The impact of flight noise on urban housing markets: Evidence fromthe new landing flight paths of Haneda Airport in Japan
Takeru Sugasawa∗

Yuta Kuroda†

Kai Nomura‡

Shohei Yasuda§

Jun Yoshida¶

September 11, 2024

Abstract
The study focuses on the announcement and introduction of the new landing flight paths of Haneda
Airport during south wind conditions and examines the impact of the newly generated flight noiseon the directly below property markets. The new landing flight paths can be considered as a naturalexperiment because the flight paths fly straight along the runway extensions over urban areas that
did not previously experience flight noise. Our analysis shows the considerably smaller impact offlight noise on housing values than the results suggested by previous studies. Our estimates indicatethat a 1dB increase in flight noise decreases property prices directly below the new flight paths byapproximately 0.1 percent. Additionally, the average impact of the new flight paths on property
rents was either insignificant or negligible. We interpret these findings to mean that in urban areas
already experiencing serious noise from other noise sources such as automobiles, the additional
impact of flight noise is minimal. Furthermore, three years after the introduction of the new flightpaths, the significant impacts on property price had disappeared. A possible explanation is that the
progression of population selection in areas exposed to severe aircraft noise gradually increased theregion’s tolerance to noise over time. Previous studies have mainly focused on suburban areas, andtheir findings may be insuﬀicient for examining the impact of flight noise in urban environments.

1 Introduction
The transportation of people and goods by airplanes is a crucial factor that influences the economic
activities of urban areas. However, it is well-known that airports cause public nuisance such as flight∗

Housing Research and Advancement Foundation of Japan. E-mail: sugasawa@hrf.or.jp.
Osaka Metropolitan University. E-mail: kuroyu0725@gmail.com.
‡
Yamanashi Eiwa College. E-mail: k.nomura@yamanashi-eiwa@ac.jp.
§
Nihon University. E-mail: yasuda.shohei@nihon-u.ac.jp.
¶
Tohoku Gakuin University. E-mail: junyoshida@mail.tohoku-gakuin.ac.jp.
†

1

noise pollution. Noise pollution poses health risks, such as headaches, particularly in areas with highaircraft traﬀic, and its economic impacts are significant (Boes et al., 2013; Beghelli et al., 2023).1
Economists have analyzed the willingness to pay to avoid exposure to flight noise pollution throughchanges in housing prices and rents (McMillen, 2004; Pope, 2008; Boes and Nüesch, 2011).
However, there is the possibility that the results of the previous studies include some endogeneity.
In concrete, arbitrary selection of flight paths or the issue of sample selection of surrounding residents
and properties can introduce endogeneity to estimation results, making it challenging to strictly identify the impact of flight noise on housing transactions. If aircraft chooses paths that minimize theamount of damage from flight noise, the impact of noise occurs in relatively quiet areas and may beoverestimated compared to the same level of noise in urban areas with larger noise from other noise
sources such as roads with heavy traﬀic. Additionally, areas surrounding airports or flight paths thatexperience severe noise may be populated by individuals more tolerant of noise or have a higher concentration of properties with strong soundproofing. Therefore, it is debatable whether the estimation
results obtained from such cases are representative of the general impact of aircraft noise.
To address these endogeneity issues and strictly identify the impact of flight noise pollution on
urban housing markets, we conduct hedonic approach with focusing on the two new landing paths
introduced at Haneda Airport in Japan on March 29, 2020. These two landing paths run straight overthe metropolitan area of Tokyo towards the runways, and the areas newly exposed to aircraft noisedue to the flight paths can be interpreted as areas that coincidentally lie along the extended paths of
the runways. Additionally, the regions newly affected by flight noise due to these two landing paths
were previously unaffected by aircraft departing from or arriving at Haneda Airport, thus addressing
the potential selection biases of people and buildings adapting to flight noise. Furthermore, the areas
directly beneath the landing paths are in the metropolitan area of Tokyo, one of the most developed
urban areas in the world, providing an opportunity to examine the impact of flight noise in urban
areas where noise pollution is a serious concern. For these reasons, the two landing paths of Haneda
Airport serve as an ideal case study to investigate the impact of flight noise on the housing market inurban areas.
The study makes three key contributions. First, as mentioned above, we are the first to investigate
the impact of aircraft noise on the peripheral housing market using a natural experiment approach.
1

There is extensive research in the field of medicine on the impact of noise on human health. High levels of noise
can directly increase the risk of hearing loss (Alberti, 1992). Additionally, the stress induced by noise exposure has
been reported to elevate health risks for individuals without chronic illnesses (Babisch et al., 2003). These health risks
associated with noise can act as a deterrent, and cause people to avoid residing in areas with serious noise exposure.

2

Our estimation results suggest that the introduction of the new landing flight paths led to a decreasein property prices directly below by approximately 0.10% per 1 dB increase in noise. This coeﬀicient issignificantly smaller compared to previous studies that examined the impact of flight noise on housing
prices, e.g. approximately 1.7% decreases in housing prices per 1 dB increase in noise found by Winke(2017). Additionally, our analysis did not find significant effects on rental prices directly beneath the
new flight paths. This result contrasts with previous research that discovered significant impacts ofchanges in aircraft noise levels due to variations in flight volume on surrounding housing rents (Boesand Nüesch, 2011). Those results imply that in urban areas already exposed to high noise levels,
the additional impact of flight noise might be limited. By using data on automobile noise publishedby the Ministry of the Environment (ME), we found that the extent of the decline in housing prices
decreases approximately by 64% within 50 meters of roads that are sources of high road noise level.
These results suggest that the negative externalities of transportation noise may vary between urban
and suburban or rural areas. Additionally, our results indicate that properties with lower prices or in
older buildings, which are associated with lower soundproofing, experience a greater negative impact
from noise exposure. Since living in lower prices or older properties correlate with lower income, theresult suggests the presence of income disparities in noise damage within urban areas.
Second, this study compares the impact of flight noise on property sales prices and rents. Ourestimation results indicate that while housing prices significantly decreased directly below the newlanding flight paths, the impact on property rents is insignificant or negligible. This difference in theimpact of noise on sales and rental markets remained robust across various robustness checks. Thesefindings are consistent with previous research that suggests that in the housing market, the living
environment is more highly valued in sales transactions than in rental transactions (Caplan et al.,2021; Kuroda and Sugasawa, 2023; Sugasawa et al., 2024). A possible explanation is that in urban
areas, rental housing occupants are often young singles, such as university students, who are less likely
to be at home during the day when the new flight paths are in use, thereby limiting their exposure tonoise. An analysis limited to the subsample of properties with an area of 50 square meters or morerevealed a decrease in rents for properties directly below the new flight paths after the flight paths
were introduced.
Third, we discusse the progression of selection in population and property characteristics following
the introduction of the new landing flight paths. Interestingly, the impact of Haneda Airport’s new
landing flight paths on property prices directly below the flight paths was no longer statistically3significant about two years after the paths were introduced. A possible interpretation is that, following
the introduction of the new flight paths, people who are sensitive to noise relocated away from areas
directly under the new flight paths. An analysis focusing on different population groups showed thatthe proportion of the 25 − 34 years old population with high mobility decreased in areas directly below
the flight paths after the introduction. This change might reflect a progression in population selection,
where people who are more sensitive to noise move from directly under the flight paths. On the other
hand, the characteristics of transacted properties directly below the new landing flight paths were notsignificantly affected after the flight path’s introduction. While previous studies (Boes et al., 2013;
Huss et al., 2010; Von Graevenitz, 2018) have shown a bias in population and property characteristics
in areas with high noise levels, our analysis suggests that the progression of population selection
following the introduction of noise precedes changes in the selection of property characteristics.
The rest of the paper is structured as follows. In Section 2, we review previous studies focusing on
the effects of flight noise on property values. Section 3 describes the background about the new flightpaths of Haneda Airport. Section 4 outlines this study’s empirical design, data sources, and variables.
Section 5 shows the main results and robustness checks. Section 6 discusses the factors affecting theimpacts of flight noise and generating small magnitudes of our estimation results. Finally, Section 7
concludes the paper.

2 Previous studies
The section overviews previous studies that examine the impact of airport noise on housing values
using quasi-experimental methods, with focusing on the endogeneity that may be inherent in theirfindings. Focusing on quasi-experimental cases, prior research that estimates the impact of aircraft
noise on housing values can be categorized into two types. The first type examines the exogenous
occurrence, increase, or decrease of airport noise in a specific region (Boes and Nüesch, 2011; Winke,
2017; Zheng et al., 2020). The second type investigates the announcement effects of decisions that
introduce additional noise to a region (Jud and Winkler, 2006; Mense and Kholodilin, 2014; Winke,2017).2
2

In the realm of quasi-natural experiments related to aircraft noise and housing prices, Pope (2008) stands out despite
not fitting into the two mentioned categories. Pope (2008) examines the impact of reducing information asymmetry
between buyers and sellers by publicizing aircraft noise levels peripheral areas of Raleigh-Durham International Airport.
The study finds that making noise levels known led to an additional decrease in the prices of affected houses by up
to approximately 2.9%. Pope (2008) provides crucial evidence on how information about flight noise impacts propertytransactions. However, because our study does not focus on scenarios where supplementary information about existing
noise is introduced, we do not directly compare the research design of Pope (2008) with the design of the study.

4

Studies that examine the impact of changes in airport noise on housing values include Boes andNüesch (2011), and Zheng et al. (2020). Boes and Nüesch (2011) focused on changes in flight noiseat peripheral areas of Zurich Airport, estimating the impact of changes in aircraft noise on property
rents. They found that in areas where airport noise increased by more than 3 decibels, rental prices
decreased by approximately 0.5% per decibel. In addition, Zheng et al. (2020) investigated the impactof the disappearance of aircraft noise following the relocation of Hong Kong’s Kai Tak Airport on
peripheral property prices. Their results suggest that the disappearance of airport noise due to the
airport’s relocation increased property prices around the former airport area approximately by 24.4%
on average.
Research examining the impact of announcements related to new noise sources on surrounding
housing prices includes Jud and Winkler (2006) and Mense and Kholodilin (2014). Jud and Winkler (2006) investigated the impact of the announcement of an aviation hub in the Greensboro/High
Point/Winston-Salem metropolitan area on peripheral property prices. Their findings indicate thatthe announcement of the aviation hub’s construction caused a decrease in property prices by approximately 9.2% within 2.5 miles of the expected site. Mense and Kholodilin (2014) estimated the impactof anticipated changes in noise exposure due to the expansion of Berlin Brandenburg International
Airport on housing prices around the expected flight paths. Their results show that the predicted
additional noise exposure decreased property prices below the planed flight paths by about 9.6% to10.2%.
Winke (2017) is recent research focusing on both the announcement effect and actual new noise
exposure. Winke (2017) focuses on the construction of a new runway at Frankfurt Airport andthe impacts of the events on surrounding property prices. The study found a significant decreasein property prices by about 1.7% per 1 dB of noise exposure. On the other hands, no significant
announcement effect was detected. This inconsistency in the results regarding the announcement
effects within the quasi-experimental studies highlights a divergence in findings. However, there is aconsensus among the previous studies that actual flight noise exposure negatively impacts surrounding
property prices and rents.
These prior studies using quasi-natural experimental methods provide valuable insights into theimpact of aircraft noise exposure and its announcement on housing transactions. However, a common
challenge in these studies is that they do not completely eliminate the issue of endogeneity related tothe sample exposed to flight noise.

