The Impact of Flooding on Real EstateTransactions in Densely Populated Areas:
Evidence from the 2019 Typhoon Hagibis in Japan∗
Ikuto Aiba†

Daisuke Hasegawa‡
July 1, 2024

Abstract
We examine the impact of flooding on real estate transactions in an urban area, using asa natural experiment the flooding caused by Typhoon Hagibis, which hit Japan in October
2019. Applying the difference-in-differences method, we find that the properties located in
the inundated areas experienced significant declines in contract and offer prices by about
6.4% and 5.9% on average, respectively, indicating that discount rate, defined as a percentagechange from offer to contract prices, became larger by about 0.5 percentage points. Focusing
on transactions of apartments, we show that the negative effects are significant for higher
floors, whereas insignificant for lower floors. This suggests a possibility of buyers becoming
more aware that higher floors are also vulnerable to flooding because of the risks of power
and water outages. It is also revealed that the negative effects on detached houses are more
serious and appear more slowly than those on apartments.

Keywords: flood, typhoon, real estate, apartments, bargaining power
JEL classification: Q54, R11, R31∗ We are grateful to Yudai Higashi, Yusuke Makino, Hiroaki Shirai, Takeru Sugasawa, Atsushi Yamagishi, and

Jun Yoshida for valuable comments and discussions. We also appreciate the River Planning Division in the Ministryof Land, Infrastructure, Transportation and Tourism (MLIT) for providing the data on the areas flooded by Typhoon
Hagibis. This research is financially supported by Center for Real Estate Innovation, the University of Tokyo. Allremaining errors are our own.
† Graduate School of Economics, the University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan.
E-mail: aiba@e.u-tokyo.ac.jp.
‡ Graduate School of Engineering, the University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan.
E-mail: hasega60@e.u-tokyo.ac.jp.

11Introduction

Climate change may increase the threat of natural disasters in the near future. In fact, theintensity of tropical cyclones has increased over the past few decades and the most intense
cyclones are predicted to become more frequent by the end of the twenty-first century (Emanuel,2005; Knutson et al., 2010). Furthermore, an increase in precipitation can result in making more
people and properties face the risk of flooding (Intergovernmental Panel on Climate Change,
2012; Wing et al., 2022). Under these circumstances, it becomes more and more important
to grasp how floods affect our lives and properties, particularly in urban areas with a large
population and many economic activities.1
Densely-populated urban areas are often characterized by a lot of high-rise buildings. Compared with lower floors, higher floors are seemingly safe even if a flood occurs because suchfloors would not be flooded above the floor level. However, flooding may bring about power andwater outages around the flooded areas due to damages on electrical power facilities, harming
not only lower but also higher floors. This implies that higher floors are also not free from theflood risk as long as the building is located in a flood-prone area. As pointed out by Gourevitch
et al. (2023), residential properties at flood risks tend to be overvalued. Here in the context of
populous cities, a question is whether the housing market adequately reflects the risk of higher
floors in case of flooding.
To examine whether flood risks are perceived and capitalized into the market prices, it isuseful to study what happened to the housing market when floods actually occurred because
natural disasters can update people’s risk perception (Carbone et al., 2006). Thus, the present
study evaluates the impact of flooding on the housing market in an urban area with a large
population, using as a natural experiment the flood in Tokyo metropolitan area induced by the2019 Typhoon Hagibis, one of the costliest typhoons that ever hit Japan.
A notable feature of this flood is that it brought about power and water outages in apartmentbuildings due to flooding of electric equipment, forcing residents in such buildings to live an
inconvenient life until recovery, even on high floors without direct damages of the inundation. For
example, in one tower block residence, the rising water level caused power outages in electricaland mechanical facilities, shutting down the water supply and elevators. It took one week torestore power and two weeks to restore water (NIKKEI, 2020). Thus, this flood is considered
to have updated people’s perception of flood risks in apartment buildings and suitable for our
research purposes.
As an estimation strategy, we employ the difference-in-differences approach. Along withthe literature, we take the contract price of a property as the outcome variable. In addition,
the data that we use in the present study lets us know not only contract prices but also prices
initially offered by sellers. Note that a feature of real estate transaction is that properties areoften discounted during the process of transaction. Thus, the availability of the data on offer
1Kocornik-Mina et al. (2020) point out that economic activities are more concentrated on low-elevation urban
areas, although these areas are flooded more frequently. They also document that economic activities do not moveout of these areas in response to floods.

2prices enables us to see the effects on discount rates, from which we can obtain an insight into
how bargaining powers of sellers and buyers are affected by disasters.
We first conduct the regression combining both apartments and detached houses into a single
sample. The results suggest a significantly negative impact of flooding. Specifically, the contract
and offer prices of properties located in the flooded areas declined by about 6.4% and 5.9% on
average, respectively, compared with those located outside the inundated areas. Consistent withthese observations, it is also shown that the discount rates were significantly larger in the flooded
areas by about 0.5 percentage points. These results indicate that the flood not only had a negative
impact on the transaction price itself, but also made the bargaining power of buyers stronger andthat of sellers weaker. Stated differently, the decline in contract prices in the flooded areas canbe partly attributed to changes in bargaining powers of sellers and buyers.
We then examine transaction of apartments. Dividing the sample into properties located onhigher and lower floors, we find that the effects were insignificant for lower floors and significantly
negative for higher floors. Because the flooding induced by Typhoon Hagibis brought about
power and water outages in apartment buildings due to flooding of electric equipment, it can bethought that buyers updated their risk perception and became more aware that not only lower
but also higher floors are vulnerable to flooding. On the other hand, the flood risk of lower
floors, such as inundation above the floor level, had long been recognized among buyers and had
already been capitalized into the prices. From these, the results exhibit significantly negative
impacts on higher floors, whereas not on lower floors.
Focusing on transaction of detached houses, we find that the quantitative impacts on detachedhouses are larger than those on apartments, regarding both prices and discount rates. A possible
explanation for this observation is that most of detached houses are made of wood, more
vulnerable to flooding than reinforced concrete or steel, resulting in more serious impacts.
Furthermore, we also investigate the dynamic effects of flooding, applying the event study
design. The results indicate that the effects on apartments appeared rather immediately, about ayear after the typhoon hit, whereas those on detached houses appeared more slowly, about three
years after the hit. There are three possible reasons for these observations. First, the liquidityof detached houses is lower than that of apartments. Second, owners of the flooded houses
postponed putting up for sale to restore the parts damaged by the flood. Third, it became difficult
for the damaged houses to be sold until the prices are largely discounted, resulting in the longer
time-on-market.
The present study is related to the ones that investigated the effects of flooding on real estatemarkets. It is mostly agreed by many extant studies that housing markets are negatively affectedby flood events: for example, the 1992 Hurricane Andrew (Hallstrom and Smith, 2005); the1993 flood on the Missouri and Mississippi rivers (Kousky, 2010); the 1994 flood in DoughertyCounty, Georgia (Atreya et al., 2013); the 1999 Hurricane Floyd (Bin and Polasky, 2004; Bin
and Landry, 2013); the 2005 Hurricane Katrina (Vigdor, 2008); the great Iowa flood in 2008
(Yi and Choi, 2020); and the 2012 Hurricane Sandy (Ortega and Tas.pınar, 2018; Cohen et al.,3

