Discussion Paper No. 128
The value of scattered greenery in urban areas:
A hedonic analysis in Japan
Yuta Kuroda and Takeru Sugasawa

July, 2022

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku

Sendai 980-8576, JAPANThe value of scattered greenery in urban areas:
A hedonic analysis in Japan

Yuta Kuroda a and Takeru Sugasawa b

aGraduate School of Economics, Osaka Metropolitan Universityb

Housing Research and Advancement Foundation of Japan

Acknowledgments
This work benefited from a project funded by the Housing Research and Advancement
Foundation of Japan. Satellite images and vegetation data were collected and generated incooperation with JAPAN SPACE IMAGING CORPORATION. The views expressed are thoseof the authors and do not necessarily reflect those of any organizations with which the authors
are affiliated.

aCorresponding author; Graduate School of Economics, Osaka Metropolitan University, 3-3-

138, Sugimoto, Sumiyoshi-ku, Osaka, 558-8585 Japan (E-mail: kuroyu0725@gmail.com)b

Housing Research and Advancement Foundation of Japan, 6-3 Nibancho, Chiyoda-ku, Tokyo

102-0084, Japan (E-mail: takeru.sugasawa@gmail.com)
1

Abstract
This study investigates the impact of scattered greenery (street trees and yard bushes), rather
than cohesive greenery (parks and forests), on housing prices. We identify urban greenspacefrom high-resolution satellite images and combine these data with data on both sales and rentals
of condominiums to estimate hedonic pricing models. We find that scattered urban greenerywithin 100 meters significantly increases housing prices, while more distant scattered greenery
does not. Scattered greenery is highly valued near highways but is less valued near the central
business district (CBD). Additionally, the prices of inexpensive and small for-sale and of forrent properties are less affected by scattered greenery. These results indicate that there issignificant heterogeneity in urban greenery preferences by property characteristics and location.
This heterogeneity in preferences for greenery could lead to environmental gentrification since
the number of more expensive properties increases in areas with more green amenities.

JEL classifications: Q51, R3, R21, Q57
Keywords: Environmental amenities, Urban greenness, Hedonic house price model, Housing
value, Remote sensing

2

1.

Introduction

Urban green spaces provide a variety of benefits, including improved landscapes, air pollution
abatement, noise reduction, soil conservation, and mitigation of the heat island effect, and thesebenefits have a substantial impact on the physical and mental health, quality of life, and overall
well-being of residents (Bertram and Rehdanz, 2015; Taylor and Hochuli, 2017). However,green amenities, such as urban forests, parks, and street trees, are public goods with many
positive externalities, so in the absence of public intervention, they are underprovided. The
attempt to increase agglomeration effects by allocating spaces to more productive uses tends
to result in substitution away from or elimination of less competitive uses, such as green
amenities, particularly in highly urbanized areas. Therefore, in urban areas in manyindustrialized countries, local administrations and policy makers have implemented greening
policies, such as imposing land use restrictions and planting trees in public spaces. However,if the value of green amenities is not properly measured, the amount of publicly provided green
space might be less than optimal, which worsens people's welfare.
Rosen's hedonic pricing framework, as a method for measuring the value of urban green
amenities, has been widely used in the fields of urban and environmental economics (Rosen,1974). The general hedonic pricing approach assumes that the buyer of a home is paying forthe amenities and services provided by the environment surrounding the house in addition to
the property itself. By estimating housing prices as a function of various characteristics that are3bundled with the housing, such as the distance from a hospital or the amount of greenery, we
can derive an implicit value (willingness to pay, WTP) for each characteristic. As theavailability of geographic data on land use has increased, numerous studies have used hedonic
pricing approaches to measure the value of urban green space (e.g., Baranzini and Schaerer,
2011; Gibbons et al., 2014; Sander et al., 2010; Tyrväinen and Miettinen, 2000). Previous
studies have suggested that urban green amenities have a generally positive impact on realestate prices (Brander and Koetse, 2011; Czembrowski and Kronenberg, 2016; Perino et al.,2014; Siriwardena et al., 2016).
Many recent studies have focused on the variety of green spaces and the heterogeneity ingreen-space preferences (Stromberg et al., 2021). According to Barrio and Loureiro (2010),
who conducted a broad survey of studies on the value of greenery, people's WTP for greeneryvaries greatly depending on the type of greenery, the residential environment, and thecharacteristics of the people. For example, Czembrowski and Kronenberg (2016) classified
green spaces into several categories by type, use, and size and showed that each dimension has
different impacts. Panduro et al. (2018) also found that heterogeneous preferences for the same
type of green space depend on the socioeconomic and demographic characteristics ofhouseholds. Most of these existing studies have considered greenery of a certain size (i.e.,cohesive greenery), such as parks and forests, as “urban green space” and have classified such
spaces according to their use (e.g., sports fields, landscape preservation, and air quality4improvement).
In contrast to the richness of studies of cohesive green space, prior research has provided
little information about the value of scattered greenery, such as street trees and yard bushes.
Unlike parks and forests, for which official statistics and geographic data are more widely
available, such scattered greenery is not mapped, and data often do not exist. Measuring thevalue of scattered greenery requires very detailed vegetation data at the street or site level,
which can be costly to obtain. Therefore, scattered greenery has been either overlooked orintentionally excluded from analyses (Perino et al., 2014), although its total area is large andcould have a meaningful effect on people.
To bridge the gap in the current literature, this study investigates the value of street trees
and yard bushes. Green density is calculated using the normalized difference vegetation index(NDVI) from high-resolution (1.5-meter pixel resolution) satellite imagery and is combinedwith large-scale real estate data that include detailed information about various characteristics
to provide insights into the value of scattered greenery. The analysis covers the area around the
Setagaya and Suginami wards in Tokyo, the most urbanized residential areas near the center ofJapan. We also used greenery data from two different years, 2008 and 2013, to analyze changes
in effects over time.
Our results show that a 10% increase in scattered greenery within 100 meters of a property
increases the price of apartments for sale by 2% to 2.5%. Conversely, the impact of scattered5greenery on rental properties is weak or insignificant. These main results are not sensitive tochanges in the estimation specification or variable definitions and are robust to changes in thesample. We also find that the value of scattered greenery depends greatly on the characteristicsof the property and its location. Street trees are highly valued along highways because of theirrole in mitigating noise and emissions. Higher priced and roomier properties are associatedwith higher values for greenery, but this outcome is also due to the large supply of both goodquality properties and greenery in areas suitable for habitation. Furthermore, the analysis ofchanges in effects over time suggests that there might be a gradual increase in the heterogeneityof the value of greenery by property price and quality.
Scattered greenery is especially important in urban areas where available land is scarce andpricey. In urban areas, high land prices often prevent the construction of additional parks, urban
forests, and other large open spaces. Additionally, converting existing residential andcommercial areas into green space is a very costly alternative because it undermines theattractiveness of the city as a center of productive activity. Therefore, a feasible strategy would
be to construct nonexclusive small-scale green spaces and plant scattered greenery. Prior
studies have pointed not only to the benefits of using greenery, such as exercise and recreation,
but also to the benefits of the existence of greenery, such as improved air quality andtemperature, and the benefits of seeing greenery, such as stress reduction (Mullaney et al.,2015). Such effects can be achieved even with scattered greenery, which does not require large
6

tracts of land, so if scattered greenery has a positive impact on property values, it might
additionally improve the welfare of urban areas. Therefore, knowing what function scatteredgreenery performs in a city and where and to whom it provides utility is expected to generate
new insights for urban planning. In recent years, the uneven distribution of urban green spaceand environmental gentrification has become an issue, and it is also important to understandthe widely scattered greenery that exists in cities from an environmental justice perspective.
Despite the potential benefits of scattered greenery, there is a lack of empirical evidence
for the value of scattered greenery and the heterogeneity of its impacts. A small number ofstudies have examined the impact of tree cover on property values by generating data throughsight counts of trees. Donovan and Butry (2010), who counted street trees during a detailed
field survey, found that street trees within 100 feet significantly increased real estate prices.
Pandit et al. (2013) visually identified street trees from aerial images and found positive
neighborhood externalities due to broadleaf street trees. However, such visual identification ofstreet trees is very costly, making it difficult to target large areas or multiple regions and thus
results in the disadvantage of small sample sizes and missing data. Additionally, the fieldsurvey did not reveal greenery in hard-to-see areas such as behind walls, and grasses and bushes
other than trees were completely overlooked, so their value was not revealed. Therefore, remotesensing with high-resolution aerial or satellite imagery has been used in recent years (Francoand Macdonald, 2018; Tsurumi et al., 2018). Troy and Grove (2008) used remote sensing to7

study the heterogeneous impact of parks on the real estate market in Baltimore, Maryland, U.S.
Sander et al. (2010) estimated the value of urban tree cover in Dakota and Ramsey counties,
Minnesota, U.S., and found that a 10% increase in tree cover within 100 meters increased theaverage home sale price by $1371 (0.48%) and that a 10% increase within 250 meters increased
the average price by $836 (0.29%). However, the resolution of the images used by Sander etal. (2010) was 30 meters, and since one pixel on an image represented 30 meters of groundsurface, nothing is revealed about the value of scattered greenery because it cannot be identified.
To our knowledge, few studies have analyzed the impact of scattered greenery on property
values using large sample sizes and detailed green coverage data.
We contribute to the literature on the evaluation of urban green spaces by identifying
greenery in detail using satellite images that have a higher resolution than those used in existingstudies. Using satellite images that allow us to identify trees and bushes on a plant-by-plantbasis, we can determine the amount of greenery covering a large area without missing anything
and provide robust evidence for the value of scattered greenery. Our results highlight the highvalue of trees and bushes along roadsides, which has been overlooked in previous studies.
Additionally, while existing studies have emphasized the availability of green spaces such asparks and forests, our results indicate that greenery that is not directly usable is also considered
an important amenity. These results generate new insights for development plans in cities whereland is scarce and expensive.
8

We also reveal the heterogeneous valuations of green amenities by property characteristics
and location by combining detailed green coverage data with rich real estate transaction datafor both sales and rentals. Most previous studies have focused on either sales or rentals or on
highly homogeneous real estate markets and might have overlooked heterogeneity in the ways
in which green amenities affect the market. However, Łaszkiewicz et al. (2019) suggested thatgreenery could be a luxury item and that people with higher incomes might seek better-quality
green amenities. We identify the heterogeneity in preferences for green amenities by comparing
the transactions data on properties for sale, which are more expensive and longer-term
investments, with those on properties for rent, which are less expensive and shorter-termholdings. Additionally, we contribute to the discussion about environmental gentrification byfinding suggestive evidence that such heterogeneity in preferences could lead to residential
segregation or stratification.
The paper proceeds as follows. Section 2 describes the study area and details the data usedin this study. Section 3 presents the empirical strategy. Section 4 presents the main results, aseries of robustness checks, and insights into the underlying mechanism. Section 5 discusses
the policy implications, and Section 6 concludes the study.

2.