5The endogeneity issues in previous studies can be categorized into two main points. Firstly, theshocks focused by the previous research involve pre-existing variations in flight volumes or the introduction of new flight paths in areas already exposed to flight noise, the possibility of selection basedon the existing population composition or property characteristics in response to the noise cannot
be excluded. Von Graevenitz (2018) found that properties within 200 meters of major roads tend tobe smaller and lower in price compared to other properties. Additionally, these areas have a higher
concentration of residents who are students or single households. Boes et al. (2013) indicate that
residents might select their locations with consideration for their health conditions when examining
the health impacts of aircraft noise on surrounding residents. In addition, since aircraft noise tends tolower property prices, it could attract lower-income households, thereby influencing the demographic
characteristics of the area (Hener, 2022). Furthermore, in areas with significant noise, there may be extensive soundproofing installations such as double-glazed windows, leading to property characteristics
that differ from those in relatively quieter surrounding areas. The bias arising from these interregional
differences in the impact of noise on housing transactions is generally addressed by including regional
fixed effects, such as postal codes. However, if the noise present before the change induces selection,
causing the impact of noise changes on housing prices to vary, the average estimated results might
still contain bias even after considering regional fixed effects. In areas where population and property
selection have adapted to pre-existing noise pollution, it is conceivable that these areas might be lessaffected by additional pollution exposure compared to cases where no such selection has occurred(Greenstone and Gayer, 2009). To address these potential selection biases and examine the impact ofaircraft noise on property transactions, it is necessary to focus on cases of new aircraft noise exposurein areas previously not exposed to such noise. To our best knowledge, no prior research has focusedon such cases.
The second issue is that the location of aviation hubs or flight paths might be determined in sucha way that the noise impact on surrounding areas is minimized. For instance, Winke (2017) treats
the additional noise exposure due to the new runway at Frankfurt Airport as an exogenous changeof the amount of flight noise. The areas newly exposed to noise levels of 55 decibels or more dueto the new flight path are limited to regions outside the city center. Similarly, Boes and Nüesch
(2011) and Boes et al. (2013) focused on the changes in noise levels due to alterations in flight volume
around Zurich Airport in Switzerland. Still, areas with average daytime noise levels exceeding 50 dB
are concentrated in regions far from the city center. If such selection exists in the analyzed cases,

6the observed treatments cannot be considered as randomly occurring, leading to potential bias in theestimated results. Suburban and rural areas are likely to have fewer alternative noise sources, such as
traﬀic noise and urban hustle, compared to urban areas. Therefore, there is room for doubt regarding
the generalizability of findings on the impact of additional noise in such regions (Greenstone andGayer, 2009). To address this issue, it is necessary to focus on areas where flight paths are determined
exogenously and investigate the impact of noise on the region. However, no such studies have beenreported to date.
Related to the second problem mentioned above, is the scarcity of studies that identify the impactof additional noise on housing prices and economic activities within urban areas due to the endogenous
nature of noise source location selection. Noise is widely discussed as a major urban pollution issue,
and accumulating rigorous evidence on the impact of noise on the living environment and economic
activities in urban areas is an important topic in urban economics. When additional traﬀic noise occurs
in a city, it is expected that the negative externalities of agglomeration will increase, and selection of
population and property characteristics would progress. However, due to the aforementioned reasons,
there are no studies examining cases where additional flight noise has exogenously occurred in urban
areas.
We address the endogeneity issues that previous studies were unable to resolve due to the limitations of their cases by focusing on the announcement and introduction of the new landing flightpaths of Haneda Airport. The new landing flight paths can be interpreted as a case that new flightnoise exogenously occurred in a highly developed urban area. The characteristics of the flight paths
allow us to investigate the impact of noise on the surrounding environment while overcoming the
aforementioned endogeneity issues that prior research could not address.

3 New flight paths of Haneda Airport
Haneda Airport, located in Ota Ward, Tokyo, is the largest airport in Japan, serving approximately
78.7 million passengers in 2023. 3 According to Airports Council International (ACI) , Haneda Airport
ranked fifth globally in terms of annual passenger volume in 2023, making it one of the world’s busiest
airports.4 Haneda Airport is situated about 15 km southeast of central Tokyo. Tokyo itself is ranked
as the fourth most developed city in the world according to the Global Cities Index published by
3

The breakdown includes 60.8 million domestic passengers and 17.9 million international passengers, respectively.
https://airportindustry-news.com/aci-world-reveals-top-10-busiest-airports-in-2023/, retrieved 5 July
2024
4

7

Oxford Economics, highlighting Haneda’s significant proximity to a major urban area.5
Haneda Airport has four runways (Runways A, B, C, and D), and eight runway surfaces are utilized
depending on whether it is for takeoff or landing, the wind direction (north or south), and weather
conditions. Figure 1 shows the locations of Haneda Airport’s runways, runway surfaces, and terminalbuildings.
[Figure 1 around here]
Among these, Runway B began operations in March 2000, and no changes in the number of runways
have been made since then. On August 8, 2019, there was the announcement about that the new flightpaths of Haneda Airport would be introduced on March 29, 2020. Before the new flight paths were
implemented, the flight paths either passed over Tokyo Bay or took alternative paths that avoided
flying directly over the central Tokyo area. There were no flight paths that passed directly over central
Tokyo.
The new flight paths involve six of the eight runway surfaces and is to be used in conjunction
with the existing paths. According to the Ministry of Land, Infrastructure, Transport and Tourism(MLIT), this change will allow for an increase of 39,000 international flights annually.
The blue lines of Appendix Figure A1 shows the records of flights using the new landing flight pathsof Haneda Airport.6 Under the new flight paths, the paths for landing on Runways A and C during
southerly winds traverse Saitama Prefecture, and then fly directly over central Tokyo to reach the
runways during the daytime.7 These landing paths pass over some of Tokyo’s most developed areas,
including Minato Ward, Shinagawa Ward, and Shibuya Ward. As mentioned earlier, prior to theimplementation of these new paths, no flight paths passed directly over these regions. Therefore, theintroduction of the new Haneda Airport paths can be interpreted as introducing new noise exposure to
the areas underneath the two southerly landing paths. Additionally, the directness of these new flightpaths suggests that the areas experiencing new noise exposure were determined by their alignment
with the extended lines of Runways A or C.
From the above, it can be interpreted that the two southerly landing paths of Haneda Airport
introduced new aircraft noise exogenously to central Tokyo. Due to the characteristics of the Haneda
5

https://www.oxfordeconomics.com/global-cities-index/, retrieved 5 July 2024
The flight paths was recorded on 28 May 2024, and retrieved on 24 July 2024.
7
The use of Haneda Airport’s new flight path during southerly winds is restricted to the hours between PM 3:00 andPM 7:00.
68

Airport landing paths, we are able to address the endogeneity issues outlined in Section 2 and examine
the impact of aircraft noise on the urban housing market.

4 Study area, data, and empirical strategy
4.1 Study area
To examine the exogenous impact of new flight noise on the housing market directly beneath the newlanding paths of Haneda Airport, this study focuses on the areas surrounding the linear part of theflight paths over Tokyo. Specifically, the analysis targets the area from approximately 17 kilometersfrom Haneda Airport along the new straight flight path to Runway A. The straight part of the flightpath for Runway C is longer than that for Runway A; however, to simplify the selection of treatment
and control groups, we analyze the area where both flight paths are straight.
Limiting the analysis to the straight flight paths is crucial for the identification strategy, as deviating from the runway’s extension could lead to selecting flight paths over areas where the impact
of noise on residents’ well-being is minimized. While there are other new paths at Haneda Airport
besides the two southerly landing paths, these are excluded from this study due to factors such as
flying over Tokyo Bay shortly after takeoff or having too few residences directly below them.
The municipalities included in the areas directly beneath the new straight flight paths to therunways in this study are Minato Ward, Shinagawa Ward, Shinjuku Ward, Meguro Ward, Shibuya
Ward, and Nakano Ward. These areas are among the most densely populated within Tokyo’s 23
wards, allowing for the observation of noise impacts in highly developed urban areas.

4.2 Property transaction data
We adopt property transaction data supplied by the Real Estate Transaction Promotion Center(RETPC), an association of realtors. The RETPC provides Multiple Listing Service (MLS) dataset, called as the Real Estate Information Network System (REINS). REINS data contains information of the property transactions recorded by each member real estate agent. This dataset includes
both sales and rental transactions of the properties in apartments for residential purposes. Each recordin the dataset contains information about the transacted price or rent, transaction date, address, anda range of characteristics of the transacted property and its building, such as room count, floor area,
floor level, building age. We convert addresses of apartment buildings into longitude and latitude

9

coordinates, and merge the property data with the other variables based on the location information.
To adjust our estimation period to the timing that the new flight paths of Haneda Airport begun
to be used (29 March, 2020), we adopt the properties transacted between 29 March, 2017 and 28
March, 2023. In the period, REINS dataset records approximately 110,000 sales and 680,000 leases
property transactions in our study area. We construct our analytical sample by restricting the propertytransaction records to those properties estimated to experience severe noise pollution from Haneda
Airport’s new landing paths and those in the surrounding areas.

