Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Earthquake risk reduction and land value along geographic
boundaries of extremely high-risk urban districts in Tokyo
Mizuki Kawabata, Michio Naoi, Shohei Yasuda
10 July, 2023
DP2023-013
https://ies.keio.ac.jp/en/publications/22893/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
10 July, 2023

Earthquake risk reduction and land value along geographic boundaries of extremely
high-risk urban districts in Tokyo
Mizuki Kawabata, Michio Naoi, Shohei Yasuda
Keio-IES DP2023-013
10 July, 2023
JEL Classification: R38, C21, Q54
Keywords: Earthquake risk reduction, land value, spatial regression discontinuity
design, Tokyo
Abstract
We examine the impact of earthquake risk reduction on land values along thegeographic boundaries of dense urban districts with an extremely high risk ofearthquakes (high-risk DUDs) in Tokyo. Our analysis employs a spatial regression
discontinuity design that takes advantage of the discontinuity in earthquake risk at thehigh-risk DUD boundary. Our conservative estimate indicates that removing high-risk
DUDs increases land value by 5.2% near the boundary. Moreover, we find that theboundary effect increases as the road width at the boundary widens, suggesting thatthe sample limited to wide roads mitigates the spatial spillover of fire risk.
Mizuki Kawabata
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyo, 108-8345, Japanmizuki@econ.keio.ac.jp
Michio Naoi
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyo, 108-8345, Japannaoi@econ.keio.ac.jp
Shohei Yasuda
College of Economics, Nihon University
1-3-2 Kanda misaki-cho,Chiyoda-ku, Tokyo 101-8360, Japan
yasuda.shohei@nihon-u.ac.jp
Acknowledgement : We are grateful to Tadao Hoshino, Kentaro Nakajima, Ishino
Takuya, Atsushi Yamagishi, and the participants at various conferences and seminars
for their constructive comments. This research was supported by JSPS KAKENHI Grant
Numbers JP20K01617, JP21K01447, and JP23K17559, as well as Keio Gijuku Academic
Development Funds (Collaborative Research).

Earthquake risk reduction and land value along geographic boundaries
of extremely high-risk urban districts in Tokyo *Mizuki Kawabata †, Michio Naoi ‡, Shohei Yasuda §July 10, 2023

Abstract
We examine the impact of earthquake risk reduction on land values along the geographic boundaries
of dense urban districts with an extremely high risk of earthquakes (high-risk DUDs) in Tokyo. Our
analysis employs a spatial regression discontinuity design that takes advantage of the discontinuity inearthquake risk at the high-risk DUD boundary. Our conservative estimate indicates that removing
high-risk DUDs increases land value by 5.2% near the boundary. Moreover, we find that the boundary
effect increases as the road width at the boundary widens, suggesting that the sample limited to wide
roads mitigates the spatial spillover of fire risk.
Keywords: Earthquake risk reduction, land value, spatial regression discontinuity design, Tokyo
JEL codes: R38, C21, Q54

We are grateful to Tadao Hoshino, Kentaro Nakajima, Ishino Takuya, Atsushi Yamagishi, and the participants at
various conferences and seminars for their constructive comments. This research was supported by JSPS KAKENHI
Grant Numbers JP20K01617, JP21K01447, and JP23K17559, as well as Keio Gijuku Academic Development Funds(Collaborative Research).
† Faculty of Economics, Keio University. 2-15-45 Mita, Minato-ku, Tokyo, 108-8345, Japan. Email:mizuki@econ.keio.ac.jp‡ Faculty of Economics, Keio University. 2-15-45 Mita, Minato-ku, Tokyo, 108-8345, Japan.
§ College of Economics, Nihon University 3-2 Kanda-Misakicho 1-chome, Chiyoda-ku, Tokyo 101-8360, Japan.
*

11. Introduction
How does earthquake risk reduction affect land value? Earthquake-prone cities worldwide haveattempted to reduce earthquake risks and enhance disaster resilience. Tokyo, one of the mostearthquake-prone cities, has promoted urban redevelopment to reduce earthquake risk. In fact, recent
estimates indicate a marked decrement in expected earthquake damage. According to the Tokyo
Metropolitan Government’s (TMG) damage estimates for the imminent Tokyo Inland Earthquake,revised for the first time in 10 years in 2022, the number of buildings that will completely collapse
decreased from 120,000 to 80,000; those that will be destroyed by fire declined from 200,000 to
120,000; and the number of deaths from shaking and fire lessened from 9,200 to 5,700 (TMG, 2012b,
2022). Despite the ample literature on the economic impact of earthquake risk, (e.g., Brookshire et al.,
1985; Beron et al., 1997; Naoi et al., 2009; Hidano et al., 2015; Aguirre et al., 2022), the economic
benefits of urban redevelopment for earthquake risk reduction remain underexplored.
We provide new evidence on such benefits by examining the impact of earthquake risk onland values along the geographic boundaries of dense urban districts with an extremely high risk ofearthquakes (high-risk DUDs) in Tokyo. Our analysis employs a spatial regression discontinuity (RD)design that takes advantage of the discontinuity in earthquake risk at the high-risk DUD boundary.
Specifically, we ask two questions: (1) What is the impact of earthquake risk reduction on land value
along the high-risk DUD boundary? (2) Does the impact increase as the road width at the high-risk
DUD boundary widens? The second question is examined since the impact of earthquake risk may beunderestimated owing to a possible spatial spillover of fire risk outward from the high-risk DUD
boundary. Such spatial spillover violates the stable unit treatment value assumption (SUTVA) for the
spatial RD design. We expect the earthquake risk impact to be greater when the sample is restricted towider roads as they are more likely to prevent the spread of fire.
We find highly significant boundary effects, which demonstrate that being located outside2

