The effect of proximity to different types of parks on housing prices is estimated using a unique data set of single family homes sold between 1990 and 1999 in Greenville, South Carolina. The value of park proximity is found to vary with respect to park size and amenities, as well as household income and family size. The greatest impact on housing values was found with proximity to small neighborhood parks, with property values as much as 13 percent higher for homes within 600 feet of such parks. The positive impact of proximity to attractive medium size parks extended to homes as far as 1500 feet from the park. The value of park proximity increases with family size and is greater, as a percentage of housing value, for households with income below the median.Resource /Energy Economics and Policy,

Espey, Molly

Owusu-Edusei, Kwame

English

Research Papers in Economics

 
 
 
 
Demographics and the Value of Park Proximity in Greenville, South Carolina 
 
 
Molly Espey 
Kwame Owusu-Edusei 
 
Department of Agricultural and Applied Economics 
Clemson University 
 
 
To be presented at the annual meeting of the American Agricultural Economics Association 
 
 
May 15, 2001 
 
 
 
 
 
Copyright 2001 by Molly Espey and Kwame Owusu-Edusei.  All rights reserved.  Readers may make 
verbatim copies of this document for non-commercial purposes by any means, provided that this 
copyright notice appears on all such copies.Abstract 
 
The effect of proximity to different types of parks on housing prices is estimated using a unique data set 
of single family homes sold between 1990 and 1999 in Greenville, South Carolina.  The value of park 
proximity is found to vary with respect to park size and amenities, as well as household income and 
family size.  The greatest impact on housing values was found with proximity to small neighborhood 
parks, with property values as much as 13 percent higher for homes within 600 feet of such parks.  The 
positive impact of proximity to attractive medium size parks extended to homes as far as 1500 feet from 
the park.  The value of park proximity increases with family size and is greater, as a percentage of 
housing value, for households with income below the median. 
 Introduction 
 
Urban sprawl has been blamed for loss of wildlife habitat, farmland, and wetlands.  Many states have 
been increasing their efforts to protect remaining open space.  For example, in November 2000 voters 
across the country considered at least 205 ballot measures that proposed to raise funds for conserving 
open spaces (Barber 2000).  Eighty-two percent of these measures were approved raising more than $7.3 
billion.   
 
But what is the protection of open space worth?  One way to quantify the benefit of protecting open space 
in an urban environment is to determine the impact of open space on housing prices.  The objective of this 
study is to determine the impact of parks on residential property values in Greenville, South Carolina and 
the relationship between those values and certain demographic characteristics. 
 
Parks can provide recreational opportunities and attractive views for nearby residents.  They might also 
lead to increased traffic and noise.  This study will estimate the net impact of proximity to parks and park 
type on housing sales price by using a data set that includes housing and neighborhood characteristics and 
park size and proximity.  The value of parks reflected in residential property values would provide a 
lower bound on the overall value of parks and open space protection to residents. 
 
 
Background 
 
According to a recent Sierra Club report (1999), South Carolina lags behind the rest of the nation in terms 
of open space protection, ranking third to last among the fifty states.  In funding for parks and recreation, 
Greenville County households provide at least thirty percent less than the state's other metropolitan areas, 
Spartanburg, Richland, and Charleston counties (Romain 2000).  City planners, however, have displayed increased focus on protection of the Reedy River, downtown revitalization, and improving the quality of 
life for Greenville residents. If the acquisition and protection of open space increases residential property 
values, property tax revenues would also increase, providing a possible funding mechanism for purchase, 
development, or maintenance of open space.  Quantification of the impact of open space protection on 
residential property values could guide local and state land use decision-makers in preservation efforts 
and planning for future growth. 
 
A number of other studies have used hedonic models to estimate the effect of different open space types 
on a house's sales price or assessed value.  Weicher and Zerbst (1973) studied parks in Columbus, Ohio.  
Correll, Lillydahl, and Singell (1978) studied greenbelts in Boulder, Colorado.  Frech and Lafferty (1984) 
estimated that actions by California Coastal Commission to preserve open space increased home prices by 
between $990 and $5,000. Do and Grudnitski (1995) found that proximity to golf courses increased 
property values. 
 
