Running head: FLOOD RISK IMPACT ON PROPERTY MARKET

Quantifying Flood Risk Impact on Residential Property Market in Tokyo

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FLOOD RISK IMPACT ON PROPERTY MARKET

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Introduction
Environmental disasters have threatened the resilience of society, bringing about big
enough damages to even destroy civilizations throughout the human history. Recently, an
addition has been made to the family of environmental risks, as intensified human economic
activities have resulted in one of the major threats humanity has faced, that is, climate change(IPCC, 2014). Despite uncertainties, its impact is estimated to be at least 5% of the world’s
annual GDP with no adaptation policies in place (Stern, 2007), and excessive interferencewith the earth systems has now driven the rate of climate change to the point where it isirreversible (Rockström et al., 2009). Abrupt and irreversible changes in one of the earthsystems then trigger changes in the subsystems, posing multifaceted environmental risks thatneed to be dealt with on many different levels.
Among such climate change-induced environmental risks, flood risk has been given an
intense academic attention. Scientific evidence suggest that precipitation pattern has been
increased in both intensity and frequency by climate change, thereby causing more frequentand intense floods than before all over the world (IPCC, 2014). In fact, in the U.S., the annual
cost of damage from inland flooding is higher than any other disasters (U.S. Climate
Resilience Toolkit, 2020). Under the 4℃ scenario, the population in Central Europe, SouthAsia, South America and Japan that is projected to experience river flood is known to amountto ten times of that during 1976-2005 (Alfieri et al., 2017).

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Given these, the recent trends in the real estate sector seem to show that the actors in
the market started to take flood risk into serious considerations as a physical risk to assets andbusiness operations. Global Estate Measurement Code for Occupiers, the unified valuation
criteria for occupancy performance of properties offered by IPD (Investment Property
Databank Limited), explicitly included flood risk in the checklist of factors used to measuresustainability of a property (IPD, 2013). The enforcement of TCFD (Task Force on Climaterelated Financial Disclosures) worldwide, with the prevalence of ESG investment in the
background, also drives recognition and integration of flood risk into portfolio management.
GPIF, the largest institutional investor in the world, recently published a comprehensive
report on its ESG status, reporting to what extent its domestic and overseas assets are exposed
to climate change-related risk for each asset class based on TCFD framework (GPIF, 2019).
The aim of this paper is to reveal how flood risk is priced using big data that containover 180,000 properties in the metropolitan areas of Tokyo. Provision of qualitative accountof to whom and why quantified flood risk matters is also within the scope of this paper. This
study uniquely targets the residential property market of Tokyo, Japan. In Asia, priceimplications of flood risk to property markets have generally remained unknown. Most floodrisk studies have taken place mainly in the U.S., whereas in Asia these studies are scarce
despite its geographical traits subject to significant flood risk and growing public concern.
Following the methodology of the previous studies using hedonic techniques while utilizing

FLOOD RISK IMPACT ON PROPERTY MARKET

4market data of Japan, this paper discusses implications of quantified flood risk for the
relevant actors in the housing market.
Literature review
It is essential that how flood risk might affect different actors in the real estate market
be revealed, not only in quantitative terms but also qualitatively. Rather, quantitative evidence
is utilized only when its qualitative implications are fully understood. However, while studies
quantifying flood impact to property market have been accumulated as climate change
became an increasingly well-discussed topic, those that qualitatively consider why it isimportant in the first place to investigate into flood discount are disproportionately scarce. A
few meta-analyses of flood discount literature (Beltrán et al, 2018; Daniel et al., 2009b) and astudy by Pryce and Chen (2011) are among the latter group of flood risk research. This
chapter discusses why and to whom flood risk studies matter by reviewing previous literature.
Studies that investigate into flood risk impact on property price often utilize hedonic
property price method, where it is assumed that a house as a differentiated market good
represents a bundle of characteristics that constitutes the price (Rosen, 1974). In other words,
the price of a house can be explained by its attributes, such as accessibility to the station or
central business district (CBD), building area and the environmental factors such as noise,sunlight and air quality. Flood risk can theoretically be one of such attributes. Assuming
rational and informed buyers, a house exposed to flood risk is expected to be discounted

