# RECORD 7059eb726206d6598f1ef88ae9a43a35a98c6937
# TITLE: The relationship of single-family detached house prices with neighborhood walkability and disaster risk
# YEAR: 2023.0  VENUE: PLoS ONE  DOI: 10.1371/journal.pone.0292657
# AUTHORS: Hongjik Kim; H. Baba; C. Shimizu; Kimihiro Hino
# PAGES: 23  CANDIDATE TABLE PAGES (1-based): [6, 7, 10, 11, 12]
# ABSTRACT: People’s preferences regarding their neighborhood environment can vary depending on their socioeconomic status and the cities where they live. This study aims to discern the relationship between neighborhood environment factors and single-family detached house sales by sale price and by central and noncentral cities. We analyzed sale prices in the Tokyo Metropolitan Area from 2015 to 2020. The neighborhood environment was assessed using flood/sediment risk and neighborhood walkability measured by net residential density, intersection density, and facility density (walking opportunity). Flood and sediment risk is a major concern that restricts the available land and is included as a negative aspect of the neighborhood environment, taking the topographic features into consideration. A comparison of the results showed that the preference for neighborhood walkability varies by socioeconomic status as well as by target cities. For most facility types, the number of walking opportunities within walking distance from houses was found to be positively related to the sale price of single-family detached houses in all quantiles. The relationship of house price with population and intersection density was found to vary depending on the price level, with a negative relationship with the sale price of relatively more expensive houses being exhibited. People who considered buying houses with relatively higher sale prices were found to devalue houses located in flood/sediment-hazardous areas more. However, it was also found that the negative relationship was slightly mitigated in the highest quantile of sale prices for houses in areas with a moderate flood risk (maximum flooding depth: 3–5 m). Plains near rivers with amenities offer high walkability but pose a flood risk, resulting in a trade-off between flood risk and neighborhood walkability. The findings suggest the use of indices representing diverse preferences in accordance with the target socioeconomic status when policymakers assess the neighborhood environment.


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RESEARCH ARTICLE
The relationship of single-family detached
house prices with neighborhood walkability
and disaster risk
Hongjik KimID1*, Hiroki BabaID2, Chihiro Shimizu2, Kimihiro HinoID1
1 Department of Urban Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo,
Japan, 2 Center for the Promotion of Social Data Science Education and Research, Hitotsubashi University,
Tokyo, Japan
* hongjik.kim@ua.t.u-tokyo.ac.jp
Abstract
People’s preferences regarding their neighborhood environment can vary depending on
their socioeconomic status and the cities where they live. This study aims to discern the rela-
tionship between neighborhood environment factors and single-family detached house
sales by sale price and by central and noncentral cities. We analyzed sale prices in the
Tokyo Metropolitan Area from 2015 to 2020. The neighborhood environment was assessed
using flood/sediment risk and neighborhood walkability measured by net residential density,
intersection density, and facility density (walking opportunity). Flood and sediment risk is a
major concern that restricts the available land and is included as a negative aspect of the
neighborhood environment, taking the topographic features into consideration. A compari-
son of the results showed that the preference for neighborhood walkability varies by socio-
economic status as well as by target cities. For most facility types, the number of walking
opportunities within walking distance from houses was found to be positively related to the
sale price of single-family detached houses in all quantiles. The relationship of house price
with population and intersection density was found to vary depending on the price level, with
a negative relationship with the sale price of relatively more expensive houses being exhib-
ited. People who considered buying houses with relatively higher sale prices were found to
devalue houses located in flood/sediment-hazardous areas more. However, it was also
found that the negative relationship was slightly mitigated in the highest quantile of sale
prices for houses in areas with a moderate flood risk (maximum flooding depth: 3–5 m).
Plains near rivers with amenities offer high walkability but pose a flood risk, resulting in a
trade-off between flood risk and neighborhood walkability. The findings suggest the use of
indices representing diverse preferences in accordance with the target socioeconomic sta-
tus when policymakers assess the neighborhood environment.
