Who Gives a Dam? Capitalization of Flood Protection in
Fukuoka, JapanDavid Wolf
Kenji Takeuchi
February 2022
Discussion Paper No. 2203

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANWho Gives a Dam? Capitalization of Flood Protection in Fukuoka, JapanDavid Wolf
Kobe University
Kenji Takeuchi
Kobe University

Abstract
Large-scale flooding is becoming increasingly common due to prolonged and intensive rainfall
caused by climate change. Communities have mitigated this risk by building flood protection,
though it is unclear whether residents are aware of these public works or the protection they
confer. We provide insight on this matter by examining whether apartment rental prices (2015 –2019) responded to the completion of the Gokayama Dam in Fukuoka Prefecture, Japan. We findapartments protected by the Gokayama Dam experienced a 1.8% price increase relative toapartments in other floodplains after Typhoon Prapiroon hit western Japan and tested the dam.
Renters used the natural disaster as a learning experience to update their perceptions of flood risk
as opposed to the completion of the dam, suggesting a possible disconnect between perceptionsof flood risk and objective risk. In addition, we find the benefits from flood protection were
unevenly distributed with higher premiums observed in first floor units, units closer to rivers, andin areas where floodwaters are expected to exceed two meters, while rental units designed as
temporary housing received no premium. Homeowners and commercial renters also benefited
from the added flood protection but to an even greater extent than apartment renters. In
aggregate, the Gokayama Dam provides $11.3 million in benefits to downstream apartment
renters each year which offsets more than one-third the annualized cost of the dam.

Keywords: hedonic analysis; flood protection; difference in difference; natural disasters; JapanJEL Codes: R30, Q51, Q54, Q25

Funding: This work was supported by the Japanese Society for the Promotion of Science,[grant number 21H00709, 2021 - 2024].

Corresponding author: Assistant Professor at Kobe University, Graduate School of Economics
Address: Frontier Hall for Social Sciences, Room 812, Kobe University, Kobe, Japan 657-0013
Telephone: 078-803-7245
E-mail address: wolf@econ.kobe-u.ac.jp
Professor at Kobe University, Graduate School of Economics.
E-mail address: takeuchi@econ.kobe-u.ac.jp

Who Gives a Dam? Capitalization of Flood Protection in Fukuoka, Japan
1. Introduction
Despite 41% of its population living in flood-prone areas, Japan manages flood risk well thanks
in part to its extensive network of flood defense (Kundzewicz et al. 2014). At the center of thisdefense are more than 2,500 dams nationwide which control water levels across thousands of
streams, rivers, and lakes. Many of these dams need to be repaired soon due to their age.
Approximately 24% of Japanese flood control dams are more than 50 years old, which isconsidered a critical threshold of when stress begins to show and action needs to be taken (Perera
et al. 2021). The decision to rebuild or reinvest in dams is difficult as policymakers need toweigh the benefits of flood control with the cost of replacement and potentially adding to the $11
trillion national debt (Ministry of Finance, 2021).1
To complicate matters, public opinion on dams is divided, and recent controversies
surrounding dam construction have sprouted anti-dam movements. One particularly controversial
project is the Yamba Dam, which is located 130 kilometers northwest of Tokyo. Construction of
the dam started in 1967 despite fierce opposition from locals who wanted to save the area frombeing submerged. The project was once cancelled in 2009 to curb public spending (Fackler,2009) but was later restarted in 2012 due to a change in the national ruling party. Finally, in 2019
the dam was completed billions of dollars over budget and decades beyond the original timeline.
The mismanagement of the Yamba Dam led to a public opinion that dams are a product ofwasteful government spending and caused many politicians to run on dam-free platforms in the
2000s (Mishima, 2015).

1

Estimates referenced in the text that are originally measured in Japanese Yen (JPY) are converted into
U.S. Dollars (USD) using a ratio of 110 JPY to 1 USD.
1Climate change may tip the scales towards greater investment in water infrastructure,despite these concerns, as extreme weather events are expected to become more common in
Japan (Mizuta et al., 2017; Hoshino et al., 2020). In Tokyo, for instance, the probability of
having a 250+ mm rainfall day is predicted to at least double by the end of the 21st century(Mizuta et al., 2017). With greater rainfall comes greater flood risk. The national, prefectural,and local governments have responded by updating floodplain maps (MLIT, 2015), raising riverbanks (Ikeuchi, 2012), improving emergency warning systems (Japan Meteorological Agency,2021), and building upstream flood protection. The extent to which residents value theseprecautionary measures, however, is not well understood.
In this study we investigate how apartment rental prices in Fukuoka Prefecture, Japan
responded to the completion of the Gokayama Dam and its subsequent testing four months later
caused by the arrival of Typhoon Prapiroon. We argue the latter event induced floodplain renters
to update their perceptions of flood risk and use this as a natural experiment to causally identify
the relationship between flood protection and downstream apartment rents. We use the former
event, on the other hand, to test whether objective changes in flood risk are also capitalized inrental prices.
From this analysis we make three contributions to the flood risk literature. We are thefirst study to examine how improvements in flood protection, specifically the completion of an
upstream dam, influence real estate markets. Although capitalization of coastal defense is a wellstudied topic (McNamara et al., 2015; Qiu and Gopalakrishnan, 2018), little is known in regards
to the benefits conferred by upstream dams. This may be due to lack of awareness as dams areoften built several kilometers away from the area it protects. Second, we focus on how apartment
renters respond to changes in flood risk. Renters are an important stakeholder group in Japan but

2are typically unaccounted for in hedonic applications due to data limitations. They are also likely
to value flood protection differently than homeowners due to their more transitory nature andtheir lower level of risk. Finally, we contextualize these results by examining how flood
protection is capitalized within the commercial rental and housing market. Business owners areanother important stakeholder group concerned with mitigating flood risk but are often excluded
from hedonic applications, while several studies have priced flood risk using housingtransactions (Hallstrom and Smith, 2005; Kousky, 2010; Bin and Landry, 2013; Ortega andTaspinar, 2018; Georgic and Klaiber, 2021). The valuations we recover from all three real estatemarkets provide a more holistic understanding of the benefits conferred from flood protection.
Using a difference-in-difference estimator and a rich dataset of rental transactions from
Fukuoka Prefecture, Japan (2015 – 2019), we find apartment rental prices increased by 1.8% ifthey were protected by the recently constructed Gokayama Dam relative to rental units in otherfloodplains. Capitalization also occurred in commercial rentals and housing values, but to aneven greater extent, increasing by 9.1% and 11.6% respectively. This price increase is only
observed after Typhoon Prapiroon hit western Japan and tested the dam, suggesting that nearby
flooding altered renters’ perception of flood risk while the completion of the dam did not. Higher
flood protection premiums are also observed in first floor apartments, apartments closer to rivers,
and in areas where floodwaters are expected to exceed two meters, while no premium isobserved in apartments designed as temporary housing. Overall, the aggregate benefits from theresidential rental market ($11.3 million per year) cover more than one-third the annualized costof the Gokayama Dam ($27.9 million per year), which suggests the dam is cost-effective given
the number of other stakeholder groups that benefit from flood protection.

3

2. BackgroundTyphoons are a regular occurrence in Japan and typically occur between the months of May andOctober. The timing of Typhoon Prapiroon in late June and early July of 2018 was a little
unusual though as it coincided with a stronger than average rainy season2, which was fueled bywarm air from the Pacific Ocean (Japanese Meteorological Agency, 2018). The synergy between
the typhoon, the seasonal rain front, and the warm air brought record-breaking rainfall to westernJapan between June 28th and July 5th and resulted in widespread flooding, destruction of over
17,000 homes, and water outages for over 260,000 families. The Ministry of Land,
Infrastructure, Transport and Tourism (MLIT) estimate the floods caused over $10 billion indamages, the costliest in Japanese history. Fukuoka City, the 6th largest metropolitan in Japanand home to over 1.6 million residents (Figure 1), was especially hard hit receiving over 85 cm(33 inches) of rain over a 10-day period (Appendix Figure 1). Although much of the city is built
on a floodplain, only 59 properties were damaged thanks in part to the city’s extensive flood
defense network (Fukuoka Prefecture, 2019).

The Gokayama Dam
One of the largest flood control structures that protects Fukuoka City is the Gokayama Dam,
which is located 10 km upstream from the city center (Figure 2). Construction of the dam
finished four months before the arrival of Typhoon Prapiroon and is one of the largest publicprojects in Fukuoka Prefecture, costing $600 million and taking 6 years to complete. Based on a
cost-benefit analysis conducted by the prefectural government, the dam provides $4.3 billion in

Japan’s rainy season, often referred to as tsuyu in Japanese, is a period of continuous rain brought by astationary front that can last several weeks or even a few months.
24benefits to the local community (Fukuoka Prefecture, 2011). The concrete embankment is over
100 meters tall, 550 meters wide, and is designed to reduce waterflow by as much as 33%, or
450 m3 /s (Fukuoka Prefecture, 2011). The dam generates only enough electricity to power itself
and is primarily used for drought prevention and flood control, redirecting water from the NakaRiver which flows through Fukuoka and Nakagawa City. Prior to the completion of the
Gokayama Dam, minor flooding events were reported along the Naka River in 1999, 2001, 2003,
and 2009.
The prolonged rainfall brought by Typhoon Prapiroon was undoubtedly the first realworld test for the Gokayama Dam. There was no major flooding reported along the Naka River,and it fared well when compared with other nearby rivers. This is evident when looking at
Google Trends data where search volume for the Naka River within Fukuoka Prefecture wasrelatively flat during July of 2018 (Appendix Figure 2) while Google searches for other Fukuoka
City rivers, like the Muromi, Tatara, Hii, Umi, and Mikasa (Figure 2), reached a four-year high.
In contrast to the Naka River, most of these rivers are either undammed or are protected bysmaller and older dams built several decades before the study period.