high-risk DUDs increases land value. Our conservative estimate indicates that removing high-risk
DUDs increases land value by 5.2% along the high-risk DUD boundary. Furthermore, the boundary
effect increases as the road width at the high-risk DUD boundary widens, suggesting spatial spillover
owing to fire spread from inside the high-risk DUDs to the outside. The boundary effect is as large as
20.1% when the road width is 13.0 m or wider. Given that wider roads are more likely to prevent fire
spread than narrower roads, our results suggest that restricting the sample to wide roads at theboundary reduces spatial spillover and leads to a more reliable estimate.
The contributions of this study are threefold. First, this study provides empirical evidence
on the economic impact of earthquake risk reduction, which has been seldom explored in the literature.
(Related literature is reviewed in Section 2.) Second, by using high-risk DUDs as a measure ofearthquake risk, we provide new evidence for the evaluation of urban redevelopment policies forearthquake risk mitigation. The high-risk DUDs are among the highest-risk urban districts that arepriority areas for disaster prevention policies in Japan. The Ministry of Land, Infrastructure, Transport,
and Tourism (MLIT) in Japan defines high-risk DUDs as dense urban districts with particularly highfire-spread risk and evacuation difficulties, potential for large fires, loss of evacuation routes out ofthe district owing to roads blocked by an earthquake, and extreme difficulty in securing the safety oflife and property (MLIT, 2021). The national government and TMG have implemented various urban
redevelopment projects to eliminate the high-risk DUDs, including the promotion of earthquakeresistant buildings and redevelopment of close-set old wooden residential areas. Owing to such urban
redevelopment efforts, the high-risk DUD area in Tokyo has decreased markedly, from 1,683 ha in2012 to 103 ha in 2021 (MLIT, 2022). However, little is known about the economic benefits of suchurban redevelopment for earthquake risk reduction. Third, our results demonstrate that a sample
restricted to wider roads at the high-risk DUD boundary has a greater boundary effect, suggesting thatthe use of wider roads mitigates the spatial spillover of fire risk or violation of SUTVA. This finding3

also has policy implications since the construction of wide roads that serve as fire spread barrier zones
is one of the main measures in Tokyo’s urban redevelopment to reduce earthquake risk.
The structure of this paper is as follows. Section 2 presents the related literature, and Section
3 provides the background information on earthquake risk and urban redevelopment for earthquake
risk reduction in Japan and Tokyo. Section 4 explains our empirical specifications, and Section 5
describes the data. Section 6 reports the results, and Section 7 presents the robustness checks. Section
8 concludes the paper.

2. Related literatureThis study ties in with a large body of literature on the economic impact of earthquake risk. Particularly,
the relevant literature examines the effects of earthquake risk information on housing and land prices.
Brookshire et al. (1985) demonstrate that after the publication of California’s high seismic risk zone
map in 1974, housing prices in high-risk zones in Los Angeles and the San Francisco Bay Area became
significantly discounted. Several studies use TMG’s community earthquake risk measures at the
district level for Tokyo. Using building collapse risk as part of the community earthquake risk
measures, Nakagawa et al. (2007, 2009) show that housing rents and land prices are significantly lower
in high-risk areas. Hidano et al. (2015) employ building collapse risk and combined risk, which areincluded in the TMG’s community earthquake risk measures. Their results indicate that the unit pricesof residential properties in low-risk districts are approximately 13,970–17,380 JPY higher than thosein high-risk districts, depending on the type of risk. Note that the TMG’s community earthquake risk
measures differ from the high-risk DUDs employed in our analysis in that the community earthquake
risk is a relative measure ranging from 1 (lowest risk) to 5 (highest risk). Therefore, even if the absoluterisk decreases, community earthquake risk levels may remain unchanged or even increase. On theother hand, high-risk DUDs are defined based on the absolute risk levels; if the actual risk declines,4the high-risk DUD area also decreases. Moreover, whereas TMG’s community earthquake risk is anassessment of the relative level of risk status, high-risk DUDs are a policy target that should be
eliminated. A recent study by Singh (2019) utilizes revisions to California earthquake fault zone maps
over time. The results indicate that, on average, the delineation of the fault zone reduces property
values by 6.6% and lowers rents by approximately 3.3%.
Another stream of literature examines housing and land prices before and after specificearthquakes, often demonstrating changes in the subjective perception of earthquake risk. Beron et al.
(1997) show that the hedonic price in the San Francisco Bay area declined after the 1989 Loma Prieta
Earthquake, suggesting that consumers had overestimated the market value of the earthquake risk.
Önder et al. (2004) find that the distance from fault lines had a significant positive impact on housing
values in Istanbul in 1995 and 2000, but the impact was greater in 2000, after the 1999 Kocaeli
earthquake. Naoi et al. (2009) demonstrate that immediately after a strong earthquake, owner-occupied
house prices and housing rents in earthquake-prone areas were significantly discounted compared tothose before the earthquake in Japan. Their results suggest that earthquake risk tends to beunderestimated in areas where earthquakes have not occurred in recent years. Gu et al. (2018) showthat non-residential land prices closer to the Uemachi fault in Osaka were significantly discounted
after the Great Hanshin earthquake. Fekrazad (2019) finds that a high-casualty earthquake outside ofCalifornia reduced the home value index by approximately 6% and the median listing price byapproximately 3% in high-risk areas compared to low-risk areas.
A strand of literature also examines the macroeconomic and local economic impacts ofearthquakes. The empirical evidence of these studies is mixed, perhaps owing to differences in study
areas, time periods, earthquakes, and methods. The cross-country analysis of Skidmore and Toya
(2002) indicates that geologic disasters, including earthquakes, are negatively correlated with longterm economic growth. Using a difference-in-difference approach, Aguirre et al. (2022) find that 8–95