Introduction
Urban agglomeration has proceeded through the gathering of firms and workers in cities,
alongside urban growth [1–3]. Urban agglomeration creates clusters of amenities that hinge
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OPEN ACCESS
Citation: Kim H, Baba H, Shimizu C, Hino K (2023)
The relationship of single-family detached house
prices with neighborhood walkability and disaster
risk. PLoS ONE 18(10): e0292657. https://doi.org/
10.1371/journal.pone.0292657
Editor: Bing Xue, Institute for Advanced
Sustainability Studies, GERMANY
Received: June 10, 2022
Accepted: September 26, 2023
Published: October 11, 2023
Copyright: © 2023 Kim et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data used in the
study are owned by third party organizations. The
contact information for the organizations to which
data requests may be sent is as follows: Sales price
data: SUUMO research center (which is an institute
of the Recruit Co., Ltd.; https://www.suumo-
research.com/) Shibaura 3 Chome, 12-7-12,
Minato City, Tokyo, 108-0023, Japan Telepoint
Pack! and Advanced Digital Road Map Database
2015: Center for Spatial Information Science, The
University of Tokyo (https://joras.csis.u-tokyo.ac.
jp/); each also can be purchased by the vendors
(Telepoint Pack! from Zenrin Co., Ltd.: https://


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of the maximum projected rainfall [64]. Furthermore, the ministry has designated hazardous
sediment areas based on surveys of topography, geology, and land use [64]; the provision of
hazard information (e.g., hazard maps) is mandatory before transactions take place [65].
Variable descriptions
Table 1 shows the characteristics of single-family detached houses sold between 2015 and
2020. The sale price was recorded for each single-family detached house sold. The characteris-
tics of each house, such as years/months of transactions, floor space, land area, age of house,
road width in front of the house, and main window orientation, were also recorded and served
as control variables. Years/months of transactions were included on a quarterly basis to
account for the impact of temporal factors on sale price (e.g., inflation). The maximum flood-
ing depth of the area where each house was located was assessed and the results were classified
Table 1. Characteristicsa of single-family detached houses sold between 2015 and 2020.
Target area
Central cities
Other cities
Sale price (JPY10,000)
4,529 ± 3,221
5,314 ± 3,797
3,487 ± 1,764
Year of transactions (month is omitted)
2015
9,082 (12.7)
5,357 (13.2)
3,725 (12.2)
2016
8,841 (12.4)
5,010 (12.3)
3,831 (12.5)
2017
9,032 (12.7)
5,089 (12.5)
3,943 (12.9)
2018
13,603 (19.1)
7,640 (18.8)
5,963 (19.5)
2019
15,438 (21.7)
8,845 (21.8)
6,593 (21.5)
2020
15,293 (21.5)
8,720 (21.4)
6,573 (21.5)
Floor space (m2)
106.47 ± 38.42
108.22 ± 41.90
104.14 ± 33.10
Land area (m2)
113.22 ± 52.19
107.79 ± 54.18
120.44 ± 48.49
Road width in front of houses (m)
5.38 ± 2.48
5.30 ± 2.42
5.48 ± 2.55
Age of houses (months)
179.9 ± 101.07
187.4 ± 101.97
170.0 ± 99.00
Main window orientation
Facing south
12,626 (17.7)
6,949 (17.1)
5,677 (18.5)
Other
58,663 (82.3)
33,712 (82.9)
24,951 (81.5)
Travel time to Tokyo Station from the nearest station in daytime (min)
44.72 ± 13.09
39.98 ± 12.18
51.00 ± 11.51
Maximum flooding depth (m)
0.0
56,440 (79.2)
32,653 (80.3)
23,787 (77.7)
(0.0, 3.0)
9,287 (13.0)
4,410 (10.8)
4,877 (15.9)
[3.0, 5.0)
4,692 (6.6)
3,176 (7.8)
1,516 (4.9)
[5.0, inf]
870 (1.2)
422 (1.0)
448 (1.5)
Sediment-hazardous area
Houses inside the area
2,107 (3.0)
1,256 (3.1)
851 (2.8)
Houses outside the area
69,182 (97.0)
39,405 (96.9)
29,777 (97.2)
Land use zones
Exclusively low-rise residential zone
29,194 (41.0)
16,469 (40.5)
12,725 (41.5)
Mid/high-rise-oriented residential zone
15,112 (21.2)
8,492 (20.9)
6,620 (21.6)
Residential zone
14,341 (20.1)
8,925 (21.9)
5,416 (17.7)
Commercial zone
3,614 (5.1)
2,523 (6.2)
1,091 (35.6)
No land use zone designated
3,868 (5.4)
1,377 (3.4)
2,491 (8.1)
Sample number
71,289
40,661
30,628
a The mean and standard deviation (SD) are displayed for the continuous variables (and the number of samples for the binary and categorical variables).