2021).2 In line with these studies, we also find that prices of properties located in the areas
flooded by Typhoon Hagibis declined. A unique point of our study is that we focus on not only
contract prices but also offer prices. We reveal that contract prices were affected significantly
more than offer prices, implying that properties located in the inundated areas became more
likely to be discounted during the process of transaction.
Among the studies focusing on the impact of flooding due to hurricanes, Atreya et al.
(2013), Bin and Landry (2013) and Cohen et al. (2021) found that housing prices in theflood plains declined immediately after the flood, although these effects became smaller andeventually disappeared within several years, indicating that the negative effects of flooding aretemporary. Conversely, Ortega and Tas.pınar (2018) suggested an evidence on the persistence ofnegative impacts. They studied the case of Hurricane Sandy and found that damaged properties
experienced a large drop in prices immediately after the storm, and then the prices partially
recovered, but did not come back to the pre-flood level. In the present study, it is difficult toprovide a conclusive evidence on whether the negative impacts of flooding are temporary or
persistent, partly because we can observe only four years after the typhoon hit. On the other
hand, our study can give an insight into the timing at which the negative impacts appear.
Some studies also pointed out heterogeneity in the effects of flooding. For instance, Zhang(2016) revealed that lower-priced houses are affected more than higher-priced ones, based ona quantile regression analysis. Our study contributes to the literature by showing anotherkind of heterogeneity in the negative impact of flooding. We find that, regarding transactionsof apartments, the effect is different depending on floors on which the property is located:properties on higher floors are more affected than those on lower floors. Accounting for this“vertical heterogeneity” is crucial, particularly when managing the flood risk in large urban
areas with a dense population and many high-rise buildings.
The present study is also related to the literature of bargaining in housing markets (Han andStrange, 2015). In this literature, Harding et al. (2003b) is a seminal paper that accounted forbargaining powers of buyers and sellers in an empirical model. Their approach revealed thatbargaining powers were influenced by household wealth, gender, and other demographic traits,
and is followed by a lot of studies.3 Merlo and Ortalo-Magné (2004) is also an innovative study
in that they looked into the bargaining process in housing markets by analyzing the records oflisting price changes and sales agreements. They found that listing price revisions were caused
by a lack of offers, and the size of the reduction in the listing price tended to be larger when
the property had been on the market longer. The present study contributes to this strand ofresearch by showing a possibility that natural disasters affect the balance of sellers’ and buyers’
2Other studies also examined the impacts of hurricanes or typhoons on, for example, business activities (Elliott
et al., 2019; Indaco et al., 2021; Okubo and Strobl, 2021; Meltzer et al., 2021), local labor markets (Belasen andPolachek, 2008; McIntosh, 2008; Deryugina et al., 2018; Groen et al., 2020), fiscal costs (Deryugina, 2017; Jerch etal., 2023; Capuno et al., 2024), homeowners’ behavior (Gallagher, 2014; McCoy and Zhao, 2018), and residential
sorting (Smith et al., 2006; Varela, 2023).
3Applications in early years include Harding et al. (2003a) and Colwell and Munneke (2006). More recently,
their framework is applied in, for instance, Steegmans and Hassink (2017), Ling et al. (2018), and Hayunga andMunneke (2021).

4

bargaining powers in the damaged areas.
In Japanese context, our study is also related to Zhai et al. (2003), who investigated the caseof the flood caused by a heavy rain in Aichi Prefecture, containing the central part of the thirdlargest metropolitan area in Japan. Their study is similar to ours in that they examined flooding
in densely-populated urban areas, but different in that their focus was on the effects on appraised
land values. We rather focus on the impacts on transaction prices and elucidate how the impacts
are different between apartments and detached houses. To the best of our knowledge, the present
study is the first to quantify the impact of Typhoon Hagibis on the real estate market.
The remainder of the paper is organized as follows. Section 2 briefly explains the backgroundon Typhoon Hagibis. Section 3 describes the data that we use. Section 4 provides the econometricframework. Section 5 shows the regression results. Section 6 examines the dynamic effects.
Finally, Section 7 concludes the paper and provides some implications derived from the present
study.

2

BackgroundOn October 6th, 2019, a tropical storm grew into a typhoon in east of the Mariana Islands andwas named Hagibis. It rapidly intensified and reached a central pressure of 915 hPa on October7th. Heading north, Typhoon Hagibis made landfall on October 12th at the Izu Peninsula,
southwest of the Tokyo metropolitan area, Japan, with a central pressure of 955 hPa and amaximum wind speed of 40 m/s (Japan Meteorological Agency, 2019). Heavy rains, windstorms, tidal waves, and storm surges were caused by this extremely strong typhoon. The
damage was extensive throughout Japan, affecting 33 of the 47 prefectures. In particular, many
areas where the typhoon passed through experienced the largest amount of precipitation everrecorded. This extreme rainfall caused rivers to overflow and landslides in many areas. After
passing over Japan, the typhoon became an extratropical low in northeast of Japan on October
13th.
According to the available sources, more than 100 lives were lost, over 100,000 homes
were damaged, and flood damage was estimated at JPY 1.88 trillion. It was the largest amountof damage since 1961, the start of Japanese flood statistics (Ministry of Land, Infrastructure,
Transportation and Tourism, 2019, 2021; Fire and Disaster Management Agency, 2019). For
example, the flooding of the Chikuma River in Nagano Prefecture caused the submergence ofa Shinkansen rail yard. In Fukushima and Miyagi Prefectures, the Abukuma River overflowed,
killing more than 50 people, about a half of the total deaths due to this typhoon. Because thistyphoon caused extensive damages over a wide area in eastern Japan, it is also known as the“East Japan Typhoon” in Japan.
The Tokyo metropolitan area, where the population is concentrated, also suffered distinctive
damage. Concentrated precipitation in Tokyo and Kanagawa Prefectures caused internal floodingin the lower reaches of the Tama River, where many high-rise residential buildings are located.

5

③Tokyo Prefecture④⑨

CBD of Tokyo

②
⑧

⑦

①

⑥

⑤

Kanagawa Prefecture① Ōta Ward
② Setagaya Ward
③ Komae City
④ Chōfu City
CBD of Yokohama⑤ Kawasaki Ward (Kawasaki City)⑥ Saiwai Ward (Kawasaki City)⑦ Nakahara Ward (Kawasaki City)⑧ Takatsu Ward (Kawasaki City)⑨ Tama Ward (Kawasaki City)

Figure 1: Locations of the municipalities.
Notes: The figure shows the map of Tokyo and Kanagawa Prefectures. The thick and thin lines represent
prefectural and municipal boundaries, respectively. The colored municipalities with numbers are the ones ofour focus.

The heavy rain made it difficult to remove rainwater from sewers to rivers, causing water fromthe river to flow back through drainpipes and making some areas along the river flooded. This
results in power outages and water supply disruptions in these areas.