Lupi et al (1991), Doss and Taff (1993), and Mahan, Polasky, and Adams (2000) all estimated a positive 
value of proximity to different types of wetlands.  Finally, Netusil and Bolitzer (2000) and Lutzenheiser 
and Netusil (1999) examined the impact of proximity to various types of open space on property values in 
Portland, Oregon. 
 
 
Data 
 
Housing sales data used in this study includes all sales of single family houses in the City of Greenville 
between 1990 and 1999.  Housing prices are deflated using monthly consumer price indices.  The first 
explanatory variable is based on the county assessor's percentage depreciation factor used to assess 
effective house age, taking into account both actual age and the condition of the house.  This variable (DEPR) has a maximum value of 100 for a new house. Other explantory variables are the number of 
bathrooms (BATH), square footage of the house (SQFT), whether or not the house has air conditioning 
(AC=1 if yes, and 0 otherwise), whether or not the house has a garage (GARAGE=1 if yes, and 0 
otherwise), and lot size.  Lot size data was limited to properties over an acre and two variables accounting 
for lot size were used in the final regressions: ACR24=1 if the lot acreage is between 2 and 4 and 0 
otherwise, and ACR4=1 if lot acreage is greater than four acres and 0 otherwise.  Twenty-eight census 
tracts in the city limits serve as proxies for neighborhood characteristics. Table 1 shows the summary 
statistics for the housing characteristics. 
 
Parks are categorized into four groups.  There are twelve small parks, ranging in size from 15,620 to 
87,687 square feet, that are group together as basic neighborhood parks (Type 1).  All of these parks have 
some playground equipment in a sandy area and a small grassy area, typically mottled with weeds and 
bare spots.   None of these parks could be considered particularly attractive.  Four other small parks, 
ranging in size from 17,541 to 69,921 square feet, are grouped together as generally attractive as well as 
having some playground equipment (Type 2).  Six medium size parks, ranging in size from 210, 635 to 
1,101,310 square feet, are grouped together (Type 3).  These parks vary in terms of the type of amenities 
available, including baseball fields, tennis courts, a frisbee golf course, and playgrounds, but all included 
some walking trails and more natural areas.  Finally two other medium size parks (95,425 and  169,751 
square feet) were group together as being generally less attractive with fewer amenities and no natural 
area (Type 4).  The proximity of each house sold to each park type was determined by creating buffer 
zones of various distances around each park in ArcView, a widely used GIS software package. 
 
 Model 
 
The price of a house reflects the value of a bundle of attributes including structural characteristics, 
neighborhood characteristics, and environmental characteristics.  The hedonic housing price technique 
can be used to model the price of a house as a function of these various characteristics as follows: 
 
Pi = f(Si, Ni, Ei) 
 
where Pi is the price of a given house, Si is a vector of structural characteristics, Ni is a vector of 
neighborhood characteristics, and Ei is a vector of environmental characteristics. The first derivative of P 
with respect to any one variable reflects the marginal value of that characteristic.  For example, if an 
environmental variable that measures proximity to a park in miles is included, the price model would 
show the value of being one mile closer to a park. 
 
In this study, Si includes condition (DEPR) with a higher value indicating better condition, the number of 
baths, square footage of the house, air conditioning, lot size, and whether or not the house has a garage.  
Ni is approximated here by census tract dummy variables and Ei is park proximity.  The specific measures 
of park proximity are explained in the next section.  This study uses ordinary least squares estimation of a 
semi-log model, the structural form found to produce the best results in previous hedonic studies. 
 
To determine the relationship between the value of park proximity and demographic characteristics, the 
sample was stratified by household income and family size using census block information.  Separate 
regressions were estimated for those above the median household income and below, and for those with 
three or more persons per household and those with fewer.  These results are then compared to determine 
if there are statistically significant differences in the value of park proximity based on household income 
or family size.   
Estimation Results 
 
First the general impact of park proximity was estimated without regard to park size or type.  These initial 
results indicated that proximity to parks has a positive impact on housing values, with homes located 
within 1500 feet of any park selling for 6.5 percent more than homes greater than 1500 feet from a park.  
This impact appears most significant for small neighborhood parks, with homes within 1500 feet selling 
for 8.5 percent more than those further away. 
 
