# RECORD 79a14cd6c47cfe07a6fd907132f2ed86427b4831
# TITLE: Are Homeowners Willing to Pay More for Access to Parks? Evidence from a Spatial Hedonic Study of the Cincinnati, Ohio, USA Park System
# YEAR: 2018.0  VENUE: Journal of Regional Analysis & Policy  DOI: 10.68479/001c.3851
# AUTHORS: Rainer Vom Hofe; Oana Mihaescu; Mary Lynne Boorn
# PAGES: 17  CANDIDATE TABLE PAGES (1-based): [7, 11, 13]
# ABSTRACT: This paper examines the impacts of the City of Cincinnati urban park system on residential property values using two local spatial hedonic model specifications: the spatial Durbin error model (SDEM) model, and the spatial lag of X model (SLX). Specifically, we examine how the distance to parks, the size of the nearest park, and the presence of park facilities for recreational activities, affect property values. Our results show that decreasing the distance to a park by one meter appreciates of the value of the average house in our sample by $3.44 to $6.27. A one hectare increase in the park size appreciates house values by $22.24 to $22.48 and the presence of park facilities for recreational activities depreciates house values by an estimated $4,612 to 5,300 for properties within a 500 meter radius to the nearest park.


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Journal of Regional Analysis & Policy
48(3): 66–82
Are Homeowners Willing to Pay More for Access to Parks?
Evidence from a Spatial Hedonic Study of the Cincinnati, Ohio,
USA Park System
Rainer vom Hofe
University of Cincinnati
Oana Mihaescu
HFI, Handelns Forskningsinstitut
Mary Lynne Boorn
University of Cincinnati
Received: 08/04/2017
Accepted: 11/28/2017
Abstract
This paper examines the impacts of the City of Cincinnati urban park system on residential property
values using two local spatial hedonic model specifications: the spatial Durbin error model (SDEM)
model, and the spatial lag of X model (SLX). Specifically, we examine how the distance to parks, the size
of the nearest park, and the presence of park facilities for recreational activities, affect property values.
Our results show that decreasing the distance to a park by one meter appreciates of the value of the
average house in our sample by $3.44 to $6.27. A one hectare increase in the park size appreciates house
values by $22.24 to $22.48 and the presence of park facilities for recreational activities depreciates house
values by an estimated $4,612 to 5,300 for properties within a 500 meter radius to the nearest park.
1
Introduction
Cincinnati has enjoyed a long-standing tradition of public parks. The formation of the Cincinnati Board
of Park Commissioners in 1906 and the master plan for Cincinnati’s parks A Park System for the City of
Cincinnati by George Kessler in 1907 laid the foundation for what has become one of the nation’s most
recognized urban park systems. Cincinnati Parks is ranked 7 on the ParkScore® in 2015, published by
The Trust for Public Land.
One hundred years later, Cincinnati City Council adopted the Cincinnati
Parks 2007 Centennial Master Plan, which guides the Cincinnati Board of Park Commissioners’ mission “to
conserve, manage, sustain, and enhance parks’ natural and cultural resources and public greenspace for the
enjoyment, enlightenment and enrichment of the Cincinnati community” for the decades to come. Today,
the City of Cincinnati park system entails over 5,000 acres of city parklands, including five regional parks,
70 neighborhood parks, and 34 nature preserves, numerous natural areas and scenic overlooks, and a total of
50 miles of hiking and bridle trails. The park system amounts to approximately ten percent of Cincinnati’s
total land area, including the Cincinnati Zoo.
While voters supported the Cincinnati Board of Park Commissioners’ mission, they declined the slight
property tax increase necessary to maintain and improve the parks system. In the 2015 election, Cincinnati
voters overwhelmingly rejected a bill to add a one mill tax levy that would have raised an estimated $5 million
annually to improve the existing park system and expand bike trails in the area. Many local governments
face increasing financial pressure to balance budgets amidst rising expenses without increasing revenues.