years after the 2010 Chile earthquake with a magnitude of 8.8, economic activity in affectedmunicipalities is approximately 10% lower than in unaffected municipalities. Conversely, Fujiki and
Hsiao (2015) find no persistent effects after the 1995 Great Hanshin-Awaji earthquake in Japan.
Several studies exhibit heterogenous impacts. Using a cross-country panel, Fomby et al.
(2013) find negative and insignificant effects of earthquakes on economic growth but a positive effecton non-agricultural growth one year later, associated with reconstruction activities in housing,
infrastructure, and production plants. Barone and Mocetti (2014) demonstrate that large earthquakes
in two different regions of Italy, Friuli in 1976 and Irpinia in 1980, have opposite long-run effects on
GDP per capita, with higher pre-earthquake institutional quality having a positive impact. Cole et al.
(2019) show that building-level damage negatively affects the survival of manufacturing plants after
the Kobe earthquake in Japan. Their analysis further reveals cleansing and ‘build back better’ effects,
as the plants that are most likely to exit are the least productive, while those that survive aretemporarily more productive. Nguyen and Noy (2020) report that after the Canterbury Earthquake
Sequence (2010–2011) in New Zealand, insurance payments significantly affected the recovery
process of the local economy, as measured by night-time luminosity.
Thus far, few studies have focused on the economic impact of earthquake risk reduction.
Using spatial panel data models, Kawabata et al. (2022) demonstrate that a reduction in high-risk
DUDs, as well as in fire risk and the combined risk of the TMG’s community earthquake risk measures,increases land prices not only in the lot’s own district but also in neighboring districts in Tokyo.
However, the causal relationship is not specified in their analysis. By using a spatial RD design, thisstudy provides policy-relevant and novel empirical evidence to the literature.

63. Background
3.1 Earthquake risk in Japan and Tokyo
Located at the collision of the four tectonic plates, Japan faces a particularly high earthquake risk.
Although the country covers only 0.25% of the world’s land area, it accounts for 18.5% (326) of the1856 earthquakes of magnitude 6.0 or greater that occurred between 2004 and 2013 (Cabinet Office,2016). Over the past decades, Japan has suffered tremendous damage from massive earthquakes. The
1995 Great Hanshin-Awaji Earthquake, the largest inland earthquake recorded up to that time in Japan,
resulted in more than 6,400 deaths and 40,000 injuries (Fire and Disaster Management Agency, 2006).
This inland earthquake also caused severe damage to a wide range of structures, including houses andbusinesses, highways, railroads, ports, and lifelines, with the total damage amounting to approximately
10 trillion yen (Cabinet Office, 2017). The devastation from collapsed buildings and fires wasparticularly serious in urban areas where old wooden houses were densely located. Subsequently, thestrengthening of earthquake resistance standards and the elimination of urban areas with close-set old
wooden houses have emerged as new disaster prevention policies. In 2011, the even more devastating
Great East Japan Earthquake struck the Tohoku region. With a magnitude of 9.0, it caused fatal tremors,tsunamis, and fires, leaving more than 22,200 people dead or missing (Fire and Disaster Management
Agency, 2022). More than 120,000 dwellings were completely destroyed, 280,000 were partiallydestroyed, and approximately 750,000 were partially damaged (Fire and Disaster Management Agency,2022). The total damage from this earthquake was estimated at approximately 16.9 trillion yen(Cabinet Office, 2011).
Furthermore, Japan is at imminent risk of several massive earthquakes, including themagnitude 8–9 class Nankai Trough Earthquake and magnitude 7 class Tokyo Inland Earthquake.
Within the next 30 years, the probability of the Nankai Trough Earthquake is predicted to beapproximately 70–80%, and that of the Tokyo Inland Earthquake is approximately 70% (Cabinet

7

Office, 2021; The Headquarters for Earthquake Research Promotion, 2022). The Nankai TroughEarthquake is expected to cause a major disaster throughout western Japan. According to the damage
assumptions for the landward case of this earthquake, the maximum number of houses totally
destroyed or burned down is estimated at approximately 2,386,000, the maximum number of fatalities
at 323,000, and the economic damage at approximately 214 trillion yen (Cabinet Secretariat, 2021;
Central Disaster Management Council’s Nankai Trough Earthquake Countermeasures Study Working
Group, 2013).
Regarding the Tokyo Inland Earthquake, the most damage is expected from collapsedbuildings and large fires in urban areas where old wooden houses are densely built, as in the case of
the 1995 Great Hanshin-Awaji Earthquake. According to the government’s report in 2013, in the eventof an earthquake with an epicenter directly under the southern part of Tokyo (assuming a magnitudeof 7.3), the maximum number of fatalities is estimated at approximately 23,000, with approximately
72,000 people requiring rescue assistance, and approximately 610,000 houses completely destroyed
or burned to the ground. The total economic damage, including direct damage to assets and indirect
damage from reduced production and services, is estimated at approximately 95 trillion yen (Central
Disaster Management Council’s Tokyo Inland Earthquake Countermeasures Study Working Group,
2013; Cabinet Office, 2021).