Flood Insurance in Japan
Flood insurance in Japan is typically packaged with fire insurance and is only provided byprivate insurers as there is no national flood insurance program like the National Flood Insurance
Program (NFIP) in the United States. According to the General Insurance Rating Organization ofJapan (GIROJ), about 70% of residential fire insurance policies also cover flood damage.3
Insurance prices do not reflect locational differences in flood risks, although major insurance

3

Fire insurance is compulsory in most apartment rental contracts (GIROJ, 2022).

5

companies are considering to differentiate it in response to the increasing damages caused byclimate disasters. Typically, flood damage is compensated when damages exceed 30% of theproperty’s value or when inundation levels are greater than 45cm (GIROJ, 2022).

3. Literature Review
Capitalization of flood risk is a well-studied topic and is typically derived from housing pricedifferentials between properties sold inside and outside of a floodplain (Bin and Kruse, 2006;
Kousky, 2010) or from price changes inside a floodplain (Hallstorm and Smith, 2005; Bin andLandry, 2013; Georgic and Klaiber, 2021). Homeowners are expected to be compensated forliving in riskier areas by paying less for a house (Kousky, 2010), though floodplain pricediscounts are typically not observed unless a flood recently occurred (Bin and Polasky, 2004;
Bin and Landry, 2013). This suggests floodplain homeowners are not fully aware of the risk they
face and use recent experiences to update their perceptions of risk (Kousky, 2010; Atreya et al.,2013; Bin and Landry, 2013; Beltrán et al., 2019).
Heightened awareness of flood risk may not be permanent even if it is induced by anatural disaster. Housing prices have been observed to revert back to pre-flood levels 5 to 10
years following a natural disaster (Atreya et al., 2013; Bin and Landry, 2013; Beltrán et al.,2019). Flood insurance follows a similar pattern as households are more likely to purchaseinsurance immediately after a storm. The number of households with insurance then reverts backto its mean level 5 to 10 years out (Gallagher, 2014; Atreya et al., 2015). This short-sightedness
may be due to bounded rationality (Simon, 1957) where individuals are unable to evaluate all of
the risk they face but instead rely on recent or dramatic events (Tversky and Kahneman, 1973) tohelp them prioritize which risk to mitigate. Alternatively, the temporal nature of the price6

discount may depend on the severity of the natural disaster, with more extreme events having
longer-lasting effects (Ortega and Taspinar, 2018).
An additional concern is whether floodplain price discounts are caused by inundation
and/or an updating of flood risk perceptions. In most instances it is difficult to separate the two
as spatial data detailing where inundation occurs is sparse (Hallstrom and Smith, 2005; Atreya etal., 2013). Only a few studies have linked housing values with inundation damage (Oretega andTaspinar 2018; Beltrán et al., 2019; Gibson and Mullins, 2020), finding inundated homes lose up
to 48% of their value following a flood. Losses from inundation are not permanent as pricerecuperation can take as little as 5 years, though the recovery time will depend on the type offlooding and the initial price of the home (Beltrán et al., 2019).
Houses that are not directly damaged by a flood also lose value due to a change inperceptions, inconveniences caused by the flood, and damage to the community. Non-floodplain
homes in St. Louis, for instance, experienced a 4.5% price decrease following the flooding of theMississippi and Missouri River in 1993 (Kousky, 2010). The flooding of the Mississippi and
Missouri River indirectly affected many homeowners by forcing highways to close, damaging
municipal water and sewer treatment plants, and shuttering hundreds of businesses. “Near miss”
floodplain properties in Lee County, Florida, where Hurricane Andrew was expected to hit in1992 but did not, also experienced a price reduction as home buyers and sellers used theinformation conveyed by the storm to update their perceptions of flood risk (Hallstrom andSmith, 2005).
We make three contributions to this literature using real estate data from Fukuoka
Prefecture, Japan. Unlike previous studies which are concerned with measuring homeownerwillingness to pay, we focus on how apartment renters responded to flood risk. Apartment

7

renters likely respond differently to hazards as they are more transitory and have fewer financial
investments tied to their home. There are also additional benefits from using rental data. The
rental market in Japan is generally more active than the housing market as renters are moremobile, making it easier to identify small price effects due to the higher volume of transactions.4
Second, renters constitute a large fraction of the population in Japan. Homeownership rates are
5% to 15% lower in Japan than in the United States and European Union (OECD Social Policy
Division, 2021; U.S. Department of Commerce, 2021), which highlights the importance of thisgroup and the need to recover reliable valuation estimates for non-homeowners. Third,
apartments tend to be more uniform in quality and price than single-family residences (Naoi etal., 2009), lessening the chances of omitted variable bias and other confounding factors that
could hamstring causal identification. Finally, hedonic price differentials will not reflect the
future stream of benefits and costs tied to a property if amenity conditions included within thehedonic price schedule deviate from expectations (Bishop and Murphy, 2019). This is less likely
to occur when rent is used in place of sale price, as renter expectations extend over a shorter time
horizon.
As a second contribution we examine how improvements in flood protection, specificallyconstruction of an upstream dam, influence rental values. Large scale investments in flood
protection are common along coastal communities and confer significant benefits to nearby
homeowners (McNamara et al., 2015). The extent of these benefits will depend on whether the
infrastructure obscures oceans views, if neighboring communities have flood protection, and if
the property is oceanfront or inland (Gopalakrishnan et al., 2011; Qiu and Gopalakrishnan,

430% of renters reported moving within the past 5 years as compare to only 8.6% of homeowners(Statistical Bureau of Japan, 2019).
8

2018). Capitalization of inland flood protection is less understood as only a handful of studies
have linked housing prices to dam removal or other inland defenses (Lewis et al., 2008;
Provencher et al., 2008), while no academic study to our knowledge has estimated the capitalized
value from the completion of a dam. The relative scarcity of inland flood protection studies may
be due to a lack of awareness of the protection provided by upstream infrastructure, which can beseveral kilometers away from the area it protects. Our study provides a unique opportunity to test
this hypothesis by examining how rental prices responded to the completion of the Gokayama
Dam in March of 2018 and the arrival of Typhoon Prapiroon four months later.
Finally, we estimate the value flood protection provides to homeowners and renters ofoffice and retail space to help contextualize our primary contribution within the broader realestate market. Commercial property applications of the hedonic method are scarce as it isdifficult to obtain attribute data for these property types. The commercial property market is alsoless active as there are fewer listings at any given time and properties are generally more
expensive. Despite these challenges, commercial property prices have been found to increase
with population density (Nagai et al., 2000), energy efficiency (Eicholtz et al., 2013), andproximity to the nearest rail station or highway (Cohen and Brown, 2017), while decreasing withcrime rates (Lens and Meltzer, 2016).
Flood risk is likely capitalized in commercial properties as floodwaters have caused
significant damage to businesses in the past. Over $14 billion in insurance payouts have been
distributed to businesses through the National Flood Insurance Program in the United States as of2021 (NFIP, 2021). Many businesses also experience uninsurable losses. Hundreds of small to
medium enterprises across Japan reported production delays, destroyed inventory, and logisticalissues following the 2018 floods, which totaled over $4.3 billion in damages (Nikkei Newspaper,

9

2018). Insurance payments only covered some of these losses, as business owners were notcompensated for things like foregone revenue and preparations made to protect their businessprior to the flood.
Indaco et al. (2021) also find that a business’ income generating potential can be
negatively impacted by flooding. Following Hurricane Sandy, flooded businesses in New York
City experienced a reduction in average wages, employment, wage income, and were more likely
to exit or relocate. Taken altogether, Hurricane Sandy likely reduced the income-generating
potential of the area, which in turn lowered commercial property values. This hypothesis wasnever formally tested, however (Indaco et al. 2021). We provide clarity on this matter byexamining how commercial rents respond to nearby flooding and flood protection provided by
the Gokayama Dam.

4. Data
We collect real estate listing data from the National Institute of Informatics (NII) to test if flood
protection from the Gokayama Dam is capitalized in downstream rents (At Home Co. Ltd.,
2020). The NII’s database includes a vector of structural characteristics, locational features, andrent for apartments listed on the market. The total monthly payment for each unit is calculated by
dividing the deposit by the number of months on the lease and adding that to the monthly rental
payment.5 Rentals with extreme or incorrectly measured characteristics (below the 1st percentile

5

Deposit and rental prices come from the last month the unit is listed on the market. In most cases, there
is no difference between the final listed rental price and transacted rental price in Japan as renters havelittle to no negotiating power when determining the final rental price. Sadayuki (2020) estimates the ratio
between the two to be 0.99937 using transactions of apartment units between 1995 and 2016. Despite thislimitation, renters indirectly alter equilibrium rental prices through their decision of where to live.
Hedonic applications of listed rental price in Japan are also common (see for example Nakagawa et al.
(2007), Shimizu et al. (2010), and Sadayuki (2018)).
10

or above the 99th percentile) are removed from the analysis to limit the impact of outliers. We
follow a similar process when collecting rental information on commercial properties, which
include office and retail locations, and housing transactions. Appendix B provides additional
information on these secondary datasets. Summary statistics for apartment rentals are provided inTable 1, while a description of the variables is provided in Appendix Table A1.
The NII dataset also includes the latitude and longitude of the rental unit building. Four
proximity measures are created with this information – distance to the nearest river, public park,
school, and shinkansen (bullet train) station – using GIS and shapefiles collected from the MLIT.
An indicator of whether the rental is located in a 100-year floodplain is also created using GIS.
100-year floodplains are areas where there is a 1% chance of a major flood occurring within agiven year. For the Naka River floodplain, a major flood is defined as an instance where over
328mm of rainfall occurs over a 24-hour period. The expected flood depth from such a storm isalso known and is discretized by the MLIT into three categories: greater than two meters,
between one to two meters, or between zero and one meter. We create dummies for the former
two categories and include them as controls within our hedonic price regression.
City, ward, and neighborhood identification numbers are also identified using GIS and
MLIT shapefiles. We define neighborhoods in Fukuoka Prefecture at the chocho level. Chocho
are geographical regions used by the postal service to indicate the city and neighborhood of eachaddress. There are over 3,500 chocho across Fukuoka Prefecture, with the average chocho
comprised of 355 households (Statistical Bureau of Japan, 2015). We use the terms chocho andneighborhood interchangeably throughout the text and tables given their similar definitions.
Walking distance to the nearest local train station is also provided by the NII and therefore does
not need to be computed using GIS.