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relevant hedonic literature, we included the following explanatory structural characteristics: the lot size
(sizeland), the size of the house in square meters (sizehouse), the number of bathrooms (bathrooms), the
age of the house (age), and the car capacity of the basement garage (garage) (Hess and Almeida, 2007;
Cebula, 2009; Landry and Hindsley, 2011). Following Bin et al. (2008), we added a variable denoting the
condition of the house, which includes seven categories, as defined by the Hamilton County Auditor’s Office:
very poor, poor, fair, average, good, very good, and excellent. We argue that the property condition is an
important contributor in determining its market value. We define the middle condition category (average) as
the baseline in our regression model. Consequently, we expect that houses in worse than average condition
(very poor, poor, or fair) will have lower values, while houses in better than average condition (good, very
good, or excellent) will have higher values. Definitions and summary statistics for the variables used in our
analysis are presented in Table 2.
Table 2: Variables: Definitions and Summary Statistics (n=36,167)
Definition
(Abbreviations in parenthesis)
Mean
Median
Minimum
Maximum
Market values of single-family residential
properties (P)
122,852
96,300
10,000
668,750
Structural characteristics of the property:
Size of the land, in hectares (sizeland)
0.074
0.056
0.005
2.017
Size of the building, in square meters (sizehouse)
162.943
146.601
22.297
547.756
Total number of bathrooms (bathrooms)
1.709
1.500
0.000
8.000
Age of the building, in years (age)
83.878
85.000
1.000
210.000
Basement garage capacity, # of cars (garage)
0.335
0.000
0.000
8.000
Condition of the house:
very poor (condvpoor)
0.005
0.000
0.000
1.000
poor (condpoor)
0.016
0.000
0.000
1.000
fair (condfair)
0.096
0.000
0.000
1.000
average (condaverage)
0.455
0.000
0.000
1.000
good (condgood)
0.362
0.000
0.000
1.000
very good (condvgood)
0.065
0.000
0.000
1.000
excellent (condexcellent)
0.001
0.000
0.000
1.000
Neighborhood characteristics:
Poverty rate (poverty)
26.469
24.000
2.500
82.500
% of people with bachelor degree (bachelor)
30.859
26.900
1.300
82.500
Crime rate (crime)
18.886
17.916
0.000
62.857
Public school performance indicator (school)
84.521
83.200
68.800
111.800
Distance to the closest park, in meter (parkdist)
248.930
245.438
0.000
499.997
Size of the closest park, in hectares (parksize)
45.081
11.569
0.018
576.135
Presence of park facility (parkfacility)
0.375
0.000
0.000
1.000
The poverty rate (poverty), crime rate (crime), and educational attainment (bachelor) are three com-
monly used neighborhood characteristics (Lynch and Rasmussen, 2001; Geoghegan, 2002). We use the 129
Census tracts in the City of Cincinnati as neighborhood proxy in our study.
Poverty rates and educa-
tional attainment were obtained from the 2010 US Census. The poverty rate is defined as the percentage
of the population below the poverty line; educational attainment refers to the percent of people with a
bachelor’s degree.
We define the crime rate as number of crimes / (population/1000).
Crime data are
publicly available for 2012/2013 from the City of Cincinnati police department.
Only property-related
crimes (i.e., aggravated burglary; inflicted harm; aggravated arson; breaking and entering; burglary; crim-
inal trespassing; fire weapon into habitat; property damage; safecracking; tampering with coin machine;
unauthorized use of property; and non-school vandalism) are considered for this analysis, since non-property
related crimes can take place randomly through the whole city and are thus not specifically related to
real-estate properties.
In addition, we include school district information to control for neighborhood
characteristics (Brasington and Haurin, 2006; Clapp et al., 2008; Dougherty et al., 2009).
Specifically,
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we use a performance indicator for public schools, made available by the Ohio Department of Education
(http://reportcard.education.ohio.gov/Pages/default.aspx).
