichotomous 

treatment based on a continuous tax rate. Propensity score matching directly controls for observables that affect both outcomes and the likelihood of treatment. Thus 

the researcher needs a dataset that includes all relevant observables. Differencein-difference methods, on the other hand, control for unobserved time-invariant 

characteristics without the necessity of data collection. Difference-in-difference 

methods measure the effect of a treatment at a given point in time. The idea behind 

this method is to compare the treated group to itself before treatment as well as to 

some other untreated control group. Simply evaluating treated observations relative 

to themselves before treatment does not account for events or trends that occur during treatment and affect the entire treatment group. If the researcher fails to include 

a non-treatment control group, then changes attributable to trends affecting the 

general population will be attributed inappropriately to treatment. In the property 

tax context, local population changes should not be attributed to changes in local 

tax rates without first accounting for the population change trends in the region. If 

the region as a whole is growing, then it is likely misleading to attribute local population growth entirely to local changes in tax rate. The researcher can net out the 

regional trend by comparing the treated group to an untreated control group.

Spatial autocorrelation poses another challenge to the estimation and arises 

when changes in population and other profile variables in nearby communities 

directly affect each other or are affected by the same unobserved factors. The 

former situation is referred to in the literature as spatial lag dependence (or spatial 

interaction), and the latter situation is referred to as spatial error dependence. In 

Propensity score 

matching estimates 

the treatment effect 

by systematically 

comparing pairs of 

observations from the 

treatment and nontreatment groups that 

are otherwise alike. 

Difference-in-difference 

methods compare the 

treated group to itself 

before treatment as 

well as to some other 

untreatred group. 
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General technical report pnw-gtr-905

the preceding case study, both types of spatial dependence may occur. Appropriate 

tests should be conducted to detect their existence. If found, appropriate estimation 

techniques must be used to address the issue. 

The preceding case study details the data collection and processing used to 

construct a model of household locational choice (population change) in the Portland, Oregon, metropolitan area. Geographic information system data and software 

facilitate creative solutions for data at conflicting geographies. Still, substantial care 

is necessary in model specification and estimation to avoid the pitfalls associated 

with interactions and endogeneity. 

In fact, the organization of the case study, particularly the model estimation 

section closely follows the authors’ efforts to take advantage of rich data for the 

study area, while avoiding the aforementioned pitfalls. Although detailed presentation of results is beyond the scope of this report, a brief discussion of the empirical 

work that provides the basis for the case study follows. 

The initial focus of this research was causal links between municipal profile 

and land-use change, broadly, and, specifically, the effect of urban forest and 

open-space amenities on household location choice and community characteristics. 

The inclusion of a broad set of municipal attributes, some of which are undoubtedly endogenous, precludes unbiased OLS estimation. Broad controls also render 

instrumental variable estimation infeasible in practice owing to the difficulties of 

identifying appropriate instruments. Without sacrificing broad controls, a reduced 

form model explaining endogenous municipal attributes in terms of exogenous 

attributes remains a feasible option. In this case, reduced-form estimation revealed 

that exogenous natural and historical amenities do indeed influence the level of 

endogenous municipal characteristics, including population change and density, 

median income, school quality, property taxes, and home values. Results indicated 

how natural characteristics (e.g., slope, elevation) and proximity to different natural 

amenities (e.g., water bodies, parks by type) influenced endogenous characteristics. 

Of course, reduced-form estimation does not shed light on the underlying relationships between location choice and endogenous municipal characteristics. Furthermore, although they illustrate preferences, reduced-form results may have little 

policy relevance because natural features are difficult to change.

To quantify the underlying relationships in the absence of appropriate instruments, one alternative approach is to abandon broad controls and focus on a single 

municipal feature. In this case, though biased, preliminary OLS estimates highlighted the impact of race/ethnicity on population change. Neighborhoods with high 

concentrations of black residents tended to shrink. Other minority neighborhoods 

grew fast, especially Asian neighborhoods, while majority neighborhoods grew 
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Urban Green Space and Vibrant Communities: Exploring the Linkage in the Portland-Vancouver Area

modestly. These observations gave rise to a more focused research question: how do 

minority concentrations affect local municipal profile or neighborhood quality?

Simple correlations revealed that high minority concentrations were associated 

with lower school quality and higher crime. However, this approach ignores systematic differences between minority and majority groups, e.g., in terms of income 

and educational attainment. In order to isolate the effect of minority concentration 

from the effect of these systematic differences, a more sophisticated method was 

required. In this case, treatment effects methods, specifically propensity score 

matching, were appropriate. Under propensity score matching, pairs of observations 

that differed in terms of minority concentration but which were similar in other 

dimensions of municipal profile were compared. In this context, that meant that a 

higher minority observation was compared to the lower minority observation with 

the most similar characteristics. Controlling for other dimensions of municipal 

profile can yield strikingly different results than simple correlations. For example, 

once other relevant municipal attributes were controlled for, observations with 

higher concentrations of black residents exhibited significantly lower crime rates 

than observations with black-resident concentrations closer to the study area mean.