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into four groups [66] according to the risk of damage to the floors, ranging from no risk (0 m
of flooding depth) to high risk (5 m and higher of flooding depth, where the water level on the
second floor becomes hazardous). In the case of a sediment disaster, we identified whether
each house was inside a hazardous sediment area; this was defined as a binary variable. Land
use zones and travel time to Tokyo Station from the nearest station were included to account
for designated zoning restrictions on building height and building types for the area where
each house is located and the distance to the central business district, respectively.
The neighborhood walkability—net residential density, intersection density, walking time
to the nearest station, and facility density—was assessed for each single-family detached house
(Table 2). Net residential density, intersection density, and facility density are popular indica-
tors used to measure neighborhood walkability in many countries [67–71]. An area with high
net residential density is considered to be one that is densely developed. Net residential density
was measured at the level of the census district where each single-family detached house was
located. Facility density was measured as the density of each facility type within a catchment
area of 1,200 m from each house along a road network. The distance of 1,200 m corresponds
to a 15-minute walk, which is the average walking distance to neighborhood destinations [72].
The density of neighborhood facilities enabled us to assess the number of destinations that res-
idents can travel to within a walkable distance. Witten et al. [71], for example, considered eight
types of facilities (education, transport, recreation, social and cultural, food retail, financial,
health, and other retail). Similarly to the classification by Witten et al. [71] (but with more
Table 2. Neighborhood characteristicsa of single-family detached houses sold between 2015 and 2020.
Target area
Central cities
Other cities
Net residential densityb
1,918 ± 1,887
2,250 ± 2,137
1,477 ± 1,374
Intersection density (four-way)
53.5 ± 25.46
61.50 ± 26.94
42.94 ± 18.68
Walking time to the nearest station (min)
13.04 ± 5.75
12.29 ± 5.66
14.03 ± 5.72
Facility density
Bars
13.46 ± 22.15
16.64 ± 26.62
9.23 ± 13.03
Cafe´s
2.73 ± 4.67
3.54 ± 5.65
1.65 ± 2.51
Restaurants
25.27 ± 32.49
31.71 ± 39.39
16.72 ± 16.39
Hobby classes
4.24 ± 4.55
4.98 ± 4.91
3.27 ± 3.81
Cultural institutions
3.58 ± 4.34
4.23 ± 5.16
2.71 ± 2.68
Parks
0.15 ± 0.34
0.21 ± 0.38
0.09 ± 0.27
Gyms and fitness clubs
2.75 ± 2.61
3.19 ± 2.95
2.16 ± 1.93
Convenience stores
4.31 ± 3.26
5.27 ± 3.77
3.04 ± 1.75
Schools
2.13 ± 1.32
2.55 ± 1.45
1.57 ± 0.86
Childcare facilities
4.73 ± 2.67
5.64 ± 2.80
3.52 ± 1.89
Retail shops
5.28 ± 10.30
6.52 ± 12.01
3.64 ± 7.12
Welfare facilities for older adults
8.32 ± 4.72
9.99 ± 4.84
6.11 ± 3.48
Hospitals
0.24 ± 0.61
0.30 ± 0.66
0.16 ± 0.51
Pharmacies
6.15 ± 4.45
7.35 ± 4.77
4.56 ± 3.38
Bus stops
7.39 ± 2.94
7.35 ± 2.54
7.43 ± 3.40
Stations
0.47 ± 0.64
0.59 ± 0.72
0.30 ± 0.45
Sample number
71,289
40,661
30,628
a The mean and standard deviation (SD) are displayed for the neighborhood characteristics. All the densities are estimated per square kilometer.
b Net residential density is estimated for each census tract where the targeted samples are located (4,126 census tracts); that is, buffered areas based on a 1,200 m network
distance away from houses are used in the case of intersection and facility density.