4.3 Noise data
Detailed distribution data on aircraft noise around Haneda Airport is not publicly available. Therefore, we estimate the noise levels under the new flight paths based on the noise measurement results
published by MLIT. For our noise estimation, we use the data from 29 noise measurement devices
located within 1 km directly below the straight portions of the new landing flight paths for southward
winds. The detailed calculation process and the distribution of the noise measurement devices aredescribed in Appendix A.
As a result of the calculations, it was estimated that the noise exposure levels exceed 70dB, whichis the environmental standard for serious daytime noise set by the Tokyo Metropolitan Government,
within approximately 10 km from Haneda Airport and within 500 meters of the new flight paths.
Properties located within this range are interpreted as the treatment group significantly affected bynoise due to the new flight paths of Haneda Airport.
In addition to aircraft noise, road traﬀic noise and railway noise can also affect property prices(Ossokina and Verweij, 2015; Von Graevenitz, 2018; Diao et al., 2023). Von Graevenitz (2018) examined the impact of road traﬀic noise on housing prices in Copenhagen, while also considering thelevels of aircraft noise and railway noise. We obtained road traﬀic noise information published by
the Ministry of the Environment (ME: 2017, 2018, 2019, 2020, 2021, 2022, and 2023). The road
traﬀic noise data records noise levels at observation points along major roads. We matched emergency transport roads for each year with the nearest noise observation point to create road traﬀic
noise information. Emergency transport roads, designated by the Ministry of Land, Infrastructure,
Transport and Tourism (MLIT), are key roads comprising major roads such as national roads androads connecting national roads. Furthermore, we matched each property with the nearest emergency
transport road. We use the distance from each property to the nearest major road with recorded10

noise information as an explaining variable. Additionally, highways might be a significant source ofnoise pollution. We calculated the straight-line distance from each property to the nearest highway.
Unfortunately, we could not obtain noise information related to railways in Tokyo. As an alternative,
we calculated the distance from each property to the nearest railway track and used this as a variable.
Since the Ministry of the Environment’s road traﬀic noise information is not recorded for all
emergency transport roads, there are properties for which road traﬀic noise information is missing
when matched to the nearest emergency transport road. Considering the potential reduction in sample
size, we conduct analyses that both include and exclude noise information other than aircraft noise.

4.4 Other control variables
We control for a variety of characteristics that can affect property values. We referred various government statistics and obtained GIS data regarding the locations of stations, busstops, entrancesof highways, urban parks, post oﬀices, hospitals, schools, police stations, fire departments, culture
departments (museums and libraries), attraction facilities (sports fields, martial arts facilities, andswimming pools), welfare facilities, general waste disposal sites, industrial waste disposal sites, heliport, the Haneda Airport terminal building, and we calculated the distances from each property to
the closest instance of each type of amenity.

4.5 Identification strategy
Our baseline DiD model, constructed based on the hedonic price model (Rosen, 1974), is as follows:

ln priceisymd = α + βAf terN F Pymd +3∑γb Distbandbi +

b=1

3
∑

δb Af terN F Pymd ∗ Distbandbi

b=1

(1)

+ϕXisym + µs + τym + σsy + ϵisymd ,where the dependent variable, ln priceisym , is the natural logarithm of the nominal price or rent of property i in street (cho-cho, called in Japanese) s, transacted on day d month m, year y. Af terN F Pymd
indicate property transactions occurring after the commencement of the new flight paths of Haneda
Airport on March 29, 2020. The variable takes the value of one for transacted date ymd is after thisdate. In addition to analyzing the impact of the new paths starting from March 29, 2020, we also
estimation treating the announcement date of the new flight paths, August 8, 2019, as the treatment.

11For identifying the effect of the announcement, the dummy variable Af terN F Pymd is adjusted totake the value of 1 for transactions made on or after August 8, 2019. Additionally, for this analysis,
the sample is restricted to transactions occurring up to March 28, 2020, the day before the new paths
were actually implemented.
Distbandbi represents that property i is located within the b-th buffer from directly below the newlanding flight paths of Haneda Airport. The coeﬀicient γb measures the value of locating within theb-th buffer. The cross-term between Af terN F Pymd and Distbandbi identify the properties transacted
after the new flight paths of Haneda Airport begun to be used by their proximity to the landing flightpaths. We interpret δb as the treatment effects of new flight paths of Haneda Airport on directly
below real estate markets. Xisym controls for various characteristics, such as property characteristics,
accessibility characteristics. µs is the street (cho-cho) fixed effects controlling for unobserved and timeinvariant characteristics, such as the culture and residential environment common to each street. τym
is the fixed effect of the transacted year and month that controls for property market variations causedby economic tendencies and other events in each year and month. σsy is the fixed effect controlling forproperty market variations base on each street in each year. For instance, the impact of COVID-19
epidemic on real estate markets could differ among both of street and year. This specification allows
us to estimate the impact of the new flight paths of Haneda Airport within the same street, controllingfor property market trends. We estimate model (1) using two separate datasets on sales and rental
properties.
To identify the attenuation of noise impact with increasing distance from the new landing flightpaths of Haneda Airport, we divide the sample into distance buffers of 500 meters each from the newflight paths. The first distance band is 0-500 meters, the second band is 500-1000 meters, and thethird band is 1000-1500 meters. The properties locate within these buffers are used as the treatmentgroup in model (1). The sample is restricted to properties within 5000 meters of the new paths, withthose located 1500-5000 meters from the new paths serving as the baseline. Our hypothesis is thatthe impact of the new flight paths on property prices and rents will be greatest in the buffer closest to
the new paths. Figure 2 shows the distribution of our estimation sample and the new landing flightpaths of Haneda Airport.
[Figure 2 around here]
In addition to Model 1, we estimate the impact of a 1dB increase in aircraft noise (Lden) on

12

property prices. As discussed in the Appendix Appendix A, since noise monitoring devices are only
located directly below the new flight paths, it is not feasible to estimate noise exposure levels for areas
outside directly below the flight paths. The estimation model is as follows:

ln priceisymd = α + βAf terN F Pymd +3∑γb Distbandbi +

b=1

+

3
∑

3
∑

γb Distbandbi ∗ Ldeniym

b=1

(2)

δb Af terN F Pymd ∗ Distbandbi ∗ Ldeniym + ϕXisym + µs + τym + σsy + ϵisymd ,b=1

where, Ldeniym represents estimated noise level (Lden) that property i exposed in month m, year y.
The top panel of Table 1 describes summary statistics of the variables used in the estimation, andthe bottom panel shows statistics for the overall transaction sample before selection. To eliminate
outliers from the estimation sample, we exclude properties with prices and rents within the top andbottom 1% of the sample. In the estimation sample, the average property price is JPY 57.0 million(approximately USD 380 thousand, based on an exchange rate of JPY 150/USD 1), and the averagemonthly property rent is JPY 103.9 thousand (USD 693). Table 1 indicates that the properties in
the analytical sample are more upscale compared to the overall sample. This can be interpreted asreflecting that our estimation sample is located in highly developed areas of Tokyo with higher housing
demand. In addition, sales properties tend to be superior to rental properties in terms of property
characteristics, reflecting the general socioeconomic difference between buyers and renters.8[Table 1 around here]

5 Estimation results
This section presents the primary results on the impact of the announce and the introduction of the
new landing flight paths on directly below property price. We begin by presenting the estimation andthe interpretation of the baseline model (1) results, followed by introducing alternative estimations
that use different samples and variables specifications to ensure the robustness of the findings.
8
According to Housing and Land Survey (MIC, 2018), property owners generally have higher incomes than property
renters in Japan.

13

5.1 Main results
Table 2 presents the results of DiD estimates for sales properties to observe the effects of beginningto be used and announcement of the new landing flight paths of Haneda Airport.
[Table 2 around here]
Columns (1)-(3) describes the results identifying the effect of new flight paths being used. Before
the new flight paths began to be used, there are no statistically significant differences between thetreatment and control groups in sales price, supporting the validity of the parallel trend assumption.
In column (1), we observe a decline in prices following that the new landing flight paths of Haneda
Airport began to be used with statistical significance. The coeﬀicient shows that the new landing
flight paths decrease property price by approximately 4.5% in the directly below housing markets.
This reduction equals to JPY 2.57 million (USD 17,100: assuming an exchange rate of JPY 150/USD
1) for a unit with the average price of our estimation sample. Column (2) shows that the new landing
flight paths have negative effects on directly below housing price even with controlling for other noise
sources. Column (3) describes the effect of the new landing flight paths on property price by per
decibel (Lden) level. The estimation result reveals that an increase in 1 dB decreases property priceby approximately 0.1% with five percent statistical significance.
Columns (4) and (5) present the estimation results identifying the effects of the announcement of
the new flight paths and their commencement date of Haneda Airport. The sample for these estimations is restricted to transactions occurring before the actual commencement of the new flight paths on
March 29, 2020. Unlike the results in columns (1)-(3), these results indicate that the announcementof the new flight paths did not significantly decrease housing prices in areas directly under the newflight paths compared to the control group. These findings align more closely with Winke (2017),
who suggested that the price declines directly under the new flight paths were caused by the actual
occurrence of noise rather than the announcement, in contrast to the results by Mense and Kholodilin(2014), which found that price declines occurred at the time of the announcement.
The decision of the new flight paths for Haneda Airport was under discussion before the announcement of their commencement, suggesting that the selection process may have been ongoing prior to
the actual announcement.9 However, the estimates of the dummy variable representing areas directly
9

The initial discussion regarding the decision for Haneda Airport’s new flight paths was a proposal by the Ministryof Land, Infrastructure, Transport and Tourism (MLIT) in June 2014, aimed at enhancing the functionality of airports
in the Tokyo metropolitan area.

14

under the new flight paths described in Columns (4) and (5) (0 − 500m from new flight paths) arenot significant, indicating no significant price difference between areas directly under the new flightpaths and other areas before the announcement. To further observe differences in trends prior to thecommencement of the new paths, we present the results of the placebo tests in Table 3.
[Table 3 around here]
Column (1) of Table 3 uses one year before the actual commencement of the new flight paths(March 29, 2019) as the placebo treatment date, while Column (2) uses two years before (March 29,2018). Each sample includes only properties transacted before the actual commencement of the newflight paths. The results in Columns (1) and (2) of Table 3 both indicate that the placebo tests do notshow a significant effect on areas directly under the new flght paths. These results suggest that theprice decline observed in areas directly under the new flight paths, as revealed in Columns (1)-(3) of
Table 2, was triggered by the actual noise generation after the commencement of the new flight paths.
In other words, it can be interpreted that the parallel trends assumption between the treatment groupand the control group is satisfied.
We find that property prices significantly declined in regions directly beneath the new flight paths
after the commencement of the Haneda Airport new flight paths. However, the magnitude of thisdecline is substantially lower compared to results from previous studies. For instance, the impact of
new flight paths on housing prices in Frankfurt showed a price decrease of 1.7% per 1 dB increase innoise level (Winke, 2017).