Data and settings

2.1. Study area
9

Our study area covers the Setagaya and Suginami wards, which are located in the westernpart of central Tokyo, the capital of Japan. The satellite images used to create the green
coverage data cover an area of approximately 131 km2, including 545 streets. 1 This area isadjacent to the central business district (CBD) of Tokyo and is one of the most attractive realestate markets in Japan. The 2010 population (and density) of the Setagaya and Suginami wards
was approximately 880,000 (15,000/km2) and 550,000 (16,000/km2), respectively. The area
has many high-income residents: the average taxable income across residents in allmunicipalities in 2010 was 2,765,000 JPY, whereas the average for the Setagaya and Suginami
wards was 4,971,000 JPY and 4,354,000 JPY, respectively. Consequently, land and housing
prices are also known to be quite high.
This area is considered to be a “just right” residential area, with the central commercial area
to the east and the suburbs to the west. The entire area is fairly well developed, with very little
farmland, wasteland, or vacant land. There are several forests, but they are all managed planted
forests within parks; there are no natural forests. To maintain a comfortable residential
environment, there is a large amount of scattered greenery, with street trees along the roads andbushes surrounding buildings. Therefore, we can identify the impact of scattered greenery in a1

Technically, these passages are not streets but are called “cho-chos.” A cho-cho is the smallest

geographical unit in Japan and is similar in concept to a street in the U.S. For simplicity, thispaper uses the term “street.”10highly developed city while reducing the problem of misidentification of greenery areas.

2.2. Urban greenness
We use Maxar Technologies' high-resolution optical satellite imagery to identify greencovered areas in to create our GIS data. Satellite images taken on April 30, 2008, and October
13, 2013, the two days with the least cloud cover among the available dates in 2008 and 2013,
were used. 2 The images include four spectral bands, the blue-green-red visible bands and thenear-infrared band, and are available with a 1.5-meter spatial resolution. We created our NDVI
image data using the red (𝑅𝑅) and near-infrared (𝑁𝑁𝑁𝑁𝑁𝑁) spectral bands to extract green-covered
areas. NDVI is calculated as (𝑁𝑁𝑁𝑁𝑁𝑁 − 𝑅𝑅)⁄(𝑁𝑁𝑁𝑁𝑁𝑁 + 𝑅𝑅) and indicates the relative greenness ofthe pixels. Because plants absorb visible (red) light during photosynthesis, and plant cellstructures reflect near-infrared light, NDVI is used as a relative indicator of greenness (Franco

2

We assume that using April data from one year and October data from another year does notcause serious problems because the region does not experience significant changes in plantconditions except during the winter (December-February). However, given the concern that thedifference in green cover between 2008 and 2013 is due to the month of observation, this study
does not focus on the increase or decrease in green cover from 2008 to 2013 but only on thechange in the impact of green cover on the real estate market in each year. Due to budget
constraints, other data were not available, and this study is limited by the inability to consider
changes in vegetation due to seasonal differences.
11and Macdonald, 2018). In general, an NDVI value close to 1 represents rich greenery, while an
NDVI value close to -1 represents a water area. We focus on pixels with high NDVI values andsubsequently process the data by changing the threshold value and checking for false positives
to produce the most appropriate identification of green coverage. 3
The green coverage data generated based on the NDVI values tells us only that the area has
green cover and does not allow us to identify the type of greenery that is present. Therefore,
we identify the type of greenery by combining our NDVI data with the Urban Area Land Use
Subdivision Mesh Data published by the Ministry of Land, Infrastructure, Transport andTourism (MLIT). These GIS data are based on satellite images and field surveys and identify
land at the 100-meter mesh (100-square meter) level for each type of use (rice fields,
agricultural land, forests, building lots, roads, parks, rivers, etc.). We match the 2009 and 2014
Urban Area Land Use Subdivision Mesh Data to the 2008 and 2013 green coverage data,
respectively.
Specifically, if the land use category is buildings, roads, or railroads, then the greenery in
the area overlapping that mesh is identified as “scattered greenery.” This definition isreasonable because the greenery present in areas used for buildings and roads consists of the3

The green coverage identified using only NDVI images contains misclassified objects.

Therefore, we confirmed and corrected these misclassified areas with the support of JAPANSPACE IMAGING CORPORATION, a company specializing in satellite image manipulation.
12

trees between roads and sidewalks or the bushes around buildings. Similarly, if the land use
category is farmland, wasteland, or vacant land, the category is “farmland and vacant land
greenery”; if the category is rivers or lakes, the category is “waterfront greenery”; and if thecategory is forests, parks, or public facilities, the category is “park and public facility greenery”.
The Urban Area Land Use Subdivision Mesh Data define the land use for the entire mesh
as use that accounts for the largest percentage within each mesh. Thus, if the mesh consists of
70% buildings and 30% parks, it is assigned a land use of “buildings,” and the greenery in theparks is thus defined as scattered greenery. However, the greenery in such small parks can bethought of as similar to street trees or garden bushes because of their low availability forspecific purposes, such as exercise and recreation. Appendix Figures A1 and A2 show
comparisons of high-resolution aerial photographs and NDVI-based green coverage data.
Figure A1, showing residential areas, illustrates that what is defined as scattered greenery ismainly bushes and trees around houses, beside roads, and along railroad tracks. Figure A2,
which shows parks with sports fields, indicates that the greenery around parks and sports fields
is classified as cohesive greenery. However, the 100-meter mesh is used, so the greenery at theboundary of the park is classified as scattered greenery. Although such classification errors
potentially bias the results, the boundary between parks and other areas is not clearly defined,
and the area is small, so the analysis in this study considers the area as scattered greenery.
Figures 1 and 2 show the green areas by type in 2008 and 2013, respectively. As shown,13

even the data classified at the 100-square meter level are sufficiently smooth to distinguishbetween the different types of greenery. 4 Many green areas are spread throughout the study
area, emphasizing the importance of scattered greenery in urban areas. The locations of thegreen areas did not change significantly between 2008 and 2013, but the percentage of green
coverage decreased slightly. Scattered greenery accounted for approximately 18.5% of the areain 2008 and approximately 14.9% in 2013. Of course, these figures should be interpreted withcaution since the decrease could have been caused by the difference in the dates of observationor the processing of the satellite images.

4

In the 2009 Urban Area Land Use Subdivision Mesh Data, forests within parks are classified

as "parks,", but in 2014 they are classified as "forests.". This is because the categoryclassification was changed by the MLIT and not because the actual land use has changed. Sincealmost all forests in the area are within a parks, parks and forests are treated the same as whencreating the variables.
14

Figure 1. Green coverage by type in 2008
Note: The location and amount of greenery are based on satellite images from 2008. The
classification of green spaces is based on the 2009 Urban Area Land Use Subdivision Mesh
Data.

15

Figure 2. Green coverage by type in 2013
Note: The location and amount of greenery are based on satellite images from 2013. The
classification of green spaces is based on the 2014 Urban Area Land Use Subdivision Mesh
Data.

Most studies related to urban green space have focused on two different measures: thedistance to a green space and the amount of green space. Unlike parks and other large open
spaces, scattered greenery is not something people travel to and use. The effects of scattered16

greenery include improved air quality due to its presence and reduced stress due to a beautiful
landscape. Therefore, it is not the distance to the nearest scattered greenery but the total amountof scattered greenery around the property that matters. We constructed five doughnut-shaped
concentric buffers (defined at 100-meter intervals up to a maximum of 500 meters) around thecoordinates of the building's center of gravity and measured the amount of each type ofgreenery within each buffer. Descriptive statistics are provided in Appendix A3.

2.3. Property data
We use housing transaction data provided by the Real Estate Transaction Promotion Center(RETPC), an association of real estate agents. The RETPC provides the largest Multiple Listing
Service (MLS) in Japan, called the Real Estate Information Network System (REINS). REINS
contains records of contracts for the properties handled by each member real estate agent, andits database includes transaction information for the property (contract price or rent, date ofcontract, exact address of the building, and various property characteristics). This dataset
includes both sales and rentals of apartments for residential purposes. We convert building
addresses into longitude and latitude coordinates and then merge the real estate data with theother variables based on these coordinates.
For our analysis, we use the sales and apartment rentals that were transacted in the analyzed

17area during the 10 years from 2006 to 2015. 5 Because green coverage does not change
substantially over a few years, the 2008 and 2013 green coverage data are connected to property
data from 2006 to 2010 and from 2011 to 2015, respectively. We removed from our sample
properties for which the exact latitude and longitude were unknown, that were missing primarycharacteristics, that had extremely high or low prices or rents, or that suffered from suspected
typographical errors. Totals of 17,552 properties for sale and 137,851 properties for rent areused for estimation. Each property observation includes information about the number of rooms,
the square footage, the age of the building, the floor on which it is located, the number of floors
in the building, the type of layout, the type of building structure, and the zone of the location. 6
Descriptive statistics can be found in Appendix A3.

5

Apartments (condominiums) are important when effectively using small, densely populatedareas, such as those in Tokyo, and are the main option for residential housing. Our data include
detached properties, but the number of transactions is very small, and the transaction prices are
extremely high. Additionally, detached houses are able to have more greenery in their own
yards, causing endogeneity problems in the estimation. Thus, we focus on the price of or rent
for apartments.
6

The zones of a location define the types of buildings that can be constructed in these areas(low-rise residential, high-rise residential, commercial, industrial, etc.), and the building-toland ratio and floor-area ratio are also defined for each zone. By controlling for the fixed effectsof the zones, the estimation considers the effects of confounders such as the size of the yard
and the height of the building.
18

2.4. Other control variables
We control for a variety of characteristics that can affect property values. We prorate thecensus-based street-level population, household count, population younger than 20, andpopulation older than 65 within a 500-meter radius of the property to create variables for thedemographic characteristics around the property. To control for real estate market conditions
around the properties, we generated the number of transactions, the average price or rent, andthe average ground floor level for each property within a 500-meter radius for both sales andrental properties. Additionally, we obtained GIS data on various government statistics
regarding the locations of hospitals, schools, police stations, fire stations, post offices, parks,
museums, libraries, sports fields, martial arts facilities, swimming pools, municipal offices,
stations, bus stops, major roads, highways, Tokyo Station (the CBD), and the Tama River, andwe calculated the distances from the properties to the nearest instance of each type of amenity.
These accessibility measures are logarithmically transformed because the effect of access toamenities is expected to decrease as distance increases. Descriptive statistics can be found in
Appendix A3.

3.

Empirical strategy
Hedonic property pricing models have been widely used to estimate the contribution of19

various characteristics to the value of a property. This paper uses a hedonic model to estimate
the marginal implicit price of scattered greenery. The estimation equation is as follows.
5ln(𝑃𝑃)𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼 + � 𝛽𝛽𝑟𝑟 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 + 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 + 𝑌𝑌𝑦𝑦 + 𝑀𝑀𝑚𝑚 + 𝑆𝑆𝑠𝑠 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝑟𝑟=1

(1)

where the dependent variable ln(𝑃𝑃)𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the natural logarithm of the price or rent ofproperty 𝑖𝑖 on street 𝑠𝑠 that was contracted in month 𝑚𝑚 of year 𝑦𝑦. 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 represents

the percentage of scattered greenery within the 𝑟𝑟 -th concentric buffer from the center ofproperty 𝑖𝑖 . The coefficient 𝛽𝛽𝑟𝑟 measures the value of the greenery within the 𝑟𝑟 -th buffer.

𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 controls for various characteristics, such as property characteristics, neighborhood

characteristics, accessibility characteristics, and other green coverage. 7 𝑌𝑌𝑦𝑦 , 𝑀𝑀𝑚𝑚 , and 𝑆𝑆𝑠𝑠control for the contract year, contract month, and street fixed effects, respectively. We estimate
Eq. (1) using four separate datasets on sales and rental properties for 2008 and 2013.
The hedonic model in Eq. (1) does not consider spatial relationships among theobservations. In estimating hedonic price models, heteroskedasticity and spatial
autocorrelation issues can render ordinary least squares (OLS) estimators inefficient. Some
previous studies have considered spatial dependence by applying spatial hedonic models using

7

The study area is a well-developed urban area, and as Figures 1 and 2 show, the other typesof greenery (e.g., parks and waterfront greenery) are scarce and unevenly distributed. Therefore,
this study uses green spaces other than scattered greenery as a control variable only and does
not provide a detailed interpretation of the corresponding impact.
20

spatial weight matrices that define adjacencies (e.g., Sander et al., 2010; Votsis, 2017).
However, since our data contain separate rooms in the same building, some samples have acommon longitude and latitude (i.e., zero distance), making it difficult to define the spatial
weight matrix. Additionally, we have the technical problem that maximum likelihood
estimation is difficult due to the large sample size and large number of independent variables.
We therefore report our estimation results from a general hedonic pricing model that
controls for various amenities and fixed effects as our main results. While not accounting for
spatial dependence might seem problematic, Mueller and Loomis (2008) confirmed that
estimates obtained by accounting for spatial autocorrelation in a hedonic property model arenearly identical to OLS estimates. We also estimated a spatial error model using samples thatuse only properties with unique latitudes and longitudes as a robustness check, but the results
were almost identical to those obtained using OLS. Therefore, the presence of spatial
dependence should not seriously affect our results.

4.

Results

4.1. Main results
Table 1 shows the main results. Columns (1) and (2) are estimated using data on properties
for sale and show that scattered greenery within 100 meters of a property significantly increases
the contract price. Scattered greenery more than 100 meters from the residence has a barely21significant impact. This result is consistent with the results of a previous study (Donovan andButry, 2010) and suggests that scattered greenery is not something that is accessed for use andis therefore highly valued when it is easily visible on a daily basis (Lo and Jim, 2012; Tsurumi
et al., 2018). Columns (3) and (4) present estimation results using data on rental properties.
Column (3) uses 2008 data and shows that scattered greenery within 100 meters slightly
increases rents, while Column (4) uses 2013 data and shows that scattered greenery at any
distance has no significant effect on rents.
Our results show that a 10% increase in scattered greenery within 100 meters increases theprice of apartments for sale by approximately 2% to 2.5% (from 740,000 to 930,000 JPY).
Sander et al. (2010), who analyzed green space in Minnesota, reported that a 10% increase in
the tree canopy within 100 meters increased the average housing price by 0.48% and that the
average tree canopy within 250 meters increased the average price by 0.29%. Our estimatedimpact, which is larger than those in previous works, could be caused by the characteristics ofthe study area. Our study area has little green space, so the value of greenery could be high(Brander and Koetse, 2011; Siriwardena et al., 2016). Additionally, trees and grasses that
reduce noise and pollution might be highly valued due to the high population density and trafficin our study area (Perino et al., 2014; Votsis, 2017). We provide a subsample analysis in thefollowing sections and address the mechanisms underlying the results of these green
assessments.
22

Properties for rent
2008
2013
(3)
(4)

Properties for sale
2008
2013
(1)
(2)
% Surrounding greenness
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Property characteristics
Neighborhood characteristics
Accessibility characteristics
Measures of urban greenness
Street FE
Year FE
Month FE
Observations
Adjusted R-squared

0.251***(0.057)-0.137(0.093)-0.096(0.116)-0.270*(0.136)-0.085(0.152)
YES
YES
YES
YES
YES
YES
YES
7,872
0.94240.204**(0.076)-0.092(0.118)-0.159(0.140)-0.189(0.156)-0.324(0.178)
YES
YES
YES
YES
YES
YES
YES
9,680
0.94520.055*(0.027)0.019(0.043)0.038(0.056)-0.054(0.060)-0.023(0.080)
YES
YES
YES
YES
YES
YES
YES
43,1880.91740.016(0.024)0.005(0.043)0.044(0.046)0.038(0.056)-0.111(0.066)
YES
YES
YES
YES
YES
YES
YES
94,6630.9106

Table 1. Effects of scattered greenery on property prices and rents
Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively. The full results
are provided in Appendix Table A4.

Tsurumi and Managi (2015) analyzed the value of green space using the life satisfaction
approach (LSA) for areas close to ours. They indicated that the marginal WTP for a 1% increasein green space within a 100- to 300-meter radius from home is 93,714, which is fairly close to
our result. However, Tsurumi and Managi (2015) found that parks and other green spaces
23within 100 meters have no significant impact. Several previous studies have found thatgreenery too close to a house has a negative effect or no effect at all on housing prices, butthese studies focused their analyses on cohesive green spaces, such as parks and urban forests(Pandit et al., 2013; Stromberg et al., 2021). Too much proximity to a cohesive green spaceprovides disamenities, such as increased noise, decreased public safety, and the presence of
unpleasant animals and insects, which can reduce the value of a property. However, scatteredgreenery is less likely to generate such disamenities, so closer proximity could be important.
Compared to properties for sale, rental properties are less affected by scattered greenery.
There could be several explanations for this fact. Individuals entering into a purchaseagreement, with its higher price and longer ownership period, might place more importance on
the living environment, while those entering into a less expensive and easier rental agreement
might place more importance on accessibility to commercial areas, workplaces, schools, etc.
Appendix Table A5 shows the number of households within 10 kilometers of the CBD by yearsof residence in the current house, indicating that the number of years of residence for owned
and rented households is quite different. More than 60% of households living in sales properties
have lived in their current home for more than 13 years, and about 35% have lived in theircurrent home for more than 28 years. In contrast, about 40% of households living in rental
properties have lived in their current homes for less than two years, and about 80% have lived
in their current homes for less than 12 years. Therefore, it is plausible to explain that24

environmental amenities are strongly considered in sales contracts, which are long-termholdings, but not in short-term rental contracts.
Alternatively, there could be heterogeneity in ownership preferences since wealthy people
and families are more likely to live in properties for sale, and single individuals are more likely
to live in properties for rent. Łaszkiewicz et al. (2019) suggested that green spaces are luxury
goods and that individuals with higher incomes might be more environmentally oriented. It isalso plausible that families are more likely to be concerned about their residential environments
due to a focus on the health and growth of their children. Further insights into theseheterogeneities in preferences are also provided in the subsample analyses in the following
section.

4.2. Robustness checks
The results of our series of robustness checks are presented in Table 2 and Appendix Table
A6. Panels A, B, C, and D show the results using data from the properties for sale in 2008, theproperties for sale in 2013, the properties for rent in 2008, and the properties for rent in 2013,
respectively. In what follows, due to space limitations, we report only the results for scatteredgreenery within 100 meters that are significant, and the impacts at greater distances areprovided in the Appendix. Column (1) shows the results using the natural logarithm instead of
the percentage of scattered greenery, while Column (2) shows the results estimated using a25

dummy variable that has a value of 1 when the amount of scattered greenery is in the top 25%.
The results in Columns (1) and (2) are consistent with the main results, and our results arerobust to changes in the measure of scattered greenery.
Columns (3) through (6) confirm that the main results are not sensitive to changes in thesample. Column (3) shows the results after excluding the top and bottom 5% of observationsin terms of prices/rents in each sample, confirming that the main results are not driven by
extremely expensive or inexpensive properties. Column (4) excludes the impact of very large
apartment buildings with various amenities, such as lush gardens (called high-class tower
condominiums in Japan), by excluding from the sample properties with more than 10 floors.
Column (5) is estimated using only properties contracted in 2008 and 2013 (the years for which
the green coverage data were obtained). Although the smaller sample size increases thestandard errors and slightly decreases the significance of our results, the magnitudes of thecoefficients are consistent. Column (6) confirms that the inclusion of multiple rooms in a single
building does not affect the results. Specifically, properties with an exact latitude and longitude
match in a contract year are assumed to be in the same building, and average values arecalculated for the number of rooms or floors on which rooms are located to create a uniquedataset at the year and building levels.
Column (7) shows the results of the estimation after considering spatial dependence. We
conduct this estimation using only properties contracted in 2008 and 2013 from the unique26

sample created in Column (6). Using the distance at which every property has one or moreneighbors (approximately 500 meters) as the threshold for adjacency, a spatial weights matrix
is created using the inverse of the distance and is analyzed using a spatial error model (SEM).
The estimation results from the SEM are in close accordance with the main results estimatedwith OLS, confirming that spatial dependence does not seriously affect our results.

Alternative green
space measures
Natural Top 25%
logarithm dummy(1)Panel A: Sales (2008)
Scattered greenery (0–100 m) 0.023**(0.007)
Observations
7,872
Adjusted R-squared
0.9422
Panel B: Sales (2013)
Scattered greenery (0–100 m) 0.014(0.008)Observations
9,680
Adjusted R-squared
0.9451Panel C: Rentals (2008)
Scattered greenery (0–100 m) 0.009**(0.003)Observations
43,188Adjusted R-squared
0.9174
Panel D: Rentals (2013)
Scattered greenery (0–100 m) 0.003(0.003)Observations
94,663Adjusted R-squared
0.9106
Property characteristics
YES
Neighborhood characteristics
YES
Accessibility characteristics
YES
Measures of urban greenness
YES
Street FE
YESYear FE
YES
Month FE
YES

(2)

Trimmed Samples
Remove Exclude
Only
Only
top and large-scaleuniquesingle year
bottom 5% properties
buildings
(3)
(4)
(5)
(6)

Spatialdependence
(SEM)
(7)

0.041***(0.009)7,872
0.94230.218***(0.058)7,084
0.92440.210***(0.062)
6,245
0.94430.288*(0.128)1,473
0.94520.233***(0.058)7,586
0.94000.287**(0.099)1128

0.024**(0.008)9,680
0.94510.151*(0.077)8,712
0.92570.175*(0.078)7,601
0.94760.203(0.124)2,146
0.95120.220**(0.078)9,356
0.94040.231*(0.104)1529

0.0004(0.004)
43,1880.91740.024(0.020)39,0390.9017

0.056*(0.028)41,2630.91570.042(0.052)
6,814
0.91730.078***(0.022)35,3610.90930.079*(0.039)3961

0.001(0.003)
94,6630.9106
YES
YES
YES
YES
YES
YES
YES

-0.019(0.024)85,3290.8885
YES
YES
YES
YES
YES
YES
YES

0.019
(0.025)90,7680.9085
YES
YES
YES
YES
YES
YES
YES

0.068
(0.035)
19,2660.9110
YES
YES
YES
YES
YES
NO
YES

0.042*(0.020)73,5720.9074
YES
YES
YES
YES
YES
YES
NO

0.066*(0.030)
9107
YES
YES
YES
YES
YES
NO
YES

Table 2. Robustness checks
Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***27

indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively. The independent
variables are the same as in Table A2, and the parameter estimates and standard errors for thecontrol variables and for scattered greenery more than 100 meters away are omitted due tospace limitations. The impact of scattered greenery more than 100 meters away is reported inAppendix Table A6.

