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Using Machine Learning and Google Street View to
Estimate Visual Amenity Values
Timothy L. Hamilton
Department of Economics
University of Richmond
Erik B. Johnson
Department of Economics
University of Alabama
May 31, 2018
Abstract
In this paper, we estimate nonmarket values for public parks and open space, distin-
guishing between proximity-based use value and visual amenity value. We use machine
learning to conduct visual analysis and classify the view of an individual household.
In particular, we identify visual characteristics that are similar to those of publicly
provided park space and thus provide a similar a visual amenity. This technique al-
lows us to estimate the value of proximity to open space and separately identify values
associated with visual amenities. We find positive capitalization rates associated with
household views of park-like properties. From a policy perspective, such identification
could indicate the optimal size, location, and shape of open space. Furthermore, ma-
chine learning methods used in construction of our view variable provide a potentially
powerful tool for nonmarket valuation studies.
1 Introduction
Hedonic housing price analysis has emerged as a common tool for nonmarket valuation in
urban and environmental economics. Derived primarily derived from Rosen (1974), a large
literature explores the degree to which local amenities are capitalized into housing markets.
The advent of geographic information system (GIS) software provided researchers with the
ability to observe and measure local amenities in a spatial context1. Thus, location attributes
could be defined based on continuous measure of distance and spatial overlap, rather than
based on local political boundaries. This allowed for a richer characterization of location-
specific goods and arguably more accurate valuation estimates. The recent development
of machine learning may offer economists another step forward in being able to properly
measure local amenities. More specifically, the combination of deep learning and image
recognition gives researchers the ability to account for nuanced characteristics of an amenity
that may not appear in a discrete set of descriptors. Similarly, these techniques may be able
to categorize observations with more accuracy than standard classification, in such a way as
to better represent the consumption perspective. Machine learning in this context improves
our characterization of inputs to the hedonic price function, resulting in improved valuation
estimates.
The nonmarket valuation literature has extensively examined open space as an envi-
ronmental amenity. Studies often identify physical attributes of the open space, including
land-cover designation, size, biodiversity measures, or recreational opportunities. In addi-
tion, analyses also measure the magnitude of a household’s consumption of such open space
through several different channels, including distance to open space, the percent of nearby
land that exists as open space, or simply the presence of open space within some distance.
Each of these metrics implies a slightly different consumption good.
An important attribute of open space that may provide considerable benefits to nearby
households is that it serves as a visual amenity. Defining the degree of visual amenity is
a difficult task. Several studies (Paterson and Boyle (2002); Walls et al. (2013)) use a
combination of digital elevation models and land-cover data to characterize a household’s
view. In this paper, we develop a machine learning approach to measuring the visual appeal of
properties to account for an important location attribute. In particular, we train and utilize
an image recognition algorithm to estimate the visual amenity that households consume.
This measure becomes a component of the hedonic price function with an associated marginal
price.
Our analysis rests on the notion that open space conveys value in several ways, including
1See Bateman et al. (2002) for an overview of GIS in environmental economics.
2
recreational value, ecosystem services, and visual amenity value. Research in nonmarket
valuation of open space tends to focus primarily on the first two aspects. First, the omitted
visual amenity of open space may generate a bias in attempts to estimate recreation values.
For example, proper measurement of the visual characteristics of open space ensures unbi-
ased estimation of recreation demand and correctly informs policies that alter recreational
opportunities. Second, estimates of these visual amenity values that are distinct from recre-
ational value play an important role in determining the optimal provision of open space. In
the context of recreation value, it may be important to consider attributes such as the size
and layout of open space to maximize benefits to the surrounding community. To maximize
visual amenity value, however, such attributes become less important as only access to a
view of open space matters. Given a fixed quantity of land dedicated to open space (rat-
her than residential or commercial development), the spatial allocation of that space could
such that it offers an area and services for recreation. Alternatively, it could be allocated in
a way to expand the perimeter and thus maximize household exposure to a view amenity
through the use of, for example, boulevards, local squares, or small parks. Determining the
optimal balance between visual and recreation amenities requires the relative values of such
amenities.