3

Data

In the present study, we focus on nine municipalities located in the lower reaches of the TamaRiver. In these areas, the Tama River coincides with the prefectural border: that is, the left
and right banks belong to Tokyo and Kanagawa Prefectures, respectively. The locations of themunicipalities are shown in Figure 1. The colored municipalities with numbers are the oneson which we focus in the present study. The municipalities labeled with one through four —Ōta Ward, Setagaya Ward, Komae City, and Chōfu City — are located on the left bank of theTama river and belong to Tokyo Prefecture. The municipalities labeled with five through nine
are Kawasaki Ward, Saiwai Ward, Nakahara Ward, Takatsu Ward, and Tama Ward, all of which
are sub-municipal districts of Kawasaki City. These municipalities are located on the right bankof the Tama River and belong to Kanagawa Prefecture.
Tokyo Prefecture contains the capital of Japan, Tokyo, and the location of its central business
district (CBD) is also shown in Figure 1. Kanagawa Prefecture is the second most populous
6prefecture of Japan after Tokyo. Although a large part of Kanagawa Prefecture is regarded as
the suburban areas of the Greater Tokyo, the capital city, Yokohama, hosts one of the greatest
ports in Japan and has large business and commercial districts. In Figure 1, we also show thelocation of Yokohama’s CBD.
Located between two large cities, our study areas host a large population.4 About two million
people live in the four municipalities of Tokyo Prefecture, and about one million people residein the five municipalities of Kanagawa Prefecture. In total, there are more than three million
people living in our study areas.

3.1

Real Estate Transaction Data

The data for real estate transactions is derived from Real Estate Information Network Systems(REINS), which has a large administrative transaction dataset. From this dataset, we extract thedata on apartments and detached houses that are located in the municipalities shown in Figure
1 and transacted between January 2016 and August 2023.5
The data provides not only contract prices, but also the prices initially proposed by a seller,
which we refer to as offer prices. The availability of these two kinds of prices enables us tocalculate how much a property was discounted during the process of transaction. Thus, wedefine the discount rate as follows:(discount rate) = 100 ×(contract price) − (offer price).
(offer price)

Besides the prices, the data also contains a large set of information about characteristics ofproperties, which we can use as control variables in a regression. For example, the informationon the walking time to the nearest train station and the age of a structure is available for bothapartments and detached houses. In the sample of apartments, we can also use the informationon the exclusive area of an apartment and the floor where an apartment is located. On the other
hand, the sample of detached houses provides us of the information on the area of land, the widthof a road faced by the house, and the floor-area ratio. The details of all other control variables
are provided in Appendix A.

3.2

Treatment and Control Groups

Although we use the data on transactions of real estates located in the municipalities illustrated
in Figure 1, ex ante risks of flooding are different across locations even within these areas. Since
the flooded areas were thought to face a risk of flooding even before the typhoon hit, we should
4According to the 2020 Census, the number of population is 748,081 in Ōta Ward, 943,664 in Setagaya Ward,
84,772 in Komae City, 242,614 in Chōfu City, 232,965 in Kawasaki Ward, 171,119 in Saiwai Ward, 263,683 inNakahara Ward, 234,328 in Takatsu Ward, and 221,734 in Tama Ward. The total population of these municipalities
adds up to 3,142,960.
5In our full sample, observations for apartments account for about 78%, while the other 22% is for detachedhouses.

7

Table 1: Summary statistics.
VariableN
Mean
Sd Median Min
Max
Common to apartments and detached houses
contract price (ten thousand yen)
28330
4499
2492
4230
9 52800
offer price (ten thousand yen)
28330
4608
2561
4300
12 53800
discount rate (%)
28330-2.611 4.279 -1.389 -72.5
0flooded by Typhoon Hagibis (dummy)
28330 0.02993 0.1704001after flooding (dummy)
28330 0.4999
0.5
0
0
1
walking time to the nearest station (minutes) 26891
8.751 4.785
8135
age of structure (years)
27442
18.92 13.01
160
96
Only for apartments
exclusive area (m2 )
21966
62.62 21.45
66.33 7.99 330.6
floor
21490
5.603 6.283
4
1
57
Only for detached houses
land area (m2 )6364
89.32 47.25
79.38 21.2 704.8
width of facing road (m)5578
5.194 2.429
4.5
0.8
36
floor-area ratio
5971
154.6 58.97
160
40
500
Notes: In the column labeled with “N,” we report the number of observations that are not missing. Sd, Min,and Max represent standard deviation, minimum value, and maximum value, respectively.

focus on the properties located in the areas that were also considered to have a similar risk, inorder to uncover the causal impacts of the 2019 flooding. For this purpose, we refer to flood
hazard maps provided by the Digital National Land Information System and use the one forflooding from rivers.6 Then, we extract the data on transactions of real estates located in these
expected flooded zones.
In the subsequent analysis, we take the difference-in-differences approach. Since our focus
is on the effects of flooding caused by the typhoon, the properties located in the flooded areas areclassified as the treatment group; whereas those which are located within the expected flooded
zones but did not experience the flooding are classified as the control group. The polygon data of
the inundated areas is provided by Ministry of Land, Infrastructure, Transportation and Tourism.
In preparation for the analysis, we construct two dummy variables. One is a variable thattakes one if a property under transaction is located in the areas flooded by the typhoon, and theother is a variable that takes one if the date of contract is after the typhoon strike.7

3.3

Summary Statistics

Table 1 shows summary statistics for main variables used in the analysis.8 It indicates thatproperties are often discounted to some extent during the process of transaction. The mean ofthe discount rates is about −2.6%, and the median is about −1.4%.
6The hazard maps are the ones in 2022 and available at the webpage of the Digital National Land Information
System: https://nlftp.mlit.go.jp/ksj/ (last accessed on April 22, 2024).
7Specifically, the after-typhoon dummy takes one if the date of contract is October 14th, 2019 and beyond,because the typhoon passed over Japan and became an extratropical low on October 13th.
8Summary statistics for other categorical variables are also shown in Appendix A.

8

3500

21490
20000

3000

Frequency

2500

15000
12500

2000

10000

1500

7500

Cumulative frequency

17500

1000

frequency

5000

cumulative frequency

500

2500

0

0
1

4

7

10

14

18

22

26

30

34

38

42

46

50

54

58

Floor

Figure 2: The number of observations for apartments by floor.
Notes: The horizontal axis represents floor. The vertical axis on the left side and green bars show the frequencyof each floor, while the vertical axis on the right side and the red line show the cumulative frequency.

As the properties in our sample are located in densely populated areas, we have a rich set ofobservations for apartments. Figure 2 gives the distribution of floors on which the properties arelocated, showing that about 40% of the apartments in our sample are located on fifth or higher
floors. This enables us to examine whether the impacts of the flooding are different between
higher and lower floors.
We also have about six thousand observations for detached houses, although relatively fewer
than those for apartments. Thus, it is possible to investigate the difference in the quantitative
impacts between apartments and detached houses.

4

Empirical Framework

To examine the impact of the flooding caused by Typhoon Hagibis, we employ the differencein-differences approach and specify the estimation equation as follows:⊤
𝑦𝑖𝑟𝑡 = 𝛼 + 𝛽1 × ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 + 𝛽2 × 𝑎 𝑓 𝑡𝑒𝑟 𝑡 + 𝛾 × ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 × 𝑎 𝑓 𝑡𝑒𝑟 𝑡 + 𝒛𝑖𝑟𝑡
𝜹 + 𝜇𝑟 + 𝜏𝑡 + 𝜀𝑖𝑟𝑡 ,(1)where 𝑦𝑖𝑟𝑡 is an outcome variable for property 𝑖 located in municipality 𝑟 and sold on date 𝑡;ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 is a dummy variable that takes one if 𝑖 is in the area flooded by the typhoon; 𝑎 𝑓 𝑡𝑒𝑟 𝑡
is a dummy variable that takes one if 𝑡 is after the typhoon strike; 𝒛𝑖𝑟𝑡 is the vector of control
variables; 𝜇𝑟 is the municipality fixed effect; 𝜏𝑡 is the contract year fixed effect; and 𝜀𝑖𝑟𝑡 is theerror term. The coefficient of our focus is 𝛾, which captures the treatment effect.
As the outcomes, we mainly consider three kinds of variables. One is the logarithm of the9Table 2: Baseline results.

intercept
hagibis × after
hagibisafter
Num.Obs.
R2
R2 Adj.