5.2 Robustness checks for the main finding
To confirm the robustness of our main results, we conduct additional estimations: (1) using a sample
excluding the units with the top 5% and bottom 5% of prices instead of excluding the units with the
top 1% and bottom 1%, (2) using a sample excluding the properties in apartments built after thecommencement of the new flight paths, (3) using a sample excluding the properties with information
showing reformed experience during the study period, (4) using the natural logarithm of per-floor-area
price as the explained variable instead of the natural logarithm of price. In addition, we confirm therobustness of our main results for the changes in distance definition of our sample: (5) change thedifference buffer defining our treatment groups, (6) using other borders limiting our control group, (7)using subsamples divided by distance from Haneda Airport.

15

The results for these robustness checks (1)–(4) are described in columns (1)–(4) of Appendix Table
A2, respectively. By these estimations, we confirm that our main results remain robust and are
unaffected by (1) different trimming levels of sample, (2) the possibility that the sample constructed
after the treatment might have adapted to the noise, (3) the possibility that the existing sample might
have adapted to the treatment, (4) changes in the form of the dependent variable.
As robustness check (5), we confirm the robustness of our main results by different distance definition of our treatment group and control group. Appendix Table A3 shows the estimation results
when varying the buffer distances used to define the treatment group based on the distance from the
new landing flight paths. The buffers are set at intervals of 50 meters, ranging from 400 meters to
600 meters, and columns (1)–(5) are numbered with shorter buffer distances defined in order. The
buffer for the control group is uniformly set within 5000 meters from the new flight paths. The resultsof the 500-meter buffer shown in column (3) are the same as those in column (1) of Table 2, which
are the main results. The results in Appendix Table A3 consistently indicate a decrease in property
prices in the areas directly below the new flight paths after their use began, suggesting that the results
described in Table 2 are robust to variations in the buffer distances.
As robustness check (6), we change the distance from the new landing flight paths of Haneda
Airport to define the control group. Appendix Table A4 presents the results of the analysis bychanging the distance limitations of properties from the new flight paths used in the sample. Columns(1)–(5) use samples divided at intervals of 1000 meters, ranging from 3000 meters to 7000 meters. The
estimation for the sample constrained by the 5000-meter distance shown in column (3) corresponds
to the estimation results in column (1) of Table 2. From Appendix Table A4, we find that significant
property price declines occurred in the areas directly below the new flight paths after their use began.
This suggests that our main results are robust to the definition of the control group.
Since the measured noise levels decrease with distance from Haneda Airport in Appendix A, theeffect on property prices from the new flight paths is likely to vary based on the distance from the airport. As robustness check (7), Appendix Table A5 presents the results of an analysis using subsamples
divided by distance from the airport, ranging from 0m to 6000m, and from 6000m to 14000m in 2000m
intervals. The estimation results are consistent with our hypothesis, showing a statistically significant negative impact on property prices in the subsample closest to the airport. In the 6000m-8000m
distance bands, the coeﬀicients were negative but not statistically significant. Within 8000m-10000m
distance bands, we find the negative effects on directly below property prices with 10% statistical16

significance, and the coeﬀicient is smaller than one shown in column (1). For the subsample morethan 10000m away from Haneda Airport, the coeﬀicients were positive and statistically insignificant.
This trend, where the negative impact and statistical significance decrease with increasing distancefrom the airport, aligns with the trend of noise levels decreasing with distance. The result reinforces
our discussion that the observed negative impact is due to noise exposure.

5.3 Property rent
To compare with the results of property sales, we examine the impact of the new landing flight paths onproperty rent of located directly below the flight paths. For this analysis, we use the same estimation
model as Model (1), but replace the dependent variable, the natural logarithm of property prices, with
the natural logarithm of rental prices. The results of these estimations are presented in Table 4.
[Table 4 around here]
The estimation results in Table 4, in contrast to the sales results, suggest that the new landing flightpaths did not have a significant effect on property rent. In Column (3), there is a significant decline
in property rent for properties located within the 1000-1500 meters buffer from the new landing flightpaths after the announcement. However, there is no observable trend that rental prices decrease more
as properties closer to the new flight paths, which does not align with the hypothesis that anticipatednoise exposure from the new flight paths would decrease property rent. Furthermore, the finding thatthe new flight paths do not affect property rent directly below, remains consistent even when applying
the robustness checks used for sales property in Section 5.2 to property rent.10
In interpreting the difference between sales and rental results, our findings align with previous
research suggesting that home renters value amenities less than homeowners (Caplan et al., 2021).
Similarly, empirical studies focusing on the Tokyo metropolitan area have shown that property rents
are less sensitive to environmental conditions than property prices (Kuroda and Sugasawa, 2023;
Sugasawa et al., 2024). Our findings are consistent with prior research examining differences in amenity
valuation between property rents and prices.
Compared to previous research, our findings are not consistent with studies that found significant
effects of flight noise level changes on property rents. Boes and Nüesch (2011) focused on the areaaround Zurich Airport and demonstrated that property rents significantly decreased in areas where10

Due to space constraints, the results of the robustness checks on rental prices are omitted but will be made available
upon request.

17

noise levels increased by more than 3dB. Urban areas may have a higher proportion of young singleperson households, such as university students, compared to suburban or rural areas. Single-personhouseholds are often away from home during the day, making them less likely to be affected by daytime
noise exposure compared to family households. To verify this, we divide the sample of rental properties
into subsamples based on whether the property is larger than 50 square meters.11 The results of thisanalysis are shown in Table 5.
[Table 5 around here]
Column (1) of Table 5 presents the results for rental properties smaller than 50 square meters. The
result is consistent with those shown in Table 4, and indicates that aircraft noise does not have asignificant effect on rental prices. In contrast, the result in Column (2) reveals that the rent for
properties larger than 50 square meters located directly below the new landing flight paths of Haneda
Airport significantly decreased after the introduction of the new flight paths. The coeﬀicient suggeststhat property rents directly below the new flight paths decreased by approximately 3.6%. Thesefindings support our hypothesis that the insignificance of the impact of the new landing flight paths
on property rents, as shown in Table 4, may be due to the tendency of single-person households tobe away from home during the day. Thus, while rental properties do not show significant effects fromthe new flight path on average, there is a significant decrease in rents for the subsample likely to beoccupied by family households.
We find smaller impact of new noise exposure from the new landing flight paths of Haneda Airport
on both property prices and rental prices than that found in previous research. The lower estimatedvalues in our results might be attributable to the average quality of the housing or the surrounding
noise levels. This aspect is discussed in more detail in Subsection 6.1.

6 Mechanisms of the effects of the flight noise
The analysis in the preceding section confirmed that the new landing flight paths of Haneda Airport
decreases directly below property prices, but the effects are smaller than one shown in the previous
studies. In this section, we discuss two potential factors that could help understanding the small
11

According to the Minimum Housing Standards in the Basic Plan for Housing set by the MLIT (2021), the minimum
housing standard for a four-person household is 50 square meters. Therefore, we define properties of 50 square meters
or more as those likely to be occupied by family households.

18

effects: (1) the heterogeneity of the property characteristics and peripheral noise level, (2) observing
the effects across time elapsed from the commencement of the new landing flight paths.

6.1 Property characteristics and residential environment
The level of noise exposure experienced by residents can be influenced not only by location but also
by the quality of the property and the surrounding environment. We use an interaction term model(DDD model) to examine how heterogeneity in factors such as property quality and the surrounding
environment affects the trend of property price decline due to aircraft noise. First, we examine thedifferences in the impact of flight noise based on property price level. The DDD model used for theestimation is as follows:

ln priceisymd = α + βAf terN F Pymd +3∑γb Distbandbi +3∑

δb Af terN F Pymd ∗ Distbandbi

b=1
3
∑

b=1

+ζHigherpricei + ηAf terN F Pymd ∗ Higherpricei +θb Distbandbi ∗ Higherpricei

(3)

b=1

+

3
∑

ιb Af terN F Pymd ∗ Distbandbi ∗ Higherpricei + ϕXisym + µs + τym + σsy + ϵisymd ,b=1

where an explaining variable, Higherpricei is a dummy variable that becomes one if the transacted
property i’s price is higher than the median price of our estimation sample. We focus on the coeﬀicientof ιb and interpret the difference between properties with lower price and ones with higher price inthe effects from the new landing flight paths of Haneda Airport.
Table 6 displays the results of DDD estimates for sales properties to observe the difference of theeffects of beginning to be used of the new landing flight paths of Haneda Airport between higher andlower price level.
[Table 6 around here]
The results in Table 6 suggest that the average property prices directly below the new flight pathsof Haneda Airport have decreased after the commencement of the flight paths. The coeﬀicient indicatesa price decline of approximately 6.5%, which is statistically significant at the 1% level. On the other
hand, the triple interaction term in Table 6 suggests that properties directly below the new landing
flight paths with prices higher than the median experienced a significant increase in prices following
19the new flight paths begun to be used.
The coeﬀicient indicates that the new landing flight paths decrease housing price approximatelyby 6.5% in properties with price lower than median. However, we also find the impact is smaller
in properties with price higher than median price by about 3.6%, with the difference being around
2.8 percentage points. Taken together, these results indicate that while higher-priced properties also
experienced a price drop after the commencement of the new flight paths, however, the impact of
this decline was smaller compared to properties priced below the median. A possible interpretationof these findings is that lower-quality properties generally have inferior soundproofing capabilities,
making them more susceptible to the impact of new flight noise exposure.
To further examine the variation in the impact of noise exposure on property prices due to property
attributes, we focus on the age of the apartment buildings in which the properties are located. For
our estimation model, we use the DDD model employed in estimating Model (3) in Table 6. However,instead of a dummy variable that equals one for properties priced above the median, we use a dummy
variable that becomes one for properties located in apartment buildings with an age above the sample
median. The results of this estimation are presented in Table 7.
[Table 7 around here]
We observe Table 7 and find that significant price decreases due to the new Haneda Airport flight paths
occur in properties located in older building apartments. Conversely, the effect of the new flight paths
on properties in newer building apartments directly below the flight paths, while having a negative
coeﬀicient without statistical significance. The result suggest that the negative impact of noise fromthe new flight paths primarily affects properties in older building apartments. The consistent findings
across Tables 6 and 7 are that attributes indicative of lower property quality increase the impact ofnoise. In urban areas with higher housing demand and a higher concentration of affluent residents, it
is likely that there are more high quality real estates compared to suburban or rural areas, potentiallyreducing the average impact of noise. Furthermore, these results imply that there may be a severe
disparity in noise exposure effects between individuals with higher income and individuals with lower
income who may only be able to resident in lower-quality housing.
In addition to property characteristics, other peripheral noise sources can affect noise damage
that properties expose from the new flight paths. To examine the point, we focus on the proximity
to roads generating traﬀic noise. We use the traﬀic noise data linked to emergency transport roads

20

(Ministry Environment) as used in the main results in columns (2) and (5) in Table 2. For theestimation model, instead of Higherpricei in model (3), we use a dummy variable that becomes one
for properties located within 50 meters of a road where noise levels of 65dB or higher have been
observed.12 Following Von Graevenitz (2018), we limit the sample to properties within 200 meters ofroads where noise levels of 65dB or higher have been observed. Table 8 presents the results of thisanalysis.
[Table 8 around here]
The result in Table 8 indicate that property prices directly below the new landing flight paths decreased
after the paths began to be used. According to the coeﬀicient, property prices under the flight paths
decreased by approximately 11.6% on average. In contrast, the triple interaction term using thedummy variable for properties located within 50 meters of noise generating roads shows different
results. According to its coeﬀicient, properties located within 50 meters of noise generating roads
experienced an approximately 7.4% increase in property prices after the new flight paths began, witha 1% statistical significance. The coeﬀicient is smaller than the average price decline (approximately
11.6%) directly below the new flight paths. In summary, properties close to roads already expose totraﬀic noise experienced a price decline of approximately 4.2% after the new flight paths began, whichis smaller than the average price decline.
This result suggests that in areas with existing noise, the additional noise impact from the newflight paths may be reduced. One explanation for the smaller noise impact observed in our results
is that urban areas may already have high levels of noise from traﬀic and commotion, leading to asmaller negative impact from newly introduced noise compared to studies focusing on suburban orrural areas.