This paper is organized as follows. First, we review the literature on hedonic valuation of
open space, including analyses that consider visual amenities. We then introduce a method
to define a visual amenity and discuss the few places such a technique has been used in
the economics literature. After reviewing the current state of the literature, we present our
empirical model and describe our data. Then, we present our image recognition algorithm
in detail and its role in our hedonic analysis. Finally, we present results of a housing price
hedonic model that accounts for visual amenity values and conclude with a discussion of our
estimates.
2 Open Space Hedonic Analyses
While there exists an extensive hedonic literature that attempts to value open space, we limit
our review to focus on studies that estimate the value of visual amenities. To begin with,
empirical evidence indicates that type of open space matters. Neumann et al. (2001) and
Liu et al. (2013) find positive housing price premiums associated with proximity to National
Wildlife Refuges, a form of open space with a natural aesthetic. These studies also find
that effects are highly localized around wildlife refuges, potentially driven by houses with
a view of the refuges. These results suggests that the corresponding visual amenity could
be an important component. Shultz and King (2001) also report positive amenity values
3
associated with natural areas and wildlife habitat, but find that housing prices decrease
with proximity to city/county parks that may be used primarily for recreation. While this
could be the result of aversion to congested areas, the visual difference in the type of open
space may also be a contributing factor.
Several studies have attempted to directly value a view amenity. Weicher and Zerbst
(1973) estimate a hedonic housing price model that includes indicator variables for homes
that are adjacent to a municipal park, further distinguishing between homes that face a
park, back into a park, or face a park with heavy recreational use. They find positive effects
associated with facing a park that does not have heavy recreational use and insignificant or
negative effects in other cases of close proximity.
While Weicher and Zerbst (1973) define the visual amenity as a discrete variable, other
studies use GIS software to define a magnitude of visual amenity. Paterson and Boyle
(2002) employ a digital elevation model to determine how much is visible from a household’s
location. This is combined with land cover data to measure the percent of a household’s
view defined by different land use categories. The study finds that visible developed land
generates a negative price impact, but other visual measures are insignificant. However, the
inclusion of visual measures does have substantial effects on other model estimates. Walls
et al. (2013) take a similar approach and using ArcGIS Viewshed tool to characterize a
household’s view of different land covers. They find negative price effects of forest views,
but positive price effects of farmland and grassy area views.
3 Machine Learning for Image Classification
The method in Paterson and Boyle (2002) and Walls et al. (2013) relies on classifying land
cover into a specific category, such as residential, forest, or grassland. A view amenity is then
defined based on the amount of each category in a household’s viewshed. We take a different
approach, relying on a machine learning image classification algorithm. This approach has
two advantages. First, we are able to avoid potentially restrictive land-use categories and
instead determine the elements of a visual amenity in the process of estimating our an
image classification model. This allows us to measure heterogeneity in residential space. For
example, residential areas may vary considerably on other land use attributes. Second, by
separately measuring view and land-use type we can separately identify the visual amenity
value of open space from the recreation value.
We define the view amenity in our model based on its similarity to a pristine natural
view. We discuss the required data and details of our technique later in the paper, but
here we briefly outline our approach to measuring the view amenity. First, we use Google
4
Streetview to capture images that replicate the view from each household in our dataset: i.e.
looking outwards from the property. We then use Streetview images of parks that include
natural amenities (trees, shrubs, grass) to define a “green” view. Using these park images,
we estimate the parameters of an artificial neural network to build a predictive model that
can score any image based on its similarity to the “green” view. This method is similar
to commonly used discrete choice models in economics, with chosen park images serving as
observations with a dependent variable equal to 1.2 The model then allows us to predict the
score of each household’s view amenity from 0-100.
To our knowledge, no studies have used this type of image classification to define en-
vironmental attributes. Several studies, however, use machine learning image classification
methods in the context of empirical economics. Glaeser et al. (2015) demonstrate the use of
support vector regression to address a lack of economic statistics. Using data from Google
Streetview data, the authors predict median income in a census block group based on pixel
data from images of the neighborhood. The model not only fits the data well, but is able
to predict out of sample median income in a separate city with a high degree of accuracy.