The variables of interest in our study are the Euclidean distances from each property to the closest park
(parkdist) in meter, the size of the closest park (parksize) in hectares, and the presence [absence] of park
facilities in the closest park (parkfacility). With the park facility binary variable, we distinguish between
natural green space and parks with built facilities, such as tennis, basketball, or soccer courts. Regarding
the distance measure (parkdist), we chose Euclidean over network distances for numerous reasons. First,
Euclidean distances are conceptually straight forward, are easy to compute, and have been successfully
used to capture location-related neighborhood characteristics in previous studies (Troy and Grove, 2008;
Conway et al., 2010). Earlier studies by Newell (1980) and O’Sullivan and Morrall (1996) reported that
network distances are approximately 1.20 to 1.23 times the Euclidean distance in an urban environment,
while Sander et al. (2010a) found the mean of the differences in distance to be as high as 48.4%. Further,
while Sander et al. (2010b) argue that road distances better reflect of actual travel distances to the nearest
park, we follow Eiser et al. (2007) who argue that Euclidean distances should be preferred to network (street)
distances when the perception of being in close proximity to the investigated feature is more important for
housing prices than accessibility. With respect to parks, either argument has some validity as some residents
perceive parks as a pleasant amenity. Without setting a foot into a park, they understand that parks build
communities and create a sense of place, thus contributing to a higher quality of life. Other residents see
parks as a welcome opportunity to walk their dogs, get some exercise through running, playing tennis or
basketball, or just socialize. While we do not know the specific percentages of active park users, we assume
that residents perceive nearby parks as a positive amenity. Last, the majority of parks can be accessed
literally from anywhere on foot. Consequently, it would be almost impossible to identify all park entrance
points.
According to the statistics reported in Table 2, the average house in our study has a market value of
$122,852. Our average house has 162.9 square meter, 1.71 baths, and is built on a 742 square meter lot.
The majority of houses in our study area are in either average (45.52%) or good condition (36.17%). Just
under 12% are in below average condition, while 6.65% are in above good condition. With respect to the
neighborhood characteristics, the average house is located in a Census tract with 26.5% of households living
below the poverty line, with 18.9 property-related crimes per 1,000 inhabitants, where 30.9% of the population
has at least a bachelor’s degree, and with an average school performance indicator of 84.5. Important for
our study is that the average house is located 248.9 meter away from the nearest park. This specific nearest
park is 45.1 hectares in size and there is a 37.5% chance that this nearest park includes some type of facility.
3.2
Empirical models
The centerpiece of this research is a hedonic price model, where potential buyers value the elements of the
bundle that compose a housing unit. The bundle is made up of structural and neighborhood characteristics,
including park characteristics. Following LeSage and Ha (2012) and Mihaescu and vom Hofe (2013), we
applied a Bayesian spatial Durbin error model (SDEM), defined as:
lnP = α + S ∗β + N ∗γ + W ∗S ∗θ + C ∗ϕ + u
(6)
u = λ ∗W ∗u + ϵ
ϵ ∼N(0, σ2
ϵ In)
where lnP is a vector of log-transformed market values of single-family residential properties in the City of
Cincinnati; S is a matrix of structural characteristics of the properties, C is a matrix of quadratic controls of
longitude and latitude, and N is a matrix of neighborhood characteristics, including the distances from each
property to the closest park (parkdist), the sizes of the closest park (parksize), and the presence of park
facilities (parkfacility). The four vectors β, γ, θ, and ϕ are the corresponding parameters to be estimated
and W is the spatial weight matrix. The fact that economic theory does not imply the functional form of
hedonic models (Bolitzer and Netusil, 2000) and the common practice of using log-transformed prices, while
not transforming distance measures (Czembrowski and Kronenberg, 2016), we chose a log-linear functional
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Table 3: Effects of Parks on Residential Property Values in the City of Cincinnati (n = 36,167)
Variables
SDEM
SLX
Parameter
p-Value
Parameter
p-Value
constant
10.201465
<0.01
10.211803
<0.01
Structural characteristics of the properties
sizeland
0.000053
<0.01
0.000048
<0.01
sizehouse
0.002684
<0.01
0.002859
<0.01
bathrooms
0.051512
<0.01
0.055278
<0.01
age
-0.010605
<0.01
-0.010174
<0.01
agesquared
0.000038
<0.01
0.000036
<0.01
garage
0.027471
<0.01
0.017696
<0.01
condvpoor
-0.923445
<0.01
-1.072054
<0.01
condpoor
-0.49363
<0.01
-0.514455
<0.01
condfair
-0.219315
<0.01
-0.211597
<0.01
condgood
0.135936
<0.01
0.124734
<0.01
condvgood
0.291110
<0.01
0.262003
<0.01
condexcellent
0.441086
<0.01
0.465527
<0.01
Neighborhood characteristics
poverty
-0.001252
<0.01
-0.001140
<0.01
bachelor
0.011986
<0.01
0.011502
<0.01