11

Finally, we create two variables of interest: a dummy if the unit was leased after June,2018 and a dummy if the rental is located within the Naka River floodplain. Rentals inside the
Naka River floodplain receive additional protection from the Gokayama Dam, while rentals
outside do not.6 In our analysis we limit our sample to floodplain rentals, though our results arerobust to the inclusion of non-floodplain units.

5. Identifying the impact of flood protection on rental values
Transactions between heterogenous buyers and sellers define a hedonic price equilibrium(Rosen, 1974) that maps underlying attributes of a good to its market value. By estimating thehedonic price function from price and attribute data, practitioners are able to indirectly value
attributes of goods that are not sold in a conventional market. We apply the hedonic pricing
method to estimate the value flood protection provides to renters living in Fukuoka Prefecture,
Japan in equation (1).
(1) Ln Rent ijt = γSit + πNakaRiveri + δ(NakaRiveri ∗ Post t ) + Fi + 𝜁t + ηj + εijt
The natural log of rent for apartment i, leased in location j during month t is given by
Ln Rent ijt . Sit is a vector of structural and locational features that define the unit, NakaRiveri
indicates whether the apartment is located within the Naka River’s 100-year floodplain, Post t isa dummy equaling one if the apartment was leased after June, 2018 and 0 otherwise, Fi , 𝜁t , andηj are vectors of floor, time, and spatial fixed effects respectively, while εijt is an idiosyncraticerror term. We define 𝜁t at the year by month level to control for any seasonal and annual shifts

6

In addition to indicating which areas are flood-prone, the MLIT links each floodplain to a specific river.
This allows us to identify which properties are protected by the Gokayama Dam and which are not. It isalso important to note that we use the most recent prefectural-wide floodplain map, which was publishedin 2012.
12

in the rental market. Finally, the shape of the hedonic price equilibrium is defined by thecoefficients γ, π, and δ. Taking the derivative of the rental price function with respect to any of
the observable attributes will reveal the capitalized value of that attribute (Rosen, 1974;
Kuminoff and Pope, 2014).
The hedonic specification outlined in equation (1) follows a traditional difference-indifference (DD) approach and relies on the occurrence of a flood as a source of exogeneous
variation. Focusing on the first difference, NakaRiveri classifies apartments into a treated orcontrol group where group assignment is dependent on whether the apartment is located in the
Naka River floodplain. The parameter π is referred to as the group effect (Athey and Imbens,
2006; De Chaisemartin and d’Haultfoeuille, 2020) and captures any price difference associatedwith living within the Naka River floodplain prior to the 2018 floods. The second differenceclassifies apartments as being leased before (Post t = 0) or after the 2018 floods (Post t = 1)
and controls for any price differential common to all floodplain apartments. Post t is not directly
included in equation (1) as the year by month fixed effects (𝜁t ) already control for this difference.
Finally, the coefficient on the interaction term δ reveals the average treatment effect on thetreated, or put more simply: the effect of having flood protection from the Gokayama Dam onapartment rental values in the Naka River floodplain.
It is worth mentioning an implicit assumption made in equation (1). We define treatment
based on when the dam was first tested by severe rainfall (July, 2018) rather than when it wascompleted (March, 2018). This is intentional as the added flood protection needs to be salient inorder for it to be capitalized (Bradley, 2017). This decision aligns with prior findings whichindicate differences in flood risk are only capitalized in periods following a flood (Bin andPolasky, 2004; Kousky, 2010; Atreya et al., 2013; Bin and Landry, 2013) and is consistent with13

the availability heuristic (Tversky and Kahneman, 1973) which suggests decision makers areunable to recognize all of the risk they face but instead rely on recent or dramatic events to teachthem which risk to respond to. We test the validity of this assumption in section 6.
The capitalization of flood protection is likely heterogeneous across property types. We
provide three alternatives to equation (1) which allow for additional heterogeneity. First, we
implement a triple-difference estimator.
(2) Ln Rent ijt = γSit + πNakaRiveri + αIi + δ(NakaRiveri ∗ Post t ) + θ(Ii ∗ Post t ) +ρ(NakaRiveri ∗ Ii ) + φ(NakaRiveri ∗ Ii ∗ Post t ) + Fi + 𝜁t + ηj + εijt
Equation (2) differs from equation (1) due to the inclusion of the third-difference Ii and its
interactions with Post t , NakaRiveri , and NakaRiveri ∗ Post t . We describe Ii only in genericterms as several sources of heterogeneity are tested, including whether the apartment unit is on
the first floor or if the apartment building is located near a river. Ii takes a different form in eachcase. The coefficient of interest in equation (2) is φ as it indicates whether the average treatment
effect of the treated varies across apartment types identified by the indicator Ii .
As an alternative to equation (1), we also estimate a hedonic equilibrium for commercial
properties and housing transactions in equations (3) and (4) respectively. A separate hedonic
equilibrium needs to be estimated for each property type as apartments, commercial real estate,and homes are transacted in separate markets.
(3) Ln ComRent ijt = γC S̃it + πC NakaRiveri + δC (NakaRiveri ∗ Post t ) + 𝜁t + ηj + υijt
(4) Ln HousePriceijt = γH Šit + πH NakaRiveri + δH (NakaRiveri ∗ Post t ) + 𝜁t + ηj + σijt
The natural log of rent for commercial rental unit i, leased in location j during time t is given by
Ln ComRent ijt , while the natural log of housing price is given by Ln HousePriceijt . The

14

covariates in equations (3) and (4) are defined as before, though accents are added to Sit toindicate that different characteristics are available for each property type (see Appendix B for
more details). Subscripts C and H are also added to indicate differences in capitalization rates,
while υijt and σijt are idiosyncratic error terms. We expect δC > δ and δH > δ as commercial
renters and homeowners have more to lose monetarily than residential renters and therefore placea higher value on flood protection.

6. Results
Hedonic estimates from four variants of equation (1) are reported in Table 2, with thecoefficients associated with the group effect and DD term listed at the top. Starting with thegroup effect in model 1, we find renters are willing to pay 9.1% more to live near the Naka River
compared to living in another floodplain prior to July of 2018.7 This aligns with expectations as
the Naka River flows through some of the most densely populated areas in Fukuoka Prefecture
where rental and housing prices are higher. This premium increased to 10% following the July
2018 floods, indicating the added flood protection provided by the Gokayama Dam increased
downstream rental values by 0.9%. Model 1 is unlikely to provide a causal relationship though as
unobserved amenities (distance to the nearest convenience store, average crime rates, access toopen space, etc.) are spatially correlated, likely biasing the DD coefficient.
One solution is to add more stringent spatial fixed effects which forces identification tocome from within a tighter spatial grouping (Kuminoff et al., 2010). We explore different
transformations of the spatial fixed effect in models 2 - 4 and discuss their role on identification.

7

Dummy variable coefficients referenced in the text have been corrected using the technique suggested
by Halvorsen and Palmquist (1980) due to the use of a semi-log functional form.
15

We begin by adding ward and neighborhood fixed effects to models 2 and 3 respectively.
Neighborhood characteristics like access to open space, average school quality, and distance tothe nearest shopping center are likely correlated with rent and the Naka River indicator, biasing
the DD coefficient. Models 2 and 3 are better suited to mitigate this bias by controlling for timeinvariant features specific to the apartment’s ward and neighborhood respectively.
In both models, the DD coefficient is positive and statistically significant indicating thepremium associated with living in the Naka River floodplain increased following the 2018floods.8 Renters likely updated their perceptions of how often a flood could happen and thedamage it could inflict after seeing reports of widespread destruction across western Japan.
These updated preferences manifested themselves in higher rental rates within the Naka Riverfloodplain where a newly constructed dam prevented downstream flooding. Relative to rental
prices in other floodplains, rental prices along the Naka River increased between 1.8% and 2.1%.
This suggests a yearly benefit between $120 (model 3) and $140 (model 2) when evaluated at the
average monthly rent within the Naka River floodplain ($556). We attribute this premium to theadded flood protection provided by the Gokayama Dam.
The DD specification in models 2 and 3 causally identifies the relationship between
rental values and flood protection so long as unobserved apartment characteristics are
independent of the treatment and group status. Inclusion of apartment unit fixed effects are oneof the strongest controls to ensure this independence as they control for characteristics that are
difficult to measure, such as the view from the apartment unit, whether there is a noisy neighbor,and the overall flow and design. Similar to a repeat-sales or property fixed effect model, we limit

8

The Naka River indicator is removed from model (3) due to perfect collinearity with the neighborhood
fixed effects.
16the sample to rentals leased multiple times (16,904) while also including a fixed effect for eachrental unit.
Flood protection provided by the Gokayama Dam continues to be positively capitalized
in model 4 with rental prices increasing by 1.4% after July 2018. This point estimate is similar tothe previous measures as it is within model 3’s 95% confidence interval (0.012 to 0.024). One
concern is that more frequently transacted apartments may not be representative of the entire
rental market (Palmquist, 2005). We find evidence of this when comparing attributes ofapartments sold once vs multiple times in Appendix Table A2. The difference across samples isoften meaningful and statistically significant, with one-time rentals 12% less likely to have an
AC unit. Large differences are also observed in relation to whether the apartment comes with astove, is near the Naka River, has an elevator within the building and whether there are security
cameras nearby. Given these differences, we use model 3 as our baseline model as it retains thefull sample, includes stringent spatial fixed effects, and produces estimates similar to model 4.

First Floor Units
The extent to which flood protection is reflected in rent is likely heterogeneous across apartment
units. We explore four sources of heterogeneity using a triple-difference estimator (equation (2)).
For each triple-difference model, we include an indicator for the third difference directly in themodel as well as its interaction with NakaRiveri , Post t , and NakaRiveri ∗ Post t . For brevity, weonly report the coefficients from NakaRiveri ∗ Post t and the triple-difference term.