Additional analysis shows that predicted income is strongly correlated with housing prices,
indirectly linking image data to housing prices. In Naik et al. (2014), the authors employ a
model that predicts the perceived safety of a city from Streetview images. The study gat-
hers data on perceived safety based on location images via crowdsourcing, which can then
be used to estimate a support vector regression. Results show a strong positive correlation
between a city’s median family income and perceived safety. Finally, Naik et al. (2017) use
crowdsourced data to determine the preceived safety of a location and estimate a predictive
model. The authors find positive correlation between perceived safety and education levels
in a neighborhood. In general, this line of research establishes a connection between visual
data and economic outcomes but stops short of formally estimating a specific economic value,
such as a capitalization value or willingness to pay in a hedonic analysis.
4 Visual Amenity in the Hedonic Analysis
We estimate a hedonic housing price function with the objective of identifying the marginal
price of the visual amenity associated with open space. To accomplish this, we build a
standard hedonic housing price model which includes a view amenity variable as an additional
2Our model more closely resembles a multivariate discrete choice model in which we define a park categoryin addition to several other image classifications.
5
attribute. Specifically, we estimate
lnPijt = β0 + βXX + τt + γj + β1Proxi + β2Prox2i + βAV iew Parki + εi, (1)
where Xi is a vector of housing attributes, including number of bedrooms, gross living square
feet, lot size, and dummies for the year the home was built. We also include year-of-sale
fixed effects, τt, and spatial fixed effects, γj, based on US Census block groups.
The variable Proxi measures the inverse of distance to the nearest city/county designated
park. This follows a conventional approach in the hedonic literature that treats open space
as an amenity consumed via proximity. The variable V iew Park denotes the visual amenity
for house i. This measure represents the quality of the view that household i has when
looking across the street. We define a high-quality view as a view that is similar to a
forested/landscaped park. This includes the view of a designated city/county park, but
may also the view of undeveloped open space, well-manicured private land, or any other
land that looks similar to a park. We use several different definitions for V iew Park that
include both continuous and discrete designations. Our objective is to estimate the value
of the visual amenity. We are able to separately identify this value by directly estimating
the proximity value to designated parks. While very close proximity to a park will often
imply a high-quality view, the visual amenity will quickly dissipate as distance to the park
increases. Furthermore, many non-park areas provide a visual amenity but do not provide
the use-value through proximity since they are not designated parks. Thus we are able to
separately identify proximity and visual amenity values. As additional controls, we also
include visual scores for residential and commercial property in Equation 3, V iew Res and
V iew Com, respectively.
Since the view score is a somewhat indeterminate measure, we also estimate a dummy
variable specification in which V iew Park Indi is assigned a value 1 if the household’s view
score is above a particular threshold. Several specifications define this threshold as a percen-
tile in the observed distribution of V iew Park, while one specification defines the dummy
variable equal to 1 if the park score is the highest score among all categories. This appro-
ach fits better with the interpretation of the artificial neural network as a predictive model.
We discuss construction and interpretation of this variable in more detail in a subsequent
section, following a discussion of our data. We then estimate
lnPijt = β0 + βXX + τt + γj + β1Proxi + β2Prox2i + βAV iew Park Indi + εi. (2)
In addition to the above regressions, we estimate alternative specifications to test for
other price impacts. For example, we include the variable Own Parki as an additional re-
6
gressor to capture the visual amenity of a household’s own property that may be capitalized
into its price. Also, interaction effects may suggest more complex consumption patterns. For
example, by including V iew Parki ∗ Proxi, we can test for whether view and use amenities
are complements or substitutes. A similar interpretation follows for Own Parki∗Proxi. The
interaction V iew Parki ∗ Own Parki indicates potential substitution between view ameni-
ties. Further discussion of these effects follows with estimation results.
5 Data
The first data component of our analysis is housing transaction data. This includes 121,045
observations of sales transactions in Denver, CO, from 1999-2017. Summary statistics for
housing data are shown in the top panel of Table I. In addition to the previously mentioned
structural housing variables and transaction prices, we observe the exact address of each
home. This allows us to obtain images of the home and the view across the street and to
accurately measure distance to the nearest designated park. Next, the exact location of
city/county designated parks is obtained from the Denver Department of Parks and Recrea-
tion. Figure 1 shows the location of designated parks in the study area. GIS software allows
us to overlay housing transaction locations and park locations to measure the distance from
the edge of each housing parcel to the edge of the nearest park.