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detailed facility types), we considered 16 types of facilities, including cafe´s, restaurants, gyms,
hobby classes, schools, hospitals, train stations, bus stops, and cultural institutions (museums,
libraries, community centers, etc.). These facilities provide leisure, recreation, and welfare
opportunities (e.g., schools can also be perceived as recreation facilities by making their play-
grounds available as public places for recreation activities), which are considered attractive
components of the neighborhood environment and are major destinations in daily living [56,
71]. Thus, a high density of neighborhood facilities indicates more neighborhood walking
opportunities.
Stations were also assessed using the walking time to the nearest station from each house
based on the average walking speed. Because we included both the distance and the density of
stations, it was possible to consider not only the proximity to the nearest station from resi-
dents’ homes but also their options to access a station (e.g., when there are many stations, peo-
ple who visit a destination within their neighborhood can more easily access the nearest
station from that point). In addition, the proximity to the nearest station enabled us to con-
sider each house’s proximity to a local central area (i.e., the Tokyo Metropolitan Area was
developed alongside railways, and areas near stations were considered to be local central
areas). With regard to intersection density, we selected four-way intersections (20.5% of all
intersections) because they represent good neighborhood walkability and frequent use of pub-
lic transportation better than three-way intersections [73–75]; their density was measured in
the same way as facility density. High intersection density indicates that the road network near
each house is more likely to be a grid pattern.
Analytic method
A conditional quantile regression model was used to investigate the relationships between the
neighborhood characteristics and the conditional quantiles of the sale price rather than the
mean value [76–78]. Given that the quantiles were conditional, the model could consider the
sale price after controlling for other factors, such as the floor space and age of the house, which
enabled us to test the relationships between sale price and neighborhood characteristics. As
previously mentioned, assuming a budget constraint, those who can pay more for a house with
similar features to others are more likely to be high-income earners. In other words, the differ-
ence by conditional quantiles in the relationship between the sale price and the neighborhood
characteristics indicates the difference by income level in home buyers’ revealed preference for
the neighborhood characteristics. The sale price was included in the model as a natural loga-
rithmic scale owing to the large variance. The net residential density, intersection density, and
facility density were transformed into a nonnegative form, ln (1 + density), which is a special
case of Box–Cox transformation; this enabled us to escape a negative infinity issue (i.e., zero
values) and transform the right-skewed distribution to make it proximal to a normal one [79].
The years/quarters of transactions, floor space, land area, age of the houses, road width in
front of the houses, main window orientation, land use zones, and travel time to Tokyo Station
from the nearest station were adjusted. The models were estimated in accordance with condi-
tional quantiles ranging from the 0.05 to the 0.95 quantile at 0.05-point intervals. The 0.50
quantile corresponds to the median.
We estimated two models as follows:
Model 1: Regression model considering differences by centrality
Model 2: Regression model considering differences by income level
The analysis was conducted in three steps. We initially estimated the conditional quantile
regression model (Model 1) to capture the relationships regarding the median of the sale price,
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to 0.95 at 0.05-point intervals) of the sale price and their relationships with neighborhood
characteristics. Based on a comparison of each neighborhood characteristic, we discerned the
relationship between neighborhood environment and conditional quantiles of single-family
detached house prices.
Results
Results of regression models by centrality
Table 3 summarizes the results of the conditional quantile regression models (for the median
sale price, which is the 0.50 conditional quantile; Model 1) for all samples and subareas (cen-
tral and other cities). Column 3(a) shows the results for all samples from the target area.
High net residential density was negatively related to the median sale price of single-family
detached houses (β = -0.003). Variables related to flood and sediment risk also showed nega-
tive relationships (β = -0.122, β = -0.188, and β = -0.219 for flood risk by maximum flooding
depth; β = -0.026 for sediment risk). In terms of facility density, the density of most facility
types was positively related to the median sale price, except for bars and cultural institutions
(β = -0.077 and β = -0.060, respectively). A shorter time distance to the nearest station was
negatively related to the median sale price (β = -0.011). The intersection density was found
to be insignificant.