3.2 Urban redevelopment for earthquake risk reduction in Tokyo
In preparation for the impending Tokyo Inland Earthquake, the national government and TMG havebeen promoting urban redevelopment to mitigate disaster risk. In 2011, the Cabinet approved the Basic
Plan for Housing and Living Standards (National Plan), whose goal is to eliminate most of the highrisk DUDs that existed in Japan at that time, approximately 6,000 ha, by 2020 (MLIT, 2011). In 2012,
the government announced that the number of high-risk DUDs in Tokyo was 113, covering an area of8

1,683 ha (MLIT, 2012). The high-risk DUDs are identified as areas with particularly high fire spreadrisk and evacuation difficulty. The fire spread risk is determined based on indicators such as residential
unit density, proportion of fire-resistant buildings, and open space area. The evacuation difficulty
represents the difficulty of evacuating from inside the district to outside, which is evaluated based on
indicators such as district area, road width and shape, and earthquake and fire resistance of buildings(MLIT, 2012).
In 2013, the national government announced 15 priority programs to promote national land
resilience and listed the avoidance of fire-related casualties in dense urban districts in the first of the
15 programs (MLIT, 2013). Examples of specific urban redevelopment measures to avoid fire-related
casualties are: promotion of earthquake resistance of houses and buildings; development of parks,
green spaces, and open spaces as evacuation sites; development of roads and greenways as evacuationroutes and widening of narrow roads; adoption of nonflammable measures around evacuation sites,
evacuation routes, and firebreak zones; renovation and reconstruction of buildings along evacuationroutes; and joint reconstruction of old buildings into fire-resistant buildings in conjunction with theremoval of old buildings (MLIT, 2013).
The TMG has also promoted urban redevelopment for earthquake reduction. A major policy is
the promotion of earthquake-resistant buildings. In 2011, the TMG enacted the Ordinance to Promote
Earthquake Resistance of Building along emergency transportation roads in Tokyo (TMG, 2011).
Since 2012, seismic diagnosis has been mandatory for buildings along specified emergency
transportation roads, and subsidies have been provided to cover the costs of seismic diagnosis andrenovation. Furthermore, the TMG has developed the Tokyo Earthquake Renovation Promotion Plan(for FY2016–2025) to promote earthquake resistance in buildings systematically and comprehensively(TMG, 2016). Another major policy is the promotion of fire resistance, with an emphasis on theredevelopment of dense wood-frame residential districts with a high fire risk. The TMG initiated a 10-9year redevelopment project to enhance the fire resistance of close-set wooden housing districts in 2012
(TMG, 2012a). The project has supported the joint reconstruction of old buildings and theimprovement of roads, parks, and other public facilities to secure evacuation routes and open spaces.
The project has also promoted the use of special zones to enhance fire resistance and the constructionof specific maintenance roads to block fire spread.
Consequently, the area of close-set wooden housing districts defined by the TMG decreased from
16,000 ha in 2012 to 8,600 ha in 2020 (TMG, 2022b), and the area of high-risk DUDs in Tokyodeclined from 1,683 ha in 2012 to 103 ha in 2021 (MLIT, 2022). The high-risk DUDs in Tokyo largely
overlap with the TMG’s close-set wooden housing districts, but the area of the former is smaller, thus
reflecting its extremely high risk level. In 2021, the Cabinet approved the new Basic Plan for Housing(National Planning), which includes the goal of eliminating most of the remaining high-risk DUDs by2030 (MLIT, 2021).
Despite such substantial reductions in earthquake risk, the economic benefits of urban
redevelopment for mitigating earthquake risks, such as high-risk DUD removal, have been rarely
explored in the empirical literature.

4. Empirical specification
We employ a spatial RD design that exploits the discontinuity in earthquake risk at the high-risk DUD
boundary. As the high-risk DUD boundary is two-dimensional, our spatial RD specification uses the
distance to the nearest high-risk DUD boundary as a one-dimensional forcing variable, following theapproach often employed in the literature (Holmes, 1998; Black, 1999; Kane et al., 2006; Lalive, 2008;
Dell, 2010; Ambrus et al., 2020). To examine the impact of earthquake risk reduction on land value,
we estimate the following local linear regression equation with a sample restricted to locations withinbandwidth h of the high-risk DUD boundary:10

ln 𝑝𝑝𝑖𝑖 = 𝛼𝛼𝑏𝑏(𝑖𝑖) + 𝜏𝜏𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 + 𝛽𝛽𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 + 𝛾𝛾𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 × 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 + 𝑿𝑿′𝑖𝑖 𝛿𝛿 + 𝜀𝜀𝑖𝑖 for |𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 | < ℎ, (1)where 𝑝𝑝𝑖𝑖 is the roadside land value of location 𝑖𝑖 , the midpoint of each road (line) segment;

𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂_𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 is an indicator equal to 1 if location 𝑖𝑖 falls outside the high-risk DUDs and 0

otherwise; 𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝑖𝑖 is the distance between location 𝑖𝑖 and the closest high-risk DUD boundary, whichtakes a negative value for locations inside the high-risk DUDs; and 𝑿𝑿𝑖𝑖 is a vector of additional control

variables. Equation (1) includes location fixed effects 𝛼𝛼𝑏𝑏(𝑖𝑖) , where 𝑏𝑏(𝑖𝑖) is an index for location 𝑖𝑖’s

closest line segment of the high-risk DUD boundary. In our dataset, the high-risk DUD boundary
consists of 1,607 line segments, with an average line segment length of approximately 24.5 m.