The final data component is the “park view score” for each housing observation. We use
Google Streetview to obtain pictures of each property in our sample. These pictures define
the view across the street for each household, as well as the image of the property itself. We
also obtain parcel maps from Denver Open Data Catalog to accurately measure the view
angle for each household. In a later section, we describe in detail the process of using these
images to calculate V iew Park and Own Park, as well as scores for other categories.
In important component of this analysis is separately estimating the amenity value of
proximity, presumable a recreation-based value, from the amenity of a view. For econome-
tric identification, we need to observe properties with park-like views that are not in close
proximity to designated parks: i.e. properties with high view scores but low proximity. The
correlation coefficient for these two variables is 0.013. In Table II we report the mean and
standard deviation of our park view score for deciles of the proximity distribution. Mean
and standard deviation of predicted park score are calculated for all observations that are
bounded below and above by proximity values. It is clear that park view score is relatively
independent of our proximity measure. Along with a simple correlation coefficient, these
statistics demonstrate a joint distribution of variables that supports empirical identification.
7
5.1 Preliminary Regressions
Before discussing our approach to measuring the view amenity, we estimate a hedonic price
function in which a house is determined to have a park view if it is facing a designated
park, similar to Weicher and Zerbst (1973). Here, we simply hope to motivate the role
of a view in the hedonic price function. This approach considers only designated parks.
Therefore, estimates cannot capture the value of any other park-like land that may convey
the same view amenity. In addition, all properties with a view of a park will also be in very
close proximity to a usable park, so that this model does not have the same identification
power in separately estimating values derived from a view amenity versus those derived from
proximity (i.e. recreation value).
We limit our sample to roughly 17,000 observations of only those homes that are within
100 meters of an officially designated park. Using residential parcel maps, we determine
which houses have a park view based on the angle of the property’s frontage and it’s proximity
to a park. Specifically, we assign Park V iew = 1 if an orthogonal line drawn from the house
parcel boundardy nearest to the park intersects the park boundary prior to intersecting
another parcel. We than estimate
lnPijt = β0 + βXX + τt + γj + β1Proxi + β2Prox2i + βV Park V iewi + εi. (3)
Results show a positive and statistically significant effect of Park V iew. The estimated
coefficient is .0197, with a standard error of .0061. This coefficient can be estimated as
approximately a 1.97% increase in the price of a home when it is facing a park. Based on a
sample mean home price of $444,834, this translates to an increase of $8,763.
We treat these results, along with results from the literature, as preliminary evidence
that view matters as an amenity. Given the shortcomings of this approach, we now move on
to our image classification technique to better construct the view amenity.
6 Image Classification
To create the variables V iew Park and Own Park, we re-train Google’s image recognition
program to detect an array of visual property-type attributes. Our objective is to score
each property in our dataset based on its visual amenity. We define this visual amenity as
similarity to a designated park. This technique involves two steps: 1) Train the model on
a subset of data to understand what is a park-like view, and 2) Apply the model to the
rest of the sample to classify view types for each house observation in our dataset. This
classification involves predicting a view score for different view types.
8
6.1 Training
We use Google’s implementation of the Inception V3 algorithm, a deep convolutional neural
network. This is an open source software library that is trained for image recognition. The
algorithm is trained to identify 1000 different images. However, our analysis is focused on
identifying views that look like parks. Therefore, we re-train (re-estimate) the final layer of
the neural network, effectively teaching the algorithm to distinguish park-like images from
other urban land uses.3
To train the algorithm to propertly classify view types, we obtain pictures of parks and
other categories from Google Streetview. These images serve as input data to estimate pa-
rameters of the algorithm. Figure 2 shows images designated as parks for training purposes.
In addition to training the model on what a park is, we also train to model on other types of
views to avoid false positives. Figure 3 shows four other classifications used in the training
stage. We define four alternative categories: residential, commercial, intersection, and park
usage. While the first three are fairly obvious, the final category, park usage, indicates the
view of a park that is made up of parking lot and/or facilities: i.e. attributes that are not
associated with a high-quality visual amenity.
6.2 Prediction
The image classification algorithm is trained to classify images based on how much the image
resembles each of the categories. Classification assigns a score for each category to an image.