17

We begin with an examination of whether flood protection is capitalized differently
between first and higher floor units. First floor units in general are sold at a discount9 as theylack the privacy, view, and security that is afforded to units higher up (Nakagawa et al., 2007)
but are also likely to receive the most flood damage. We capture this difference by transforming
our DD model into a triple-difference model using a first-floor indicator (Ii = First i) and report
the coefficients under model 5 in Table 3.
First floor units within Naka River floodplain received an additional premium following
the 2018 floods, increasing in value by approximately 3.3% while higher-floored units
experienced a 1.6% price increase. Interestingly, model 5 indicates that flood protection iscapitalized in all apartment units, even those above the water inundation level. This conforms
with findings observed in other markets (Kousky, 2010). Residents living on higher floors arestill inconvenienced by floodwaters as flooding can make local roads and railways impassable,cause power outages, shutter nearby businesses, cut off water and sewage services, and damage
local amenities. In addition, any vehicles parked nearby may be damaged or completely lost
during a flood.

One Room Apartments
The rental market provides housing services for both short and long-term residents. We expect
short-term renters have a lower capitalization rate as the probability of experiencing a flood
reduces with shorter residency. Although we do not have information on how long renters stay,some units are designed specifically for renters searching for temporary housing. Approximately

9

We observe a monotonic relationship in the floor fixed effects in model 3 where higher-floored units are
1% to 10% more expensive than first floors units.
188.4% of the rental units in our sample are designated as “one room” apartments. One room
apartments are distinctive as they are similar in quality and layout to a college dorm, with theentire apartment consisting of one multi-purpose room and one bathroom. The average one room
apartment is 20.0% cheaper and 30.3% smaller than the average unit in our sample.
To test whether one room apartment renters are responsive to flood protection, weconvert the DD model into a triple-difference estimator using a third difference term
(Ii = One RoomI ) and report the results under model 6. The triple interaction term is similar inmagnitude and significance as the baseline coefficient but has an opposing sign, indicating thatflood protection is not capitalized into one room apartment rents. The lack of a relationship is
intuitive as these units are often rented to a more transitory segment of the population (students
and single-person households). According to a survey conducted by the Japanese Property
Management Association, only 9.5% of students and 22.6% of single-person households have
lived within their current residence for more than 4 years, while 73.4% of multi-personhouseholds meet this criteria (Japan Property Management Association, 2020). As flooding is anextreme but infrequent event, we do not expect short-term residents to prioritize or even consider
flood protection when searching for their new apartment, which is what we observe in model 6.

Expected Flood Depth and Proximity to the Nearest River
Floodplain renters in low elevation areas are expected to incur greater damage from floodwaters
relative to renters higher up. To capture this difference, we use expected flood depth datacontained with the floodplain maps. In particular, the MLIT details not only the location of the
100-year floodplains but also the expected flood depth within these areas (see section 4 for moredetails). We define a triple-difference model using the deepest flood depth category (>2 meters)

19and report the results under model 7 of Table 3. Apartments within the Naka River floodplain
where floodwaters are predicted to exceed two meters experienced an additional 4.6% pricepremium after the 2018 floods.
Similar to renters living in low elevation areas, renters living near rivers may be moresensitive to flood protection as they are the first to be impacted. We consider this possibility bycreating a spatial indicator of whether an apartment is within 50 meters of a river (Adjacent i =1) and use this dummy to form a triple-difference model, which we label as model 8 in Table 4.
The positive and statistically significant coefficient on the triple interaction term indicates rentersliving near rivers benefit more from additional flood protection than those living farther away.
Specifically, Naka River floodplain rents increased by 4.6% if the property was river adjacent,while non-adjacent properties experienced a 1.7% price increase. This triple interaction term
attenuates to zero as more distant properties are defined as river adjacent in models 9 – 11, where
adjacency is defined using a 75-meter, 100-meter, and 125-meter cutoff respectively.

Commercial Rentals and Housing Sales
Residential renters are not the only beneficiaries of flood protection; business and home owners
are also concerned about protecting their property from flood damage. According to a survey of
900 business establishments in Ozu City, which east of the study area, 6% of businesses had toclose or relocate due to damage caused by Typhoon Prapiroon (Nikkei Newspaper, 2021). We
examine how commercial rentals responded to Typhoon Prapiroon by re-estimating models 1 – 3using listing data from office and retail locations (see Appendix B for more details) and present
these results in Table 5. Focusing on the first two rows, we find commercial rents were morereactive, increasing in value between 8.3% and 9.5%. For the average commercial renter within

20

the Naka River floodplain with a monthly rent of $3,010, this translates into an annual pricepremium between $3,088 and $3,431.
Homeowners are another stakeholder group that benefited from the Gokayama Dam.
Their capitalized gains likely exceed the price premiums observed in apartment rental contracts
as their chances of encountering a 100-year storm within their current residence are higher. In
addition, they are at risk of losing more as homeowners need to replace not only their lostbelongings but also pay for any structural repairs. We examine this issue by collecting housingtransactions information from the MLIT and re-estimating models 1 – 3 once again (seeAppendix B for the full set of results and additional information about the data). Overall,downstream housing prices increased by 9.6% to 11.6%. Assuming a 4% rate of interest over a
30-year timespan (MLIT, 2018), this suggests the average homeowner within the Naka Rivergained a yearly benefit between $2,299 and $2,783 from the Gokayama Dam.10

Threats to Identification and Robustness
Apartment rents within the Naka River floodplain may have increased over time for reasonsoutside of the completion and subsequent testing of the Gokayama Dam. We test this concern bymodifying the baseline model (model 3) to allow the DD coefficient to vary across time(quarters) and present the updated coefficients and their 95% confidence intervals in Figure 3.
All coefficients in Figure 3 should be interpreted in relation to the omitted category, which is thequarter of the flood. There does not appear to be any significant pre-trends in apartment rental

10

We follow the guidelines provided in section 6 of National Center for Environmental Economics (2014)
when converting total cost or benefit estimates into annual terms throughout the text. A 4% interest rate isalso assumed when making these calculations (MLIT, 2018).
21

prices as most of the DD terms are statistically insignificant prior to the flooding event. Had
several of the coefficients been statistically significant or if there was an increasing trend overtime, this would suggest Naka River apartment rental prices would have increased regardless ofwhether the Gokayama Dam was built.
Instead, we observe a discrete increase immediately after the flood that is retained until atleast the end of the study period. The temporal pattern observed in Figure 3 is consistent with theidea that renters updated their perceptions of flood risk after the July 2018 floods and acted on
those new perceptions by paying more for apartments downstream from the recently constructed
Gokayama Dam. Three other major milestone events occurred between 2015 and 2019 thatshould be noted. The main embankment of the Gokayama Dam was completed in January of
2016, test flooding began later that year in October, and finally the dam became operational in
March of 2018. Each milestone could have been taken as a signal by downstream residents thatthe area was better protected against major flooding. Residents do not appear to have acted inthis manner though as rental prices were relatively flat, suggesting a possible information gap
between renters’ perceptions of flood risk and actual flood risk.
A common robustness check within the quasi-experimental literature is to perform
falsification tests by incorrectly defining the group assignment or treatment variable and seeing
whether the relationship revealed in prior models holds. The expectation is that the DD
coefficient should be statistically insignificant as there should be no treatment effect onobservations that did not receive a treatment. We perform two falsification tests by changing thedefinition of the group assignment variable. In model 18 (model 19) we assume the treatmentgroup is defined by whether the apartment is within the Hii (Tatara and Umi) River floodplain or
not (see Figure 2 for locations of the Hii and Tatara and Umi River floodplain). If our estimates22

are causally identified, we should expect the DD coefficient to be statistically as no dam wasrecently built in either of these floodplains. We find this to be the case in Table 6 with both DD
coefficients not statistically different from zero.
Rental properties along the Naka River could have been renovated following the floods,
biasing the DD coefficient as the effect of improved rental unit quality would be confoundedwith an updating of risk perceptions (McCoy and Xiaoxi, 2018; Gibson and Mullins, 2020). We
find little evidence this occurred, however. Only 59 properties in Fukuoka City11 were damagedby the floods (Fukuoka Prefecture, 2019) making it unlikely that widespread renovation
occurred. Reconstruction of public facilities along the Naka River (i.e., public access points,boardwalks, parks, etc.) could also confer secondary benefits that would confound identification.
After discussing this matter with local river managers from Fukuoka Prefecture, we found nosuch projects occurred as there was minimal damage to the Naka River or its embankments.

Policy Implications
Our capitalization estimates suggest apartment renters are less responsive to flood risk thanhomeowners or commercial renters, with apartment rents increasing by 1.8% (model 3) after thecompletion and subsequent real-world testing of an upstream dam. This premium is small incomparison to the price effects observed in the commercial rental (9.1%) and housing (11.6%)
market. This difference is magnified even further when evaluated in dollar terms as apartment
renters gained a yearly benefit of $120, while homeowners and commercial renters gained$2,783 and $3,280 respectively.

11

Similar to other studies, spatially-fine data detailing where flooding occurred is unavailable. Damage
estimates are only reported at the city level (Fukuoka Prefecture, 2019).
23

There are at least two reasons why we see smaller capitalization estimates within theapartment rental market. First, apartment renters tend to be more mobile than other demographic
groups. According to a survey conducted by the Statistical Bureau of Japan, 30.8% of renters
moved within the past 5 years as compared to only 8.6% of homeowners (Statistical Bureau ofJapan, 2019). Renters therefore face systematically lower levels of risk due to their shorter
residency. Second, apartment renters have less at stake financially as they are only responsible
for covering the cost of their lost or damaged possessions. Homeowners need to replace their lostbelongings as well as pay for structural repairs, while businesses may suffer financial losses fromproduction delays and damage to commercial equipment (Nikkei Newspaper, 2018).
To better understand the cost-effectiveness of the Gokayama Dam, we calculate anaggregate benefit measure using information from the downstream apartment rental market andcompare to the total cost of the dam. We do not include benefits from the housing or commercial
rental market as there is a lower likelihood, in either case, the identifying sample isrepresentative of the larger market. Both datasets are also less detailed in terms of the available
structural and locational attributes that define each property (see Appendix B for more details).
Turning our attention to the average floodplain apartment renter, their yearly benefit from living
downstream from the Gokayama Dam is $120. If we aggregate this measure across all rental
apartments within the Naka River floodplain (94,532)12, we find a yearly benefit of $11.3 million
associated with the completion of the Gokayama Dam.