Columns 3(b) and (c) show the results for the central and other cities. Most of the vari-
ables were related to the sale price, and the results were similar to those for the total sample.
However, there were also differences in the results for each subarea. A high intersection den-
sity was insignificant in the case of central cities but negatively related to the sale prices of
houses in the case of other cities (β = -0.099). A high density of restaurants was positively
related to the sale price in the case of central cities (β = 0.048) but was insignificant in the
case of other cities. In contrast, a high density of bus stops was positively related to the sale
price in the case of other cities (β = 0.122) but was insignificant in the case of central cities. A
high density of pharmacies was negatively related to the sale price in the case of central cities
(β = -0.016) but positively in the case of other cities (β = 0.037). Flood and sediment risk
were negatively related in the case of central cities (β = -0.118, β = -0.248, and β = -0.213 for
low to high levels of flooding risk, respectively; β = -0.078 for sediment risk), but moderate
flood risk (maximum flooding depth ranging from 3.0 to 5.0 m) and sediment risk were
insignificant in the case of other cities.
Results of regression models by price range
Table 4 summarizes the results of the conditional quantile regression models regarding repre-
sentative conditional quantiles, which are the (a) 0.15, (b) 0.50 (median), and (c) 0.85 quantiles
(Model 2). High net residential density was negatively related to sale price in the 0.85 quantile
(β = -0.009), a similar result to that of the model for the median sale price (β = -0.003). How-
ever, it was insignificant in the 0.15 quantile. High intersection density was negatively related
to sale price in the 0.85 quantile (β = -0.010); however, it was insignificant in the 0.15 and 0.50
quantiles. The facility density and time distance to the nearest station showed a similar trend
(with some differences) to the model for the median sale price, except for restaurants (negative
relationship of restaurant density with sale price in the 0.15 quantile, β = -0.003) and pharma-
cies (negative relationship of pharmacy density in the 0.85 quantile, β = -0.029). Single-family
detached houses in flood- and sediment-hazardous areas had lower sale prices than those in
nonhazardous areas.
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Results of regression models for each neighborhood characteristic by
conditional quantiles
Figs 3 and 4 present the estimates of the conditional quantile regression models for the condi-
tional quantiles of the sale price, ranging from the 0.05 to the 0.95 quantile at 0.05-point inter-
vals. Fig 3(a)–3(d) show the coefficients for flood and sediment risks. As mentioned in the
brief comparison above, being located in a hazardous area was negatively related to the sale
Table 3. Comparison of the resultsa,b of the conditional quantile regression models (50% quantile) between central and other cities.
(a) Target area
(b) Central cities
(c) Other cities
Difference in the direction of the coefficientd
(a) ! (b) ! (c)
Higher net residential densityc
-0.003 (0.001) ***
-0.010 (0.001) ***
-0.004 (0.001) **
−! −! −
Higher intersection densityc
-0.006 (0.003)
0.001 (0.004)
-0.099 (0.004) ***

 ! 
 ! −
Shorter time distance to the nearest station
-0.013 (0.000) ***
-0.015 (0.000) ***
-0.009 (0.000) ***
−! −! −
Higher facility densityc
Bars
-0.077 (0.003) ***
-0.086 (0.004) ***
-0.049 (0.003) ***
−! −! −
Cafe´s
0.104 (0.003) ***
0.095 (0.004) ***
0.104 (0.004) ***
+ ! + ! +
Restaurants
0.014 (0.004) ***
0.048 (0.006) ***
-0.006 (0.004)
+ ! + ! 