We also check whether the amount of scattered greenery has nonlinear effects. Previous
studies have suggested that the amount of urban green space and real estate prices or life
satisfaction exhibits an inverted U-shaped relationship (Bertram and Rehdanz, 2015;
Siriwardena et al., 2016) because too much green space can result in negative impacts, such as
noise, soil dust, insect damage, etc. Alternatively, perhaps this nonlinear relationship occurs
because more green space is correlated with fewer other important amenities. Table 3 andAppendix Table A7 show the results using dummy variables created by dividing the scatteredgreenery variable into quintiles. Columns (1) and (2) show the results using sales properties,
and in contrast with previous studies, we find that sales prices are significantly higher,
especially in areas with more greenery. Scattered greenery, unlike parks and urban forests, isless likely to produce negative externalities, such as noise, or to exclude other amenities.
Therefore, too much scattered greenery is not expected to reduce real estate values.
Alternatively, it might simply be the case that the study area is a well-developed urban area.
Siriwardena et al. (2016) suggested that the value of surrounding green space reaches a28

maximum at approximately 40% and decreases thereafter, while the scattered greenery in ourstudy area is approximately 15% to 20%. Therefore, it is possible that the study area has not
yet reached the point of “too much” greenery.
Among properties for sale, a large amount of scattered greenery is highly valued, whichmight indicate that a green environment is considered desirable for long-term residence. In
contrast, Columns (3) and (4), which present the estimates for rental properties, show different
results. There is no consistent relationship between the amount of scattered greenery and themagnitude of the impact using either the 2008 or the 2013 green coverage data. This finding
might occur because renters do not live in their properties for long periods, making a green
environment less important. To discuss the characteristics of sales and rentals that cause thisdifference, we next estimate subsamples defined by the price or location of the property.

29

Scattered greenery (0–100 m)
2nd quintile
3rd quintile
4th quintile
5th quintile
Property characteristics
Neighborhood characteristics
Accessibility characteristics
Measures of urban greenness
Street FE
Year FE
Month FE
Observations
Adjusted R-squared

Properties for sale2006-20102011-2015(1)(2)

Properties for rent
2006-20102011-2015(3)(4)-0.018(0.010)-0.005(0.010)0.013(0.011)0.034*(0.013)
YES
YES
YES
YES
YES
YES
YES
7,872
0.94240.008*(0.004)0.003(0.004)0.013**(0.005)0.011(0.006)
YES
YES
YES
YES
YES
YES
YES
43,1880.91750.015(0.011)0.013(0.012)0.018(0.012)0.052***(0.015)
YES
YES
YES
YES
YES
YES
YES
9,680
0.94530.003(0.004)0.0002(0.004)0.004(0.004)0.002(0.004)
YES
YES
YES
YES
YES
YES
YES
94,6630.9107

Table 3. Nonlinear effects of scattered greenery
Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively. The independent
variables are the same as in Table A2, and the parameter estimates and standard errors for thecontrol variables and scattered greenery more than 100 meters away are omitted due to space
limitations. The impact of scattered greenery more than 100 meters away is reported inAppendix Table A7.

4.3. Subsample analysis
Table 4 and Appendix Table A8 present the results of the subsample analysis. Columns (1)and (2) present the results of the estimation by dividing the sample into two parts: (1) greaterthan the median price or rent and (2) less than the median price or rent. For both sales and30

rentals, we see that the higher-priced properties are more strongly affected by the scatteredgreenery, and the differences in property prices are more noticeable in 2013. This finding is
consistent with related studies showing that people with higher incomes are more concerned
about environmental amenities (Fuerst and Shimizu, 2016; Łaszkiewicz et al., 2019).
Interestingly, while the analysis using the full sample showed that scattered greenery had agreater impact in 2008, the impact was greater in 2013 when the properties were divided byproperty price. This outcome come be due to increased residential sorting and segregation in2013, polarizing the population into two groups: wealthy residents who care about greeneryand poor residents who do not.
Columns (3) and (4) show the results of dividing the sample by the number of rooms, i.e.,
one room or at least two. We can see that properties with two or more rooms are affected byscattered greenery, but single-room properties are not significantly affected regardless of year
or whether the property is a rental or a sale. The interpretation could be similar to that for the
results in Columns (1) and (2), according to which higher-income people living in higherquality homes are more concerned about green amenities. Alternatively, it might be more
appropriate to say that students and single individuals living in one-room apartments tend tomove out after a few years and do not care about the residential environment.
Anderson and West (2006) suggested that open spaces and amenities are valued
heterogeneously depending on neighborhood characteristics. Scattered greenery can also be
31valued not only for its role in maintaining the landscape in residential areas but also for its role
in reducing exhaust emissions and noise along busy roads. To check this possibility, Columns(5) and (6) show the results of an estimation that uses subsamples divided by the median
distance to the highway. The results in Column (5) for properties far from the highway have apositive coefficient but almost no significance or very weak significance in each of the samples.
In contrast, in Column (6), which was estimated using properties close to the highway, for-saleproperties are very strongly positively affected by scattered greenery, while rental properties
are not significantly affected. This finding is counterintuitive to the results obtained from theprice and number of rooms subsamples since the more relatively inexpensive and lower quality
properties are located closer to the highway, which could be interpreted as an evaluation of thepollution and noise reduction benefits of scattered greenery rather than its visual benefits(landscaping and relaxation). In other words, different aspects of the same scattered greenery
are appreciated depending on where they are located. The finding that there is little
heterogeneity in the price of rental properties based on highway distance is consistent withprevious arguments that people who live in rental properties do not care much about their living
environment.
Columns (7) and (8) show the results from dividing the sample by the median linear
distance from the CBD, Tokyo Station. We can see that, among the properties for sale, scatteredgreenery has a significantly positive impact when the properties are far from the CBD, whereas32

it has no significant impact when they are close to the CBD. This outcome is the opposite ofwhat related studies (e.g., Votsis, 2017) have found, i.e., that green space is valued positively
in areas with higher population densities and less greenery. Our results could be driven by thearea around the CBD of Tokyo being too urbanized to be suitable for residence. The areas near
the CBD provide housing for those who prefer access to commercial areas over a residential
environment, while residential areas farther from the CBD might have a better environment.
As in the previous cases, the sales and rental markets behave differently, with rental properties
exhibiting less heterogeneous effects based on their distance from the CBD.
The subsample analysis suggests that scattered greenery is valued heterogeneously byproperty characteristics and location. We can see that residents of larger and pricier properties,
as well as those in locations more suitable for residence, value green amenities more highly.
Such heterogeneity in valuation has intensified over time, perhaps because the heterogeneityin people’s preferences and demands has also affected the supply side of the property market.
In other words, high-quality properties with large and plentiful rooms might be supplied inareas with large amounts of greenery, and conversely, small and low-quality properties could
be supplied in areas with little greenery. To address this concern, we next analyze the impactof scattered greenery on housing quality.

33

Housing prices or
rents
High
Low
(1)
(2)

# of rooms
High
(3)

Panel A: Sales (2008)
Scattered greenery (0–100 m)

Low
(4)

0.211** 0.177* 0.288*** 0.187(0.065) (0.084) (0.060) (0.127)
Avg. %-age of greenery within 500 18.2% 16.5%
18.1% 15.1%
Observations
3,936
3,936
5,828
2,044
Adjusted R-squared
0.8567 0.8835 0.9172 0.9291
Panel B: Sales (2013)
Scattered greenery (0–100 m)
0.297*** 0.030
0.183* 0.105(0.071) (0.113) (0.084) (0.144)
Avg. %-age of greenery within 500 14.9% 13.2%
14.8% 12.3%
Observations
4,840
4,840
6,663
3,017
Adjusted R-squared
0.8326 0.9013 0.9089 0.9332
Panel C: Rentals (2008)
Scattered greenery (0–100 m)
0.078 0.035
0.087 0.028(0.042) (0.024) (0.053) (0.026)
Avg. %-age of greenery within 500 15.9% 16.5%
17.5% 15.8%
Observations
21,364 21,824
9,778 33,410
Adjusted R-squared
0.8940 0.6012 0.9161 0.8751
Panel D: Rentals (2013)
Scattered greenery (0–100 m)
0.090** 0.003 0.118** -0.013(0.034) (0.028) (0.039) (0.026)
Avg. %-age of greenery within 500 13.1% 13.5%
14.2% 13.1%
Observations
46,776 47,887
19,341 75,322
Adjusted R-squared
0.8743 0.6574 0.9094 0.8804
Property characteristics
YES
YES
YES
YES
Neighborhood characteristics
YES
YES
YES
YES
Accessibility characteristics
YES
YES
YES
YES
Measures of urban greenness
YES
YES
YES
YES
Street FE
YES
YES
YES
YES
Year FE
YES
YES
YES
YES
Month FE
YES
YES
YES
YES

Distance fromhighway
High
Low
(5)
(6)

Distance from
CBD
High
Low
(7)
(8)

0.098 0.397*** 0.291*** 0.133(0.078) (0.082) (0.074) (0.087)
17.9% 16.8%
20.8% 13.9%
3,936
3,936
3,936
3,936
0.9444 0.9435 0.9387 0.9468
0.097 0.321**(0.122) (0.102)
14.5% 13.6%
4,837
4,843
0.9468 0.9455

0.278** 0.068(0.096) (0.122)
16.2% 11.8%
4,838
4,842
0.9453 0.9464

0.073*(0.033)
16.8%
21,5930.91060.055(0.036)19.3%
21,2630.92030.037(0.043)
15.6%
21,5950.92430.017 0.032(0.035) (0.033)
13.9% 12.7%
47,331 47,332
0.9063 0.9170
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES

0.039
(0.043)
13.1%
21,9250.91630.037 -0.024(0.027) (0.044)
15.5% 11.4%
44,425 50,238
0.9142 0.9079
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES

Table 4. Subsample analysis
Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively. The independent
variables are the same as those listed in Table A2, and the parameter estimates and standard
errors for the control variables and scattered greenery more than 100 meters away are omitted
due to space limitations. The impact of scattered greenery more than 100 meters away isreported in Appendix Table A8.

34

4.4. Residential environment and house quality
Table 5 shows the estimated results when variables measuring property quality are used as
the explained variable, instead of price or rent. Columns (1) and (2) use the number of rooms
and square footage as the explained variables, respectively, and indicate that the size of theproperty increases as the amount of scattered greenery within 100 meters increases. Unlike themain results for price and rent as the explained variables (shown in Table 1), in theseestimations, the results for both sales and rentals are highly significant. Thus, the value of
scattered greenery in the main analysis might be overestimated since larger, roomier homes
more suitable for habitation tend to be built in greener areas. Interestingly, however, scatteredgreenery increases the quality of both sales and rentals, but it increases prices only for sales
properties. In other words, scattered greenery on properties for sale is valued as a green amenity,
but it is not valued as an amenity on properties for rent. Additionally, among both sales andrentals, there is a stronger relationship between scattered greenery and housing quality in 2013
than in 2008. This finding suggests that environmental gentrification might be occurring.
In Column (3), the age of the building is the explained variable, and none of the results are
statistically significant. Thus, there is no relationship between scattered greenery and thenewness of buildings; qualities such as livability are important. Column (4) shows the resultsfrom estimations in which the number of floors in the building is the explained variable,
indicating that scattered greenery slightly increases the number of floors in the case of35

properties for sale. This outcome suggests that areas with more green amenities are in higher
demand as residential areas; thus, larger multiunit residential buildings are likely to be built.