Next, parks were categorized as explained in the previous section.  Various buffer zones around parks in 
each category were analyzed to determine if and where park proximity had a negative impact on housing 
price, for example where the negative impact of noise or lights of being adjacent to a park outweigh the 
positive value of easy access.  Then various buffer zones were analyzed to determine for each park type 
the distance at which there was no longer any significant positive or negative impact related to park 
proximity.  Finally, various ranges between these inner and outer bounds of significance were tested to 
determine ranges within which there was not a statistically significant variation in impact of the park 
proximity.  Dummy variables were then created for houses within each of these distinct ranges.  These 
results are shown in Table 2.  Note that the distance categories are not mutually exclusive as some houses 
were, for example, within 1500 feet of one park and within 500 feet of another.  In addition, some ranges 
weren't statistically significant but were included for comparability to other park types. 
 
Table 3 shows the estimation results using each of these proximity measures.  Model 1 isolates the 
analysis to proximity to the small basic parks, Model 2 includes only the small attractive parks, Model 3 
includes only the more attractive medium size parks, and Model 4 includes only the less attractive 
medium size parks.  Model 5 includes all of the parks with the various ranges used in the previous 
models.  Coefficient estimates for the census tract dummy variables are included in the Appendix.  
The estimates indicate a negative impact of park proximity for houses within 300 feet of the small basic 
neighborhood parks, reducing property values by about 14 percent.  On the other hand, there is a 
significant positive impact on housing prices for homes between 300 and 500 feet of about 15 percent.  
Further, there is a significant positive, though smaller, impact on housing values for homes between 500 
and 1500 feet from a Type 1 park, equal to about 6.5 percent higher housing values.   
 
There is also a significant positive impact of proximity to small attractive parks (Type 2) for homes 
within 600 feet, but no significant impact beyond that.  Homes within 600 feet of Type 2 parks sold for 
almost 11 percent more than other homes.  For the attractive medium size parks, there was no statically 
significant impact on houses within 200 feet but a positive impact on homes between 200 and 1500 feet, 
raising values by about 6 percent.  Finally, Type 4 parks were estimated to have a significant negative 
impact on home values for homes within 600 feet, reducing housing sales values by just over 50 percent, 
but no statistically significant impact (positive or negative) beyond that.  
 
The median income of households located within 1500 feet of the small basic parks is about $27,000, 
compared to close to $34,000 citywide, indicating that  park proximity has a greater value, as a percentage 
of housing value, for lower income households.  There was also a strong positive correlation between 
household size and the value of park proximity, suggesting that families with children are willing to pay 
more for houses located close to parks. 
 
 
Conclusions 
 
It is possible that acquisition of land for new parks, particular in the growing suburbs surrounding 
Greenville, could be partially financed by higher property tax revenues that would result from increased home sales prices.  Better estimates of the impact of parks on home sales values could be valuable 
information to local parks and recreation departments attempting to justify current expenditures on land 
acquisition in rapidly growing areas.  Such information could also be useful to developers deciding 
whether or not to include parks or other open space in new subdivisions, or to land use planners 
attempting to implement open space requirements for newly developed areas.  Demographic information 
obtained from census tract data can help city planners determine the differential impacts of their public 
expenditures and perhaps modify their plans based on equity considerations. References: 
 
Barber, R. (2000).  "Legislative Update", South Carolina Out of Doors, November/December, p. 4. 
 
Correll, M.R., J.H. Lillydahl and L.D. Singell (1978).  "The Effects of Greenbelts on Residential Property 
Values:  Some Findings on the Political Economy of Open Space", Land Economics 54:  207-217. 
 
Do, A.Q. and G. Grudnitski (1995).  "Golf Courses and Residential House Prices:  An Empirical 
Examination", Journal of Real Estate Finance and Economics, 10:  261-270. 
 