Our parameter of interest is 𝜏𝜏, which represents the boundary effect of being located outside

high-risk DUDs, that is, discontinuous changes in land values at the high-risk DUD boundary. Thisboundary effect is interpreted as the average treatment effect of a reduction in earthquake risk justoutside the high-risk DUD boundary. We estimate Equation (1) with the optimal bandwidth determined
as in Calonico et al. (2014a). 1 As the optimal bandwidth varies with the regression specification, we
also present the results using a fixed bandwidth of 100 m to examine whether our results are robust to
bandwidth selection. While additional control variables 𝑿𝑿𝑖𝑖 are, in principle, not required for

identification in the RD design, they can improve the precision of the estimates (Imbens and Lemieux,
2008; Lee, 2008). Therefore, we start with a model with no control variables other than boundary fixed
effects, and then add a set of controls: distance to the nearest rail station, land use categories, elevation,slope angle, and flood hazard (the depth of potential flooding) at location 𝑖𝑖. Furthermore, we estimate

Equation (1) for each subsample by road width at the high-risk DUD boundary to examine whether
the boundary effect is greater for the subsample with wider roads that could prevent fire spread. Finally,
we perform standard robustness and sensitivity checks on our baseline results, including sensitivity to

1

The optimal bandwidth is calculated using Stata’s rdrobust package provided by Calonico et al. (2014b).

11

the bandwidth selection and the choice of polynomial orders, and a falsification test using fakeboundaries.

5. Data
We employ high-risk DUD data in 2017 obtained from the MLIT. At that time, there were 30 highrisk DUDs remaining in Tokyo (Figure 1a). For land value, we use property tax roadside land value
data published in 2018; the property tax roadside land values are assessed at land prices as of January
1, 2017, the previous year. We obtain shapefiles of the property tax roadside land value data from the
Research Center for Property Assessment System (RECPAS). 2 Each roadside land value is the price(in JPY) per square meter of standard land along a road. In our analysis, the midpoint of each road(line) segment is used as the location of the roadside land value (Figure 1b). The spatially detailed
roadside land value data allow us to examine the difference in land value just outside and just inside
the high-risk DUDs using a spatial RD design that addresses the discontinuous change in earthquake
risk at the boundary.

The shapefile is a data format for data processing in geographic information systems. Publicly available roadsideland value data are not in such a data format. Another type of roadside land value data exists: annual inheritance tax onroadside land values; however, shapefiles of these data are not available from the RECPAS.
2

12

Roads with roadside land values
High-risk DUD boundaries
High-risk DUDs
Ward boundaries
Locations (midpoints) of roads

High-risk DUDs
Ward boundaries(a) High-risk DUDs in Tokyo(b) High-risk DUDs and roads with land values

Figure 1. High-risk DUDs and locations of roadside land values

Railway station, land use, slope angle, and flood hazard data are obtained from the National
Land Numerical Information download service. The data are selected for the years closest to 2017, theyear of the high-risk DUD data. The railway station data are from 2017, and land use data are from2019. We categorize the original 12 land use types into three categories: residential, commercial, andindustrial. The slope angle data are from the average slope angle for 250-meter grid included in theelevation and slope data in 2011. The flood hazard data are from potential flood zone data in 2012,
which include the depth of potential flooding. Our flood hazard data categorizes each location into
one of the following four groups based on potential flood depth: (1) no potential flood risk; (2)potential flood depth of less than 1.0 m; (3) potential flood depth of 1.0 to 2.0 m; and (4) potential
flood depth of 2.0 m or greater. We use dummy variables for each of these categories in our regression.
The elevation data are from the 5-meter digital elevation model (DEM) for 2014 and 2017, dependingon the location of the study area. We obtain the 5-meter DEM data from the basic map information
maintained by the Geospatial Information Authority of Japan. Road width data are extracted from the2017 Digital Road Map, which was provided by the Japan Digital Road Map Association. As the road13

width is categorical data, we use three dummy variables indicating road width of 13.0 m or wider,between 5.5 and 13.0 m, and less than 5.5 m.
Table 1 presents summary statistics for our samples. The full sample is restricted to locations
that have a distance to the high-risk DUD boundary of less than 402.5 m on either side of the highrisk DUD boundary. The distance of 402.5 m is selected because it is the maximum distance inside thehigh-risk DUDs in our dataset. We exclude locations where the nearest high-risk DUD boundary
coincides with a ward boundary, river, railroad (excluding subways), or large park because district
characteristics are likely to differ inside and outside such boundaries. We also exclude locations that
could not be identified as inside or outside the high-risk DUDs. 3 The sample restricted to locations
within a 100-meter bandwidth (i.e., locations closer to the high-risk DUD boundary) is used forcomparison. (As shown later in the study, the optimal bandwidths in our regression are less than 100m.)
Columns 1 and 2 of Table 1 denote the means and standard deviations for the full sample
outside and inside the high-risk DUD boundary, respectively. To assess the continuity assumption,column 3 presents the RD estimates using a local linear specification with each variable in Table 1 as
the dependent variable. Columns 4, 5, and 6 provide the same information for locations within 100 mof the boundary. As expected, the average land values are lower inside high-risk DUDs than outside,
but the difference is somewhat smaller within the 100-meter bandwidth than in the full sample. For
the full sample, land use inside the high-risk DUDs is more likely to be industrial and less likely to beresidential or commercial. There are some topographic differences between the outside and inside of
the high-risk DUDs. Locations inside the high-risk DUDs have, on average, lower elevations, smaller
slope angles, and higher flood hazards with potential flood depths of 2.0 m or greater. Columns 3 and6 indicate the local linear RD estimates. While the RD estimates are significant for some control
There are locations very close to the high-risk DUD boundary that might be recognized as inside when they should
be outside the high-risk DUDs, or outside when they should be inside the high-risk DUDs.

3

14

variables in the full sample, they are statistically insignificant in the 100-meter bandwidth sample. The
RD estimate for potential flood depths of 2.0 m or greater in the 100-meter bandwidth sample issignificant, but we do not find any firm evidence that the other control variables are changed
discontinuously at the high-risk DUD boundary.