Panel A: Sales (2008)
Scattered greenery (0–100 m)
Observations
Adjusted R-squared
Panel B: Sales (2013)
Scattered greenery (0–100 m)
Observations
Adjusted R-squared
Panel C: Rentals (2008)
Scattered greenery (0–100 m)
Observations
Adjusted R-squared
Panel D: Rentals (2013)
Scattered greenery (0–100 m)
Observations
Adjusted R-squared
Property characteristics
Neighborhood characteristics
Accessibility characteristics
Measures of urban greenness
Street FE
Year FE
Month FE(2)ln(Age ofproperty)(3)# of floors inthe building(4)

0.914***(0.264)7,872
0.22410.707***(0.163)7,872
0.31460.322(0.322)
7,872
0.2786

4.46**(1.72)
7,872
0.60091.34***(0.310)9,680
0.27280.935***(0.196)
9,680
0.34560.256(0.352)9,680
0.26804.29*(1.87)
9,680
0.63470.325*(0.137)43,1880.11680.415***(0.112)
43,1880.16970.385(0.260)43,1880.18170.525(0.601)
43,1880.49400.579***(0.143)
94,6630.1148
NO
YES
YES
YES
YES
YES
YES

0.496***(0.110)94,6630.1520
NO
YES
YES
YES
YES
YES
YES

0.128
(0.236)
94,6630.1909
NO
YES
YES
YES
YES
YES
YES

-0.323(0.602)
94,6630.4705
NO
YES
YES
YES
YES
YES
YES

# of roomsln(Sq. metres)(1)

Table 5. Effects of scattered greenery on property quality
Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively. The independent
variables are the same as those in Table A2 except for property characteristics, and the36parameter estimates and standard errors for the control variables and scattered greenery morethan 100 meters away are omitted due to space limitations. The impact of scattered greenery
more than 100 meters away is reported in Appendix Table A9.

5.

Discussion and policy implications
The findings of this study provide insights into how people value scattered urban greenery.

We showed that scattered greenery, such as street trees, significantly increases housing prices.
Because the workings of the real estate market reflect resident demand, which is relevant topolicy, findings from hedonic price analyses can be used to design policy. Policy makers andurban planners could benefit from increasing property values through a focus on increasing andimproving the scattered greenery in urban areas. Further positive impacts might also accrue
since higher urban property values induce private investment. Especially in urban areas suchas Tokyo, where it is difficult to convert land already in use into green space, it would be
beneficial to consider installing scattered greenery that does not require much space. The
property price increase from a 10% increase in scattered greenery within 100 meters of aresidential location is as high as 2% to 2.5%, likely justifying the cost of providing andmaintaining this scattered greenery.
Our results also suggest that scattered greenery is valued heterogeneously depending on its
location and users. Since properties along busy streets tend to have lower values due to poor
air quality and noise, scattered greenery that can reduce such environmental concerns is highly37valued. Therefore, the maintenance of street trees around roads could have a considerable
impact on housing prices. In contrast, scattered greenery is rarely appreciated in areas near the
CBD, where population density is high and greenery is scarce. Those who value their living
environments are unlikely to live in the CBD in the first place since people who prefer urban
convenience and access to commercial areas are more likely to reside there. Increasing
scattered greenery in such areas might not have the expected impact. However, of course, theeffects of scattered greenery, such as reducing air pollution and reducing the heat island effect,cannot be ignored. Such indirect effects should be investigated in the future using more detailed
geographical data.
Furthermore, because individuals with different characteristics differ in their appreciationof scattered greenery, the characteristics of residents must be considered to effectively increase
welfare through urban environmental policies. Failure to consider the heterogeneity in people's
preferences could lead to policies that disregard equity. Since the prices of properties for saleand rent respond quite differently, we must be careful when discussing not only scatteredgreenery but also other urban green spaces. People who live in rental properties and who thus
tend to move after short periods of time might not care about amenities that have long-term
effects, such as the surrounding green environment. Thus, in areas where there are many rental
properties or where resident turnover is high (e.g., areas with many students living alone),greenery could be undersupplied. There is also a concern that analyses using the hedonic
38

pricing approach for rental properties might underestimate environmental amenities.
Additionally, the results suggest that more expensive and larger properties are significantly
affected by scattered greenery, while less expensive and smaller properties are hardly affected
at all and, moreover, that the effects could become stronger over time. This finding suggeststhat landscape preservation, relaxation, and the other benefits of scattered greenery might bevalued only by high-income individuals, which is relevant to the argument that environmental
amenities have a luxury dimension (Fuerst and Shimizu, 2016; Łaszkiewicz et al., 2019).
Recent urban public policy research has focused on issues of unequal access to environmental
amenities and environmental gentrification, in which a quality environment attracts wealthypeople, increases land prices, and causes the displacement of the original residents (Melstrom
and Mohammadi, 2022; Schaeffer et al., 2016). In urban areas, people face a trade-off between
the negative effects of noise or pollution and the positive effects of access to a variety of otheramenities, such as commercial facilities and cultural assets. The wealthy can counteract thenegative aspects of urban life by living in the greenest areas of the city, but poorer people might
not have such an option. Urban greening strategies, while successful from the perspective ofwealthy individuals and corporations, could eventually exclude socially vulnerable groups.
Previous studies have found that the distribution of urban green space often provides unevenbenefits to wealthier (or white nonimmigrant) communities (Wolch et al., 2014).
Because of the price premium charged for high-quality neighborhoods, only people who
39can afford to pay the additional costs of green space can live in those neighborhoods, while theless wealthy are excluded from neighborhood green space. Additionally, if higher-income
people show a preference for environmental goods, more luxurious new developments could
be built, land prices could escalate, and only higher-income people could enjoy comfortablegreen living, which might increase environmental injustice when high-income groups that
consume more and have a negative impact on the environment enjoy a good environment, andlow-income groups that are less involved in environmental degradation suffer. If such an
outcome is caused by the greening policies of cities under the guise of being “for theenvironment,” the problem is even more serious.
Suggestive evidence for these arguments is shown in Figure 3. We divide the dataset byquartiles of the amount of scattered greenery within a 500-meter radius of each property andplot the change over time in the number of contracted properties by price range within eachsubsample. The left side of the figure shows properties for sale, and the right side shows
properties for rent, with (1) and (5) indicating the properties with the most surrounding
greenery and (4) and (8) indicating the properties with the least surrounding greenery. For (1),the greenest properties for sale, the number of contracts was approximately the same in all priceranges in 2006, but the difference in the number of transactions by price range gradually
increased, with more than twice as many properties in the top 25% of prices being traded as
those in the bottom 25% of prices in 2015. The same trend applies to properties for sale in the40third quartile of green space in (2), with the number of contracts for more expensive properties
increasing over time. In contrast, there is little difference in property prices in the second
quartile of the amount of space green, as shown in (3). The properties with the least amount ofsurrounding greenery, shown in (4), have been relatively inexpensive since 2006, and this trend
is continuing. These results are consistent with the environmental gentrification argument that
better environments attract higher-income residents and drive out lower-income residents,
resulting in increasingly polarized neighborhoods and segregated settlements. Note also thatthe data are the number of contracts in each year, so the cumulative effect is even stronger.
Unlike for-sale properties, there is not much difference between the amount of greeneryand the number of transactions by price range for rental properties. It is worth noting, however,that the number of contracts for expensive rental properties surged around 2010 in areas withlittle greenery. This surge might have been due to the construction of luxury tower
condominiums for the wealthy, suggesting the existence of a different property market fromthat of properties for sale. Interestingly, even in the main results presented in Table 1, the impactof greenery was barely reflected in rental prices, indicating that, by living in a rental property,
one could enjoy the benefits of scattered greenery without paying a premium. It should be noted,
however, that rental properties tend to be unsuitable for family residence or long-termownership because of the small number of rooms and the lack of tax breaks and other incentives
available through mortgages.
41

In summary, urban planners must develop urban strategies that protect not only ecologicalsustainability but also social sustainability. The establishment of small, scattered green spaces,
rather than large urban green spaces where resources tend to be geographically concentrated,
could be one solution. Alternatively, complementary anti-gentrification strategies, such as theprovision of affordable housing, could be effective (Franco and Macdonald, 2018). Because
environmental policies such as urban greening are difficult to overrule, it is necessary toconsider who will receive the benefits of greening when designing cities. It is important toadopt an environmental equity perspective, for example, by considering whether green
amenities require implicit compensation or whether certain people are excluded from the green
amenities.

42

Figure 3. Number of properties traded by greenery and price tier quartile
Note: In each panel, the vertical axis represents the number of properties traded, and thehorizontal axis represents the year of the contract. The red circles, yellow triangles, green
squares, and blue diamonds correspond to the highest (0%-25%), upper middle (25%-50%),
lower middle (50%-75%), and lowest (75%-100%) housing price or rent quartiles, respectively.
43

6.

Conclusions
The value that urban green spaces provide to residents has attracted interest in a variety offields, not only economics. While many studies have analyzed the value of usable greenery ofcertain sizes, such as parks and urban forests, using a hedonic pricing approach, we
complement this literature by measuring the value of scattered greenery. The results of thisstudy contribute to the literature on the value of urban green space and further our
understanding of how these values vary by resident and location characteristics. Since large
resources are invested in policies that improve the urban environment, understanding the roleof amenities is important for improving the efficiency of public welfare.
Because this study focuses on a very developed urban area, the results should be
extrapolated with caution. Scattered greenery might not be valuable in areas with sufficient
overall levels of greenery; conversely, it might be more highly valued in areas where green
space is scarce. Therefore, our results could be applicable only in cities such as Tokyo. Similar
studies for other cities are a future task, for which the use of remote sensing to measurescattered greenery would be useful.
Analyzing the heterogeneity in individual-level preferences for scattered greenery is alimitation of this study, as well as an avenue for future work. Because this study uses a hedonic
pricing model with property data, only the average WTP for scattered greenery is revealed. It
44

is important to understand the heterogeneity in preferences at the individual level sinceindividuals with different demographics within a region require different policies. Data
including individual preferences, methods such as two-stage hedonic analysis (Panduro et al.,2018) or the LSA (Tsurumi et al., 2018) could be used to reveal preferences for scatteredgreenery. It would be a fruitful task for the future to determine which characteristics affectwhether individuals do or do not receive benefits from scattered greenery.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement
The data on green coverage can be provided upon request. The real estate data are restricted
and were used under license for this study. Other geographic data are publicly available andcan be freely obtained from official government websites.