6.2 Time elapsed effects
To further investigate the mechanism through which noise affects property prices, we identify the
impact of Haneda Airport’s new flight paths on property prices on a yearly basis. The estimation
model is as follows:
12
According to Von Graevenitz (2018), the reduction in the level of road noise exposure for residences diminishes with
distance. We use properties within 50 meters of the road as the group assumed to be most strongly affected by traﬀic
noise.

21

ln priceisymd = α +2022∑
t=2017t̸=2019βt Y earN F Pt +3∑

γb Distbandbi +

3
2022
∑
∑

δbt Y earN F Pt ∗ Distbandbi

b=1 t=2017t̸=2019b=1+ϕXisym + µs + τm + σsy + ϵisymd ,(4)where, Y earN F Pt is a dummy variable which becomes one if transacted period of property i is t. t is
the year adjusted to the start date of the new flight paths of Haneda Airport, which began on March
29, 2020. For example, t = 2020 indicates properties that were transacted between March 29, 2020,
and March 28, 2021. Here, one year before the commencement of the new flight paths, t = 2019,serves as the reference period. We focus on δbt to observe the effect of the new flight paths on theproperty price within distance band b in each year t
Figure 3 shows the evolution of DiD estimates for sales (left panels) and rents (right panels) acrosstime elapsed from the commencement of the new flight paths of Haneda Airport. Figure 3 consists ofthree rows. The first row shows the results for properties located within 500 meters of Haneda’s new
flight paths. The second row displays the results for properties located within 500 to 1000 meters.
The third row presents the results for properties located within 1000 to 1500 meters. The light blue
shading represent the 95% confidence intervals with clustered standard errors at the street (cho-cho).
[Figure 3 around here]
Before the commencement of the new flight paths of the Haneda Airport, there are no statisticallysignificant differences between the treatment and control groups in each panel, supporting the validityof the parallel trend assumption. However, post-trendlines represent different patterns for the panels.
In the Figure 3a, we observe a significant decrease in prices after that the new flight paths started
to be used. The estimated coeﬀicients indicate that the new landing flight paths cause a reductionof 4.9–5.8% in the property prices located within 500 meters from the flight paths. Interestingly, thetrend of price decline continued for only two years and disappeared by 2022. We will use the remaining
part of the paper to discuss the factors that caused this price recovery.
Even in the coeﬀicients of property prices, properties located within 500-1000m and 1000-1500m
from the new landing paths of Haneda Airport did not show a consistent trend of price decline.
Moreover, the panels focusing on rental properties did not indicate any significant changes in property
22rents following the commencement of the new flight paths. As an exception, properties located 10001500m from the new flight paths exhibited a significant price decline as of 2022. However, consideringthat the buffer is the farthest from the new flight paths within the treatment group, it is unclear
whether this is due to the impact of flight noise.
Despite the reduced usage of the new flight paths during the COVID-19 pandemic in 2020 and
2021, we find the significant and strong decline in property prices directly below the new landing
flight paths. The result suggests that the impact of aircraft noise exposure on property prices might
be primarily influenced by the occurrence of significant noise in the area, while the number of flights
might have a limited effect. To verify this possibility, we focus on the difference in flight numbers
between the two landing paths of Haneda Airport. Appendix Figure A3 shows the annual landing
counts for Runway A and Runway C. The usage of Runway C consistently exceeds that of Runway A
in our study period.13
We utilize the difference in the usage numbers between two flight paths to examine the impact offlight volume on property prices. The estimation model replaces Higherpricei in Model (3) with adummy variable that equals one if the property is closer to the C flight path than to the A flight path.
Table 9 describes the estimation results.
[Table 9 around here]
We examine Table 9 and find that the significant price decline due to the introduction of the new flightpath of Haneda Airport occurred directly below the new flight paths. However, the triple interaction
term indicating the impact of the new flight path under the C flight path did not show statisticalsignificance. These results suggest that the negative noise impact from the new flight paths was notdifferent between the areas below the A and C flight paths, despite the difference in flight numbers.
The results of Table 9 support our hypothesis that property prices are significantly impacted by theoccurrence of large noise levels in the surrounding area, while the frequency of exposure has a limited
effect. From the above discussion, we can interpret that the reduction in flight numbers during the
COVID-19 pandemic did not significantly influence the noise impact trends observed in Figure 3.
The new landing flight paths of Haneda Airport are used only during the daytime. This suggeststhat the impact of noise may be greater for family households, where members are likely to stay athome during the day, compared to single households, where members are more likely to be out working
13

According to the MLIT, the usage counts for Runway A (Runway C) for the new landing paths were 2,358 (5,168)
in 2020, 3,444 (6,770) in 2021, 4,281 (9,482) in 2022, and 6,340 (13,197) in 2023.

23

during the day. To confirm this, we create subsamples based on whether the property area exceeds
50 square meters and analyzed the effects over time like 5. Although we do not have information on
the household characteristics of property purchasers, we alternatively adopt information on property
characteristics to discuss the differences in noise response by household type. The estimation result is
discussed in Appensix Appendix B.3.
Additionally, we focus on the impact of Haneda Airport’s new landing flight paths on the numberof property transactions directly below the flight paths. Since the REINS data includes only properties
that have been successfully sold, properties that were not transacted due to excessive negative noise
impacts are excluded from our sample. This potential exclusion of properties with decreased demand
is a common issue in studies using property transaction data. For our analysis, we count the numberof transactions by street (cho-cho), year adjusted to the start of the new flight paths, and 500-meterbands from the new landing flight paths. The estimation model is as follows:2022∑

ln transactionsbst = α +βt Y earN F Pt +t=2017t̸=2019

+

3
2022
∑
∑

3
∑

γb Distbandbs

b=1

(5)

δbt Y earN F Pt ∗ Distbandbs + µs + ϵbst ,b=1 t=2017t̸=2019

where, T ransactionsbst represents the number of transactions the area within distance band b fromthe new landing flight paths of Haneda Airport of street s in period t.
Figure 4 presents the estimation results.
[Figure 4 around here]
From the left panel, we find that in the area directly below the new landing flight paths, the numberof transactions significantly decreased immediately after the onset of noise, with this impact gradually
recovering over time. In contrast, consistent negative effects on the number of transactions were notobserved in areas within 500-1000 meters and 1000-1500 meters from the new flight paths.
Several implications can be drawn from these results. First, the observed decline in property prices
directly below the new landing flight paths does not appear to be driven by a supply-side.14 Thissuggests that the negative effects on prices in our estimations are driven by a decrease in demand of
14

For a discussion on the mechanism of property price declines through increased transaction volumes due to external
negative impacts on properties, see Bhattacharya et al. (2021).

24

buyers. Second, there is a possibility that properties significantly impacted by the introduction of the
new landing flight paths were excluded from the sample. Our estimation results could underestimate
the actual negative impact of the new landing paths of Haneda Airport on sales property transactions.
We find that the significant decrease in the number of transactions was resolved by 2022 like
property prices. The result suggests that buyers gradually became less concerned about the noisecaused by the new landing flight paths, resulting in the resolution of the price decline trend. The
Appendix Figure A5 shows the trend in the number of complaints regarding Haneda Airport’s new
flight paths, published by MLIT. The figure suggests that the number of complaints about the flightpaths decreased over time. This data is consistent with our interpretation that people’s awareness offlight noise from the new flight paths diminished over time.
A potential reason for the diminishing impact of the new landing flight path over time could be theprogression of selection directly below the flight path. In areas with high noise levels, demographic andproperty characteristics might adapt to the noise, which could reduce the impact of noise on propertytransactions over time (Boes et al., 2013; Huss et al., 2010; Von Graevenitz, 2018). In heavily affectedareas, it is likely that the population of individuals with not good health conditions that make themmore susceptible to noise decreases. However, we have not been able to obtain health data for theregion. Instead, we focus on changes in the population ratio within the area. People with high mobility
can quickly relocate if they find the noise from Haneda Airport’s new flight path undesirable. The
Appendix Table A7 shows the 2020 census data, detailing the population in the municipalities within
our study area, categorized by five-year age bands and duration of residence at the current address.
The table indicates that the highest proportion of individuals with less than five years of residence isfound among aged 25 − 29 and 29 − 34. Based on this, we conduct an analysis using the population
ratio of individuals aged 25 − 34 as the dependent variable.
We utilized population data from the Basic Resident Register published by each municipality,
which includes population figures by street (cho-cho) and age group. By aggregating this data, weobserved the population in five-year age groups from 2018 to 2023 at an annual level for streets (chocho) centered within the areas used in our main analysis.15 We calculated the population ratio foreach street by dividing the population aged 25 − 34 by the total population of the street (cho-cho).
The specific estimation model is as follows.
15

The population statistics for each year record the population as of January 1st. To align with the timing of the newflight path introduction in our main analysis, the statistics recorded on January 1, 2018, are considered as 2017 data.
The municipalities included in the analysis are Meguro Ward, Minato Ward, Ota Ward, Setagaya Ward, and Shinagawa
Ward.