Table 1. Summary statistics
Outside(1)
12.52(0.35)

Land price (in logs)
Distance to nearest
387.0(210.1)
railway station (m)Land use
Residential
0.412(0.492)
Commercial
0.304(0.460)Industrial
0.284(0.451)
Elevation
9.974(9.069)Slope angle
0.599(0.704)
Flood risk (depth of potential flooding)
N/A
0.567
(0.496)
0.0-1.0m
0.025(0.157)
1.0-2.0m
0.058(0.233)2.0m+
0.350(0.477)
7,876
NFull sample
Inside(2)
12.28(0.26)

RD est.
(3)0.057 ***

100m bandwidth sample
OutsideInside
(5)
(4)
12.44
(0.32) 12.32(0.27)

RD est.
(6)0.061 ***

434.2

(193.5) -16.561 ***405.3(205.5) 448.1(197.8)

0.928

0.388
0.173
0.439
6.342
0.418

(0.487)(0.378)(0.496)(8.817)(0.642)-0.139 ***0.025
0.114 ***
-0.431 ***-0.004

0.397
0.331
0.272
9.135
0.589

(0.489)(0.471)(0.445)(9.136)(0.748)(0.499)(0.397)(0.474)(9.133)(0.742)-0.036-0.003
0.039
0.092
0.002

0.004
0.004
-0.003-0.0050.507(0.500)0.016(0.127)0.038(0.191)0.438(0.496)
1,825

0.344
(0.475)0.019(0.136)0.039(0.194)0.598(0.490)
2,350

0.464
0.196
0.339
7.995
0.556

0.440
(0.497)0.015(0.123)0.033(0.179)0.512(0.500)
1,507

0.002
-0.007-0.0110.016 **

Notes : *** and ** indicate that estimated coefficients are statistically significant at the 1% and 5% levels, respectively. Columns 1, 2, 4 and 5 show the sample
means and standard deviations in the parentheses. Columns 3 and 6 show the local linear RD estimates using each variable as the dependent variable. The distanceto the DUD boundary for locations inside the high-risk DUD takes negative values. Full sample includes all locations with distance to the high-risk DUD boundary
less than 402.5m (maximum distance inside those DUDs).

6. Empirical results
We present RD plots that graphically illustrate the discontinuity in land values at the high-risk DUD
boundary. Figure 2 plots average land values (bin averages, vertical axis) against the distance to theclosest high-risk DUD boundary (forcing variable, horizontal axis). The hollow and solid dots
represent the average land values within the 20- and 50-meter distance bins, respectively. 4 The solid
vertical line (distance = 0) represents the high-risk DUD boundary, where locations inside (outside)
the high-risk DUDs have negative (positive) distance values. We also present fourth-order polynomial

For the RD plots in Figure 2, we do not use the control variables. We have similar results with a covariate adjustmentusing these variables.

4

15

fits separately for inside and outside of the high-risk DUDs. Figure 2 reveals a spatial discontinuity inthe average land values at the high-risk DUD boundary; locations just outside the boundary have12

log(land price)
12.2
12.4

12.6

higher land values than those just inside.

-400

-200
0
200
Distance to the high-risk DUD boundary (m)20m bins

50m bins

400

Polynomial fit of order 4

Figure 2. RD plot for land values

To obtain more rigorous insights, we estimate local linear spatial RD models using Equation (1).
Table 2 reports our baseline results and summarizes the boundary effects (𝜏𝜏). Columns 1 and 2

indicate the results using optimal bandwidths of 69.6 and 66.9 m, respectively. Considering that the
optimal bandwidth varies by specification, we also present results using the fixed bandwidth of 100
m in columns 3 and 4 for comparison. Columns 1 and 3 report raw estimates with no controls otherthan boundary fixed effects, whereas columns 2 and 4 include additional controls for the distance tothe nearest station, land use categories, elevation, slope angle, and potential flood depth.
In all specifications, we find positive and highly significant boundary effects. Column 1
indicates that locations just outside the high-risk DUDs have 5.6% higher land values than those just16

inside. As shown in column 2, adding a set of control variables changes the magnitude of the boundary
effect only slightly, to 5.2%. 5 We obtain similar results for the fixed bandwidth of 100 m (columns 3
and 4). Overall, the baseline results show that locations just outside the high-risk DUD havesignificantly higher land values than those just inside, regardless of the bandwidths and additional
controls. 6

Table 2. Boundary effects on land values

Bandwidth (m)Boundary FE
Controls

Optimal bandwidth
(1)
(2)
***
0.052 ***
0.056
(0.012)(0.012)
69.6
66.9
X
X
X

Fixed bandwidth (100m)
(3)
(4)
***
0.061
0.061 ***
(0.011)(0.010)
100.0
100.0
X
X
X

N (outside)
N (inside)
N (total)

1,192
1,066
2,258

1,825
1,507
3,332

Outside high-risk DUDs

1,126
1,024
2,150

1,825
1,507
3,332

Notes : *** indicates that estimated coefficients are statistically significant at the 1% level. Standard
errors are in the parentheses. All models use the polynomial order of 1 (i.e., local linear specification)and include boundary fixed effects. Columns 2 and 4 include distance to the nearest station, land use
categories, elevation, slope angle, and potential flood depth as additional controls.