45

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49

Figure A1. Comparison of aerial photography and NDVI green coverage areas aroundresidential areas

Note: The aerial imagery is based on open data created in 2016, published by Setagaya Ward.

50

Figure A2. Comparison of aerial photography and NDVI green coverage areas aroundthe park, including sports fields

Note: The aerial imagery is based on open data created in 2016, published by Setagaya Ward.

51

Table A3. Summary statistics
Mean

Property characteristics
Property price/rent (10,000 JPY)
3704.351# of rooms
2.099
Square meters59.858Age of property (in years)
18.573# of floors in the building
7.075
Floor where located
3.879
Neighborhood characteristics
Population (1000/m2)
13.474# of households (1000)
7.330% under 19 years old
0.133% over 65 years old
0.184# of properties for sale
70.134
Avg. property price (10,000 JPY)
3392.695
Avg. # of floors in for-sale properties
6.136# of properties for rent
544.688
Avg. property rent (10,000 JPY)
9.916
Avg. # of floors in for-rent properties
3.470
Accessibility characteristics
Distance to a hospital
147.578
Distance to a school
299.645
Distance to police
359.892
Distance to a fire station
782.562
Distance to a post office311.196
Distance to a park
186.010Distance to a museum or gallery
1236.599
Distance to a library
599.255
Distance to a playground949.538
Distance to a budojo
1306.542
Distance to a pool
1387.742
Distance to city hall
2926.297
Distance to a station
491.132
Distance to a bus stop
159.710Distance to a major road
314.393
Distance to the highway
1434.394
Distance to Tokyo Station
13217.977
Distance to the Tama River
5650.367
Surrounding green spaces (2008)
Farm or vacant land (0–100 m)
0.003
Farm or vacant land (100–200 m)
0.003
Farm or vacant land (200–300 m)
0.003
Farm or vacant land (300–400 m)
0.003
Farm or vacant land (400–500 m)
0.003
Waterfront (0–100 m)
0.001
Waterfront (100–200 m)
0.002
Waterfront (200–300 m)
0.003
Waterfront (300–400 m)
0.004
Waterfront (400–500 m)
0.005
Park or public space (0–100 m)
0.007
Park or public space (100–200 m)
0.014
Park or public space (200–300 m)
0.018
Park or public space (300–400 m)
0.020
Park or public space (400–500 m)
0.021
Scattered greenery (0–100 m)
0.160
Scattered greenery (100–200 m)
0.169
Scattered greenery (200–300 m)
0.172
Scattered greenery (300–400 m)
0.174
Scattered greenery (400–500 m)
0.177

Properties for saleStd. Dev.
Min.

Max.

Mean

Properties for rent
Std. Dev.
Min.

Max.

2091.835
0.852
24.66412.0344.081
2.956

230.700
1.000
8.710
0.000
1.000
-2.00025017.755
7.000
235.580
83.38442.00036.0009.410
1.256
30.42617.997
3.719
2.355

4.445
0.539
19.58011.243
2.494
1.649

1.000
1.000
2.510
0.000
1.000
-2.000187.500
22.0002624.000
88.04785.00032.000

3.174
2.410
0.033
0.018
31.449919.0351.200
265.199
1.631
0.612

4.381
1.826
0.067
0.122
5.000
1677.500
3.000
32.0007.083
2.100

21.10813.4600.237
0.285
190.000
8343.885
9.982
1326.000
21.4586.897

14.039
7.797
0.125
0.187
66.0433248.818
6.036
624.074
9.581
3.470

2.896
2.234
0.028
0.016
30.586846.476
1.163
246.918
1.413
0.612

4.214
1.726
0.067
0.122
1.000
0.000
0.000
23.000
6.984
2.100

21.10813.4600.237
0.309
194.000
8433.533
10.2731397.000
25.2076.897

96.753141.779
188.376401.598
154.028103.974667.501
269.295
478.935
597.711647.7861603.595
310.780109.229308.9211080.814
2577.661
3018.419

0.000
647.2998.165
909.8768.626
1125.649
12.190 2059.778
6.918
985.7704.474
655.871
25.658 3182.323
3.628
1683.342
36.301 2751.802
32.621 3099.050
98.793 3117.698
55.909 10695.031
0.702
1882.441
8.955
740.180
0.028
1632.718
11.479 4694.255
8106.727 18701.929
109.113 11736.019

139.153295.600
354.552768.846306.658
206.085
1238.781
583.5071028.072
1340.025
1450.926
2609.357
432.231180.040333.9171538.345
12734.248
6216.442

89.069133.067
172.349361.935140.326110.914679.883259.381525.480594.595
627.0671548.949
265.306
124.704
288.799
1146.391
2440.231
2928.325

0.000
716.7906.192
909.8762.351
1147.036
4.973
2133.190
1.616
1022.223
1.371
706.357
17.610 3187.883
3.628
1738.656
10.595 2789.267
18.768 3109.548
11.543 3095.457
39.596 10726.757
0.591
1987.998
2.005
804.535
0.006
1703.577
5.854
4694.255
8091.710 19031.638
104.893 11768.117

0.021
0.018
0.014
0.010
0.009
0.013
0.020
0.024
0.027
0.031
0.027
0.036
0.038
0.035
0.035
0.092
0.072
0.063
0.059
0.057

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.023
0.041
0.043
0.056

0.001
0.002
0.002
0.002
0.002
0.000
0.001
0.001
0.001
0.002
0.005
0.010
0.015
0.017
0.018
0.144
0.157
0.160
0.164
0.166

0.011
0.011
0.011
0.010
0.008
0.008
0.012
0.015
0.017
0.018
0.026
0.033
0.033
0.032
0.031
0.076
0.063
0.058
0.055
0.052

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.022
0.042
0.038
0.053

0.328
0.259
0.174
0.122
0.118
0.429
0.444
0.417
0.431
0.333
0.467
0.394
0.419
0.480
0.348
0.595
0.490
0.428
0.454
0.412

0.587
0.229
0.212
0.156
0.125
0.440
0.482
0.486
0.445
0.380
0.685
0.467
0.442
0.482
0.390
0.727
0.531
0.462
0.421
0.428

(continued on next page)52

(continued)
Surrounding green spaces (2013)
Farm or vacant land (0–100 m)
Farm or vacant land (100–200 m)
Farm or vacant land (200–300 m)
Farm or vacant land (300–400 m)
Farm or vacant land (400–500 m)
Waterfront (0–100 m)
Waterfront (100–200 m)
Waterfront (200–300 m)
Waterfront (300–400 m)
Waterfront (400–500 m)
Park or public space (0–100 m)
Park or public space (100–200 m)
Park or public space (200–300 m)
Park or public space (300–400 m)
Park or public space (400–500 m)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)# of layout types# of structure types# of zoning types# of streets# of properties

0.002
0.002
0.003
0.003
0.002
0.001
0.003
0.004
0.005
0.007
0.007
0.014
0.018
0.019
0.021
0.130
0.137
0.139
0.141
0.142

0.014
0.012
0.010
0.008
0.007
0.013
0.020
0.025
0.028
0.034
0.027
0.035
0.035
0.033
0.033
0.073
0.056
0.047
0.044
0.041

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.004
0.023
0.030
0.037
0.044

9
9
10
506
17,552

0.233
0.137
0.118
0.098
0.068
0.414
0.459
0.412
0.459
0.358
0.450
0.338
0.364
0.376
0.311
0.492
0.397
0.333
0.315
0.325

0.001
0.002
0.002
0.002
0.002
0.001
0.001
0.002
0.002
0.003
0.006
0.010
0.015
0.016
0.018
0.119
0.129
0.131
0.134
0.135

0.011
0.009
0.009
0.007
0.007
0.010
0.012
0.015
0.017
0.020
0.026
0.030
0.030
0.029
0.029
0.060
0.048
0.043
0.039
0.038

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.013
0.030
0.035
0.041

9
9
10
545
137,851

0.394
0.163
0.130
0.102
0.116
0.415
0.470
0.473
0.461
0.393
0.602
0.440
0.381
0.380
0.375
0.510
0.398
0.391
0.382
0.352

Note: Layout refers to the room layout as defined by the numbers of living rooms, dining rooms,
and kitchens. Structure refers to the construction materials used, such as wood or reinforced
concrete. Zoning refers to the land use zone, i.e., commercial or industrial zones, as defined by
the City Planning Act; zoning regulates the types and sizes of buildings that can be built.

53

Table A4. Effects of scattered greenery on property prices and rents (full results)
Properties for sale
2008
2013
(1)
(2)
Property characteristics# of roomsln(Sq. meters)ln(Age of property)# of floors in the building
Floor where locatedNeighborhood characteristics
Population (per 1000)# of households (per 1000)% of population under 19% of population over 65
ln(# of properties for sale)ln(Avg. property price)
Avg. # of floors in for-sale properties
ln(# of properties for rent)ln(Avg. property rent)
Avg. # of floors in for-rent properties
Accessibility characteristicsln(Distance to a hospital)ln(Distance to a school)ln(Distance to police)ln(Distance to a fire station)ln(Distance to a post office)ln(Distance to a park)ln(Distance to a museum or gallery)ln(Distance to a library)ln(Distance to a playground)ln(Distance to a budojo)ln(Distance to a pool)ln(Distance to a city hall)ln(Distance to a station)ln(Distance to a bus stop)

Properties for rent
2008
2013
(3)
(4)

0.015***(0.004)
1.02***(0.015)-0.278***(0.005)-0.003*(0.001)0.012***(0.001)0.010*(0.004)1.01***(0.013)-0.309***(0.007)0.0001(0.001)0.010***(0.0010)0.043***(0.004)0.608***(0.010)-0.086***(0.002)0.003***(0.0009)
0.011***(0.0007)0.048***(0.004)0.603***(0.011)-0.096***(0.001)0.006***(0.0008)0.011***(0.0007)

0.010(0.024)-0.018(0.040)-2.31*(1.13)
0.355(0.703)0.016(0.038)0.003(0.075)-0.029(0.023)0.020(0.026)
0.093*(0.043)0.013(0.008)0.006(0.026)-0.019(0.043)-3.93**(1.20)-0.677(0.626)-0.011(0.041)0.267***(0.074)-0.059*(0.025)0.063*(0.026)0.102*(0.044)0.024*(0.010)-0.002(0.010)0.004(0.017)-0.997(0.515)-0.310(0.242)0.010(0.018)0.010(0.039)0.005(0.010)-0.010(0.009)0.016*(0.008)-0.0005(0.004)-0.0007(0.008)-0.002(0.013)-1.36***(0.396)-0.456(0.246)0.017(0.014)0.023(0.030)0.009(0.010)-0.006(0.008)0.007(0.005)0.005(0.003)-0.001(0.005)-0.002(0.007)0.007(0.007)-0.012(0.009)0.002(0.006)-0.006(0.005)-0.015(0.015)-0.0008(0.010)-0.042*(0.019)0.024(0.019)0.018(0.017)0.022(0.022)-0.029***(0.007)-0.004(0.005)-0.0007(0.005)0.002(0.008)0.011(0.008)-0.013(0.011)
0.003(0.007)0.0003(0.006)-0.021(0.016)-0.007(0.011)-0.053**(0.020)0.012(0.021)
0.008(0.020)-0.003(0.028)-0.025**(0.009)0.001(0.005)-0.0008(0.002)-0.005(0.003)-0.0001(0.003)-0.001(0.005)0.002(0.003)0.003(0.002)-0.009(0.007)0.002(0.004)0.003(0.008)-0.007(0.009)-0.015*(0.007)-0.007(0.008)-0.008**(0.003)0.0005(0.003)-0.003(0.002)-0.002(0.003)-0.0007(0.002)0.005(0.003)0.003(0.002)0.0004(0.002)-0.002(0.006)0.001(0.003)0.002(0.007)0.005(0.008)-0.007(0.008)-0.005(0.008)-0.005(0.003)0.004*(0.002)(continued on next page)54