Hobby classes
0.032 (0.003) ***
0.074 (0.004) ***
0.018 (0.004) ***
+ ! + ! +
Cultural institutions
-0.060 (0.003) ***
-0.050 (0.004) ***
-0.029 (0.004) ***
−! −! −
Parks
0.052 (0.005) ***
0.023 (0.005) ***
0.043 (0.011) ***
+ ! + ! +
Gyms and fitness clubs
0.063 (0.003) ***
0.070 (0.003) ***
0.045 (0.003) ***
+ ! + ! +
Convenience stores
0.092 (0.004) ***
0.067 (0.006) ***
0.050 (0.005) ***
+ ! + ! +
Schools
0.144 (0.004) ***
0.099 (0.005) ***
0.114 (0.005) ***
+ ! + ! +
Childcare facilities
0.063 (0.003) ***
0.052 (0.005) ***
0.059 (0.004) ***
+ ! + ! +
Retail shops
0.036 (0.003) ***
0.042 (0.003) ***
0.015 (0.003) ***
+ ! + ! +
Welfare facilities for older adults
0.020 (0.003) ***
0.012 (0.004) **
0.005 (0.003) ***
+ ! + ! +
Hospitals
0.028 (0.005) ***
0.017 (0.006) **
0.021 (0.006) ***
+ ! + ! +
Pharmacies
0.010 (0.004) **
-0.016 (0.005) ***
0.037 (0.004) ***
+ ! −! +
Bus stops
0.029 (0.003) ***
-0.004 (0.005)
0.122 (0.004) ***
+ ! 
 ! +
Stations
0.056 (0.005) ***
0.009 (0.006)
0.000 (0.006)
+ ! 
 ! 

Maximum flooding depth (ref: 0.0 m)
(0.0, 3.0)
-0.122 (0.003) ***
-0.118 (0.005) ***
-0.028 (0.004) ***
−! −! −
[3.0, 5.0)
-0.188 (0.005) ***
-0.248 (0.005) ***
-0.006 (0.005)
−! −! 

[5.0, inf]
-0.219 (0.007) ***
-0.213 (0.007) ***
-0.085 (0.010) ***
−! −! −
Located inside sediment-hazardous areas
-0.026 (0.005) ***
-0.078 (0.007) ***
-0.008 (0.009)
−! −! 

AICe
3507.236
1773.203
-8107.784
Pseudo R2
0.509
0.543
0.463
Sample number
71,289
40,661
30,628
a The years/quarters of transactions, floor space, land area, road width in front of the house, age of the house, main window orientation, travel time to Tokyo Station,
and land use zone were adjusted.
b The values in parentheses are standard errors.
c Density was transformed into a nonnegative natural logarithmic scale.
* p < 0.05,
** p < 0.01,
*** p < 0.001.
d Signals (+, −, and 
) correspond to positive, negative, and neutral signals, respectively; the direction (positive, negative, or neutral) was based on a criterion at the
significance level of 0.05.
e Akaike information criterion.
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price of single-family detached houses in all the quantiles. The higher the quantile, the greater
the negative relationship with the sale price for houses in hazardous areas. Interestingly, when
the maximum flooding depth was between 3 and 5 m, the coefficient increased slightly in the
highest quantile. The coefficient of net residential density was positive in the lower quantiles;
however, it became negative in the 0.25 quantile and decreased further in the higher quantiles
(Fig 3[e]). In the case of intersection density, its coefficient was negative in the 0.70 quantile
and higher but insignificant in the other quantiles (Fig 3[f]).
Table 4. Comparison of the resultsa,b of the conditional quantile regression models by price range (N = 71,289).
(a) 15%
(b) 50%
(c) 85%
Difference in the direction of the coefficientd
(a) ! (b) ! (c)
Higher net residential densityc
0.001 (0.001)
-0.003 (0.001) ***
-0.009 (0.001) ***

 ! −! −
Higher intersection densityc
-0.006 (0.003)
-0.006 (0.003)
-0.010 (0.004) **

 ! 