12

We use the following equation to estimate the total number of rental apartments within the Naka Riverfloodplain: ∑𝐽𝑗=1(𝐹𝑙𝑜𝑜𝑑𝐿𝑎𝑛𝑑𝑗 ∗ 𝑇𝑜𝑡𝑎𝑙𝐴𝑝𝑡𝑗 ∗ 0.788). 𝑗 indexes across neighborhoods, 𝐹𝑙𝑜𝑜𝑑𝐿𝑎𝑛𝑑𝑗 is thepercentage of neighborhood 𝑗’s land area within the Naka River floodplain, and 𝑇𝑜𝑡𝑎𝑙𝐴𝑝𝑡𝑗 is the total
number of apartment units in neighborhood 𝑗 (Statistical Bureau of Japan, 2016). We multiply the productof 𝐹𝑙𝑜𝑜𝑑𝐿𝑎𝑛𝑑𝑗 and 𝑇𝑜𝑡𝑎𝑙𝐴𝑝𝑡𝑗 by 0.788 as 21.2% of households in Fukuoka Prefecture own rather thanrent their apartment (Statistical Bureau of Japan, 2016).
24

The cost of the Gokayama Dam – $600 million – overshadows these benefits by morethan 50 to 1. In annual terms this is equivalent to $27.9 million per year, which is a morereasonable comparison.13 Taken together, the benefits from the apartment rental market cover
more than one-third the yearly cost of the dam. This suggests the Gokayama Dam would likely
pass a cost-benefit analysis as there are many other stakeholder groups unaccounted for within
this analysis, including: homeowners, commercial renters, owners of rental properties, non-renter
commercial entities, and the local government. The dam provides benefits to non-floodplain
properties as well through irrigation management and drought prevention.
Nevertheless, it should be noted that the benefits within apartment rental market may be
temporary. Capitalization of flood risk within housing prices is found to dissipate 5 to 10 years
following a natural disaster (Atreya et al., 2013; Bin and Landry, 2013). Persistent price effects,
on the other hand, were observed in both damaged and undamaged units in areas flooded by
Hurricane Sandy (Ortega and Taspinar, 2018). The temporal nature of the price discount depends
on the severity of the natural disaster. Only extreme events, relative to what’s normal in eacharea, impact beliefs and give rise to persistent effects (Ortega and Taspinar, 2018). We areunable to test which scenario is more likely though as our rental data is limited to listings posted
between 2015 and 2019.

7. Conclusion
Japan has one of the world’s most extensive inland flood-protection networks. At the forefront of
this network are more than 2,500 dams that generate electricity, store irrigation water, and13

We assume a 50-year timespan when annualizing the cost of the Gokayama Dam as this is the average
length of time until a dam shows sign of stress (Perera et al., 2021).
25

provide protection to millions of downstream residents. Many of these dams need to be repairedor replaced in the near future to maintain current levels of flood protection, though the cost of
replacement can be in the hundreds of millions of dollars. To complicate matters, there is
growing discontent and an opinion that public money should be spent elsewhere. Climate change
may tip the scales towards greater flood protection in the future, however, due to the increased
likelihood of flooding events caused by heavy rainfall and damaging typhoons.
We provide key information for one of the largest stakeholder groups impacted by flood
protection, downstream renters. Following the large-scale flooding in July of 2018 that caused
billions of dollars in damage across western Japan, we find apartment rental prices within the
Naka River floodplain increased by 1.8% relative to apartments in other floodplains within
Fukuoka Prefecture, Japan. We attribute this price premium to the flood protection provided by
the newly constructed Gokayama Dam as downstream residents were likely unaware of theprotection it provided until it was put to the test in July of 2018. Rental prices for commercial
properties and housing values also appreciated, increasing in value by 9.1% and 11.6%
respectively. Translated into annual terms, our capitalization estimate suggests an annual benefitof $120 to apartment renters each year from the added flood protection provided by the
Gokayama Dam, while homeowners and commercial renters experienced benefits of more than
$2,500 each year.
The capitalized gains from flood protection within the apartment rental market are smallin comparison to the premiums paid by homeowners and commercial renters. Apartment renters
may be more tolerant of flood risk as they have fewer financial liabilities and can walk awayfrom the disaster more easily. Flood protection is also observed to be heterogeneously capitalized
in downstream rents with first floor apartments receiving a larger price premium from flood

26

protection compared to higher units. Apartments designed as more temporary housing did notchange in value, while units closer to rivers and in areas where floodwaters are expected to reach
two meters benefited more.
Our capitalization measures provide key insights as to who benefits from flood protection
and the extent of these benefits. Going forward, policymakers may find these measures usefulwhen determining where and to what extent additional flood protection should be constructed.
Additional research examining the relationship between flood protection and land, housing, andcommercial property sales would complement our research and provide a more holistic
understanding of how communities benefit from reduced flood risk. We expect this line ofresearch will grow in importance as communities worldwide continue to be impacted by floods
and search for cost-effective mitigation strategies.

27

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65. U.S. Department of Commerce. (2021). QUARTERLY RESIDENTIAL VACANCIES ANDHOMEOWNERSHIP, SECOND QUARTER 2021. Census.gov.
https://www.census.gov/housing/hvs/files/currenthvspress.pdf.

36

Table 1 – Floodplain Apartment Summary Statistics
Monthly Rent (2015 USD)
Square Meters
Number of Bedrooms
Floor
Months Advertised
Living Room (0/1)
Dining Room (0/1)
WC (0/1)
Heated Toilet (0/1)
AC (0/1)
Stove (0/1)
Fridge (0/1)
Washer Hookups (0/1)
Storage (0/1)
Walk-in Closet (0/1)
One Room Apt (0/1)
Elevator (0/1)
Security Camera (0/1)
Bicycle Parking (0/1)
Fiber Internet (0/1)
CATV (0/1)
Steel (0/1)
Concrete (0/1)
Medium Water Depth (0/1)
High Water Depth (0/1)
Post (0/1)
Sale Month
Sale Year
Year Built
Distance to Station (minutes)
Distance to River (100s of meters)
Distance to Park (100s of meters)
Distance to School (100s of meters)
Distance to Bullet Train (100s of meters)

Naka River Floodplain (N=47,417)
Mean
Std. Dev.
Min
Max
555.798
168.550
209.091
1,325.758
33.00812.80820.00086.100
1.262
0.548
1.000
4.000
5.558
3.241
1.000
15.000
3.335
4.095
1.000
32.000
0.329
0.470
0.451
0.498
0.598
0.490
0.464
0.499
0.549
0.498
0.664
0.472
0.036
0.187
0.937
0.244
0.812
0.391
0.108
0.310
0.108
0.310
0.884
0.320
0.846
0.361
0.964
0.187
0.932
0.251
0.952
0.213
0.060
0.237
0.909
0.287
0.180
0.384
0.003
0.052
0.330
0.470
6.312
3.375
1.000
12.000
2017
1.279
2015
2019
2000
10.317
1928
2019
8.593
4.485
1.000
78.000
4.890
3.329
0.067
16.280
1.853
0.898
0.091
5.453
3.412
1.502
0.254
8.354
20.18913.4022.053
94.51137

Mean
514.778
41.275
1.588
3.546
4.871
0.471
0.655
0.522
0.443
0.597
0.424
0.018
0.914
0.611
0.087
0.059
0.541
0.502
0.896
0.636
0.656
0.194
0.656
0.216
0.051
0.294
6.125
2017
2000
11.856
3.353
3.605
4.481
62.927

Other Floodplains (N=44,222)
Std. Dev.
Min
Max
167.977207.976
1,325.758
16.09020.00086.040
0.744
1.000
4.000
2.593
1.000
15.0005.297
1.000
32.000
0.499
0.475
0.500
0.497
0.491
0.494
0.133
0.280
0.488
0.282
0.235
0.498
0.500
0.305
0.481
0.475
0.395
0.475
0.411
0.221
0.456
3.428
1.000
12.000
1.282
2015
2019
9.731
1933
2019
8.708
1.000
83.0002.548
0.048
23.4718.562
0.027
114.7022.595
0.324
16.20557.9451.403
412.490

Table 2 – Baseline Model and Robustness to Spatial Fixed Effect Definition
VARIABLES
Naka River*Post (0/1)
Naka River (0/1)
Ln Floor Area
Number of Bedrooms
Living Room (0/1)
Ln Months Advertised
Dining Room (0/1)
WC (0/1)
Heated Toilet (0/1)
AC (0/1)
Stove (0/1)
Fridge (0/1)
Washer Hookups (0/1)
Storage (0/1)
Walk-in Closet (0/1)
One Room Apt (0/1)
Elevator (0/1)
Security Camera (0/1)

Model
(1)
0.009**(0.004)0.087***(0.011)0.573***(0.019)0.010(0.007)0.049***(0.006)-0.007***(0.002)-0.009(0.007)0.016***(0.004)0.037***(0.004)0.011***(0.003)0.045***(0.004)0.031***(0.012)0.020***(0.006)-0.026***(0.003)0.011*(0.007)-0.034***(0.006)0.074***(0.007)0.016**(0.006)Model(2)0.021***(0.003)0.004(0.010)0.609***(0.016)0.004(0.006)0.040***(0.005)0.003**(0.001)-0.010*(0.006)0.015***(0.003)0.040***(0.003)0.011***(0.002)0.037***(0.003)0.032***(0.011)
0.021***(0.005)-0.028***(0.003)0.016***(0.006)-0.030***(0.005)0.070***(0.006)0.008*(0.005)Model(3)0.018***(0.003)0.621***(0.016)0.010*(0.006)0.039***(0.005)0.002(0.001)-0.008(0.006)0.015***(0.003)0.033***(0.003)0.009***(0.002)0.026***(0.003)0.030***(0.010)0.018***(0.005)-0.018***(0.002)0.018***(0.005)-0.034***(0.005)0.058***(0.005)0.006(0.005)Model(4)0.014***(0.002)-

VARIABLES
Bicycle Parking (0/1)
Fiber Internet (0/1)
CATV (0/1)
Steel (0/1)
Concrete (0/1)
Year Built 1970 (0/1)
Year Built 1980 (0/1)
Year Built 1990 (0/1)
Year Built 2000 (0/1)
Year Built 2010 (0/1)
Medium Water Depth (0/1)
High Water Depth (0/1)
Ln Distance to Station
Ln Distance to River
Ln Distance to Park
Ln Distance to School
Ln Distance to Bullet Train

Floor FE
YES
YES
YES
NO
Time FE
YEAR x MONTH YEAR x MONTH YEAR x MONTH YEAR x MONTH
Spatial FE
NO
WARD
NBHD
UNIT
Observations
91,63991,63991,63916,904
Adjusted R2
0.796
0.845
0.881
0.980
Notes: *, **, *** denotes significance at the 10%, 5%, and 1% level respectively.
Robust standard errors are clustered at the neighborhood level.