(1)log(contract price)(2)
log(offer price)(3)
discount rate (%)

18.094***(0.032)−0.066***(0.024)0.040(0.085)0.011(0.008)18.117***(0.032)−0.061**(0.024)0.036(0.085)0.013(0.008)−2.179***(0.288)−0.471**(0.220)0.261**(0.104)−0.211***(0.076)23 8130.595
0.594

23 8130.590
0.589

23 8130.103
0.101

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clusteredin the municipalities.

contract price of a property, which is widely used as an outcome variable in many extant studies
with a hedonic approach; another is the logarithm of the offer price that was initially proposedby a seller; and the other is the discount rate. Thus, if we take the contract or offer prices as theoutcome variables, the prices of the properties flooded by the typhoon differ by 100 × (e𝛾 − 1)%compared with those outside the flooded areas. On the other hand, if we take the discount rate
as the outcome, the discount rates of the properties in the flooded areas differ by 𝛾 percentage
points compared with those outside the flooded areas. Therefore, if the flooding caused by thetyphoon has a negative impact on the properties located within the inundated areas, 𝛾 is expected
to be significantly negative.
As a baseline analysis, we first estimate (1) using the sample with both apartments anddetached houses. In this analysis, we only use control variables available for both apartments
and detached houses: walking time to the nearest station, age of structure, building structure,zoning of areas, land right, and current status. In addition to these common covariates, we also
include a dummy variable that takes one if the property is a detached house, in order to control
the type of a property.
We also estimate (1) using only with the sample for either apartments or detached houses.
In the analysis with the sample for apartments, we control exclusive area, floor, and the directionof a balcony, besides the common covariates described above. In the analysis with the sample
for detached houses, we control land area, floor-area ratio, width of facing road, the number anddirections of sides facing roads, and terrain features, in addition to the common covariates.

10

Table 3: Effects on apartments.

intercept
hagibis × after
hagibisafter
Num.Obs.
R2
R2 Adj.

(1)log(contract price)(2)
log(offer price)(3)
discount rate (%)

15.847***(0.073)−0.036(0.027)−0.018(0.035)−0.009(0.006)15.855***(0.060)−0.032(0.026)−0.019(0.034)−0.004(0.006)−0.708(1.678)−0.453**(0.210)0.066(0.223)−0.484***(0.045)18 2950.872
0.871

18 2950.876
0.876

18 2950.098
0.095

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clusteredin the municipalities.

5

Results

We first provide the results for our baseline regression, in which both apartments and detachedhouses are integrated into a unified sample. In Table 2, the first, second and third columns show
the results for the cases where the outcome variables are the log of contract price, the log ofoffer price, and discount rate, respectively. The estimate of the coefficient of our focus, 𝛾, isreported in the row labeled with “hagibis × after.”
In columns (1) and (2), the estimates of 𝛾 are −0.066 and −0.061, implying that the contract
and offer prices of properties located in the inundated areas declined by about 6.4% and 5.9% on
average, respectively. Note that the quantitative impact is larger for the contract prices by about
0.5 percentage points. Consistent with this observation, the regression result shown in column(3) indicates that the discount rates are larger in the flooded areas by about 0.47 percentage
points.
These results imply that the flood caused a transfer of bargaining power from sellers tobuyers, resulting in larger discount rates in the flooded area. That is, it can be considered thatthe negative effects of flooding on contract prices are partly attributed to changes in bargaining
powers of sellers and buyers.

5.1

Apartments

We next provide the results for analyses only with the sample for apartments. In Table 3, columns(1) and (2) show the results for the cases where the outcome variables are the log of contract
price and offer price, respectively. Contrary to the baseline results, we do not find statisticallysignificant impacts on these prices. On the other hand, the effects on discount rate, reported incolumn (3), are significantly negative, in line with the baseline results.
11

Table 4: Effects on apartments by floor.
≥ 5th floor(1)log(contract price)intercept
hagibis × after
hagibisafter
Num.Obs.
R2
R2 Adj.

< 5th floor(2)(3)
log(offer price) discount rate (%)(4)
log(contract price)(5)
log(offer price)(6)
discount rate (%)

16.500***(0.070)−0.076***(0.011)
0.006(0.032)−0.010(0.008)16.554***(0.067)−0.068***(0.011)
0.004(0.029)−0.005(0.008)−4.572***(0.661)−0.724**(0.336)
0.187(0.256)−0.484(0.363)15.721***(0.081)
0.009(0.029)−0.037(0.028)−0.003(0.008)15.731***(0.072)0.011(0.028)−0.037(0.029)0.002(0.007)−0.869(1.796)−0.160(0.245)−0.005(0.163)−0.478*(0.253)

7695
0.879
0.878

7695
0.883
0.882

7695
0.093
0.087

10 6000.870
0.869

10 6000.874
0.874

10 6000.103
0.099

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clustered in the municipalities.

A possible reason for this mixed observation is that the impact of flooding is different
depending on floors on which the properties are located. To confirm this point, we divide thesample into two groups: apartments located on floors lower than fifth (lower-floor group), andones located on fifth floor or floors higher than fifth (higher-floor group).
The results are shown in Table 4. In columns (1)-(3) and (4)-(6), we report those forhigher- and lower-floor groups, respectively. In both groups, the first, second and third columns
correspond to the cases where the outcome variables are the log of contract price, the log of offer
price, and discount rate, respectively. Focusing on the higher-floor group, we find significantly
negative impacts for all outcomes. Columns (1) and (2) indicate that the quantitative impacts
on contract and offer prices are, on average, about −7.3% and −6.6%, respectively. In linewith these estimates, column (3) reports that the discount rates are larger for properties in theinundated areas by about 0.72 percentage points. Conversely, the results shown in columns(4)-(6) indicate that the flooding had no statistically significant impacts on any outcome for the
lower-floor group. From these observations, we can infer that the negative effects are significant
for higher floors, whereas insignificant for lower floors.
To look into this point more, we additionally conduct similar analyses by changing thethreshold floor. In Figure 3, we plot the estimates of 𝛾 obtained by regressions in which
the sample is restricted to properties located on relatively higher floors. The horizontal axis
represents the threshold floor. For instance, if the value on the horizontal axis is five, it meansthat the plotted estimate is the one obtained by defining the higher-floor group as properties
located on fifth floor or floors higher than fifth. Panel (a) shows the estimates in the cases where
the outcomes are the log of contract and offer prices. The estimates are significantly negative
for any threshold between two and ten. Panel (b) plots the estimates under taking the discountrate as the outcome variable. We do not find statistical significance when the threshold floors
are between two and four. However, if the threshold floor is made higher, the estimates are
significantly negative and the 95% confidence interval becomes narrower.
In Figure 4, we plot the estimates of 𝛾 obtained by regressions using the sample only withproperties located on relatively lower floors. Again, the horizontal axis represents the threshold
12