25

popratesmy = α +2022∑

βy Y earN F Py +y=2017y̸=2019

+

3
2022
∑
∑

3
∑

γb Distbandbs

b=1

(6)

δby Y earN F Py ∗ Distbandbs + τm + ϵsmy ,b=1 y=2017y̸=2019

We use the population ratio of individuals aged 25 − 34 in the street s within municipality m for year
y as the dependent variable. For comparison, we also use the population ratio of seniors aged 65 andover, who typically have lower mobility. Each street s is classified into a distance band b based on theproximity of its centroid to Haneda Airport. Figure 5 shows the estimation results.
[Figure 5 around here]
The left panel 5a shows that, following the introduction of Haneda Airport’s new flight paths, theproportion of the population aged 25 − 34 in streets directly under the flight paths began to decline.
This decrease in the 25− 34 age group’s population ratio gradually became more pronounced, reachinga statistically significant negative value by 2022. This finding suggests that the proportion of the
25 − 34 years old population in streets directly below the new flight paths dropped by approximately
1 percentage point. On the other hand, as seen in the right panel 5b, the population aged 65 and over
did not experience any statistically significant impact from the new flight paths. We interpret theseresults as indicative of younger individuals, who are more affected by noise, choosing not to reside inareas directly below the new flight paths. This trend suggests the possibility that areas directly under
the new flight paths gradually adapt to the new flight noise as noise-averse individuals move away.
The declining public concern regarding noise, as shown in Appendix Figure A5, may reflect the trendof adaptation to noise in the directly affected areas.
Additionally, we estimate the impact of Haneda Airport’s new flight paths on the population
size and property characteristics (floor area, building age, and floor level of sales properties) of theunderlying area. A detailed discussion of the estimation model and results is provided in Appendix
C. The estimation results indicate that the introduction of the new landing flight paths of Haneda
Airport did not lead to significant changes in the population size or property characteristics transacted
in the affected area. These findings have several implications. First, the decline in property prices
observed in the main results, as well as the recovery in price levels by 2022, were not driven by26

changes in population size or property characteristics. Moreover, when new noise disturbances arise,
the selection process begins with changes in population composition, which precede changes in property
characteristics. Based on these discussions, we conclude that the recovery of property prices under
the new flight paths can be attributed to a decline in people’s concern about noise due to changes in
population composition, and a reduction in noise exposure following the end of the COVID-19 period,
when people spent less time at home.

7 Concluding remarks
We examined the impact of the announcement (August 8, 2019) and commencement (March 29, 2020)of Haneda Airport’s new landing flight paths on the directly below housing market, using these events
as natural experiments. The straight flight paths to the runway allowed us to address the potential
endogeneity of arbitrary path deciding and sample selection, thus facilitating robust estimation. Ourfindings revealed that the commencement of the flight paths led to an approximate 4.5% decrease in
the property prices directly underneath the new flight paths. The impact corresponds to a decrease ofabout 0.1% in property proces per 1 dB (Lden) increase. Additionally, our study found that a decreasein housing prices was more pronounced for lower-priced or older properties. These results suggest thatnoise impacts are greater for lower-quality properties, highlighting the potential inequality in exposure
to noise pollution.
In contrast, the announcement of the new flight paths did not significantly affect property prices,and property rent was unaffected by both the announcement and the commencement. Overall, ourestimated results are smaller or negligible compared to those reported in previous studies. One possible
interpretation is that the presence of other noise sources in urban areas causes a smaller impact
compared to suburban or rural settings. We confirmed that the additional impact of flight noise
was reduced in the case of properties exposed to large road noise. If the disadvantages of increased
noise in urban areas are smaller than previously thought, the net benefits of expanding transportation
infrastructure may need to be revised upwards. More empirical research is needed on the effects ofexogenous changes in noise levels in urban areas on surrounding regions.
In addition, we found that the proportion of populations with high mobility characteristics decreased directly under the new flight paths of Haneda Airport. The progression of selection due tochanges in demographic composition could potentially lead the area to adapt to the noise, thereby
reducing the impact of noise on property prices. On the other hand, the population size and the27characteristics of transacted properties were not significantly affected by the introduction of the newflight paths. Changes in demographic composition could potentially influence property prices in thelong term through shifts in population size and property characteristics. Therefore, observing thetrends in housing markets newly affected by flight paths over a longer period is an intriguing future
research direction.

28

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Kuroda, Y. and T. Sugasawa (2023): “The Value of Scattered Greenery in Urban Areas: A
Hedonic Analysis in Japan,” Environmental and Resource Economics, 85, 523–586.
29

McMillen, D. P. (2004): “Airport expansions and property values: the case of Chicago O’Hare
Airport,” Journal of Urban Economics, 55, 627–640.
Mense, A. and K. A. Kholodilin (2014): “Noise expectations and house prices: the reaction ofproperty prices to an airport expansion,” The Annals of Regional Science, 52, 763–797.
Ossokina, I. V. and G. Verweij (2015): “Urban traﬀic externalities: Quasi-experimental evidence
from housing prices,” Regional Science and Urban Economics, 55, 1–13.
Pope, J. C. (2008): “Buyer information and the hedonic: the impact of a seller disclosure on theimplicit price for airport noise,” Journal of Urban Economics, 63, 498–516.
Rosen, S. (1974): “Hedonic prices and implicit markets: product differentiation in pure competition,”
Journal of political economy, 82, 34–55.
Sugasawa, T., T. Sadayuki, N. Yajima, and M. Nakagawa (2024): “The stigma of in-homedeath: Impact on housing prices and rents in the Tokyo Metropolitan Area,” CSRDA discussion
paper.
Von Graevenitz, K. (2018): “The amenity cost of road noise,” Journal of Environmental Economics
and Management, 90, 1–22.
Winke, T. (2017): “The impact of aircraft noise on apartment prices: a differences-in-differences
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Zheng, X., W. Peng, and M. Hu (2020): “Airport noise and house prices: A quasi-experimental
design study,” Land Use Policy, 90, 104287.

30Figures and Tables
FiguresFigure 1: Distribution of runways and terminal buildings of Haneda Airport
Note: The light blue polygon indicates the premises of Haneda Airport. The blue lines represent the runways. The lightgreen rectangles indicate the locations of the terminal buildings. The names of the runway directions are marked at bothends of the runways. The new landing flight path A at Haneda Airport lands on runway 16R, while the new flight pathC lands on runway 16L.

Figure 2: Map of New Landing Paths of Haneda Airport and Properties used in Estimations
Note: The lines represent the borders of municipalities in the Tokyo Metropolitan Area. The light blue circles show thelocation of transactions used in our estimation. The orange and light green dashed lines represent Haneda Airport’s new
landing flight paths A and C, respectively. The purple circle indicates the location of Tokyo Station (CBD).

31

(a) Effect on sale prices(b) Effect on rents(c) Effect on sale prices(d) Effect on rents(e) Effect on sale prices(f) Effect on rents

Figure 3: Time-elapsed effects of the introduction of the new landing flight paths
Note: The left panels show the results for the cases of sales properties, while the right panels present the effects of the
new landing flight paths of Haneda Airport on rental property deals. The blue points are the estimated coeﬀicientson the variable cross-term between a dummy variable representing distance bands and a dummy variable identifying
properties transacted after the new flight paths started for the housing price/rent. The ribbons are the corresponding
95% confidence intervals. Standard errors are clustered at the street (cho-cho) level.

32

(a) 0 − 500m from the NFP(b) 500 − 1000m from the NFP(c) 1000 − 1500m from the NFP

Figure 4: The effect of the new landing flight paths on the number of sales property transactions
Note: The left panel shows the results for the cases of sales properties within 0-500 meters from the new landing flightpaths. Then, the central panel displays the estimation results for the sales properties within 500-1000 meters from the
new landing flight paths, while the right panel presents the effects of the new landing flight paths of Haneda Airport on
property transactions within 1000-1500 meters. The blue points are the estimated coeﬀicients on the variable cross-term
between a dummy variable representing distance bands and a dummy variable identifying properties transacted after
the new flight paths started for the number of housing transactions. The ribbons are the corresponding 95% confidence
intervals. Standard errors are clustered at the street (cho-cho) level.

(a) Population ratio aged 25 − 34(b) Population ratio aged 65 and overFigure 5: The effect of the new landing flight paths on population composition
Note: The left panel shows the results for the effects of the new landing flight paths on population ratio aged 25 − 34 ofdirectly below streets (cho-cho). The right panel displays the effects of the new landing flight paths of Haneda Airport
on population ratio of individuals aged 65 or over in directly below streets (cho-cho). The blue points are the estimatedcoeﬀicients on the variable cross-term between a dummy variable representing distance bands and a dummy variable
identifying streets in each year. The ribbons are the corresponding 95% confidence intervals. To align with the timingof the new flight path introduction in our main analysis, the statistics recorded on 2018 are considered as 2017 data.
Standard errors are clustered at the municipality level.

33

Tables
Table 1: The descriptive statistics
Sample used in estimations
Sale

Rent

Mean

Std.Dev

Min

Max

Mean

Std.Dev

MinMax

57,032.96

29,305.92

5,300

159,900

103.85

44.99

36

290

# of rooms

1.88

0.81

1

6

1.24

0.50

1

6

Floor area (in m2 )

57.79

20.96

8.86

148.93

31.71

14.26

1.00

145.21# of stories in the building

18.44

14.42

2

58

6.69

6.54

1

58

Property characteristics
Property price or rent (in 1,000 JPY)

Floor level

9.76

9.59

1

58

3.83

3.93

1

53

Age of the building (in month)219.13161.530

1,034

256.38164.140

1,145

Observations# of streets# of properties

262

296

15544

90672

Whole sample in REINS
Sale

Rent

Mean

Std.Dev

Min

Max

Mean

Std.Dev

MinMax

44,546.54

25,865.95

5,300

159,980

90.30

37.83

35

290

Property characteristics
Property price or rent (in 1,000 JPY)
# of rooms

2.15

0.89

1

6

1.28

0.55

1

6

Floor area (in m2 )

60.21

20.81

8.66

149.85

31.42

14.23

1.00

150.00# of stories in the building

12.23

9.66

1

58

5.09

4.27

1

58

Floor level

6.56

6.44

1

58

3.06

2.64

1

54

Age of the building (in month)

246.78164.990

1,066

261.85166.340

1,148

Observations# of properties

10964634

680010Table 2: Main Results(1)
Model:(2)(3)

New flight paths effectSample:Dependent variable:

(4)

(5)

Announcement effectTransactions for saleln price

After new flight paths started-0.0089(0.0217)0.0783∗∗(0.0381)-0.0095(0.0221)0 − 500m from new flight paths-0.0008(0.0443)0.0095(0.0477)-0.0200(0.0623)0.0088(0.0515)0.0121(0.0522)500 − 1000m from new flight paths