FLOOD RISK IMPACT ON PROPERTY MARKET

5compared to a house that is otherwise identical, because flood risk, as a negative attribute,should be capitalized into price. The benefit of using this method is that by regressing theprice by various hedonic characteristics, one can reveal whether and how much each specificfactor contributes to the price. A number of previous studies employing hedonic property
price method have examined the average market response to flood risk in quantified
monetary value. (Bernstein et al., 2019; Bin, Crawford, et al., 2008; Bin, Kruse & Landry,
2008; Daniel et al., 2009a).
Quantifying flood risk is useful for the actors in the property market. For homeowners,
the possibility that flood risk might undermine the value of a house is obviously a serious
problem because real estate is usually the biggest asset in an average household, not tomention physical threat of flood. It also brings about systemic implications, because privately
owned real estate plays an essential role in the financial system as a collateral for mortgages(Pryce & Chen, 2011). The failure of the market in appropriately pricing flood risk might
trigger a sudden and devastating drop in the property price in case of an actual flood event(Bernstein et al., 2019). Given the importance of actively preventing such price swing, astudy on flood discount can make a valuable contribution by examining whether there isflood discount beforehand, which could be used to inform homeowners if a risk factor issufficiently perceived and capitalized or not.
Flood risk matters to investors in real estate market significantly, as it is expected to

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trigger devaluation of properties and all the relevant business operations. As climate change
intensifies the existing environmental risks, asset owners will have to equip themselves withtools to quantify and manage further climate change risks in the portfolio, which they lack
(Burgess & Rapoport, 2019). Institutional investors such as GPIF have started to quantify
flood exposure in the portfolio.
Investment managers at REITs need to deal with flood risk, too. A recent report on
REITs and climate change risk pointed out that 23% of 73,500 properties owned by 321
REITs were subject to flooding, and Japanese REITs were especially vulnerable to typhoons(Four Twenty Seven & GeoPhy, 2018). It is commonly understood that with ESG investment
trend, REITs will frequently face requests from climate-concerned investors to disclose
information about how they manage flood risk. REITs that are eager to attract ESGenthusiastic institutional investors will find it unavoidable to manage flood risk either byinvesting in protection of or by retreating from properties whose sustainability is threatened.
Quantified flood risk information is therefore valuable to both asset owners and REITs thatneed to determine the optimal risk-return with flood risk in sight.
Administrative authority has always been an actor that may benefit from a valuable
insight flood studies offer, as identifying the optimal level of the government expenditure for
future SLR flood adaptation by performing a cost-benefit analysis has long been one of thethemes of environmental economics (Fankhauser, 1995; Yohe et al., 1996; Yohe &FLOOD RISK IMPACT ON PROPERTY MARKET

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Schlesinger, 1998). This line of research, however, has its own shortcomings, and flood risk
study such as this one is capable of supplementing such shortcomings. First, the results arealmost always unreliable due to unavoidable uncertainties in climate change scenarios.
Regression approach this paper utilizes, on the other hand, has a better chance to provide
results to be actually used as a tool to grasp the quantitative impact of flood to property
market, because it is not subject to uncertainties as it reveals the average past response of themarket participants to flood risk. Second, studies concerned with anticipation focus on flood
damage rather than flood risk. The implication of this is that calculating rough estimate ofpotential flood damage does not necessarily reveal the real effect of flood damage andprotection policy in terms of welfare, while calculating marginal willingness to pay (WTP) toflood risk can be used as a potential measure of welfare loss associated with flood risk (Pryce& Chen, 2011). Quantifying perceived flood risk in terms of property price discount canmake calculation of the potential benefit of flood relief projects possible on a more accurate
and regional scale by multiplying discount rate and the average property price, as Beltrán etal. (2018) demonstrates. For these reasons, flood risk research carries useful implications foradministrative authorities.
Data and research method
Individual housing data with more than 200,000 initial observations were obtained
from a Japanese real estate firm LIFULL. The dataset contains asking price of houses andFLOOD RISK IMPACT ON PROPERTY MARKET

8other variables as of September 2019. Flood data were derived in 2012 through the following
procedures: polygonal data were made from the digitized images of flood hazard maps, GIS
data and digital cartographic data created by river administrators of the rivers flowing in or
across Tokyo, as well as Kanagawa Prefecture, Saitama Prefecture and Chiba Prefecture
which constitute the metropolitan area of Tokyo. Distinctions are made between below 0.5m,
0.5-1m, 1-2m, 2-5m, and 5m and higher, indicating the inundation depth in case of such
event. Inundation here is defined as the result of the “Expected Maximum-scale
Precipitation”, determined by Flood Control Act, of either 690mm or 710mm in the total
amount of rainfall. Flood data was then merged with individual housing data, giving
floodplain identification to each property. When, for example, a property is located in afloodplain expected to be flooded by 1 to 2 meters because of the “Expected Maximum-scale
Precipitation”, the variable FLOOD(1-2m) takes the value of 1 while other flood dummies
are 0 for this observation. The source of flood data is National Land Information Division of
the Ministry of Land, Infrastructure, Transport and Tourism of Japan. Description of thevariables and summary statistics are shown in Table 1 and Table 2, respectively. Figure 1
shows a map of the metropolitan area of Tokyo with floodplain overlay.
Given the discussion above, the research hypothesis of this study goes as follows:
Properties located in flood-prone areas are priced lower than those that are otherwise
identical. This research examined this hypothesis by regressing the following hedonic

FLOOD RISK IMPACT ON PROPERTY MARKET

9function:ln(Pi) = ∑𝐾
𝑘=1 𝛽 k Xki + βF Fi + εi(1)where Fi = 1 if a property is located in a flood-prone area and 0 otherwise. Coefficient βF is
expected to show the negative sign and be statistically significant. Pi denotes the price of aproperty, whereas Xki denotes a vector of hedonic characteristics that constitute the price.
Table 1
Description of the variables.