0.5

offer pricecontract price

0.02

0.0

−0.02

Coefficient

Coefficient

0.00

−0.04

−0.5

−1.0

−0.06
−1.5
−0.08
−0.10

−2.0

2

3

4

5

6

7

8

9

10

2

3

4

Threshold

5

6

7

8

9

10

Threshold(a) Prices(b) Discount rate

Figure 3: Effects on relatively higher floors.
Notes: The horizontal axis shows the threshold floor. For example, if the value on the horizontal axis is five,the plotted estimates are the ones obtained when we define the higher-floor group as properties located on
fifth or higher floors. Panel (a) plots the estimates obtained by taking the log of contract and offer prices
as the outcome variables. Panel (b) plots the estimates obtained by taking the discount rate as the outcome
variable. The vertical bars extending up and down from each point represent 95% confidence intervals.

floor. For example, if the value on the horizontal axis is four, it means that the plotted estimate
is the one obtained by defining the lower-floor group as properties located on fourth floor or
floors lower than fourth. Panel (a) shows the estimates in the cases where the outcomes are thelog of contract and offer prices, while panel (b) plots the ones obtained by taking the discountrate as the outcome. In both panels, we do not find statistical significance for any threshold
between two and ten. Recall that the distribution of floors on which the properties are locatedis quite right-skewed and about 60% of the sample are located on fourth or lower floors (seeFigure 2 in Section 3), implying that the estimation results for the lower-floor group are likely
to be driven by the properties on these floors. Thus, the estimates are insignificant even with“high” threshold values, such as nine or ten. In Appendix B.1, we also estimate equation (1)using the sample only with properties located on the first floor. As expected, we do not findstatistical significance for the estimate of 𝛾, supporting the robustness of the observation thatthe flood event had no impact on lower floors.
It might be counter-intuitive that the negative effects are significant for higher floors but
insignificant for lower floors, because higher floors seem not to be directly affected by theinundation. However, a remarkable characteristic of the flooding induced by Typhoon Hagibis isthat it brought about power and water outages in apartment buildings due to flooding of electric
equipment, which is often located on low or underground floors. Because of this, residents insuch buildings were forced to live an inconvenient life until recovery, even on higher floors thatwere not directly damaged by the inundation. Thus, it can be thought that buyers became moreaware that not only lower floors but also higher floors are vulnerable to flooding, whereas therisks of lower floors in case of flooding, such as inundation above the floor level, had long beenrecognized among them. This is why we observe significantly negative effects on higher floors,13

offer pricecontract price

0.10

0.5

0.0

Coefficient

Coefficient

0.05

0.00

−0.5

−0.05

−1.0

−0.10

−1.5

2

3

4

5

6

7

8

9

10

2

3

4

Threshold

5

6

7

8

9

10

Threshold(a) Prices(b) Discount rate

Figure 4: Effects on relatively lower floors.
Notes: The horizontal axis shows the threshold floor. For example, if the value on the horizontal axis is five,the plotted estimates are the ones obtained when we define the lower-floor group as properties located on fifth
or lower floors. Panel (a) plots the estimates obtained by taking the log of contract and offer prices as theoutcome variables. Panel (b) plots the estimates obtained by taking the discount rate as the outcome variable.
The vertical bars extending up and down from each point represent 95% confidence intervals.

while not on lower floors.
As an additional test, Appendix B.2 gives an investigation on how the flood event affected
properties located on relatively higher floors in the areas that were not flooded by the typhoon.
Specifically, we focus on apartments in the entire area of the nine municipalities mentioned
in Section 3, except for the areas flooded by the typhoon, and regard properties located in the
expected flooded zones as the treatment group and those located outside the expected flooded
zones as the control group. Then, we find negative impacts on the prices of properties locatedin the expected flooded zones, albeit milder compared with the effects on the properties in theinundated areas. This implies that the flood event also updated buyers’ perception of the risk ofhigh floors even outside the inundated areas, resulting in a decline in prices. On the other hand,
the impact on discount rates is insignificant, indicating that a transfer of bargaining power fromsellers to buyers was specific to the areas that actually experienced the flooding.

5.2

Detached Houses

Table 5 gives the results for regressions with the sample for detached houses. Again, columns(1), (2), and (3) show the results for the cases where the outcome variables are the log of contract
price, the log of offer price, and discount rate, respectively. The results indicate that the contract
and offer prices of detached houses located in the flooded areas declined by, on average, about
11.0% and 9.7%, respectively, compared with the ones located outside the inundated areas.
Consistent with these observations, the discount rates are larger by about 1.2 percentage points
in the flooded areas.
Note that the quantitative impacts on detached houses are larger than those on apartments.

14

Table 5: Effects on detached houses.
(1)log(contract price)(2)
log(offer price)(3)
discount rate (%)

17.160***(0.050)−0.116***(0.031)0.001(0.039)0.060*(0.031)17.175***(0.052)−0.102***(0.030)−0.015(0.037)0.054*(0.030)−1.446***(0.535)−1.224***(0.373)1.350***(0.332)0.525*(0.301)

3135
0.753
0.749

3135
0.761
0.756

3135
0.170
0.154

intercept
hagibis × after
hagibisafter
Num.Obs.
R2
R2 Adj.

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clusteredin the municipalities.

This implies that the negative effects of the flooding are more serious for detached houses
than apartments. Different from apartments, most of detached houses are made of wood, more
vulnerable to flooding than reinforced concrete or steel.9 Thus, the damage of flooding is thought
to be more serious for detached houses, resulting in the quantitatively larger impacts.
In Appendix B.3, we extend our baseline regression to a triple difference framework andtest whether there exists a significant difference in the quantitative impacts on apartments anddetached houses. The results show significant differences for prices, although we cannot reject
the hypothesis that the effects on discount rates are the same across apartments and detachedhouses. Thus, it can be considered that the negative impacts of the flooding on detached houses’
prices are significantly larger than those on apartments’ prices, and the extent to which sellers’bargaining power became weaker is not quite different across apartments and detached houses.

6

Dynamic Effects

Next, we examine the dynamic effects of the flooding, applying the event study design. Let
Δbeforerepresent the set of dates that are more than 𝑠 − 1 years before the typhoon strike but not𝑠more than 𝑠 years before. Similarly, we define Δafteras the set of dates that are more than 𝑠 − 1𝑠years after the typhoon strike but not more than 𝑠 years after. Then, we estimate the following
9In our sample for detached houses, wooden ones account for about 87%.

15

Below 5th floor−0.05

Estimate and 95% Conf. Int.

−0.15

−0.15

−0.10

−0.05

0.00

Estimate and 95% Conf. Int.

0.05

0.10

0.05 0.10 0.15 0.20

5th floor and higher

wi

hin

r
r
r
e
e
er
re
re
fte
fte
fte
for
for
aft
efo
efo
sa
sa
sa
be
be
ar
rb
sb
ar
ar
ar
rs
rs
ye
ea
ye
ye
ye
y
1
ea
ea
y
y
2
3
4
n
n
n
n
n1
thi
n3
n2
thi
thi
thi
thi
wi
thi
thi
wi
wi
wi
wi
wi
wi

t

re

offer pricecontract price

r
ea
4y

hin

t
wi

re

efo
sb

for
be

hin

hin

n
thi
wi

t
wi

r
ea
3y

r
r
r
er
fte
fte
fte
aft
sa
sa
sa
ar
ar
ar
ar
ye
ye
ye
ye
1
2
3
4
n
n
n
n
thi
thi
thi
thi
wi
wi
wi
wi
e

e

efo
sb

r
ea
4y

rs
ea
2y

t
wi

r
efo

rb
ea
1y

offer pricecontract price(a) Prices
Below 5th floor

2
−2

−1

0

1

Estimate and 95% Conf. Int.