0.0040(0.0416)0.0010(0.0426)-0.0128(0.0597)0.0108(0.0486)

0.0097(0.0467)1000 − 1500m from new flight paths

0.0251(0.0328)0.0187(0.0348)-0.0112(0.0707)0.0271(0.0339)0.0248(0.0373)

0 − 500m from new flight paths × After new flight paths started-0.0454∗∗∗(0.0157)-0.0893∗∗∗(0.0258)

500 − 1000m from new flight paths × After new flight paths started-0.0143(0.0132)-0.0589∗∗∗(0.0226)

1000 − 1500m from new flight paths × After new flight paths started-0.0135(0.0163)-0.0383(0.0259)

After new flight paths were announced-0.0505∗(0.0275)-0.0724(0.0492)

0 − 500m from new flight paths × After new flight paths were announced-0.0015(0.0162)-0.0144(0.0263)500 − 1000m from new flight paths × After new flight paths were announced

0.0202(0.0140)0.0106(0.0214)1000 − 1500m from new flight paths × After new flight paths were announced

0.0240(0.0196)-0.0038(0.0357)

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
7,751
343
0.94

Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
3,609
262
0.94

Lden level (dB) × 0 − 500m from new flight paths

0.0005(0.0013)

Lden level (dB) × 500 − 1000m from new flight paths

0.0005(0.0012)

Lden level (dB) × 1000 − 1500m from new flight paths

0.0011(0.0021)

Lden level (dB) × After new flight paths started × 0 − 500m from new flight paths-0.0010∗∗(0.0004)

Lden level (dB) × After new flight paths started × 500 − 1000m from new flight paths-0.0004(0.0004)

Lden level (dB) × After new flight paths started × 1000 − 1500m from new flight paths-0.0004(0.0005)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544
365
0.94

Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
6,752
289
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544365
0.94

The table shows the coeﬀicients and standard errors of the main estimation results. The sample is limited to the properties transacted between March 29, 2017
and March 28, 2023, and located within 10km from Haneda airport and 5km from the new landing flight paths A and C of Haneda airport. The omitted category
regarding the distance from new flight paths represents the properties located within 1.5-5km from the new landing flight paths A or C of Haneda airport. Standard
errors clustered at the street (cho-cho) level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

35

Table 3: Placebo tests(1)
Sample:Dependent variable:(2)

Transactions for saleln price
1 year placebo

2 years placebo

0 − 500m from new flight paths

0.0144(0.0510)0.0081(0.0505)

500 − 1000m from new flight paths

0.0178(0.0488)0.0181(0.0484)

1000 − 1500m from new flight paths

0.0214(0.0345)0.0154(0.0343)

1 year placebo dummy-0.0541∗∗(0.0237)

0 − 500m from new flight paths × 1 year placebo dummy-0.0133(0.0147)500 − 1000m from new flight paths × 1 year placebo dummy-0.0020(0.0174)

1000 − 1500m from new flight paths × 1 year placebo dummy

0.0353∗(0.0201)

2 years placebo dummy

0.0391∗(0.0228)

0 − 500m from new flight paths × 2 years placebo dummy-0.0108(0.0156)

500 − 1000m from new flight paths × 2 years placebo dummy-0.0149(0.0130)1000 − 1500m from new flight paths × 2 years placebo dummy

0.0198(0.0189)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
7,751
343
0.95

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
7,751
343
0.94

The table shows the coeﬀicients and standard errors of the estimation results considering trends of housing
markets before new flight paths of Haneda airport started to be used. The sample is limited to the properties
transacted between March 29, 2017 and March 28, 2020. The properties used in the estimations are locatedwithin 10km from Haneda airport and 5km from the new landing flight paths A and C of Haneda airport. The
omitted category regarding the distance from new landing flight paths represents the properties located within
1.5-5km from the new landing flight paths A or C of Haneda airport. Standard errors clustered at the street(cho-cho) level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

36

Table 4: Estimations for rental properties(1)
Model:(2)

New flight paths effectSample:Dependent variable:(3)
Announcement effectTransactions for rent
ln rent

After new flight paths started-0.0176∗(0.0099)-0.0274∗(0.0147)

0 − 500m from new flight paths

0.0145(0.0168)

0.0287(0.0224)0.0275(0.0206)

500 − 1000m from new flight paths

0.0015(0.0130)

0.0092(0.0169)0.0105(0.0168)

1000 − 1500m from new flight paths-0.0013(0.0122)0.0060(0.0156)

0.0027(0.0147)

0 − 500m from new flight paths × After new flight paths started-0.0006(0.0074)

0.0067(0.0138)

500 − 1000m from new flight paths × After new flight paths started-0.0073(0.0090)-0.0103(0.0162)

1000 − 1500m from new flight paths × After new flight paths started

0.0039(0.0085)0.0123(0.0101)

After new flight paths were announced

0.0001(0.0092)

0 − 500m from new flight paths × After new flight paths were announced

0.0128(0.0084)

500 − 1000m from new flight paths × After new flight paths were announced

0.0005(0.0068)

1000 − 1500m from new flight paths × After new flight paths were announced-0.0094∗(0.0057)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
90,672
412
0.93

Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
54,767
346
0.92

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
47,362403
0.93

The table shows the coeﬀicients and standard errors of the estimation results for rental properties. The sample is limited to the properties
transacted between March 29, 2017 and March 28, 2023, and located within 10km from Haneda airport and 5km from the new landing flightpaths A and C of Haneda airport. The omitted category regarding the distance from new flight paths represents the properties located within
1.5-5km from the new landing flight paths A or C of Haneda airport. Standard errors clustered at the street (cho-cho) level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

37

Table 5: Subsample analyses for rental properties if floor area < 50m2 or not
(1)

(2)

floor area < 50m2 floor area >= 50m2
ln rent

Sample:Dependent variable:
After new flight paths started-0.0185∗(0.0106)

0.0207(0.0239)

0 − 500m from new flight paths

0.0102(0.0173)

0.0866(0.0603)500 − 1000m from new flight paths-0.0040(0.0132)

0.0179(0.0446)

1000 − 1500m from new flight paths-0.0047(0.0107)0.0166(0.0464)

0 − 500m from new flight paths × After new flight paths started-0.0017(0.0074)-0.0364∗∗(0.0158)

500 − 1000m from new flight paths × After new flight paths started-0.0028(0.0097)-0.0070(0.0120)1000 − 1500m from new flight paths × After new flight paths started

0.0037(0.0075)

0.0148(0.0243)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of streets
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
79,813
405
0.90

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
10,291341
0.89

The table shows the coeﬀicients and standard errors of the estimation results for rental properties. The sample is limited tothe properties transacted between March 29, 2017 and March 28, 2023, and located within 10km from Haneda airport and
5km from the new landing flight paths A and C of Haneda airport. The omitted category regarding the distance from new
flight paths represents the properties located within 1.5-5km from the new landing flight paths A or C of Haneda airport.
Standard errors clustered at the street (cho-cho) level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

38

Table 6: Cross-term model for different price level(1)
Model:

Cross-term

Dependent variable:ln price

After new flight paths started-0.0163(0.0210)

0 − 500m from new flight paths-0.0100(0.0450)500 − 1000m from new flight paths

0.0045(0.0423)

1000 − 1500m from new flight paths

0.0239(0.0360)

0 − 500m from new flight paths × After new flight paths started-0.0651∗∗∗(0.0225)500 − 1000m from new flight paths × After new flight paths started-0.0233(0.0174)

1000 − 1500m from new flight paths × After new flight paths started-0.0306(0.0231)

Price higher than median

0.0490∗∗∗(0.0115)After new flight paths started × Price higher than median

0.0017(0.0095)

0 − 500m from new flight paths × Price higher than median

0.0271(0.0191)

500 − 1000m from new flight paths × Price higher than median

0.0066(0.0199)

1000 − 1500m from new flight paths × Price higher than median

0.0231(0.0237)

After new flight paths started × 0 − 500m from new flight paths × Price higher than median

0.0367∗(0.0213)After new flight paths started × 500 − 1000m from new flight paths × Price higher than median

0.0184(0.0169)

After new flight paths started × 1000 − 1500m from new flight paths × Price higher than median

0.0227(0.0233)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544365
0.95

The table shows the coeﬀicients and standard errors of the cross-term estimation results. The sample is limited to theproperties transacted between March 29, 2017 and March 28, 2023, and located within 10km from Haneda airport and 5km
from the new landing flight paths A and C of Haneda airport. The omitted category regarding the distance from new flightpaths represents the properties located within 1.5-5km from the new landing flight paths A or C of Haneda airport. Standard
errors clustered at the street (cho-cho) level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

39

Table 7: Cross-term model for different building age(1)Model:

Cross-term

Dependent variable:ln price

After new flight paths started-0.0230(0.0216)

0 − 500m from new flight paths-0.0111(0.0445)

500 − 1000m from new flight paths-0.0095(0.0428)

1000 − 1500m from new flight paths

0.0183(0.0329)

0 − 500m from new flight paths × After new flight paths started-0.0174(0.0161)500 − 1000m from new flight paths × After new flight paths started-0.0086(0.0160)

1000 − 1500m from new flight paths × After new flight paths started

0.0079(0.0157)

Building age older than median-0.0311∗∗∗(0.0112)After new flight paths started × Building age older than median

0.0327∗∗∗(0.0106)

0 − 500m from new flight paths × Building age older than median

0.0145(0.0172)500 − 1000m from new flight paths × Building age older than median

0.0191(0.0167)1000 − 1500m from new flight paths × Building age older than median

0.0102(0.0249)

After new flight paths started × 0 − 500m from new flight paths × Building age older than median-0.0557∗∗∗(0.0192)

After new flight paths started × 500 − 1000m from new flight paths × Building age older than median-0.0154(0.0183)

After new flight paths started × 1000 − 1500m from new flight paths × Building age older than median-0.0347(0.0250)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544365
0.94

The table shows the coeﬀicients and standard errors of the cross-term estimation results. The sample is limited to the properties
transacted between March 29, 2017 and March 28, 2023, and located within 10km from Haneda airport and 5km from the newlanding flight paths A and C of Haneda airport. The omitted category regarding the distance from new flight paths represents theproperties located within 1.5-5km from the new landing flight paths A or C of Haneda airport. Standard errors clustered at thestreet (cho-cho) level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

40

Table 8: Cross-term analysis for proximity to major roads(1)
Model:

Cross-term

Dependent variable:ln price

After new flight paths started

0.0856∗∗(0.0387)