crime
-0.001909
<0.01
-0.001070
<0.01
school
0.001646
<0.01
0.002347
<0.01
parkdist
-0.000051
0.018
-0.000028
<0.01
parksize
0.000183
<0.01
0.000181
<0.01
parkfacility
-0.037543
<0.01
-0.043141
<0.01
Structural characteristics of neighboring properties
W ∗sizeland
0.000001
<0.01
0.000003
0.135
W ∗sqmtrfinished
0.000833
<0.01
0.000770
<0.01
W ∗bathrooms
0.032671
<0.01
0.026945
<0.01
W ∗age
-0.006336
<0.01
-0.005147
<0.01
W ∗agesquared
0.000029
<0.01
0.000024
<0.01
W ∗garage
0.038363
<0.01
0.044361
<0.01
W ∗condvpoor
-0.489707
<0.01
-0.669583
<0.01
W ∗condpoor
-0.256236
<0.01
-0.282074
<0.01
W ∗condfair
-0.208881
<0.01
-0.230919
<0.01
W ∗condgood
0.145953
<0.01
0.101972
<0.01
W ∗condvgood
0.307800
<0.01
0.237923
<0.01
W ∗condexcellent
0.370485
0.03
0.435356
<0.01
λ
0.387626
<0.01
—
—
R2
0.771
0.6657
AkaikeInformationCriterion(AIC)
61,516
63,697
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is worth $14,937 more than the house in average condition. Houses in worse than average condition lose
$5,306, $10,497 or $16,242 when their condition drops to fair, poor, or very poor respectively.
Neighborhood-related characteristics show that the poverty rate and the crime rate have, as expected, a
negative impact on the sales price of a property; for every percent of the population below the poverty rate the
price decreases by 0.13% ($154) and for every extra crime per 1000-population, by 0.19% ($235). Meanwhile,
every 1% of people with bachelor’s degree in the census tract increases the sales value of a property by 1.20%
($1,473) and every 1 point increase in the school performance indicator adds 0.16% ($202) to the market
value.
Turning now to the variables of interest, the characteristics of the park located closest to each property,
we find as expected that the price of a property decreases as the distance from the closest park increases.
For our average property valued at $122,852 this means that for every 100 meter increase in the distance
to the closest park, the sales price of a property decreases by 0.51% ($627), or $6.27 per meter change in
distance. As such, our finding confirms research by Correll et al. (1978), Crompton (2001); Lutzenhiser
and Netusil (2001), among others, who find increases in the value of properties surrounding parks in similar
magnitude using buffer zones from approximately 150 to 1,000 meter. The size of the closest park, although
significant, has only a minimal influence on property prices. For our average house of $122,852, increasing
the size of its closest park by 1 hectare would merely increase its value by 0.018%, or $22.48. Completely
different is the outcome regarding recreational uses of the parks. If the closest park includes a recreational
facility, the average decrease in the price of a property is of 3.75%, or $4,612. Our results confirm earlier
studies by Crompton (2004); Espey and Owusu-Edusei (2001); Lutzenhiser and Netusil (2001); Shultz and
King (2001) who showed that parks providing more natural landscapes and passive uses tend to have a
greater, positive impact on surrounding residential values. On the other hand, we only find a relatively
small incremental value of slightly larger parks versus smaller parks, considering that our average house’s
closest park is 45.1 ha. It also leaves open the question for further research, whether the detrimental value
of parks with recreational facilities stems from the pure presence of the recreational facility, or is attributed
to its often poor condition and neglected maintenance. This issue also raises the question of what types of
recreational facilities are preferred over others.
The spatial spillover effects in the SDEM model
captured by the θ coefficients in equation (1)
are
purely of local nature (LeSage and Pace, 2014). As such, the θ coefficients capture how residential property
prices are influenced by changes in structural characteristics of immediately neighboring properties. All local
spillover in the SDEM model, with the exception of the W ∗sizeland and W ∗bedrooms coefficients, are
statistically significant. Adding 1 square meter to the average house size of the adjacent properties induces an
increase by 0.08% ($102) in the market value. The other structural characteristics, averaged over neighboring
properties, also induce similar, though lower, effects as compared to own structural characteristics (3.27%
($4,014) for 1 extra bath, minus 0.63% ($778) for 1 year of age and 0.0029% for agesquared, and 3.84%
($4,713) for 1 more space in the averaged capacity of the basement garage). Also interesting is the relatively
large impact the neighboring houses have on the average house with respect to the conditions they are
in. Very poorly- or poorly maintained neighboring houses negatively affect own market values by $10,432
and $6,090 respectively, while properties in fair condition still depreciate own market values by as much
as $5,079. Contrarily, neighboring houses that are in above average condition positively affect own market
values by $4,234, $9,711, and $12,082 when they are in good, very good, and excellent condition. Due to
potential multicollinearity issues, we excluded all neighborhood variables–including all three park-related
characteristics and distance to the CBD–from the construction of the spatial lags in the SDEM.