1
0
−1
−2
−3

Estimate and 95% Conf. Int.

2

5th floor and higher

for

in

th
wi

thi
wi

n

rs

ea
3y

be

n
thi
wi

re

e

e

re

efo

b
rs

ea
4y

rs

ea
2y

be

for
n

thi
wi

efo

ea
1y

rb

thi
wi

r

fte

a
ye
n1

ra

n
thi
wi

rs

ea
2y

er
aft

n
thi
wi

rs
ea
3y

er
aft

n
thi
wi

rs
ea
4y

er

wi

n

thi

for

r
efo

sb

r
ea
4y

thi

wi

n

rs
ea
3y

be

thi

wi

n

rs
ea
2y

be

wi

n

thi

er
er
er
er
aft
aft
aft
aft
rs
rs
rs
ar
ye
ea
ea
ea
y
y
y
1
n
n4
n3
n2
thi
thi
thi
thi
wi
wi
wi
wi
e

e
for

e

e

aft

r
efo

rb

ea
1y

(b) Discount rate
Figure 5: Dynamic effects on apartments.
Notes: In panel (a), we plot the estimates obtained by taking the log of contract and offer prices as theoutcome variables, while in panel (b), we plot the estimates obtained by taking the discount rate as theoutcome variable. In both panels, the left and right pictures show the estimates for the higher- and lower-floor
groups, respectively. The vertical bars extending up and down from each point represent 95% confidence
intervals.

equation:𝑦𝑖𝑟𝑡 = 𝛼 + 𝛽 × ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 +4∑︁𝛾𝑠before × ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 × 1{𝑡 ∈ Δbefore
}
𝑠

𝑠=2

+

4
∑︁

(2)

⊤
𝛾𝑠after × ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 × 1{𝑡 ∈ Δafter} + 𝒛𝑖𝑟𝑡
𝜹 + 𝜇𝑟 + 𝜏𝑡 + 𝜀𝑖𝑟𝑡 ,𝑠𝑠=1

where 1{𝑡 ∈ Δbefore} and 1{𝑡 ∈ Δafter} are the indicator functions that take one if the contract𝑠𝑠
beforeafter
date, 𝑡, is in Δ𝑠
and Δ𝑠 , respectively, and the definitions of ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 , 𝑦𝑖𝑟𝑡 , 𝜇𝑟 , 𝜏𝑡 , 𝒛𝑖𝑟𝑡 , and𝜀𝑖𝑟𝑡 are the same as those in Section 4. Here, we do not estimate 𝛾1before because we take Δbefore
1
as the base period.

16

0.3
thi

wi

n

0
−1
−3

−2

Estimate and 95% Conf. Int.

1

0.2
−0.1

0.0

0.1

Estimate and 95% Conf. Int.

−0.2
for
be

rs

ea
4y

thi

wi

n

e

re

efo
sb

r
ea
3y

n

thi

wi

rs
ea
2y

e
for
be

e
for
be

thi

n1
thi
wi

wi

ar
ye
n1

r
fte

r
r
r
fte
fte
fte
sa
sa
sa
ar
ar
ar
ye
ye
ye
2
3
4
n
n
n
thi
thi
thi
wi
wi
wi
ra

a
ye

offer pricecontract price

rs

i

a
ye
n4

th
wi

(a) Prices

e
for
be
i

th
wi

thi
wi

efo

sb

ar
ye
n2

efo

rb

a
ye
n1

thi

wi

r
fte

re

re

re

efo

b
rs

a
ye
n3

n
thi

wi

ra

ea
1y

wi

t

2
hin

r
er
er
fte
aft
aft
sa
rs
rs
ar
ea
ea
ye
4y
3y
n
n
thi
thi
wi
wi

(b) Discount rate

Figure 6: Dynamic effects on detached houses.
Notes: In panel (a), we plot the estimates obtained by taking the log of contract and offer prices as the outcome
variables, while in panel (b), we plot the estimates obtained by taking the discount rate as the outcome variable.
The vertical bars extending up and down from each point represent 95% confidence intervals.

6.1

Apartments

As already discussed, the effects on apartments are different depending on floors where theproperties are located. Thus, we again divide the sample into two groups: floors below fifth(lower-floor group), and fifth and higher floors (higher-floor group).
The results of estimating (2) with each group’s sample are plotted in Figure 5. In panel (a),we show the estimates of 𝛾𝑠before and 𝛾𝑠after when we take the log of contract and offer prices
as the outcomes. The left and right pictures provide the effects on the higher- and lower-floor
groups, respectively. For the higher-floor group, the estimates are significantly negative one year
after the typhoon strike. On the other hand, the effects on the lower-floor group are consistently
insignificant both before and after the typhoon hit. In panel (b), we plot the results of taking
discount rate as the outcome. Again, the left and right pictures show the effects on the higher- andlower-floor groups, respectively. Although the point estimate for the higher-floor group sharply
drops immediately after the typhoon strike, the 95% confidence interval is quite wide and we do
not find statistical significance. For the lower-floor group, the estimates are consistently close
to zero and statistically insignificant.

6.2

Detached Houses

The results of estimating (2) for detached houses are plotted in Figure 6. In panel (a), we show theestimates of 𝛾𝑠before and 𝛾𝑠after when the log of contract and offer prices are taken as the outcomes.
After the hit of typhoon, the point estimates gradually decline and exhibit statistical significancemore than three years after the strike. The effect on discount rate, shown in panel (b), also has asimilar trend. Note that the quantitative impacts are larger than those on apartments, consistent
with the results from the difference-in-differences analyses. These observations indicate thatthe negative effects of flooding on detached houses appear more slowly and quantitatively more
serious than those on apartments.
17

The possible reasons for the slow appearance of the effects on detached houses are as
follows. First, detached houses have lower liquidity than apartments. In Japan, as pointed out by
Ministry of Land, Infrastructure, Transportation and Tourism (2020a), the number of housing
starts are larger for detached houses than for apartments, whereas the number of transactions in
the existing housing market is larger for apartments, implying higher liquidity of apartments.
Second, owners of the damaged houses postponed putting up for sale to restore the parts damagedby the flood. Third, it became difficult for the damaged houses to be sold until the prices arelargely discounted, resulting in the longer time-on-market. Unfortunately, it is hard to confirm
these two predictions because we do not have the accurate information about the date on which a
property was put up for sale and cannot calculate the time-on-market. However, the mechanism
described as the third possibility is implied by the results plotted in Figure 6. This explanationis also in line with Merlo and Ortalo-Magné (2004), who showed that the longer a property had
been on the market, the larger the discount on it.