 ! −
Shorter time distance to the nearest station
-0.010 (0.000) ***
-0.013 (0.000) ***
-0.014 (0.000) ***
−! −! −
Higher facility densityc
Bars
-0.068 (0.002) ***
-0.077 (0.003) ***
-0.087 (0.003) ***
−! −! −
Cafe´s
0.082 (0.003) ***
0.104 (0.003) ***
0.148 (0.004) ***
+ ! + ! +
Restaurants
-0.001 (0.003)
0.014 (0.004) ***
0.019 (0.005) ***

 ! + ! +
Hobby classes
0.028 (0.003) ***
0.032 (0.003) ***
0.045 (0.003) ***
+ ! + ! +
Cultural institutions
-0.040 (0.003) ***
-0.060 (0.003) ***
-0.081 (0.004) ***
−! −! −
Parks
0.057 (0.004) ***
0.052 (0.005) ***
0.047 (0.006) ***
+ ! + ! +
Gyms and fitness clubs
0.043 (0.003) ***
0.063 (0.003) ***
0.082 (0.003) ***
+ ! + ! +
Convenience stores
0.108 (0.004) ***
0.092 (0.004) ***
0.094 (0.005) ***
+ ! + ! +
Schools
0.167 (0.004) ***
0.144 (0.004) ***
0.142 (0.004) ***
+ ! + ! +
Childcare facilities
0.048 (0.003) ***
0.063 (0.003) ***
0.059 (0.004) ***
+ ! + ! +
Retail shops
0.033 (0.003) ***
0.036 (0.003) ***
0.035 (0.003) ***
+ ! + ! +
Welfare facilities for older adults
0.028 (0.003) ***
0.020 (0.003) ***
0.015 (0.003) ***
+ ! + ! +
Hospitals
0.018 (0.004) ***
0.028 (0.005) ***
0.073 (0.006) ***
+ ! + ! +
Pharmacies
0.029 (0.004) ***
0.010 (0.004) **
-0.029 (0.005) ***
+ ! + ! −
Bus stops
0.061 (0.004) ***
0.029 (0.003) ***
0.030 (0.004) ***
+ ! + ! +
Stations
0.068 (0.005) ***
0.056 (0.005) ***
0.062 (0.006) ***
+ ! + ! +
Maximum flooding depth (ref: 0.0 m)
(0.0, 3.0)
-0.078 (0.004) ***
-0.122 (0.003) ***
-0.134 (0.004) ***
−! −! −
[3.0, 5.0)
-0.114 (0.003) ***
-0.188 (0.005) ***
-0.189 (0.006) ***
−! −! −
[5.0, inf]
-0.113 (0.005) ***
-0.219 (0.007) ***
-0.278 (0.016) ***
−! −! −
Located inside sediment-hazardous areas
-0.004 (0.004)
-0.026 (0.005) ***
-0.041 (0.007) ***

 ! −! −
AICe
22298.37
3507.236
25155.46
Pseudo R2
0.437
0.509
0.531
a The years/quarters of transactions, floor space, land area, road width in front of the house, age of the house, main window orientation, travel time to Tokyo Station,
and land use zone were adjusted.
b The values in brackets are standard errors.
c Density was transformed into a nonnegative natural logarithmic scale.
* p < 0.05,
** p < 0.01,
*** p < 0.001.
d Signals (+, −, and 
) correspond to positive, negative, and neutral signals, respectively; the direction (positive, negative, or neutral) was based on a criterion at the
significance level of 0.05.
e Akaike information criterion.
https://doi.org/10.1371/journal.pone.0292657.t004
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Fig 4 shows the coefficients of facility density by facility type. Most facility types were posi-
tively related to the sale price, but there were also certain further trends or greater, lesser, or
similar positive relationships in higher quantiles. It is also true that some facility types indi-
cated a negative relationship in either some or all quantiles. The density of bars and cultural
institutions showed a negative relationship with the sale price in all quantiles and a greater
negative relationship in higher quantiles. In the case of restaurants, their density was negatively
related to the sale price in lower quantiles but became positive in the 0.25 quantile and higher.
Fig 3. Estimates of the coefficients of flood/sediment risk, net residential density, intersection density, and percentage of
older adults for sale price quantiles ranging from 0.05 to 0.95 at 0.05 intervals. The estimates are from the conditional
quantile regression model. The years/quarters of transactions, floor space, land area, road width in front of the house, age of the
house, main window orientation, travel time to Tokyo Station, and land use zone were adjusted. Conditional quantiles are
displayed on the x-axis, and coefficients are presented on the y-axis. The black dots show the estimated coefficients. The gray area
represents the 95% confidence intervals.
https://doi.org/10.1371/journal.pone.0292657.g003
PLOS ONE
The relationship of single-family detached house prices with neighborhood walkability and disaster risk
PLOS ONE | https://doi.org/10.1371/journal.pone.0292657
October 11, 2023
13 / 23