38

Model
(1)
0.008
(0.009)0.008(0.007)0.067***(0.009)0.012(0.009)0.014(0.010)-0.030(0.031)-0.003(0.029)0.069**(0.029)0.176***(0.029)0.239***(0.031)0.035***(0.007)-0.055***(0.011)-0.047***(0.006)0.017***(0.004)-0.011**(0.004)-0.008(0.006)-0.017***(0.005)

Model
(2)
0.011
(0.007)-0.001(0.006)-0.014*(0.007)0.025***(0.008)0.012(0.008)-0.049(0.033)-0.018(0.032)0.068**(0.032)0.170***(0.032)
0.234***(0.033)0.012**(0.006)
0.034***(0.012)-0.044***(0.006)0.012***(0.003)0.001(0.004)0.001(0.005)-0.060***(0.007)Model(3)0.013*(0.007)0.003(0.005)-0.013*(0.007)0.011(0.007)0.009(0.007)-0.030(0.030)0.001(0.030)0.099***(0.029)0.201***(0.030)0.264***(0.031)-0.002(0.006)0.001(0.021)-0.008(0.006)0.008**(0.004)-0.003(0.004)0.006(0.006)-0.069***(0.019)

Model
(4)
-

Table 3 – Heterogeneity in Flood Protection Capitalization
Model (5)VARIABLES
First Floor
Naka River*Post (0/1)
0.016***(0.003)
Naka River*Post*First (0/1)
0.017**(0.007)
Naka River*Post*One Room (0/1)
Naka River*Post*High Water Depth (0/1)-

Model (6)One Room Units
0.019***(0.003)-0.020**(0.009)-Model (7)
Floodwater Depth
0.016***(0.003)0.045***(0.012)

Property-Level Controls
YES
YES
YES
Floor FE
YES
YES
YES
Time FE
YEAR x MONTHYEAR x MONTHYEAR x MONTH
Spatial FE
NBHD
NBHD
NBHD
Observations
91,63991,63991,6392
Adjusted R
0.881
0.881
0.881
Notes: Secondary interaction and level terms are included within each model but not reported for brevity.
*, **, *** denotes significance at the 10%, 5%, and 1% level respectively.
Robust standard errors are clustered at the neigborhood level.

39

Table 4 – Proximity to the Nearest River

VARIABLES
Naka River*Post (0/1)
Naka River*Post*Adjacent (0/1)

Property-Level Controls
Floor FETime FE
Spatial FE
Observations

Model (8)50 meters
0.017***(0.003)0.028**(0.012)Model (9)75 meters
0.016***(0.003)0.018**(0.009)Model (10)100 meters0.016***(0.003)0.014*(0.008)Model (11)125 meters0.016***(0.003)0.010(0.007)

YES
YES
YES
YES
YES
YES
YES
YES
YEAR x MONTH YEAR x MONTH YEAR x MONTH YEAR x MONTH
NBHD
NBHD
NBHD
NBHD
91,63991,63991,63991,639Adjusted R2
0.881
0.881
0.881
0.881
Notes: Secondary interaction and level terms are included within each model but not reported for brevity.
*, **, *** denotes significance at the 10%, 5%, and 1% level respectively.
Robust standard errors are clustered at the neighborhood level.

40

Table 5 – Commercial Rentals and Housing Sales

VARIABLES
Naka River*Post (0/1)
Naka River (0/1)

Property-Level Controls

Model
(12)
0.091*(0.046)0.410***(0.044)

Commerical Rentals
Model
(13)
0.080**(0.040)0.181***(0.061)Model(14)0.087***(0.033)
-

Model
(15)
0.092*(0.048)0.445***(0.038)

YES

YES

YES

YES

Time FE
Spatial FE
Observations

YEAR x MONTH
NO
5,456

YEAR x MONTH
WARD
5,456

YEAR x MONTH
NBHD
5,456

Adjusted R2

0.779

0.784

0.827

a

Housing Sales
Model
(16)
0.095**(0.042)0.264***(0.068)
YES
a

YES
a

a

YEAR x QUARTER YEAR x QUARTER YEAR x QUARTER
NO
WARD
NBHD
9,206
9,206
9,206
0.596

0.747

Notes: Sales of single-family homes are only identified during the quarter of the transaction. See Appendix B for more details.
*, **, *** denotes significance at the 10%, 5%, and 1% level respectively.
Robust standard errors are clustered at the neighborhood level.

41

Model
(17)
0.110***(0.036)-0.807

Table 6 – Falsification Tests
VARIABLES
Naka River*Post (0/1)

Model (18)Hii River
0.002(0.006)

Model (19)
Tatara/Umi River
0.005(0.004)

Property-Level Controls
Floor FETime FE
Spatial FE
Observations

YES
YES
YEAR x MONTH
NBHD
91,639YESYESYEAR x MONTH
NBHD
91,6392

Adjusted R
0.881
0.881
Notes: *, **, *** denotes significance at the 10%, 5%, and 1% level respectively.
Robust standard errors are clustered at the neighborhood level.

42

Figure 1 – Fukuoka Prefecture Floodplains43

Figure 2 - Major Rivers and their Floodplains in Fukuoka and Nakagawa City

44

Figure 3 – Capitalization Effects across Time

Capitalization Coefficient

0.05
0.03
0.01
-0.01
-0.03
-0.05
-13

-12

-11

-10

-9

Main embankment finished

-8

-7

-6
-5
-4
-3
-2
-1
0
1
Quarters Relative to Flood
Test flooding began
Dam completed

45

2

3

4

5

July 2018 flooding

Appendix A: Additional Tables and Figures
Appendix Table A1 – Description of Variables
VARIABLESDESCRIPTION
Monthly Rent (2015 USD)
Monthly rent
Floor Area (square meters)
Size of unit
Number of Bedrooms
Number of bedrooms
Floor
Floor of rental unit
Months Advertised
Number of months rental unit was advertised online
Living Room (0/1)
Living room
Dining Room (0/1)
Dining room
WC (0/1)
Toilet and bathroom are in separate rooms
Heated Toilet (0/1)
Self-heated toilet
AC (0/1)
Air conditioner included
Stove (0/1)
Stovetop unit included
Fridge (0/1)
Refridgerator included
Washer Hookups (0/1)
Washing machine hookups
Storage (0/1)
Extra storage space in rental unit
Walk-in Closet (0/1)
Walk-in closet
One Room Apt (0/1)
One room/studio apartment
Elevator (0/1)
Building has an elevator
Security Camera (0/1)
Building has security cameras
Bicycle Parking (0/1)
Building has bicycle or scooter parking
Fiber Internet (0/1)
Building has fiber internet
CATV (0/1)
Building has a cable TV connection
Steel (0/1)
Building is made from steel
Concrete (0/1)
Building is made from concrete
Medium Water Depth (0/1)
Expected inundation level is 1 - 2 meters
High Water Depth (0/1)
Expected inundation level exceeds 2 meters
Sale MonthThe year the rental unit was leased
Sale Year
The month the rental unit was leased
Year Built
The year the rental unit was constructedYear Built 1970 (0/1)
Rental unit was built between 1970 and 1979
Year Built 1980 (0/1)
Rental unit was built between 1980 and 1989
Year Built 1990 (0/1)
Rental unit was built between 1990 and 1999
Year Built 2000 (0/1)
Rental unit was built between 2000 and 2009
Year Built 2010 (0/1)
Rental unit was built between 2010 and 2019
Distance to Station (minutes)
Walking distance to the nearest train station
Distance to River (100s of meters)
Distance to the nearest first or second-class river
Distance to Park (100s of meters)
Distance to the nearest public park
Distance to School (100s of meters)
Distance to the nearest school
Distance to Bullet Train (100s of meters)
Distance to the nearest bullet train station
Naka River (0/1)
Rental unit is located in the Naka River floodplain
Post (0/1)
Rental unit was leased after June 2018
Notes: NII = National Institute of Informatics; MLIT = Ministry of Land, Infrastructure, Transport and Tourism.