7

Concluding Remarks

In the near future, climate change may increase the intensity of hurricanes or typhoons, making
more people and properties face the risk of flooding. This would make it more important for both
policy makers and developers to assess such a risk adequately, particularly in urban areas witha large population and concentrated economic activities. The present study examined whether
properties in densely populated areas reflected the flood risk by analyzing what happened to
the real estate market after an actual flood. Specifically, we used as a natural experiment theflooding induced by Typhoon Hagibis, which hit Tokyo metropolitan area in October 2019, andapplied the difference-in-differences method to evaluate the impact of flooding on the properties
in the inundated areas.
We found that the contract and offer prices of properties located in the flooded areas declined
by about 6.4% and 5.9% on average, respectively, compared with those located outside theinundated areas. Consistent with these observations, we also revealed that the discount rates
were significantly larger in the flooded areas by about 0.5 percentage points, implying that thebargaining power of buyers became stronger and that of sellers became weaker. That is, thedecline in contract prices in the flooded areas can be partly attributed to a transfer of bargaining
power from sellers to buyers.
Focusing on transactions of apartments, we showed that the negative impact of flooding was
insignificant for lower floors, whereas significant for higher floors. These results indicate that,
although the flood risk of properties located on lower floors had already been capitalized into
prices even before the flooding occurred, the risk of those located on higher floors was not fullyreflected in prices. Thus, it can be thought that buyers became more aware that higher floors arealso vulnerable to flooding due to the risks of power and water outages, resulting in the decline
in transaction prices. We also examined transactions of detached houses and found that the18

negative effects on detached houses were quantitatively more serious and appeared more slowly
than those on apartments.
Our study can provide implications for both policy makers and developers. Policy makers
need to establish a rule such that real estate agents precisely inform buyers of what kind of flood
risks the property is subject to and what would happen to the property in case of flooding. In fact,
in response to the flood, the Ministry of Land, Infrastructure, Transport and Tourism studied
countermeasures and established guidelines for preventing buildings’ power supply facilities
from flooding (Ministry of Land, Infrastructure, Transportation and Tourism, 2020b). Taking
appropriate precautions as pointed out in these guidelines and informing residents beforehandof what to do in case of flooding, developers can enhance the value of apartments that they selleven in areas with flood risks.

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23

Appendix
A

Other Control Variables

A.1

For Both Apartments and Detached Houses

Categorical variables that can be observed for both apartments and detached houses are thefollowing four: building structure, land right, current status, and zoning regulations. The typesof zoning districts are defined by the City Planning Act and the details of each district areprovided in the Article 9.A.1 Each category’s number of observations that are not missing isshown in Table A.1. For each category, we also report the number of every sub-category’s
observations.

A.2

Only for Apartments

For apartments, the direction of a balcony is available. If a property’s balcony faces multiple
directions, only the primary direction is reported. We show the number of each direction’sobservations in Table A.2.

A.3

Only for Detached Houses

Categorical variables that can be observed only for detached houses are the following three: thecondition of sides facing roads, the direction facing a road, and terrain features. The first one
contains the information about the number of sides facing a road, and the second one informs
us of the direction that faces a road. If a property faces roads in multiple directions, only the
primary direction is reported. Each category’s number of observations that are not missing
is shown in Table A.3. For each category, we also report the number of every sub-category’s
observations.

A.1 TheEnglish translation of the City Planning Act is available at the following page:japaneselawtranslation.go.jp/ja/laws/view/3841#je ch2sc1at5 (last accessed on June 23, 2024).

A-1

https://www.

Table A.1: Categorical variables available for both apartments and detached houses.
CategoryNbuilding structure
reinforced concrete
woodensteel reinforced concrete
steellight gauge steel
othersprecast concrete
concrete blockzoning regulation
category 1 medium-to-high-rise exclusive residential districts
category 1 low-rise exclusive residential districts
category 1 residential districts
commercial districts
quasi-industrial districts
neighborhood commercial districts
category 2 residential districts
industrial districts
quasi-residential districts
category 2 medium-to-high-rise exclusive residential districts
category 2 low-rise exclusive residential districts
exclusive industrial districts
nothingland rightownership
old leasehold
fixed-term leasehold
ordinary leaseholdold superficies
fixed-term superficies
ordinary superficies
current status
with residents
vacantfor rent
under construction

27,42517,707 (65%)
5,561 (20%)
3,403 (12%)
409 (1.5%)232 (0.8%)103 (0.4%)
8 (<0.1%)
2 (<0.1%)
25,8484,932 (19%)
4,242 (16%)
3,599 (14%)
3,267 (13%)
3,197 (12%)
2,272 (8.8%)
1,541 (6.0%)
1,180 (4.6%)
835 (3.2%)
649 (2.5%)
129 (0.5%)
4 (<0.1%)
1 (<0.1%)
27,33826,878 (98%)
299 (1.1%)
48 (0.2%)
39 (0.1%)
38 (0.1%)
30 (0.1%)
6 (<0.1%)
27,576
13,211 (48%)
11,775 (43%)
2,007 (7.3%)
583 (2.1%)

Notes: For each category, we report the number of observations that are not missing and the number andshare of observations for each sub-category.

A-2

Table A.2: Categorical variables available only for apartments.
CategoryN

the direction of a balcony
south
southwest
southeast
east
west
northeast
northwest
north

20,3986,149 (30%)
3,889 (19%)
3,399 (17%)
2,793 (14%)
2,439 (12%)
672 (3.3%)
547 (2.7%)510 (2.5%)

Notes: For each category, we report the number of observations
that are not missing and the number and share of observations
for each sub-category.

Table A.3: Categorical variables available only for detached houses.
CategoryNthe condition of sides facing roads
one side
cornertwo sides
three sides
four sides
the direction facing a road
south
north
east
west
northeast
southwest
southeast
northwest
terrain features
flat
hill
slope
tieredlowlandothers

5,538
4,570 (83%)
788 (14%)159 (2.9%)
20 (0.4%)
1 (<0.1%)
5,555
963 (17%)946 (17%)805 (14%)784 (14%)533 (9.6%)
527 (9.5%)
514 (9.3%)
483 (8.7%)
4,246
4,107 (97%)
84 (2.0%)30 (0.7%)
18 (0.4%)
4 (<0.1%)
3 (<0.1%)

Notes: For each category, we report the number of observations thatare not missing and the number and share of observations for each
sub-category.

A-3

Table B.1: Effects on the first floor.
dependent variableintercept
hagibis × after
hagibisafter

log(contract price)log(offer price)

discount rate (%)

15.928***(0.060)0.082(0.065)−0.109***(0.033)0.013(0.032)15.915***(0.055)0.086(0.065)−0.112***(0.033)0.013(0.029)1.433**(0.588)−0.339(0.553)0.187(0.456)0.040(0.575)

2299
0.863
0.860

2299
0.867
0.864

2299
0.095
0.074

Num.Obs.
R2
R2 Adj.

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clusteredin the municipalities.

B

Additional Results

B.1

Effects on Properties Located on the First Floor

We estimate equation (1) using the sample only with apartments located on the first floor.B.1
The results are shown in Table B.1. The first, second and third columns show the results forthe cases where the outcome variables are the log of contract price, the log of offer price, anddiscount rate, respectively. Focusing on the coefficient of the cross term, we do not find statisticalsignificance for any outcome variable, indicating that the flooding had no impact on apartments
located on the first floor.