(continued)ln(Distance to a major road)ln(Distance to a highway)ln(Distance to Tokyo Station)
Ln(Distance to the Tama River)% Surrounding greenness
Farm or vacant land (0–100 m)
Farm or vacant land (100–200 m)
Farm or vacant land (200–300 m)
Farm or vacant land (300–400 m)
Farm or vacant land (400–500 m)
Waterfront (0–100 m)
Waterfront (100–200 m)
Waterfront (200–300 m)
Waterfront (300–400 m)
Waterfront (400–500 m)
Park or public space (0–100 m)
Park or public space (100–200 m)
Park or public space (200–300 m)
Park or public space (300–400 m)
Park or public space (400–500 m)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Layout, structure, and zoning dummies
Street FE
Year FE
Month FE
Observations
Adjusted R-squared

0.017***(0.004)0.012(0.007)-0.503**(0.183)-0.100(0.058)0.022***(0.005)0.025**(0.009)-0.390*(0.181)
0.012(0.066)0.003(0.002)0.002(0.003)-0.149(0.088)0.002(0.029)0.004**(0.001)
0.005(0.003)-0.130(0.076)-0.002(0.021)0.170(0.202)0.441(0.252)-0.148(0.389)-0.333(0.321)
0.702(0.385)0.114(0.224)-0.309(0.267)-0.327(0.208)0.057(0.206)0.020(0.296)-0.056(0.137)-0.028(0.140)-0.007(0.184)-0.088(0.190)0.167(0.206)0.251***(0.057)-0.137(0.093)-0.096(0.116)-0.270*(0.136)-0.085(0.152)
YES
YES
YES
YES
7,872
0.94240.342(0.312)0.184(0.268)-0.098(0.448)-0.618(0.545)0.341(0.637)-0.329(0.303)0.188(0.292)0.073(0.221)-0.697***(0.162)0.384(0.255)-0.112(0.148)0.094(0.160)-0.324(0.214)-0.105(0.235)-0.137(0.240)0.204**(0.076)-0.092(0.118)-0.159(0.140)-0.189(0.156)-0.324(0.178)
YES
YES
YES
YES
9,680
0.94520.181(0.141)-0.359*(0.154)0.208(0.203)-0.108(0.150)-0.293(0.180)0.549*(0.219)-0.213(0.136)0.242(0.210)-0.310(0.231)0.631(0.350)0.009(0.062)0.081(0.084)-0.087(0.088)0.002(0.086)-0.034(0.082)0.055*(0.027)0.019(0.043)0.038(0.056)-0.054(0.060)-0.023(0.080)
YES
YES
YES
YES
43,1880.9174-0.060(0.069)0.187(0.123)0.171(0.180)-0.121(0.188)0.121(0.206)0.049(0.124)0.063(0.138)-0.037(0.107)-0.122(0.163)0.241(0.296)-0.014(0.050)0.069(0.058)-0.075(0.069)-0.019(0.079)-0.069(0.065)0.016(0.024)0.005(0.043)0.044(0.046)0.038(0.056)-0.111(0.066)
YES
YES
YES
YES
94,6630.9106

Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively.

55

Table A5. Number of households by years living in the current house (within 10
kilometers of CBD)
Owned houses
Years of residence
0–2 years
3–7 years
8–12 years
13–17 years
18–22 years
23–27 years
28–37 years
38–47 years
Over 48 years
Total

Rented houses# of households

Ratio# of households

Ratio

47,30093,60073,20073,70056,00025,70054,40045,900104,600

8.23%
16.30%
12.74%
12.83%
9.75%
4.47%
9.47%
7.99%
18.21%

201,200
145,400
50,80026,50022,40012,600
16,10014,8008,10040.41%
29.20%
10.20%
5.32%
4.50%
2.53%
3.23%
2.97%
1.63%

574,400

497,900

Note: Created using data from the 2018 Housing and Land Survey (Statistics Bureau, Ministryof Internal Affairs and Communications). The central point of the CBD is the former Tokyo
Metropolitan Government Building (now the Tokyo International Forum) in Chiyoda-ku,
Tokyo.

56

Table A6. Robustness checks
Alternative green spacemeasures

Panel A: Sales (2008)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Observations
Adjusted R-squared
Panel B: Sales (2013)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Observations
Adjusted R-squared
Panel C: Rentals (2008)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Observations
Adjusted R-squared
Panel D: Rentals (2013)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Observations
Adjusted R-squared
Property characteristics
Neighborhood characteristics
Accessibility characteristics
Measures of urban greenness
Street FE
Year FE
Month FE

Trimmed Samples(2)

Remove top
and bottom
5%(3)

Excludelarge-scale
properties(4)

0.023**(0.007)-0.009(0.014)-0.012(0.017)-0.057**(0.022)0.002(0.025)
7,872
0.94220.041***(0.009)-0.023*(0.010)0.011(0.010)-0.006(0.011)-0.004(0.010)7,872
0.94230.218***(0.058)-0.088(0.088)-0.065(0.116)-0.286*(0.137)-0.127(0.150)7,084
0.92440.014(0.008)-0.009(0.015)-0.022(0.019)-0.040(0.023)-0.047(0.026)9,680
0.94510.024**(0.008)0.0005(0.010)-0.001(0.010)0.007(0.010)-0.014(0.009)9,680
0.94510.009**(0.003)0.002(0.006)0.007(0.009)-0.008(0.009)-0.006(0.013)
43,1880.91740.003(0.003)0.003(0.006)0.006(0.006)0.006(0.007)-0.015(0.009)94,6630.9106
YES
YES
YES
YES
YES
YES
YES

Naturallogarithm

Top 25%dummy(1)Spatialdependence(SEM)

Only single
year

Only uniquebuildings

(5)

(6)

(7)

0.210***(0.062)-0.106(0.097)-0.089(0.123)-0.224(0.148)-0.123(0.157)
6,245
0.94430.288*(0.128)-0.234(0.211)0.419(0.300)-0.772*(0.301)0.024(0.305)1,473
0.94520.233***(0.058)-0.143(0.097)-0.093(0.122)-0.245(0.142)-0.044(0.152)7,586
0.94000.287**(0.099)-0.232(0.148)0.451*(0.217)-0.748**(0.245)0.103(0.269)1128

0.151*(0.077)-0.059(0.120)-0.220(0.139)-0.136(0.147)-0.345*(0.171)
8,712
0.92570.175*(0.078)-0.035(0.125)-0.198(0.144)-0.109(0.152)-0.336(0.190)7,601
0.94760.203(0.124)0.139(0.189)0.067(0.223)-0.013(0.264)-0.408(0.325)2,146
0.95120.220**(0.078)-0.075(0.114)-0.098(0.142)-0.064(0.153)-0.341(0.180)9,356
0.94040.231*(0.104)-0.008(0.167)0.123(0.210)-0.048(0.241)0.411(0.273)1529

0.0004(0.004)0.0007(0.004)0.006(0.005)-0.004(0.005)-0.001(0.006)43,1880.91740.024(0.020)-0.013(0.033)-0.010(0.042)0.004(0.048)-0.008(0.053)39,0390.9017

0.056*(0.028)0.026(0.043)0.048(0.056)-0.077(0.059)-0.027(0.082)
41,2630.91570.042(0.052)-0.018(0.077)0.082(0.103)-0.121(0.111)-0.035(0.150)6,814
0.91730.078***(0.022)0.026(0.036)0.043(0.041)-0.021(0.050)0.042(0.053)35,3610.90930.079*(0.039)-0.050(0.063)0.053(0.082)-0.119(0.092)0.006(0.103)3961

0.001(0.003)0.0005(0.003)0.004(0.003)0.004(0.003)-0.002(0.004)
94,6630.9106
YES
YES
YES
YES
YES
YES
YES

-0.019(0.024)-0.027(0.039)0.034(0.043)-0.010(0.053)-0.130*(0.058)85,3290.8885
YES
YES
YES
YES
YES
YES
YES

0.019
(0.025)-0.002(0.042)0.050(0.046)0.027(0.057)-0.123(0.068)90,7680.9085
YES
YES
YES
YES
YES
YES
YES

0.068
(0.035)0.071(0.064)0.064(0.069)-0.086(0.089)-0.013(0.096)
19,2660.9110
YES
YES
YES
YES
YES
NO
YES

0.042*(0.020)0.004(0.031)
0.031(0.039)0.004(0.046)-0.045(0.053)73,5720.9074
YES
YES
YES
YES
YES
YES
NO

0.066*(0.030)0.034(0.048)0.048(0.059)-0.119(0.070)-0.045(0.079)
9107

57

YES
YES
YES
YES
YES
NO
YES

Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively. The independent
variables are the same as those listed in Table A4. The parameter estimates and standard errors
for the control variables, which are omitted due to space limitations, are consistent with themain results.

58

Table A7. Nonlinear effects of scattered greenery

Scattered greenery (0–100 m)
2nd quintile
3rd quintile
4th quintile
5th quintile
Scattered greenery (100–200 m)
2nd quintile
3rd quintile
4th quintile
5th quintile
Scattered greenery (200–300 m)
2nd quintile
3rd quintile
4th quintile
5th quintile
Scattered greenery (300–400 m)
2nd quintile
3rd quintile
4th quintile
5th quintile
Scattered greenery (400–500 m)
2nd quintile
3rd quintile
4th quintile
5th quintile
Property characteristics
Neighborhood characteristics
Accessibility characteristics
Measures of urban greenness
Street FE
Year FE
Month FE
Observations
Adjusted R-squared

Properties for sale2006-20102011-2015(1)(2)

Properties for rent
2006-20102011-2015(3)(4)-0.018(0.010)-0.005(0.010)0.013(0.011)0.034*(0.013)0.015(0.011)0.013(0.012)0.018(0.012)0.052***(0.015)0.008*(0.004)0.003(0.004)0.013**(0.005)0.011(0.006)0.003(0.004)0.0002(0.004)0.004(0.004)0.002(0.004)0.016(0.011)0.006(0.013)0.004(0.015)-0.020(0.017)-0.009(0.013)-0.005(0.014)-0.015(0.016)-0.010(0.018)-0.006(0.004)-0.004(0.005)-0.002(0.006)-0.0001(0.007)-0.0003(0.004)0.003(0.005)0.003(0.006)0.003(0.006)0.005(0.011)-0.008(0.014)-0.011(0.015)-0.009(0.018)0.007(0.013)-0.014(0.014)-0.004(0.016)-0.001(0.018)0.006(0.005)0.003(0.006)0.011(0.006)0.012(0.008)0.002(0.004)0.002(0.005)0.005(0.006)0.006(0.006)-0.005(0.013)-0.019(0.014)-0.030(0.016)-0.029(0.019)-0.013(0.011)-0.015(0.014)-0.021(0.015)-0.019(0.017)-0.003(0.005)-0.007(0.006)-0.010(0.006)-0.013(0.008)-0.007(0.005)-0.0005(0.005)0.003(0.006)0.001(0.006)-0.003(0.013)0.005(0.015)-0.0001(0.018)0.0006(0.021)
YES
YES
YES
YES
YES
YES
YES
7,872
0.94240.010(0.013)0.007(0.016)0.004(0.017)-0.008(0.019)
YES
YES
YES
YES
YES
YES
YES
9,680
0.94530.001(0.006)-0.0008(0.007)0.0005(0.008)-0.0004(0.011)
YES
YES
YES
YES
YES
YES
YES
43,1880.9175-0.002(0.004)-0.005(0.005)-0.004(0.005)-0.007(0.007)
YES
YES
YES
YES
YES
YES
YES
94,6630.9107

Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively. The independent59

variables are the same as those listed in Table A4. The parameter estimates and standard errors
for the control variables, which are omitted due to space limitations, are consistent with themain results.