46

SOURCE
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
NII
MLIT
MLIT
NII
NII
NII
NII
NII
NII
NII
NII
NII
MLIT
MLIT
MLIT
MLIT
MLIT
NII

Appendix Table A2 – Comparison of Apartment Units Rented Once Versus Multiple Times
VARIABLES
Monthly Rent (2015 USD)

One-time Rental Multiple Rentals(1)(2)
540.388516.619

Square Meters37.00636.959Number of Bedrooms

1.428

1.380

Floor

4.700

4.089

Months Advertised

4.005

4.389

Living Room (0/1)

0.397

0.401

Dining Room (0/1)

0.547

0.558

WC (0/1)

0.568

0.532

Heated Toilet (0/1)

0.455

0.450

AC (0/1)

0.549

0.672

Stove (0/1)

0.563

0.483

Fridge (0/1)

0.029

0.021

Washer Hookups (0/1)

0.928

0.915

Storage (0/1)

0.716

0.710

Walk-in Closet (0/1)

0.099

0.091

One Room Apt (0/1)

0.085

0.081

Elevator (0/1)

0.739

0.628

Security Camera (0/1)

0.698

0.599

Observations

74,735

16,904

Difference(1) - (2)23.769***(1.442)0.047(0.128)0.048***(0.006)0.610***(0.026)-0.383***(0.041)-0.004(0.004)-0.011***(0.004)0.035***(0.004)0.005(0.004)-0.123***(0.004)0.079***(0.004)0.008***(0.001)0.013***(0.002)0.007*(0.004)0.008***(0.003)0.004*(0.002)0.111***(0.004)0.100***(0.004)

VARIABLES
Bicycle Parking (0/1)

One-time Rental Multiple Rentals
(1)
(2)
0.931
0.931

Fiber Internet (0/1)

0.803

0.730

CATV (0/1)

0.825

0.738

Steel (0/1)

0.114

0.168

Concrete (0/1)

0.807

0.701

Post (0/1)

0.311

0.320

Sale Month

6.209

6.280

Sale Year2017.283

2017.233

Year Built

1999.586

2000.011

Medium Water Depth (0/1)

0.195

0.207

High Water Depth (0/1)

0.024

0.037

Distance to Station (minutes)

9.933

11.206

Distance to River (100s of meters)

4.213

3.860

Distance to Park (100s of meters)

2.615

3.067

Distance to School (100s of meters)

3.874

4.166

Distance to Bullet Train (100s of meters)

39.07248.510Naka River (0/1)

0.542

0.411

74,735

16,90447

Difference(1) - (2)>0.001(0.002)0.073***(0.003)0.087***(0.003)-0.054***(0.003)0.105***(0.003)-0.008**(0.004)-0.071**(0.029)0.050***(0.011)-0.425***(0.085)-0.012***(0.003)-0.013***(0.001)-1.273***(0.060)0.353***(0.026)-0.452***(0.051)-0.292***(0.018)-9.439***(0.395)0.131***(0.004)

Appendix Figure A1 – Daily Precipitation (mm) in Fukuoka City

48

Appendix Figure A2 – Google Search Volume Index by Fukuoka City River

49

Appendix B: Data Appendix for Commercial Rentals and Housing Transactions

In this appendix, we discuss two additional data sources used to determine whether flood
protection is capitalized in commercial rentals and/or housing sales. We begin by describing eachdata source and reporting summary statistics. Data limitations are then discussed followed by asummary of the results and sensitivity analysis.

Commercial Rental Data
Information on commercial rentals (office and retail locations) was collected from the National
Institute of Informatics, which is also the source for our apartment listing data (At Home Co.
Ltd., 2020). As a result, many of the attributes that describe the apartment rentals are alsoavailable for commercial properties including: the size of the unit, the presence or lack ofbuilding amenities (i.e. security cameras, fiber internet, elevator, etc.), and the year in which thebuilding was constructed. These two attribute vectors are not identical, however, which we
describe in more detail in Appendix Table B1. In general, the market for commercial rentals is
far less active than the market for apartments. A total of 5,456 floodplain commercial properties
were leased between 2015 and 2019 as compared to 91,639 apartments. We provide summary
statistics for the former and latter group in Appendix Table B2 and Table 1, respectively.

Housing Transactions
Unlike many other countries, Japan does not release data on the sale of specific properties as thisis considered confidential. Real estate agencies are asked to report their transactions to the MLIT
instead. The MLIT processes this data to ensure the anonymity of the buyer and seller and thenreleases an altered form of the data to the public in quarterly reports. We collect these reports and50keep only transactions of single-family homes within our study area. Due to MLIT’s data
anonymization, we are only able to identify the neighborhood or chocho (see Section 4) of eachtransaction as well as the quarter in which the property sold.1 The lack of detail pertaining towhen the transaction occurred is less of a concern as the first two quarters in 2018 coincide withthe pre-treatment period (January – March and April – June), while the third and fourth quarters
are during the post-treatment period (July – September and October – December). One benefit ofthe data is that information on housing sales is available prior to our study period (2015 – 2019).
We therefore extend our panel to increase the precision of our estimates by collecting
transactional data from 2010 to 2019 and report summary statistics of this in Appendix Table B3.
Since each transaction is classified at the chocho level, we are unable to perfectly identify
which properties are inside or outside of a floodplain. We resolve this issue using a conservative
approach by assuming a property is within a floodplain if any part of its chocho is within afloodplain. This assumption will incorrectly label many properties as within a floodplain when,in actuality, they are not. This will attenuate the DD estimate towards zero as properties outsideof the Naka River floodplain will be considered treated despite never receiving a treatment.

Estimation and sensitivity analysis
We re-estimate models 1 – 3 using housing sales and commercial rental data. Each model isestimated twice, once for each property type, using only floodplain transactions. Coefficientestimates from the commercial rental (housing) regressions are reported in Appendix Table B4

1

Additionally, the MLIT measures distance to the nearest train discretely, preventing us from including asa control a continuous distance to station measure like we do in the baseline model. Instead, we form
three dummies indicating if the property is within 5 minutes of the station (Station 0 − 5), between 5
minutes to 10 minutes away (Station 5 − 10), and between 10 minutes to 15 minutes away (Station 10 −15). The excluded category are properties more than 15 minutes on foot from the nearest station. Our
results are robust to alternative specifications.
51

(Appendix Table B5), while Table 5 combines and abbreviates these estimates. Under models 12- 17, we find flood protection provided by the Gokayama Dam increased downstream
commercial rents by 8.3% to 9.5% and housing prices by 9.6% to 11.6%. Both ranges suggest
flood protection is capitalized to a greater extent in commercial properties and housing sales
compared to apartment rentals. It should be noted in model 14 that the indicator for expectedflood water exceeding 2 meters is dropped due to perfect collinearity with the neighborhood
fixed effect.
One of the limitations with the housing data is the inability to determine each property’s
exact spatial location, making it difficult to precisely define treatment status. As a robustness
check, we allow the DD estimate to vary based on the percentage of land area within eachchocho that is within a floodplain. We create three dummies indicating if the chocho has up to
33% of its land area within a floodplain (Flood1), between 33% and 66% of its land area within afloodplain (Flood2), and more than 66% of its land area within a floodplain (Flood3). Thesedummies are then interacted with the DD term and included as regressors in model B1 ofAppendix Table B6. We expect capitalization rates will increase with higher floodplain
percentages due to a reduction in measurement error. The results from model B1 supports thisconclusion with the magnitude of the DD coefficient increasing from 0.8% to 24.9% as thepercentage of floodplain land increases. Taken together, the DD coefficients from models 14 –17 are likely a lower bound when describing the relationship between property values and dam
flood protection.

52

Appendix Table B1 – Data Availability Across Apartments, Commercial Rents, and Single-Family Homes
VARIABLESDESCRIPTIONAPTS COMM HOUSE VARIABLES
Monthly Rent (2015 USD) Monthly rent
YESYES
NO Elevator (0/1)
Sale Price (2015 USD)
Sale priceNONOYES Security Camera (0/1)
Floor Area (square meters) Size of unit or house
YES
YES
YES Bicycle Parking (0/1)
Lot Size (square meters)
Parcel lot sizeNONO
YES Fiber Internet (0/1)
Frontage (meters)
Width of the parcel lot
NONO
YES CATV (0/1)
Number of Bedrooms
Number of bedrooms
YES
YES
NO Steel (0/1)
Floor
Floor of rental unit
YESNONO Concrete (0/1)
Whole Building (0/1)
Rental unit is the entire buildingNO
YES
NO Medium Water Depth (0/1)
Several Floors (0/1)
Rental unit spans across several floors
NO
YES
NO High Water Depth (0/1)
Months Advertised
Number of months rental unit was advertised online
YES
YES
NO Sale Month
Living Room (0/1)
Living room
YESNONO Sale Quarter
Dining Room (0/1)
Dining room
YESNONO Sale YearWC (0/1)
Toilet and bathroom are in separate rooms
YESNO
NO Year Built
Separate Toilet (0/1)
Separate bathrooms for men and womenNO
YES
NO Year Built 1970 (0/1)
Heated Toilet (0/1)
Self-heated toilet
YESNO
NO Year Built 1980 (0/1)
AC (0/1)
Air conditioner
YES
YES
NO Year Built 1990 (0/1)
Stove (0/1)
Stovetop unit
YES
NONO Year Built 2000 (0/1)
Fridge (0/1)
Refridgerator
YESNO
NO Year Built 2010 (0/1)
Washer Hookups (0/1)
Washing machine hookups
YESNO
NO Distance to Station (minutes)
Storage (0/1)
Extra storage space in rental unit
YES
NONO Distance to River (100s of meters)
Walk-in Closet (0/1)
Walk-in closet
YESNO
NO Distance to Park (100s of meters)
One Room Apt (0/1)
One room/studio apartment
YES
NONO Distance to School (100s of meters)
Office (0/1)
Office SpaceNOYES
NO Distance to Bullet Train (100s of meters)
Store (0/1)
Retail SpaceNOYES
NO Percent Floodplain
Notes: APTS = Apartments, COMM = Commercial rental units, HOUSE = Single-family homes

53

DESCRIPTION
Building has an elevator
Building has security cameras
Building has bicycle or scooter parking
Building has fiber internet
Building has a cable TV connection
Building is made from steel
Building is made from concrete
Expected inundation level is 1 - 2 meters
Expected inundation level exceeds 2 meters
The month the property was leased or sold
The quarter the property was leased or sold
The year the property was leased or sold
The year the building was constructed
Building was built between 1970 and 1979
Building was built between 1980 and 1989
Building was built between 1990 and 1999
Building was built between 2000 and 2009
Building was built between 2010 and 2019
Walking distance to the nearest train station
Distance to the nearest first or second-class river
Distance to the nearest public park
Distance to the nearest school
Distance to the nearest bullet train station
Percentage of neigborhood within a floodplain

APTS
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
N/A

COMM HOUSE
YES
NO
YES
NO
NO
NO
YES
NO
NO
NO
YES
YES
YES
YES
YES
NO
YES
NO
YES
NO
YES
YES
YES
NO
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
YES
NO
YES
NO
YES
NO
N/A
YES