We use the park category score as a measure of the visual amenity. We obtain images of
the view from each house in our dataset and an image of the house itself so that we can
then calculate V iew Park and Own Park. Figure 4 shows four observations of view images
along with the predicted V iew Park. Figure 5 displays two histograms of predicted park
scores. Panel 5a shows the full distribution of predicted park views scores. Note the high
concentrations of predictions at very low end. In Panel 5b, we show only the distribution of
predicted scores above 0.05 to better illustrate variation in predictions.
Identification of a visual amenity separate from the proximity good requires variation
in the form of 1) properties that have high park scores but are not in close proximity to
parks and 2) properties that have low park scores and are in close proximity to parks. The
correlation between between parks scores and our proximity measure is 0.030. Similarly, the
Spearman rank correlation is −.033. These low correlations indicate variation in the data
due to view amenities that are due to properties other than designated parks. In addition,
3The advantage of using a pre-trained algorithm and estimating the final layer is that 1) less data and lessvariation in the data is required for identification and 2) the computational burden is reduced considerably.For more on implementation of this alogorithm: https://www.tensorflow.org/tutorials/image retraining
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proximity to a park does not ensure a high-quality view amenity. Overall, such variation
suggests strong potential for identification.
7 Results
We now turn to estimation results from the housing price hedonic in Equation 3. Table III
shows estimates and standard errors for our baseline specification that includes continuous
scores corresponding to park, commercial, and residential views. We also report results from
a specification that includes only proximity to the nearest designated park to illustrate the
role of distance-based consumption and replicate the conventional approach in the hedonic
literature. Across specifications, the quadratic effect of proximity to parks is initially negative
but turns positive at a proximity of approximately 0.52. This value is in the 96th percentile
of the proximity distribution. The impact is consistent when view scores are accounted for.
Estimates indicate a positive and significant impact of a park-like view, evidenced by
the coefficient on V iew Park. The score variable is measured on a 0 to 1 scale, so this
coefficient can be interpreted approximately as a 9% price premium for the highest-quality
park-like view relative to no park-like view. Alternatively, this coefficient can be interpreted
as approximately a 1.1% price premium for a one standard deviation increase in park score. A
1.1% increasing measured at the mean housing value of $444,834 is roughly $4,893. The view
score associated with residential and commercial properties are statistically insignificant.
Overall, we see evidence that the view amenity, separate from proximity to parks, associated
with park-like properties conveys a considerable capitalization effects.
In Table IV, we show estimates in which our view variables are defined as dummy variables
equal to 1 if the view score is above a particular threshold. Various specifications explore
different thresholds used to define “park-like”. We define this dummy as equal to 1 if the
park view score is greater than the qth quantile in the score distribution or, as in the last
specification, if the park view score is the largest among all view categories. Again, we see
positive and significant price impacts of a park-like view. The coefficient on park score is
generally consistent across thresholds, indicating a price increase ranging from 3.2% to 5.4%
for having a park-like view. A pattern emerges in these estimates, where the estimated
effect of a park view increases when the is definition of such a park view becomes more
stringent. When assigning a park view only if park score is the highest of all the view
categories, however, the estimate falls, perhaps since some view amenities get lost in this
definition. Views of commercial properties carry a negative price premium, while the effect
of residential score is generally insignificant.
In the next set of specifications, we use Own Park to control for the attributes of the
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home itself. Results are shown in Table VI. The estimated effect shows a considerable
amount of variation, ranging from 1.5−10%. These impacts are consistently positive and sig-
nificant, indicating a price premium for aesthetically nice properties. Inclusion of Own Park
has no discernible impact on the estimated effect of the park-like view across the street.
Finally, we look at specifications that include interaction effects among our proximity
and view variables. These results are presented in Table VII. In column 1, we report
earlier estimates for comparison. In column 2, we interact V iew Park with both proximity
and Own Park. The former tests for substitutability/complementarity between proximity
(recreational amenity) and the view amenity. The coefficient on the interaction is statistically
insignificant. The interaction of V iew Park and Own Park is positive and significant,
suggesting that the positive value of a park-like view across the street is amplified by a park-
like appearance of one’s own home, and vice versa. This effect is possibly capturing a highly
localized effect of well-kept properties. In column 3, we again see that the coefficient on
V iew ParkxOwn Park is insignificant. A negative significant coefficient on the interaction
between proximity and Own Park suggests that a household considers a park-like score of
its own property as a substitute for proximity to a designated park. This effect may arise as
residential properties with high park view scores may also offer private use/recreation value.