B.2

Effects on Properties outside the Inundated Areas

We examine how the flood event affected properties located on relatively higher floors in theareas that were not flooded by Typhoon Hagibis. To do so, we focus on apartments in theentire area of the nine municipalities mentioned in Section 3, except for the areas flooded by
the typhoon. We regard properties located in the expected flooded zones as the treatment groupand those located outside the expected flooded zones as the control group. Then, we employ thedifference-in-differences approach and estimate the following equation:⊤
𝑦𝑖𝑟𝑡 = 𝛼 + 𝛽1 × 𝑒𝑥 𝑝𝑒𝑐𝑡𝑒𝑑𝑖 + 𝛽2 × 𝑎 𝑓 𝑡𝑒𝑟 𝑡 + 𝛾 × 𝑒𝑥 𝑝𝑒𝑐𝑡𝑒𝑑𝑖 × 𝑎 𝑓 𝑡𝑒𝑟 𝑡 + 𝒛𝑖𝑟𝑡
𝜹 + 𝜇𝑟 + 𝜏𝑡 + 𝜀𝑖𝑟𝑡 , (B.1)
B.1 Different from the analyses conducted in the main text, we do not include the floor on which a property is

located as a control variable, because we focus only on properties located on the first floor.

A-4

Table B.2: Effects on high-floor properties located outside the flooded areas.
dependent variableintercept
expected × after
expected
after
Num.Obs.
R2
R2 Adj.

log(contract price)log(offer price)

discount rate (%)

16.664***(0.111)−0.042**(0.021)0.001(0.040)0.022(0.016)16.689***(0.088)−0.040*(0.021)
0.004(0.041)0.026(0.019)−2.319(2.670)−0.159(0.135)−0.237(0.163)−0.371(0.393)

2623
0.865
0.862

2623
0.866
0.863

2623
0.090
0.072

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clusteredin the municipalities.

where 𝑒𝑥 𝑝𝑒𝑐𝑡𝑒𝑑𝑖 is a dummy variable that takes one if 𝑖 is in the expected flooded zones. Again,
the treatment effect is captured by 𝛾.
We estimate equation (B.1) using the sample of apartments located on relatively higher
floors.B.2 Specifically, we focus on the properties located on tenth floor or higher. The estimation
results are shown in Table B.2. The first, second and third columns provide the results for thecases where the outcome variables are the log of contract price, the log of offer price, anddiscount rate, respectively. We find negative impacts on the prices of properties located in the
expected flooded zones, although the current sample does not contain the properties located in
the areas flooded by the typhoon. The results shown in the first and second columns imply thatboth contract and offer prices of properties in the expected flooded zones declined by about 4%,compared with those outside the expected flooded zones. Note that the quantitative impacts on
the prices of properties in the inundated areas, estimated in the main text, were around 7%. Thus,
the negative impacts on higher floors were milder compared with the effects on the properties
in the inundated areas. These results suggest a possibility that the flood event also updated therisk perception of buyers even outside the inundated areas, resulting in a decline in prices, albeit
milder.
On the other hand, as shown in the third column of Table B.2, we find no significance of the
impact on discount rates. This implies that a transfer of bargaining power from sellers to buyers
was specific to the areas that actually experienced the flooding.
Although the results in Table B.2 are the ones obtained by defining “high floors” as tenthfloor and higher, we can observe similar results even with other threshold floors. In Figure
B.1, we plot the estimates of 𝛾 obtained by regressions in which the sample is restricted to
B.2 Control variables are the same as those used in the main analyses.

A-5

1.0

offer pricecontract price0.5

Coefficient

Coefficient

0.00

−0.05

0.0

−0.5
−0.10
−1.0

6

7

8

9

10

11

12

13

14

15

6

7

8

9

Threshold

10

11

12

13

14

15

Threshold(a) Prices(b) Discount rate

Figure B.1: Effects on higher floors under various thresholds.
Notes: The horizontal axis shows the threshold floor. For example, if the value on the horizontal axis is ten,the plotted estimates are the ones obtained when we define the higher-floor group as properties located ontenth or higher floors. Panel (a) plots the estimates obtained by taking the log of contract and offer prices
as the outcome variables. Panel (b) plots the estimates obtained by taking the discount rate as the outcome
variable. The vertical bars extending up and down from each point represent 95% confidence intervals.

properties located on relatively higher floors. The horizontal axis represents the threshold floor.
For example, if the value on the horizontal axis is ten, it means that the plotted estimate is theone obtained by defining high floors as tenth floor and higher. Panel (a) shows the estimates in
the cases where the outcomes are the log of contract and offer prices. In particular, the negative
effects are more significant when the threshold floor is made higher. Panel (b) plots the estimatesobtained by taking the discount rate as the outcome variable. In most cases, we do not findstatistical significance of the impacts. If any, the significance is weak.

B.3

Triple Differences

To check whether there exists a significant difference between the quantitative impacts on apartments and detached houses, we extend our baseline regression to a triple difference framework.
Specifically, we estimate the following equation using the full sample with both apartments anddetached houses:
𝑦𝑖𝑟𝑡 = 𝛼 + 𝛽1 × ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 + 𝛽2 × 𝑎 𝑓 𝑡𝑒𝑟 𝑡 + 𝛽3 × 𝑑𝑒𝑡𝑎𝑐ℎ𝑒𝑑𝑖+ 𝛽4 × ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 × 𝑎 𝑓 𝑡𝑒𝑟 𝑡 + 𝛽5 × ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 × 𝑑𝑒𝑡𝑎𝑐ℎ𝑒𝑑𝑖 + 𝛽6 × 𝑎 𝑓 𝑡𝑒𝑟 𝑡 × 𝑑𝑒𝑡𝑎𝑐ℎ𝑒𝑑𝑖⊤+ 𝛾 × ℎ𝑎𝑔𝑖𝑏𝑖𝑠𝑖 × 𝑎 𝑓 𝑡𝑒𝑟 𝑡 × 𝑑𝑒𝑡𝑎𝑐ℎ𝑒𝑑𝑖 + 𝒛𝑖𝑟𝑡
𝜹 + 𝜇𝑟 + 𝜏𝑡 + 𝜀𝑖𝑟𝑡 ,(B.2)
where 𝑑𝑒𝑡𝑎𝑐ℎ𝑒𝑑𝑖 is a dummy variable that takes one if 𝑖 is a detached house. If the sign of 𝛾 isestimated to be significantly negative, it means that the quantitative impacts on detached houses
are more serious than those on apartments.
The results of estimating equation (B.2) are provided in Table B.3. The first, second andA-6

Table B.3: Difference in the quantitative impacts on apartments and detached houses.
dependent variableintercept
hagibis × after × detachedhagibis × after
hagibis × detachedafter × detachedhagibisafter
detached
Num.Obs.
R2
R2 Adj.

log(contract price)log(offer price)

discount rate (%)

17.298***(0.051)−0.139***(0.052)−0.040**(0.020)0.005(0.069)−0.067***(0.005)0.040(0.095)0.024***(0.008)0.826***(0.035)17.302***(0.050)−0.135**(0.056)−0.036*(0.019)−0.005(0.066)−0.068***(0.005)0.039(0.094)0.026***(0.008)0.846***(0.036)−0.461(0.333)−0.261(0.644)−0.440**(0.200)0.804(0.549)0.007(0.089)0.120(0.170)−0.209***(0.078)−1.737***(0.274)23 8130.595
0.594

23 8130.591
0.590

23 8130.103
0.101

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clustered in themunicipalities.

third columns show the results for the cases where the outcome variables are the log of contract
price, the log of offer price, and discount rate, respectively. In the first and second columns, theestimates of 𝛾 are significantly negative, implying that the effects on prices of detached houses
are more serious than those on prices of apartments. However, in the third column, we find no
significance for the estimate of 𝛾. Therefore, we cannot reject the hypothesis that the effects on
discount rates are the same across apartments and detached houses.

A-7