A concern with our baseline results is that locations inside and outside of the high-risk DUDs
have inherently different characteristics owing to, for example, different topography and historicaldevelopment processes, which may distort our RD estimates. To address this issue, we also estimate
models focusing on the boundaries of the current high-risk DUDs that were inside the former high-If we further include road width at each location as an additional control, the boundary effects become somewhat
smaller but remain significantly positive (0.042 and 0.050 for optimal and fixed bandwidths, respectively). This result
is not surprising because the high-risk DUDs, by definition, have numerous narrow streets with poor accessibility andpotentially strict building restrictions. Because narrow streets also increase fire risk and evacuation difficulties, which
are considered in the definition of the high-risk DUDs, our models do not include road width at each location as acontrol.
6 Considering that unobserved variables might be spatially correlated, we also estimate models that allow for spatial
autocorrelation in the error terms (Conley, 1999), using Stata’s acreg package. The results in Table A1 indicate that theboundary effects are positive and marginally significant even when we allow for the spatially autocorrelated error terms.
517risk DUDs. Specifically, we compare locations inside and outside of the high-risk DUDs in 2017,
where all these locations are inside the former high-risk DUDs in 2012. 7 The idea is that theselocations have similar topographic or historical characteristics, but the former high-risk DUDs became
non-high-risk DUDs owing to urban redevelopment and associated risk reductions that took place
between 2012 and 2017. Table 3 summarizes the results, which show a somewhat smaller magnitude
but still significantly positive boundary effects. 8

Table 3. Boundary effects on land values using locations inside the former high-risk DUDs

Bandwidth (m)Boundary FE
Controls

Optimal bandwidth
(1)
(2)
**
0.043 ***
0.033
(0.015)(0.014)
78.3
80.9
X
X
X

Fixed bandwidth (100m)
(3)
(4)
***
0.040
0.051 ***
(0.013)(0.013)
100.0
100.0
X
X
X

N (outside)
N (inside)
N (total)

651
605
1,256

861
743
1,604

Outside high-risk DUDs

676
626
1,302

861
743
1,604

Notes :*** indicates that estimated coefficients are statistically significant at the 1% level. Standard errors
are in the parentheses. All models use the polynomial order of 1 (i.e., local linear specification) andinclude boundary fixed effects. Columns 2 and 4 include distance to the nearest station, land use
categories, elevation, slope angle, and potential flood depth as additional controls.

Another concern is spatial spillover owing to fire spreading from inside to outside high-risk
DUDs. Locations immediately outside the high-risk DUDs would have a lower risk if they were notadjacent to the high-risk DUDs. Such spatial spillover of fire risk leads to the violation of the SUTVA,
which may result in underestimating the boundary effects. To address this issue, we divide the sample
based on the road width at the high-risk DUD boundary and estimate Equation (1) separately for eachsubsample. We expect that fire spread from the inside to the outside of the high-risk DUDs is less
From 2012 to 2017, the number of high-risk DUDs in Tokyo decreased from 113 to 30.
We do not proceed with further analysis using this sample, since the results are unstable perhaps owing to smaller
sample size.
7
8

18

likely to occur if the road at the boundary is wider, making SUTVA more plausible.
Table 4 summarizes the estimation results using the subsamples by road width at the highrisk DUD boundary, controlling for the full set of control variables. The road width at the high-risk
DUD boundary is categorized into three groups: 13.0 m or wider, between 5.5 and 13.0 m, and lessthan 5.5 m. Columns 1–3 of Table 4 show the results using the optimal bandwidths, ranging from 65.1
to 86.1 m. The boundary effects are all positive, but their magnitudes differ substantially dependingon the road width at the boundary. In accordance with our expectation, the boundary effects are larger
for subsamples with wider roads at the high-risk DUD boundary. When the road width at the boundaryis 13.0 m or wider (column 1), the boundary effect is 0.201, which is almost four times as large as thatin our baseline estimate of 0.052 (column 2 of Table 2). When the road width at the boundary is
between 5.5 and 13.0 m (column 2), the boundary effect is 0.066, which is smaller than that of thewider road width but remains greater than that of the baseline estimate. When the road width at theboundary is less than 5.5 m (column 3), the boundary effect is 0.031, which is smaller than those of
the wider roads and the baseline result and is only marginally significant at the 10% level. We find asimilar pattern for specifications with a fixed bandwidth of 100 m (columns 4–6). These results suggest
a spatial spillover owing to fire spreading from inside to outside the high-risk DUDs.

19

Table 4. Boundary effects on land values by road width at the high-risk DUD boundary

Outside high-risk DUDs
Bandwidth (m)Road width
at the DUD boundary
N (outside)
N (inside)
N (total)(1)0.201 ***
(0.038)65.1

Optimal bandwidth
(2)
(3)
***
0.066
0.031 *
(0.021)(0.018)86.1
67.9

Fixed bandwidth (100m)
(4)
(5)
(6)
***
***
0.101
0.073
0.044 ***
(0.030)(0.019)(0.015)
100.0
100.0
100.0

13.0m+

5.5-13.0m

5.5m-

13.0m+

5.5-13.0m

5.5m-

147
198
345

569
513
1,082

523
413
936

258
311
569

679
582
1,261

821
592
1,413

Notes : *** and * indicate that estimated coefficients are statistically significant at the 1% and 10% levels, respectively. Standard errors are in
the parentheses. All models use the polynomial order of 1 (i.e., local linear specification) and include boundary fixed effects, distance to thenearest station, land use categories, elevation, slope angle, and potential flood depth as controls.