8 Discussion
Our results provide strong evidence for the capitalization of a view amenity in housing prices.
While the continuous magnitude of the view score variable is somewhat difficult to interpret,
the monetary value of having a park-like view, relative to a purely residential or commercial
view, is several percentage points of a home’s value. Interestingly, the lower-bound on our
range of estimates is approximately equal to our preliminary regression results in which
only houses that are facing a designated park are considered to have the park-like view
amenity. Thus failing to properly account for the view amenity across the city leads to an
underestimate.
We conduct a back-of-the-envelope calculation to determine the total capitalized value of
the view amenity in the study area. We take the average observed view amenity and housing
value in our sample and extrapolate to the total number of homes in entire City and County
of Denver. We are therefore assuming that our observed transactions are a random sample
of the locality. The average home has a view amenity of 0.0426 and the 2010 census reports
622,900 owner-occupied homes in the city. We use a coefficient estimate of .0822, coming
from the specification in which view score is treated as a continuous variable and we control
for score of the home itself. An average capitalization value of $1,557 leads to an aggregate
11
value of $970.3 million. Using 95% confidence intervals on the coefficient estimate, the total
capitalization value is between $665.5 - $1,275.1 million.
While a valuable view amenity creates potential for welfare gains through spatial al-
location of open space that maximizes total view to the area, there are tradeoffs to such
allocation. For example, while the perimeter of an open space parcel is important for the
view amenity, total size or depth of open space may generate larger values linked to recre-
ational opportunities or ecosystem services. Functional aspects, such as parking lots and
facilities may increase recreational value but diminish quality of the view. Our results sug-
gest, however, an important additional component of open space, namely the quality and
quantity of households’ view of such space, that should be considered in valuation.
9 Conclusion
In this analysis, we provide evidence for the amenity capitalization of a household’s view.
Using machine learning techniques, we develop a new approach to quantitatively measuring
view. This allows us to identify the view amenity separately from any amenity value that
works through proximity. This second channel, proximity to open space, has dominated the
literature on valuation of open space. Instead, our results indicate significant effect of view
and suggest that value is created through properties other than publicly designated parks.
From a policy perspective, such identification could indicate the optimal size and location
of open space for welfare maximization. In addition to illustrating an important component
of the nonmarket valuation of open space, our approach to defining a new variable is widely
applicable in hedonic analysis.
12
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Table I: Summary Statistics
Mean Standard Deviation 0.05 Percentile Median 0.95 Percentile
Housing
# of Bedrooms 2.48 0.88 1 2 4
# of Bathrooms 2.14 0.94 1 2 3.5
Square Feet 1437.23 673.90 685 1234 2851
Lot Area 4552.51 3013.79 432 4627 9460
# of Stories 1.45 0.61 1 1 3
Year Built 1964.88 37.38 1898 1965 2015
Year of Sale 2014 1.62 2011 2015 2017
Park Proximity 0.0283 0.1472 0.0004 0.0030 0.0358
View Scores
park 0.0426 0.1218 0 0.0009 0.2357
residential 0.5537 0.3288 0.0145 0.5971 0.9782
commercial 0.1262 0.2517 0 0.0049 0.8439
park (own) 0.0688 0.1846 0 0.0009 0.5201
Table II: Park Summary Conditional on Proximity
Proximity Quantile
(upper bound)Park Score Mean Park Score S.D.