Variable

Description

Unit

PRICE

Asking price of a property.

Yen

AREA

Size of building area.

m2

AGE

Building age.

Year

STATION DIST

Distance to the nearest station.

m

CBD DIST

Distance to Tokyo station. CBD stands for Central Business District.

m

BEDROOM

Number of bedrooms.

ｰ

STRUCTURE

Dummy variable for house with wooden structure (1 if wooden, 0 if otherwise).

ｰ

CITYPLAN

Dummy variable for house within designated urban area (1 if inside, 0 if otherwise).

ｰ

FLOOD(0-0.5m)

Dummy variable for house within 0-0.5m flood area (1 if inside, 0 if otherwise).

ｰ

FLOOD(0.5-1m)

Dummy variable for house within 0.5-1m flood area (1 if inside, 0 if otherwise).

ｰ

FLOOD(1-2m)

Dummy variable for house within 1-2m flood area (1 if inside, 0 if otherwise).

ｰ

FLOOD(2-5m)

Dummy variable for house within 2-5m flood area (1 if inside, 0 if otherwise).

ｰ

FLOOD(5m-)

Dummy variable for house within 5m- flood area (1 if inside, 0 if otherwise).

ｰTable 2
Summary statistics.

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Mean

Std. Dev.

Min

Max

ln(PRICE)

17.286078

0.428325

10.021271

20.942401

ln(AREA)

4.57973

0.165925

2.037317

9.310548ln(AGE)

0.619973

1.18706505.480639

ln(STATION DIST)

6.88135

0.675916

2.250091

9.217625ln(CBD DIST)

10.013705

0.556182

6.019051

11.502704

BEDROOM

3.610971

0.682839

1

5

STRUTCURE

0.980154

0.139471

0

1

CITYPLAN

0.937974

0.241203

0

1

FLOOD(0-0.5m)

0.065455

0.247327

0

1

FLOOD(0.5-1m)

0.055317

0.228598

0

1

FLOOD(1-2m)

0.076545

0.265869

0

1

FLOOD(2-5m)

0.060537

0.23848

0

1

FLOOD(5m-)

0.00194

0.044008

01

Variable

Note . Number of observations is 188,099. The samples are restricted to those that are identifiable to theblock-level address.

Figure 1
A map of the metropolitan area of Tokyo with floodplain identification.

Note. Grey plots indicate the location of individual houses.

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Result and discussion
Table 3 shows the regression results of (1) defined above. Statistically significant andnegative coefficients were observed for all variables designed to indicate floodplain
identification. This shows that flood risk does contribute to explaining changes in the price ofproperties. For example, properties located in 0.5-1m area were priced lower than those thatare otherwise identical by 11.42%. Different discounts were observed across inundation
depths, and the average discount rate was 16.2%. Properties within floodplain were priced
about 16.2% cheaper on average than those that are otherwise equivalent.
As for other variables, building area was positive and statistically significant,illustrating that building area affected price positively. Building age accounted for decrease in
the property price. Distance from the nearest station and CBD were both statisticallysignificant. The negative coefficients suggest that the further a property is located from these,
the lower it is priced. This is of no surprise given that in a city such as Tokyo where thepublic transportation is predominant in comparison to other means of transportation, thecentral location or proximity to the nearest stations explains much part of the amenity.
Structure dummy was negative and statistically significant, indicating that a property withwooden structures is cheaper than similar properties with more resilient structures.
Table 3
Regression results.

FLOOD RISK IMPACT ON PROPERTY MARKET

Variable

12

Coef.

SE

t

ln(AREA)

0.763

0.014

54.191***ln(AGE)-0.12780.001-151.911***ln(STATION DIST)-0.1003

0.001-92.613***ln(CBD DIST)-0.3887

0.002-253.102***

BEDROOM-0.0372

0.002-22.628***

STRUTCURE-0.16460.009-19.179***

CITYPLAN

0.247

0.004

66.964***FLOOD(0-0.5m)-0.1406

0.002-57.447***

FLOOD(0.5-1m)-0.11420.003-43.983***

FLOOD(1-2m)-0.13150.002-57.976***

FLOOD(2-5m)-0.18830.002-75.944***

FLOOD(5m-)-0.23530.016-14.837***

const.