With a profusion of spatially explicit data available to the researcher at a variety 

of spatial resolutions, this report guides the reader through a fairly specific example 

of data collection and processing. Also provided is a general guide to estimation 

procedures and several descriptions of empirical applications. The fundamental 

challenge of modeling the relationships between residential location choices and 

municipal profiles is the interconnected nature of individual location decisions and 

outcomes at the neighborhood, city, or regional scale. Quality data do not preclude 

the fundamental challenges of identification in the presence of endogeneity.

Conclusions

Land use and social and environmental well-being are simultaneously determined. 

As households move, land use changes, affecting environmental quality and social 

welfare. Similarly, land-use change—and associated changes in natural amenities 

and community characteristics—affects residential location choice. For example, 

urban forests, whether designated open spaces or trees on streets and private lots, 

have amenity value and thus attract households, all else being equal. In turn, the 

resulting pattern of households in space affects the quantity and quality of urban 

forest, not to mention other characteristics by which we gauge the vibrancy of our 

communities. Household locational choice is a central element of this process, but 

there is a dearth of research investigating how households move within a metropolitan area in response to different community characteristics. In contrast, there 
20

General technical report pnw-gtr-905

is a well-developed hedonic literature investigating the relationship between home 

price and community characteristics. The challenges presented by endogeneity are 

the primary reason for this research gap. This report reviews the literature related 

to community characteristics and household locational choice, illustrates the sorts 

of spatially explicit data available for research at a metropolitan scale, and explores 

various appropriate empirical methodologies.

Our study area, the Portland, Oregon, and Vancouver, Washington, metropolitan area, exhibits great heterogeneity in community characteristics. Furthermore, 

local governments make high-quality, spatially explicit data available at reasonable 

cost. Incorporating several other sources left us with a rich dataset, including many 

potential measures of community characteristics. Variables related to urban forests 

include land-use, open-space designation, and canopy cover. Future research may 

focus on adapting methodologies to investigate the effects of these variables on 

household location choice in the face of endogeneity.

By modeling household location choice, we can shed light on household preferences for various community characteristics. These preferences, in conjunction 

with the supply of locations, determine the pattern of land use, with real social 

and environmental implications. The recent profusion of spatial data and analysis 

tools facilitates the investigation of location choice and land-use change within 

metropolitan areas. Enhanced understanding of household preferences could 

inform policymakers trying to achieve balance between economic development and 

environmental and open-space protections. For example, residential development in 

the wildland-urban interface is a significant source of land-use change and environmental impact in many communities. To mitigate this issue, policymakers need 

to understand which features of these locations households prefer and the strength 

of these preferences. Armed with this information, policymakers could create open 

space with similar amenities within the city to mitigate the outward pull of developable land. Conversely, policymakers could use information on the magnitude of the 

preferences to appropriately calibrate incentive-based policies like development 

impact fees to push development back to the center. Suburban households reveal 

their preference for their location, but deeper understanding of which attributes 

drive this preference would be helpful to policymakers aiming to protect forests, 

farmland, and other types of open space.

Acknowledgments

We thank Emery N. Castle, Dale J. Blahna, Josh Duke, Dave Lewis, and Seonghong 

Cho for their valuable comments and suggestions. 
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Appendix
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form. You may also write a letter containing all of the information requested in the form. 

Send your completed complaint form or letter to us by mail at U.S. Department of Agriculture, Director, Office of Adjudication, 1400 Independence Avenue, S.W., Washington, D.C. 

20250-9410, by fax (202) 690-7442 or email at program.intake@usda.gov. 

Persons with Disabilities 

Individuals who are deaf, hard of hearing or have speech disabilities and you wish to file 

either an EEO or program complaint please contact USDA through the Federal Relay 

Service at (800) 877-8339 or (800) 845-6136 (in Spanish). 

Persons with disabilities, who wish to file a program complaint, please see information 

above on how to contact us by mail directly or by email. If you require alternative means 

of communication for program information (e.g., Braille, large print, audiotape, etc.) please 

contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). 

Supplemental Nutrition Assistance Program 

For any other information dealing with Supplemental Nutrition Assistance Program (SNAP) 

issues, persons should either contact the USDA SNAP Hotline Number at (800) 221-5689, 

which is also in Spanish or call the State Information/Hotline Numbers. 

All Other Inquiries

For any other information not pertaining to civil rights, please refer to the listing of the 

USDA Agencies and Offices for specific agency information. 
U.S. Department of Agriculture 

Pacific Northwest Research Station

1220 SW 3rd Ave., Suite 1400 

P.O. Box 3890 

Portland, OR 97208-3890

Official Business

Penalty for Private Use, $300