60

Table A8. Subsample analysis

Panel A: Sales (2008)
Scattered greenery (0–100 m)

Housing prices or
rents
High
Low
(1)
(2)

0.211** 0.177*(0.065) (0.084)
Scattered greenery (100–200 m)-0.041 -0.240(0.088) (0.146)
Scattered greenery (200–300 m)
0.003 -0.239(0.125) (0.168)
Scattered greenery (300–400 m)-0.093 -0.489*(0.157) (0.217)
Scattered greenery (400–500 m)-0.025 0.154(0.166) (0.195)
Avg. %-age of greenery within 500 m 18.2% 16.5%
Observations
3,936
3,936
Adjusted R-squared
0.8567 0.8835
Panel B: Sales (2013)
Scattered greenery (0–100 m)
0.297*** 0.030(0.071) (0.113)
Scattered greenery (100–200 m)-0.149 -0.147(0.108) (0.152)
Scattered greenery (200–300 m)-0.103 -0.298(0.145) (0.190)
Scattered greenery (300–400 m)
0.203 -0.512*(0.180) (0.244)
Scattered greenery (400–500 m)-0.092 -0.539*(0.189) (0.249)
Avg. %-age of greenery within 500 m 14.9% 13.2%
Observations
4,840
4,840
Adjusted R-squared
0.8326 0.9013
Panel C: Rentals (2008)
Scattered greenery (0–100 m)
0.078
0.035
(0.042) (0.024)
Scattered greenery (100–200 m)
0.054 -0.011(0.070) (0.038)
Scattered greenery (200–300 m)
0.048
0.081
(0.082) (0.048)
Scattered greenery (300–400 m)-0.062 -0.010(0.090) (0.061)
Scattered greenery (400–500 m)
0.015
0.035
(0.124) (0.070)
Avg. %-age of greenery within 500 m 15.9% 16.5%
Observations
21,364 21,824
Adjusted R-squared
0.8940 0.6012
Panel D: Rentals (2013)
Scattered greenery (0–100 m)
0.090** 0.003(0.034) (0.028)
Scattered greenery (100–200 m)
0.012
0.006
(0.065) (0.040)
Scattered greenery (200–300 m)
0.023
0.043
(0.073) (0.053)
Scattered greenery (300–400 m)
0.062
0.002
(0.078) (0.059)
Scattered greenery (400–500 m)-0.152 -0.130*(0.082) (0.065)
Avg. %-age of greenery within 500 m 13.1% 13.5%
Observations
46,776 47,887
Adjusted R-squared
0.8743 0.6574
Property characteristics
YES
YES
YES
Neighborhood characteristics
YES
Accessibility characteristics
YES
YES
Measures of urban greenness
YES
YES
Street FE
YES
YESYear FE
YES
YESMonth FE
YES
YES

# of rooms
High
(3)

Low
(4)

Distance fromhighway
High
Low
(5)
(6)

Distance from
CBD
High
Low
(7)
(8)

0.288*** 0.187(0.060) (0.127)-0.118 -0.100(0.099) (0.210)-0.189 0.179(0.132) (0.244)-0.136 -0.461(0.140) (0.291)-0.140 -0.030(0.159) (0.271)
18.1% 15.1%
5,828
2,044
0.9172 0.9291

0.098 0.397*** 0.291*** 0.133(0.078) (0.082)(0.074) (0.087)-0.309* -0.023-0.131 -0.127(0.126) (0.143)(0.131) (0.148)-0.099 -0.101-0.143 0.016(0.166) (0.172)(0.154) (0.164)-0.208 -0.448*
0.172 -0.923***(0.202) (0.193)(0.180) (0.204)-0.021 -0.141-0.174 -0.106(0.209) (0.229)(0.211) (0.201)
17.9% 16.8%
20.8% 13.9%
3,936
3,936
3,936
3,936
0.9444 0.9435
0.9387 0.9468

0.183*(0.084)-0.013(0.127)-0.181(0.154)0.018(0.164)-0.122(0.193)14.8%
6,663
0.90890.105(0.144)-0.230(0.213)0.022(0.275)-0.620(0.333)-0.763*(0.385)12.3%
3,017
0.93320.097(0.122)-0.156(0.177)-0.166(0.206)-0.398(0.220)-0.268(0.263)14.5%
4,837
0.94680.321**(0.102)-0.015(0.162)-0.198(0.195)-0.054(0.234)-0.405(0.241)
13.6%
4,843
0.94550.278**(0.096)-0.113(0.144)-0.271(0.178)0.023(0.195)-0.268(0.220)16.2%
4,838
0.94530.068(0.122)0.087(0.193)0.064(0.218)-0.552*(0.257)-0.210(0.315)11.8%
4,842
0.94640.087(0.053)0.162(0.087)0.128(0.103)-0.020(0.125)0.133(0.155)17.5%
9,778
0.91610.028(0.026)-0.032(0.040)-0.002(0.050)-0.032(0.056)-0.051(0.064)15.8%
33,4100.87510.073*(0.033)0.002(0.051)
0.003(0.066)0.057(0.080)0.002(0.084)
16.8%
21,5930.91060.037(0.043)0.022(0.068)0.097(0.089)-0.161(0.083)-0.099(0.132)
15.6%
21,5950.92430.055(0.036)0.051(0.055)0.080(0.072)-0.048(0.084)0.088(0.105)
19.3%
21,2630.92030.039(0.043)0.002(0.064)-0.074(0.083)-0.011(0.090)-0.191(0.119)13.1%
21,9250.91630.118**(0.039)0.002(0.065)0.015(0.085)0.140(0.092)-0.103(0.104)14.2%
19,3410.9094
YES
YES
YES
YES
YES
YES
YES

-0.013(0.026)0.011(0.047)0.034(0.053)0.030(0.063)-0.072(0.071)13.1%
75,3220.8804
YES
YES
YES
YES
YES
YES
YES

0.017
(0.035)-0.015(0.054)0.053(0.062)0.226**(0.072)-0.064(0.084)13.9%
47,3310.9063
YES
YES
YES
YES
YES
YES
YES

0.032
(0.033)0.048(0.062)0.011(0.075)-0.179(0.092)-0.257*(0.106)
12.7%
47,3320.9170
YES
YES
YES
YES
YES
YES
YES

0.037
(0.027)-0.018(0.047)0.105(0.054)0.063(0.065)-0.050(0.082)15.5%
44,4250.9142
YES
YES
YES
YES
YES
YES
YES

-0.024(0.044)0.041(0.075)-0.104(0.084)0.044(0.095)-0.303**(0.113)
11.4%
50,2380.9079
YES
YES
YES
YES
YES
YES
YES

61

Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively. The independent
variables are the same as those listed in Table A4. the parameter estimates and standard errors
for the control variables are omitted due to space limitations.

62

Table A9. Effects of scattered greenery on property quality

Panel A: Sales (2008)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Observations
Adjusted R-squared
Panel B: Sales (2013)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Observations
Adjusted R-squared
Panel C: Rentals (2008)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Observations
Adjusted R-squared
Panel D: Rentals (2013)
Scattered greenery (0–100 m)
Scattered greenery (100–200 m)
Scattered greenery (200–300 m)
Scattered greenery (300–400 m)
Scattered greenery (400–500 m)
Observations
Adjusted R-squared
Property characteristics
Neighborhood characteristics
Accessibility characteristics
Measures of urban greenness
Street FE
Year FE
Month FE(2)ln(Age ofproperty)(3)# of floors in thebuilding(4)0.914***(0.264)0.056(0.399)0.476(0.499)0.639(0.635)0.108(0.735)7,872
0.22410.707***(0.163)0.261(0.251)
0.094(0.313)0.187(0.407)0.003(0.448)7,872
0.31460.322(0.322)-1.11*(0.492)-0.604(0.607)-1.21(0.764)-1.46(0.837)
7,872
0.2786

4.46**(1.72)
1.09(1.78)-1.15(2.54)-6.19(3.60)-5.44(3.39)
7,872
0.60091.34***(0.310)-0.109(0.481)1.08(0.629)-0.213(0.699)0.310(0.792)9,680
0.27280.935***(0.196)-0.153(0.305)0.092(0.395)-0.687(0.485)-0.698(0.536)
9,680
0.34560.256(0.352)-0.254(0.494)-0.057(0.758)-1.15(0.831)1.97*(0.940)9,680
0.26804.29*(1.87)
3.82(2.12)-1.01(3.05)-5.00(3.92)
4.43(5.67)
9,680
0.63470.325*(0.137)0.023(0.200)0.118(0.247)-0.329(0.310)0.788*(0.375)43,1880.11680.415***(0.112)0.080(0.172)-0.056(0.198)0.047(0.257)0.079(0.334)
43,1880.16970.385(0.260)-0.345(0.390)0.751(0.468)-0.638(0.600)0.658(0.603)
43,1880.18170.525(0.601)0.934(0.917)-1.22(1.24)-1.09(1.54)-1.59(1.35)
43,1880.49400.579***(0.143)-0.130(0.200)-0.070(0.246)-0.474(0.278)0.501(0.324)
94,6630.1148
NO
YES
YES
YES
YES
YES
YES

0.496***(0.110)0.084(0.173)-0.001(0.215)-0.161(0.245)0.449(0.310)94,6630.1520
NO
YES
YES
YES
YES
YES
YES

0.128
(0.236)-0.109(0.349)0.153(0.446)0.282(0.487)-0.753(0.594)
94,6630.1909
NO
YES
YES
YES
YES
YES
YES

-0.323(0.602)0.252(0.953)0.452(1.23)-0.169(1.55)-2.89(1.83)
94,6630.4705
NO
YES
YES
YES
YES
YES
YES

# of roomsln(Sq. metres)(1)Note: Robust standard errors clustered at the street level appear in parentheses. *, **, and ***indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively. The independent
variables are the same as those listed in Table A4 except for the property characteristics. The
parameter estimates and standard errors for the control variables are omitted due to space63

limitations.

64