18.55510.064

288.819***

Adj. R-squared

0.588

max VIF

1.251

mean VIF

1.105

Note . *** means 0.1% statistical significance; Coef. = Coefficient; SE = Standard Error; t =t value. Low VIF indicates there is no multicollinearity. SE is robust to heteroskedasticity.

In comparison, the discount rate obtained above is somewhat stronger than thosederived from most previous studies. For example, a meta-regression analysis based on 314
observations performed by Beltrán et al. (2018) demonstrated that a house located in 100year floodplain exposed to river-flood risk suffered 4.6% discount in the average property
price. Another meta-analysis with 117 observations of previous estimates presented an even
weaker discount rate, which was 0.6% (Daniel et al., 2009b). The rest of this section
discusses some factors that can theoretically be responsible for this difference.
First, the parameters might reflect the effect of past flood events. Several previous
studies have verified the importance of considering the occurrences of past flood events, as

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they influence buyers’ risk perceptions (Bin & Landry, 2013; Bin & Polasky, 2004; Kousky,2010). In 2018, Japan saw twice the damage from water-related disasters including floodcompared to 2017. Heavy precipitation and resulting flood events most likely pushed up thelevel of risk perception in Japan, which might have caused strong discount. It, however,should be pointed out at the same time that the flood risk perception in Japan can have beenoriginally high, since Japan has multiple climatic and topographic conditions that haveresulted in a long history of flooding. Huang (2013) demonstrated that Japan has until today
suffered from a high level of economic loss from flood disasters since the 1930s. For thisreason, it is possible that high flood discount obtained from the Japanese market data was thehonest reflection of Japanese people’s strong risk perception.
Second, waterfront amenities this study did not control might have correlated with bothfloodplain identification and the property price, affecting the estimation of parameters. A
major waterfront amenity is view/vista, which has been pointed out to be an important factor
to control for by some previous studies, especially in the context of coastal flood (Bernstein
et al., 2019; Bin, Crawford, et al., 2008; Fuerst & Warren-Myers, 2018). It, however, is also
true that riverfront homes usually do not have as good views as coastal properties enjoy,
which means that riverfront homes are not expected to enjoy strong premium related toproximity to water. Therefore, the cancelling effect between flood risk and waterfront
amenities may not have affected the result too strongly. It is also important to note that even

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if such amenities did have positive effects on price, the lack of control of it would only haveresulted in further discount rate, not the underestimation of it. For this reason, it is likely thatthe statistical significance of the obtained parameters was robust to omitted variables.
Conclusion
The purpose of this study has been to understand flood risk implications in Tokyo by
employing regression analysis. Results of this study suggest that statistically significant andnegative coefficients of floodplain identification variables reflected the perceived andcapitalized flood risk, where discount rate amounted to as high as 11.9% on average. The
research hypothesis “properties located in flood-prone areas are priced lower than those thatare otherwise identical” is therefore correct, presenting evidence of flood discount in theJapanese housing market which was previously unknown. As this clarifies that flood risk
negatively impacts social welfare measured by house price, governmental intervention isneeded to ensure resilience and sustainability of the housing market. Those with interests inproperties located in the flood-prone areas, both professional investors and residents, are also
to be concerned by flood risk, as it could lead to significant devaluation at any moment in the
future. Flood risk information this study provided can be used to inform relevant actors of thelevel of flood risk they are facing, thereby helping them make economic decisions.
Nevertheless, there are limitations as to the interpretation of the results, and theselimitations point to the future possibilities in flood risk research in Japan. Past flood events

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that might affect the current level of risk perception, waterfront amenities that presumably
have a correlation with floodplain identification, and the quality of the properties that mightalso have a correlation with floodplain identification need to be controlled to assure
consistency in estimation of parameters. Use of a more comprehensive dataset could achieve
higher explanatory power of the econometric model and less possibility of omitted variablebias. Moreover, previous literature suggested that other factors should be taken into
consideration. Differentiating investors in terms of risk evaluation (Bernstein et al., 2019),
information disclosure that affects risk perception (Fuerst & Warren-Myers, 2019; Troy &Romm, 2004), and the price implications of insurance programs and government aids (Bin,
Kruse & Landry, 2008; Bin & Landry, 2013; McCoy & Zhao, 2018) are among the topics thatshould concern future research. Furthermore, future research should take into account thatJapanese Flood Control Act assumes 1000-year floodplain. Since flood risk studies usuallyassume 100- or 500-year floodplain, understanding the numerical implication of different risk
assumptions should also be needed for further development of the literature in Japan.

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