7. Robustness checks
We conduct a standard set of robustness and sensitivity checks on our baseline results, including
sensitivity to bandwidth selection and choice of polynomial orders, and a falsification test using fakeboundaries.
To examine sensitivity to bandwidth selection, Figure 3 illustrates the boundary effectestimates (with a set of control variables) and associated 95% confidence intervals as a function of the
bandwidth between 25 and 300 m. The dashed vertical line indicates the optimal bandwidth of 66.9 m
in our benchmark result (column 2 of Table 2). We find that the magnitude and significance of theboundary effects are largely stable within a range of bandwidths, including our baseline cases of
optimal and fixed (100-meter) bandwidths.
To investigate sensitivity to polynomial order, Table 5 summarizes the boundary effectestimates for models with polynomial orders of up to four. Overall, our baseline boundary effectestimates are not very sensitive to the choice of the polynomial order.

20

.1
.05
0

Optimal bandwidth(66.9m)-.05

Fixed bandwidth(100.0m)

0

100

200

300

Bandwidth (m)RD estimates

95% confidence interval

Figure 3. Sensitivity to bandwidth selection
Table 5. Boundary effects with different polynomial orders

Outside high-risk DUDs
Bandwidth (m)Polynomial order
N (outside)
N (inside)
N (total)(1)
0.041 ***
(0.005)
26.0
0
252
245
497

(2)
0.052 ***
(0.012)
66.9
1
1,126
1,024
2,150

(3)
0.059 **
(0.024)
75.6
2
1,321
1,168
2,489

(4)
0.051 **
(0.025)
164.6
3
3,089
2,075
5,164

(5)
0.057 **
(0.027)
268.5
4
5,234
2,310
7,544

Notes : *** and ** indicate that estimated coefficients are statistically significant at the 1% and 5% levels,
respectively. Standard errors are in the parentheses. All models include boundary fixed effects and the following
controls: distance to the nearest station, land use categories, elevation, slope angle, and flood risk.

Finally, we conduct a falsification test based on fake boundaries. We create a fakeboundary 𝛿𝛿 meters away from the actual boundary, and obtain a placebo estimate under the fakeboundary. Figure 4 illustrates the placebo estimates with 𝛿𝛿 varying between −50 and 50, where apositive (negative) 𝛿𝛿 indicates that the fake boundary lies outside (inside) the true boundary. The

placebo estimates are found to be substantially larger than our baseline result (column 2 of Table 2)

21

when the fake boundary is approximately 8 m inside the actual boundary (i.e., 𝛿𝛿 = −8). This result
is perhaps because of the specific characteristics of locations inside, but very close to the high-risk
DUD boundary. The high-risk DUDs are often divided by relatively wide roads. Locations facingsuch wide roads tend to have newer and fire-resistant buildings, even inside the high-risk DUDs.
Conversely, locations that are few meters inward from the high-risk DUD boundary tend to havedense, older wooden houses. Thus, our baseline result under the actual high-risk DUD boundary(column 2 of Table 2) is considered a conservative estimate of the impact of earthquake risk

-.1

0

.1

.2

reduction on land values.

-50

0
Delta
Placebo estimates

50
95% confidence interval

Figure 4. Placebo estimates under the fake boundaries

8. Conclusions
In anticipation of the occurrence of a massive inland earthquake, Tokyo has been promoting urban
redevelopment to reduce earthquake risk. Our findings provide novel evidence on the benefits ofearthquake risk reduction in the two points that follow. First, the estimated boundary effects suggestthat urban redevelopment for the elimination of high-risk DUDs promote sizable benefits. Our

22

conservative estimate (column 2 of Table 2) indicates that removing high-risk DUDs increases land
values by 5.2% near the high-risk DUD boundary. Second, we find that the boundary effect increases
as the road width at the high-risk DUD boundary widens. When we focus on the sample with roadwidths of 13.0 m or wider at the high-risk DUD boundary, our estimate (column 1 of Table 4) is larger
at 20.1%. Spatial spillover of fire risk was a concern in our RD analysis because fire-risk mitigation
has a spatial spillover effect, as indicated by Kawabata et al. (2022). Given that wider roads are more
likely to prevent fire spread than narrower roads, our results suggest that using a sample with wider
roads at the high-risk DUD boundary reduces the spatial spillover or the SUTVA violation, leading toa more reliable estimate. In Tokyo, the construction of wide roads that serve as fire spread blocking
zones is an important urban redevelopment policy for creating a disaster-resistant city (TMG, 2021).
Our results imply that such urban redevelopment can have substantial economic benefits.
Our estimates focus only on the effects along the boundaries of high-risk DUDs. The
economic impact of eliminating the high-risk DUDs, however, is likely to be greater when it is further
inside those DUDs. An analysis to examine such an impact is a direction for future research. Anotherdirection is to develop panel data on high-risk DUDs and conduct a similar analysis. Such analyses
deepen our understanding of the economic benefits of urban redevelopment for earthquake risk
reduction.

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(Reiwa4nen 5gatsu 25nichi Kouhyou)].
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Appendix
Table A1. Boundary effects on land values in models with spatially autocorrelated error terms

Optimal bandwidth(1)(2)

Fixed bandwidth (100m)(3)(4)

0.049 **

0.040 *

0.061 ***

0.062 ***

(0.022)(0.023)(0.019)(0.019)Bandwidth (m)

69.6

66.9

100.0

100.0

Boundary FE

X

X

X

X

Outside DUDs

Controls

XX

N (outside)

1,192

1,126

1,825

1,825

N (inside)

1,066

1,024

1,507

1,507

N (total)

2,258

2,150

3,332

3,332

Notes : ***, ** and * indicate that estimated coefficients are statistically significant at the 1%, 5% and
10% levels, respectively. Standard errors are in the parentheses. We choose the polynomial order of 1(i.e., local linear specification) in all models. All models include boundary fixed effects. Columns 2 and 4
include distance to the nearest station, land use categories, elevation, slope angle, and potential flood
depth as additional controls.

31