Appendix Table B2 –Summary Statistics of Commercial Rentals
Monthly Rent (2015 USD)
Floor Area (square meters)
Months Advertised
AC (0/1)
Separate Toilet (0/1)
Whole Building (0/1)
Several Floors (0/1)
Office (0/1)
Store (0/1)
Elevator (0/1)
Security Camera (0/1)
Fiber Internet (0/1)
Steel (0/1)
Concrete (0/1)
Medium Water Depth (0/1)
High Water Depth (0/1)
Post (0/1)
Sale Month
Sale Year
Year Built
Distance to Station (minutes)
Distance to River (100s of meters)
Distance to Park (100s of meters)
Distance to School (100s of meters)
Distance to Bullet Train (100s of meters)

Naka River Floodplain (N=4,107)
Mean
Std. Dev.
MinMax
3,009.763
2,542.335
285.857
16,487.139
78.42052.26820.000464.660
7.160
7.254
1.000
45.000
0.342
0.475
0.245
0.430
0.271
0.445
0.009
0.092
0.481
0.500
0.275
0.447
0.776
0.417
0.176
0.381
0.462
0.499
0.236
0.425
0.758
0.428
0.133
0.340
0.000
0.000
0.234
0.423
6.630
3.341
1.000
12.000
2017
1.296
2015
2019
1984
12.151
1949
2019
6.281
3.217
1.000
31.000
5.575
3.122
0.044
16.162
2.330
1.132
0.153
5.660
3.885
1.827
0.254
9.105
17.48510.4341.272
84.47554

Other Floodplains (N=1,349)
Mean
Std. Dev.
Min
Max
1,880.411
1,785.835
295.128
15,281.001
84.04466.70420.130476.650
8.182
8.657
1.000
43.000
0.090
0.287
0.156
0.363
0.372
0.484
0.029
0.168
0.277
0.447
0.383
0.486
0.438
0.496
0.110
0.314
0.099
0.298
0.268
0.443
0.669
0.471
0.153
0.361
0.012
0.108
0.305
0.460
6.580
3.301
1.000
12.000
2017
1.347
2015
2019
1988
11.297
1949
2019
7.729
6.174
1.000
34.000
2.979
2.091
0.161
12.052
2.461
5.198
0.101
105.513
4.518
2.006
0.499
12.84146.11259.8491.304
280.297

Appendix Table B3 – Summary Statistics of Single-Family Homes

Sale Price (2015 USD)
Floor Area (square meters)
Lot Size (square meters)
Frontage (meters)
Steel (0/1)
Concrete (0/1)
Post (0/1)
Sale Quarter
Sale Year
Year Built
Distance to Station (minutes)
Percent Floodplain

Mean
414,133
179.566
187.947
10.936
0.202
0.129
0.154
2.473
2015
1995
15.30944.461Naka River Floodplain (N=1,325)
Std. Dev.
Min
Max
332,049
10,9092,368,307105.900
45.000500.000
118.604
60.0001,400.000
5.298
1.400
43.000
0.401
0.335
0.361
1.097
1.000
4.000
2.745
2010
2019
17.0201947
2019
10.4031.000
60.00036.2261.000
100.000

55

Mean
217,188
135.702251.643
13.571
0.150
0.037
0.161
2.481
2015
1996
18.33237.293

Other Floodplains (N=7,881)
Std. Dev.
Min
Max
182,351
7,799
2,501,251
74.64245.000500.000
176.891
60.0001,400.000
6.356
2.000
49.000
0.357
0.188
0.368
1.113
1.000
4.000
2.758
2010
2019
17.4921946
2019
14.1451.000
90.00032.4961.000
100.000

Table B4 – Capitalization of Flood Protection in Downstream Commercial Rental Prices
VARIABLES
Naka River*Post (0/1)
Naka River (0/1)
Ln Floor Area
AC (0/1)
Separate Toilet (0/1)
Ln Months Advertised
Whole Building (0/1)
Several Floors (0/1)
Office (0/1)
Store (0/1)
Elevator (0/1)
Security Camera (0/1)
Fiber Internet (0/1)
Steel (0/1)

Time FE
Spatial FE
Observations
Adjusted R2

Model
(12)
0.091*(0.046)0.410***(0.044)1.003***(0.022)0.174***(0.026)0.102***(0.023)0.002(0.009)0.341***(0.094)Model(13)0.080**(0.040)0.181***(0.061)1.005***(0.022)0.175***(0.026)0.100***(0.021)0.006(0.008)0.257***(0.078)Model(14)0.087***(0.033)1.012***(0.022)0.167***(0.026)
0.096***(0.019)0.006(0.008)0.154**(0.075)0.121(0.085)-0.108***(0.029)-0.079(0.095)0.095***(0.026)-0.066*(0.036)0.077**(0.032)-0.049(0.060)0.052(0.065)-0.138***(0.028)0.009(0.078)0.078***(0.024)-0.077**(0.036)
0.051*(0.030)-0.065(0.056)0.011(0.043)-0.083***(0.025)0.056(0.071)
0.023(0.025)-0.152***(0.032)0.078***(0.029)-0.054(0.068)VARIABLES
Concrete (0/1)
Year Built 1970 (0/1)
Year Built 1980 (0/1)
Year Built 1990 (0/1)
Year Built 2000 (0/1)
Year Built 2010 (0/1)
Medium Water Depth (0/1)
High Water Depth (0/1) a
Ln Distance to Station
Ln Distance to River
Ln Distance to Park
Ln Distance to School
Ln Distance to Bullet Train

Model
(12)
-0.084(0.054)0.013(0.055)0.112**(0.053)0.157***(0.056)
0.327***(0.058)0.493***(0.069)-0.040(0.033)Model(13)-0.096**(0.048)0.030(0.053)0.137***(0.052)0.179***(0.052)0.324***(0.056)0.495***(0.066)-0.079**(0.031)Model(14)-0.029(0.055)0.041(0.048)0.164***(0.046)0.212***(0.050)0.345***(0.051)
0.588***(0.058)-0.138***(0.049)-0.107(0.103)-0.079***(0.026)-0.017(0.020)0.068**(0.030)0.065(0.041)-0.045***(0.015)0.029(0.108)-0.086***(0.024)-0.034*(0.020)0.062**(0.028)0.066*(0.036)-0.123***(0.044)-0.028(0.022)0.077*(0.044)0.001(0.035)-0.021(0.065)-0.420***(0.124)

YEAR x MONTH YEAR x MONTH YEAR x MONTH
NO
WARD
NBHD
5,456
5,456
5,456
0.763

0.784

0.827

a

Notes: High water depth is dropped from Model 3b due to perfect collinearity with the neighborhood spatial fixed effects*, **, *** denotes significance at the 10%, 5%, and 1% level respectively.
Robust standard errors are clustered at the neighborhood level.

56

Table B5 – Capitalization of Flood Protection in Downstream Housing Prices
Model
Model
Model
VARIABLES
(15)
(16)
(17)
Naka River*Post (0/1)
0.092*
0.095**
0.110***(0.048)(0.042)(0.036)
Naka River (0/1)
0.445***
0.264***(0.038)(0.068)
Ln Floor Area
0.845***
0.602***
0.452***(0.030)(0.024)(0.019)Ln Lot Size
0.071***
0.343***
0.470***(0.026)(0.020)(0.018)Frontage (meters)-0.005***-0.0010.003***(0.001)(0.001)(0.001)
Steel (0/1)
0.159***
0.146***
0.087***(0.021)(0.017)(0.017)
Concrete (0/1)
0.348***
0.316***
0.221***(0.043)(0.028)(0.025)
Year Built 1970 (0/1)
0.011
0.041
0.096***(0.047)(0.036)(0.032)
Year Built 1980 (0/1)
0.201***
0.217***
0.249***(0.047)(0.033)(0.031)
Year Built 1990 (0/1)
0.362***
0.413***
0.482***(0.048)(0.036)(0.033)
Year Built 2000 (0/1)
0.741***
0.728***
0.756***(0.050)(0.038)(0.037)
Year Built 2010 (0/1)
1.145***
1.129***
1.146***(0.048)(0.037)(0.034)
Station 0 - 5 (0/1)
0.301***
0.282***
0.159***(0.054)(0.031)(0.027)
Station 5 - 10 (0/1)
0.276***
0.238***
0.121***(0.037)(0.022)(0.020)Station 10 - 15 (0/1)
0.186***
0.144***
0.046***(0.031)(0.020)(0.016)Time FE
Spatial FE
Observations

YEAR x QUARTER YEAR x QUARTER YEAR x QUARTER
NO
WARD
NBHD
9,206
9,206
9,206

2

Adjusted R
0.596
0.747
0.807
Notes: *, **, *** denotes significance at the 10%, 5%, and 1% level respectively.
Robust standard errors are clustered at the neighborhood level.

57

Table B6 – Robustness to Floodplain Definition
Model
VARIABLES(B1)
Naka River*Flood1*Post (0/1)
0.008(0.027)
Naka River*Flood2*Post (0/1)
0.185**(0.073)
Naka River*Flood3*Post (0/1)
0.222***(0.071)
Ln Floor Area
0.452***(0.019)Ln Lot Size
0.470***(0.018)Frontage (meters)
0.003***(0.001)
Steel (0/1)
0.087***(0.017)
Concrete (0/1)
0.220***(0.025)

VARIABLES
Year Built 1970 (0/1)
Year Built 1980 (0/1)
Year Built 1990 (0/1)
Year Built 2000 (0/1)
Year Built 2010 (0/1)
Station 0 - 5 (0/1)
Station 5 - 10 (0/1)
Station 10 - 15 (0/1)

Model
(B1)
0.096***(0.032)0.249***(0.031)
0.482***(0.033)0.757***(0.036)
1.147***(0.034)0.160***(0.027)0.121***(0.020)0.045***(0.016)Time FE
YEAR x QUARTER
Spatial FE
NBHD
Observations
9,206
2
Adjusted R
0.807
Notes: *, **, *** denotes significance at the 10%, 5%, and 1% level respectively. Robust standard errors are
clustered at the neighborhood level.

58