Doss, C.R. and S.J. Taff (1993).  "The Relationship of Property Values and Wetlands Proximity in 
Ramsey County, Minnesota", Economic Report 93-4, Department of Agricultural Economics, University 
of Minnesota, St. Paul, Minnesota. 
 
Frech III, H. E.  and R. N. Lafferty (1984).  "The Effect of the California Coastal Commission on 
Housing Prices", Journal of Urban Economics, 6:  105-123. 
 
Lutzenheiser, M. and Netusil (1999).  "The Effect of Open Space Type and Proximity on a Home's Sale 
Price:  Portland, Oregon", Working Paper, Department of Economics, Reed College, Portland, Oregon. 
 
Mahan, B., S. Polasky, and R.M. Adams (2000).  "Valuing Urban Wetlands:  A Property Price 
Approach", Land Economics, forthcoming. 
 
Netusil, N.R. and B. Bolitzer (2000).  "The Impact of Open Space on Property Values in Portland, 
Oregon", Journal of Environmental Management, forthcoming. 
 Romain, L. (2000).  "Greenville rec chief:  Buy land now or miss out", The Greenville News, March 25, 
13A. 
 
The Sierra Club (1999). http://www.sierraclub.org/sprawl/report99/openratings.asp 
 
Weicher, J. and R. Zerbst (1973).  "The Externalities of Neighborhood Parks:  An Empirical 
Investigation", Land Economics, 49:  99-105. 
  
 
 
Table 1:  Summary Statistics for Housing Characteristics (N=4153) 
 
 
Variable 
 
Mean 
 
Std. Dev. 
 
Minimum 
 
Maximum 
# of observations = 1 
for dummy variables 
DEPR 80.2  13.2  5  100   
BATH 1.7  0.8  0.5  7   
SQFT 1453  615  240  6276   
AC 0.45  0.52  0  1  1854 
GARAGE 0.10  0.30  0  1  421 
ACR24 0.04  0.19  0  1  160 
ACR4 0.02  0.14  0  1  85 
  
 
Table 2:  Park Proximity Measures by Park Type 
 
Park Type  Proximity  Number of Houses in Range 
Type 1:  Small Basic  Within 300 feet  26 
 300-500  feet  70 
 500-1500  feet 
 
434 
Type 2:  Small Attractive  Within 600 feet  80 
 600-1500  feet 
 
289 
Type 3:  Medium Attractive  Within 200 feet  28 
 200-1500  feet 
 
289 
Type 4:  Medium Basic  Within 600 feet  5 
 600-1200  feet  79 
  
Table 3:  Estimation Results:  Dependent Variable Log of Price 
 
Variable  Model 1  Model 2  Model 3  Model 4  Model 5 
Intercept  4.31*** 4.30*** 4.29*** 4.30*** 4.30*** 
DEPR  0.008*** 0.008*** 0.008*** 0.008*** 0.008*** 
BATH  0.24*** 0.24*** 0.24*** 0.24*** 0.24*** 
SQFT  0.0005*** 0.0005*** 0.0005*** 0.0005*** 0.0005*** 
AC  -0.03*** -0.03**  -0.04*** -0.04*** -0.04*** 
GARAGE  0.10*** 0.10*** 0.10*** 0.10*** 0.10*** 
ACR24  0.12*** 0.13*** 0.12*** 0.13*** 0.13*** 
ACR4  0.11*** 0.11*** 0.11*** 0.12*** 0.11*** 
T1:  < 300  -0.15**        -0.15** 
T1:    300-500  0.13***     0.14*** 
T1:    500-1500  0.07***     0.06*** 
T2:  < 600    0.13***      0.11** 
T2:    600-1500   0.01    -0.001 
T3:  < 200      0.06    0.03 
T3:    200-1500    0.06**   0.06** 
T4:  < 600        -0.66***  -0.72*** 
T4:    600-1200     -0.007  -0.01 
 
Note:  significance levels *=0.10, **=0.05, ***=0.01 
 

DEMOGRAPHICS AND THE VALUE OF PARK PROXIMITY IN GREENVILLE, SOUTH CAROLINA

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