0.1 0.0406 0.1299
0.2 0.0720 0.1810
0.3 0.0447 0.1277
0.4 0.0372 0.1076
0.5 0.0376 0.1120
0.6 0.0359 0.1020
0.7 0.0328 0.1030
0.8 0.0333 0.1062
0.9 0.0387 0.1079
1.0 0.0289 0.0970
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Table III: Baseline Estimates
Dependent Variable = ln(pricei)
(1) (2) (3)
Proxi -0.9603*** -1.2393*** -1.2495***
(0.2778) (0.2805) (0.2806)
Prox2i 0.9289*** 1.2021*** 1.2126***
(0.2749) (0.2775) (0.2776)
V iew Park 0.0978*** 0.0919***
(0.0141) (0.0160)
V iew Res -0.0100
(0.0092)
V iew Com 0.0088
(0.0133)
n 31290 31290 31290
r.squared 0.6952 0.6957 0.6957
Standard errors in parentheses. All specifications include year-of-sale and block group fixed effects, in addition to additional housing attributes.
Table IV: View as Discrete Variable
Dependent Variable = ln(pricei)
quantile threshold Park Score Commercial Score Residential Score
0.4 0.032 -0.0288 -0.0042
(0.0051) (0.0082) (0.0128)
0.5 0.0353 -0.0241 -0.0001
(0.0052) (0.0083) (0.0128)
0.6 0.0321 -0.0194 0.0016
(0.0054) (0.0085) (0.0130)
0.7 0.0455 -0.0103 0.0107
(0.0059) (0.0087) (0.0131)
0.8 0.0535 -0.0067 0.014
(0.0070) (0.0089) (0.0132)
0.9 0.0544 -0.0127 0.0057
(0.0095) (0.0090) (0.0132)
max 0.0348 -0.0238 -0.0053
(0.0117) (0.0089) (0.0131)
Standard errors in parentheses.
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Table V: View as Discrete Variable: Park Score Coefficient
Dependent Variable = ln(pricei)
quantile threshold Coefficient Standard Error
0.4 0.0320 (0.0051)
0.5 0.0353 (0.0052)
0.6 0.0321 (0.0054)
0.7 0.0455 (0.0059)
0.8 0.0535 (0.0070)
0.9 0.0544 (0.0095)
max 0.0348 (0.0117)
Standard errors in parentheses.
Table VI: Include Own Park Score
Dependent Variable = ln(pricei)
View Park Own Park
Continuous 0.0822 0.1604
(0.0157) (0.0207)
0.4 0.0317 0.0149
(0.0050) (0.0050)
0.5 0.035 0.0174
(0.0051) (0.0050)
0.6 0.0307 0.0228
(0.0053) (0.0052)
0.7 0.0431 0.0291
(0.0058) (0.0057)
0.8 0.0502 0.0333
(0.0068) (0.0068)
0.9 0.0534 0.076
(0.0093) (0.0103)
max 0.0425 0.1046
(0.0111) (0.0179)
Standard errors in parentheses.
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Table VII: Interaction Effects
Dependent Variable = ln(pricei)
(1) (2) (3)
Proxi -1.1438 -1.1106 -1.1174
(0.2768) (0.2770) (0.2772)
Prox2i 1.1046 1.0767 1.0996
(0.2739) (0.2739) (0.2739)
V iew Park 0.0822 0.0624 0.0843
(0.0157) (0.0170) (0.0160)
Own Park 0.1604 0.1211 0.1662
(0.0207) (0.0231) (0.0210)
Proxi xV iew Park
-0.0477 -0.0684
(0.0795) (0.0796)
Proxi xOwn Park
-0.2056
(0.1278)
V iew Park xOwn Park
0.2889
(0.0765)
Standard errors in parentheses.
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Census Block GroupsParks
Figure 1: Study Area
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Figure 2: Park images for training
19
(a) Residential (b) Commercial
(c) Intersection (d) Usage
Figure 3: Images for training on other categories
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park 0.99962
com 0.00000
res 0.00016
usage 0.00021
int 0.00000
park 0.99976
com 0.00000
res 0.00001
usage 0.00023
int 0.00000
park 0.63219
com 0.00013
res 0.33452
usage 0.03316
int 0.00000
park 0.00003
com 0.00001
res 0.99970
usage 0.00023
int 0.00000
Figure 4: Predicted View Scores
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0
3000
6000
9000
0.00 0.25 0.50 0.75 1.00
Park Score
coun
t
(a) Full Distribution
0
300
600
900
0.00 0.25 0.50 0.75 1.00
Park Score
coun
t
(b) park score > 0.05
Figure 5: Distribution of Park Scores
22