When selecting the most appropriate model framework, we ended up, for reasons discussed above, with
two potential spatial models: the SDEM model and the SLX model. Though the SDEM model provides
the overall better fit to our data sample, the SLX model also provides some interesting findings. Comparing
our direct effects from the SDEM and the SLX models confirms our general conclusions. The estimated
coefficients for the SLX model all have the expected signs and are all significant, except for the lagged size of
the land parameter (p = 0.135). A direct comparison of the SDEM and SLX model coefficients reveals that
the majority of the estimated coefficients are similar in magnitude, but exhibit, on average, differences of
9.7% for the structural characteristics, 22.9% for the neighborhood characteristics, and 18.6% for the lagged
structural characteristics. Examining the three estimated parks parameters in the SLX specification–park
distance, park size, and park facility–we find that for every 100 meters increase in the distance of our property
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valued at $122,852 its market value decreases by 0.28% ($344), or $3.44 per meter. A change in the nearest
park’s size by 1 hectare translates into a 0.02% ($22.24) increase in value of our average property. The
interesting finding, however, is that according to the SLX result, recreational facilities decrease the market
value of the nearest residential property by 4.31% ($5,300) which again emphasizes that residents prefer
passive park uses over active ones.
One of the important choices we had to make when assessing the influences parks have on residential
property values is whether or not to use a buffer zone around the parks for identifying the properties to be
included in our study. Given that the relevant literature is inconclusive as to whether to apply buffer zones,
or as to the size of these buffer zones, we conducted a sensitivity analysis to find out how the parks impacts
differ for different buffer zones in our study area. Our findings from this sensitivity analysis are presented
in Table 4.
Table 4: Sensitivity of Park Effects on Property Values with Respect to Size of Buffer Zones
Buffer
Spatial Durbin Error Model
Number of
Zones
parkdist
parksize
parkfacility
Observations (n)
0-100 m
0.000022
0.000289
***
-0.029609
**
6,340
0-250 m
-0.000071
0.000249
***
-0.035509
***
18,415
250-500 m
-0.000043
0.000126
***
-0.037637
***
17,752
100-500 m
-0.000035
0.000167
***
-0.037449
***
29,827
0-500 m
-0.000051
**
0.000183
***
-0.037543
***
36,167
500-750 m
0.000015
0.000147
***
-0.038445
***
9,145
750-1000 m
0.000346
***
0.000053
-0.033261
*
3,989
500-1000 m
0.000229
***
0.000146
***
-0.037838
***
13,134
no buffer
0.000061
***
0.000141
***
-0.034785
***
51,786
Buffer
Spatial Lag of X Model
Number of
Zones
parkdist
parksize
parkfacility
Observations (n)
0-100 m
-0.000131
0.000335
***
-0.044068
***
6,340
0-250 m
-0.000063
**
0.000274
***
-0.041457
***
18,415
250-500 m
-0.000015
0.000119
***
-0.044139
***
17,752
100-500 m
-0.000009
0.000152
***
-0.044089
***
29,827
0-500 m
-0.000028
***
0.000181
***
-0.043141
***
36,167
500-750 m
0.000370
0.000091
***
-0.035325
***
9,145
750-1000 m
0.000319
***
0.000063
**
-0.026550
***
3,989
500-1000 m
0.000188
***
0.000106
***
-0.035218
***
13,134
no buffer
0.000048
***
0.000122
***
-0.037216
***
51,786
* significant at the 0.10 level. ** significant at the 0.05 level. *** significant at the 0.01 level.
Not surprising, changing the distances for the buffer zones significantly affects the outcomes of the
estimated parameters. First, the park distance parameter (γparkdist) switches sign with increasing distances,
i.e., after 500 meters, in both the SDEM and SLX model specifications. While market values within 500
meters increase as distances to the nearest park decreases, outside the inner 500-meter buffer the results
indicate that market values tend to increase with increasing distances. Unfortunately, the fact that half of
the estimated park distance parameters (ˆγparkdist) are not significant does not allow valid conclusions to
be drawn with respect to their changes in magnitude. Nevertheless, the results allow a comparison of the
first 0-500 meter buffer to the second 500-1000 meter buffer. Here we find a park effect for the 0-500 meter
buffer of negative $6.27 (ˆγparkdist = −0.000051) per meter increase in distance from the park compared to a
positive $28.13 (ˆγparkdist = 0.000229) per meter in the second 500-1000 meter buffer. Both of these values
are from the SDEM model in reference to our averagely priced property of $122,852. This finding indicates
not only the sensitivity of the nearest park effect on property values, but also questions the park effects on
property values beyond 500 meters in our study area, or beyond what we identified as a walkable distance
to the nearest park.
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Second, park sizes matter regardless of how far a residential property is from the nearest park. In both
the SDEM and SLX spatial model specifications (with significance of all but one estimated parameter),
larger park size matters regardless of the distance to the nearest park. As expected, we find that property
values generally tend to increase with an increase in park size and that the positive effect is stronger for
properties closer to their nearest park. Third, park facilities do have a negative effect on property prices, as
indicated by the significance of all estimated parameters in both our spatial models. For the SDEM model,
the negative effect of park facilities appears to be fairly constant with respect to applied buffer zones, except
the 0-100 meter zone (-0.029609), which is somewhat smaller in magnitude. For the SLX model, the effect of
park facilities is definitively larger within the first 500 meters, but decreases significantly in size beyond the
500 meter threshold. To complete this sensitivity analysis of how the park facilities differ in impact across
the various buffer zones in our study area, we included the estimated parameters when not using buffer zones
at all. Here we find that the park facility effects are similar to the effects when applying buffer zones of
greater than 500 meter. Again, the park distance parameters, while significant, do not have the expected
sign.
5
Conclusion
It is a well-known fact that location matters for homebuyers and homeowners alike, and plays an important
role when determining property values. In this paper, we show that urban parks have a significant influence
on house prices when they are located within close proximity to a park, where the distance to the nearest
park is calculated based on Euclidean distances. More specifically, using the results from a spatial hedonic
pricing model that accounts for local spatial dependence among residential property prices–the Spatial Durbin
Error Model (SDEM)–we evaluated three different park influences on property values. First and foremost,
we find that proximity of residential properties to parks benefits property owners. The price of a property
increases with decreasing distance to the closest park, which confirms previous findings. More specifically,
we estimated the influence of the Cincinnati parks in Hamilton County, Ohio, to increase the average priced
house of $122,852 in our sample by $6.27 when moving closer to the park by one meter. Secondly, increasing
the size of a nearby park by one hectare increases the average priced house by 0.018%, or $22.48. While this
confirms other research that park size maters, the magnitude of the park size influence on property values
is of lesser importance in Cincinnati, which one might explain by the large number of parks within the city.
Last, we find that properties located close to parks with recreational facilities can register a total decrease in
the price of a property of 3.75% ($4,612) for the average property according to our results. This result raises
the issue of whether the detrimental value of parks with recreational facilities stems from the pure presence
of the recreational facility, or can be attributed to its often poor condition and neglected maintenance. It
also leaves open the question of what types of recreational facilities are preferred over others.
In this paper, we presented a sensitivity analysis with the estimation results when using different buffer
zones. Overall, we conclude that the estimated results for the three park parameters–park distance, park
size, and park facility–are very sensitive with respect to the chosen buffer zone. Most interesting is the
finding that with increasing distance to the nearest park, property values clearly decrease for houses within
500 meters to the park. Beyond this 500 meter buffer, we find exactly the opposite effect; with increasing
distance, houses appear to appreciate in value. We also find that the bigger the park, the better. The size
of the nearest park positively influences property values, regardless of the distance of the property to the
nearest park. Park facilities, on the other hand, tend to negatively affect property values, if properties lie
within a 500 meter distance to the nearest park. This negative influence does, however, not extend beyond
the 500 meter, as indicated by our sensitivity analysis.
Overall, the findings of this sensitivity analysis
indicate that more research is needed to more fully understand the influence on the estimated parameters
stemming from various buffer zones. In addition, individual park facilities may also have different effects on
property values, a fact that suggests further research of this matter.
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