0 − 500m from new flight paths

0.0112(0.0465)500 − 1000m from new flight paths-0.0087(0.0412)1000 − 1500m from new flight paths

0.0212(0.0396)

0 − 500m from new flight paths × After new flight paths started-0.1155∗∗∗(0.0298)

500 − 1000m from new flight paths × After new flight paths started-0.0529∗∗(0.0249)

1000 − 1500m from new flight paths × After new flight paths started-0.0464(0.0291)

50m within the closest road-0.0308∗∗(0.0135)

After new flight paths started × 50m within the closest road-0.0266(0.0173)

0 − 500m from new flight paths × 50m within the closest road-0.0346(0.0287)

500 − 1000m from new flight paths × 50m within the closest road-0.0219(0.0198)

1000 − 1500m from new flight paths × 50m within the closest road-0.0246(0.0285)

After new flight paths started × 0 − 500m from new flight paths × 50m within the closest road0.0741∗∗∗(0.0264)

After new flight paths started × 500 − 1000m from new flight paths × 50m within the closest road0.0064(0.0285)

After new flight paths started × 1000 − 1500m from new flight paths × 50m within the closest road0.0208(0.0381)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
6,752
289
0.94

The table shows the coeﬀicients and standard errors of the cross-term estimation results. The sample is limited to the properties
transacted between March 29, 2017 and March 28, 2023, and located within 10km from Haneda airport and 5km from the newlanding flight paths A and C of Haneda airport. In addition, the estimation use properties located within 200 meters from theclosest emergency road. The omitted category regarding the distance from new flight paths represents the properties locatedwithin 1.5-5km from the new landing flight paths A or C of Haneda airport. Standard errors clustered at the street (cho-cho)level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

41

Table 9: Heterogeneity between flight paths A and C(1)Model:

Cross-term

Dependent variable:ln price

After new flight paths started-0.0174(0.0220)

0 − 500m from new flight paths-0.0737(0.0666)

500 − 1000m from new flight paths-0.0347(0.0590)

1000 − 1500m from new flight paths

0.0354(0.0515)

0 − 500m from new flight paths × After new flight paths started-0.0562∗∗∗(0.0216)500 − 1000m from new flight paths × After new flight paths started-0.0046(0.0136)1000 − 1500m from new flight paths × After new flight paths started-0.0333(0.0235)Flight path C side

-0.1237(0.0799)

After new flight paths started × Flight path C side

0.0148(0.0193)

0 − 500m from new flight paths × Flight path C side

0.1248(0.0855)

500 − 1000m from new flight paths × Flight path C side

0.0717(0.0769)

1000 − 1500m from new flight paths × Flight path C side

-0.0192(0.0661)

After new flight paths started × 0 − 500m from new flight paths × Flight path C side

0.0147(0.0323)

After new flight paths started × 500 − 1000m from new flight paths × Flight path C side

-0.0168(0.0253)

After new flight paths started × 1000 − 1500m from new flight paths × Flight path C side

0.0392(0.0342)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544365
0.94

The table shows the coeﬀicients and standard errors of the cross-term estimation results. The sample is limited tothe properties transacted between March 29, 2017 and March 28, 2023, and located within 10km from Haneda airportand 5km from the new landing flight paths A and C of Haneda airport. The omitted category regarding the distancefrom new flight paths represents the properties located within 1.5-5km from the new landing flight paths A or C of
Haneda airport. Standard errors clustered at the street (cho-cho) level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

42Appendix A

Noise data construction

We use noise data recorded and published by national and regional noise measuring devices to estimate
the amount of aircraft noise exposure for each property. Two indicators are used for noise: the monthly
average noise level caused by the new landing flight paths of Haneda Airport, and Lden which weights
the noise level according to the time of day when it occurs. The monthly average noise level is used to
identify areas where significant noise is considered to have been generated by the new flight paths ofHaneda Airport.A1 Lden is often adopted in previous studies to calculate changes in property values
per dB, and we use the indicator to compare our estimation results with previous studies.
We limit the analysis to noise measuring instruments located within 20 km of Haneda Airport andwithin 1 km of the new landing flight paths to estimate the impact of the distance from Haneda Airport
and the new landing flight paths on observed noise. The reason for setting the distance within 20 km
from Haneda Airport is to ensure a suﬀicient sample size. There were 29 noise measuring instruments
recording average noise and 14 instruments recording Lden within this range. Appendix Figure A2
shows the locations of noise measuring instruments recording average noise and those recording Lden,
respectively.
[Appendix Figure A2 around here]
The estimation model is as follows:

N oisejym = α + βDistanceHanedaj + γAsidej + δDistanceN F P Aj ∗ Asidej(A1)+ϕDistanceN F P Cj ∗ Csidej + τy + σm + ϵjym ,where, N oisejym represents noise level measured by device j in year y and month m. Asidej is adummy variable which equals to one if the noise measurement device j is closer to the new landing
flight path to Runway A than the path to Runway C. DistanceN F P Aj (DistanceN F P Cj ) is thevariable showing distance to the new flight path A (C) from the noise measurement device j.
Appendix Table A1 shows the estimation results. We find that an increase in the distance bothfrom Haneda Airport and from the new landing flight paths decreases noise level with statisticalsignificance. According to the estimation results (1), the estimated noise level is approximately 70 dB
A1

There are noise measuring devices that publish average noise levels on a monthly basis and others that publish on adaily basis. To standardize the indicators, we average the daily noise levels published to a monthly level.

43

at a distance of around 10 km from Haneda Airport. Since 70 dB is the standard of daytime noise
pollution specified by the Tokyo Metropolitan Government, we consider properties within 10 km ofHaneda Airport as the treatment group affected by the noise from the new landing flight paths. The
impact on samples located more than 10 km away from the airport will be examined in Appendix
Table A5.

Appendix B

Supplement for the analyses

Appendix B.1

Robustness checks for the main analysis

Appendix Tables A2-A5 represent the results for the robustness checks for the main results.
[Appendix Tables A2-A5 around here]
Section 5.2 considers eight robustness checks (1)–(7) conducted in this appendix. Robustness checks(1)–(4) corresponds to Columns (1)–(4) of Appendix Table A2, (5) to Appendix Table A3, (6) toAppendix Table A4, and (7) to Appendix Table A5.

Appendix B.2

Full result for analysis focusing on the time elapsed effectAppendix Table A6 shows the full results for the estimations shown in Figure 3.

Appendix B.3

Subsample analysis for sales property if floor area < 50m2 or not

Appendix Figure A4 shows the results of the subsample analysis.
[Appendix Figure A4 around here]
The left panel of Appendix Figure A4 indicates that the new landing flight paths did not have asignificant impact on the prices of properties under 50 square meters. In contrast, the right panel
shows that the negative impact of the new landing flight paths increases for properties with floor area
50 square meters or larger. These results are consistent with our hypothesis that flight noise effects
are greater for larger properties and the result shown in 5.
It was also confirmed in Appendix Figure A4 that the significant negative impact of noise was notobserved in 2022. The increase in home-based work due to COVID-19 may have increased home-staytime and, consequently, the impact of daytime noise exposure, even for single-person households. However, the left panel of Appendix Figure A4 does not show a significant price decline trend for smaller
44

properties. A possible explanation for this is the existence of investment behavior where properties arepurchased but not inhabited. If there are owners who purchase small properties anticipating future
price increases but do not live in them, the impact of noise on property prices may be limited. Since
we cannot observe the purpose of property transactions or the actual residence status after the transaction, the extent to which amenity evaluations are considered in investment property transactions
remains a subject for future research.

Appendix C

Analyses of population size and property characteristics

The appendix discusses the impact of the new landing flight paths on population size and property
characteristics of directly below areas. At first, we focus on the population size. For the estimation,
we use Model (6), but replace the dependent variable with the natural logarithm of population of eachstreet (cho-cho).
Appendix Figure A6 represents the estimation result.
[Appendix Figure A6 around here]
We find that population of the directly below area of the new landing flight paths was not received
significant effects after the introduction of the new flight paths. The result means that the new landing
flight paths do not significantly increase or decrease population size of directly below streets (cho-cho).
Next, we analyze property characteristics as the dependent variables. The estimation model is asfollows:

ln characteristicsisymd = α +2022∑

βt Y earN F Pt +t=2017t̸=2019

+

3
2022
∑
∑

3
∑

γb Distbandbi

b=1

(A2)

δbt Y earN F Pt ∗ Distbandbi + µs + τm + σsy + ϵisymd .

b=1 t=2017t̸=2019

As dependent variables, we use the natural logarithm of floor area (m2 ), building age (year), and floor
level of property i.
Appendix Figure A7 shows the estimation results.

45

[Appendix Figure A7 around here]
The result shows that there were no significant changes in the characteristics of properties transacted
directly below the new Haneda Airport flight paths after the flight paths began to be used.
These findings suggest that the observed decrease and subsequent recovery in property prices
below the new landing flight paths were not driven by changes in the population size and the property
characteristics.

46

Appendix figures and tables
Appendix figures

Figure A1: Flight record for new landing flight paths of Haneda Airport
Note: This figure is a flight path map for Haneda Airport’s new flight paths during southern winds, as published by
MLIT from https://www.ntrack.mlit.go.jp/plane/track.html. We obtained the flight path map from May 28, 2024,
on July 24, 2024. The blue lines indicate the new landing flight paths, which are the focus of this study. The red lines
indicate the new departure flight paths.

(a) Noise measurement devices for average noise(b) Noise measurement devices for Lden

Figure A2: The distribution of noise measurement devices
Note: The lines represent the borders of municipalities in the Tokyo Metropolitan Area. The blue circles of the left panel
show the location of noise measurement devices recording monthly or daily noise level of the new landing flight paths ofHaneda Airport. The orange circles of the right panel show the location of noise measurement devices recording monthly
Lden noise level. The orange and light green dashed lines represent Haneda Airport’s new landing flight paths A and C,
respectively.

47

Figure A3: The numbers of flights for the landing flight paths A and C
Note: The blue and red lines respectively represent the monthly count of flights that used Haneda Airport’s new landing
flight paths, landing on runway A and runway C.

(a) Floor area < 50m2(b) Floor area >= 50m2

Figure A4: Subsample analysis identifying if floor area >= 50m2 or not
Note: The left panel shows the results for the cases of sales properties with floor area under 50 square meters, while theright panel presents the effects of the new landing flight paths of Haneda Airport on sales properties equal or larger than
50 square meters. The blue points are the estimated coeﬀicients on the variable cross-term between a dummy variable
representing distance bands and a dummy variable identifying each year for the housing price/rent. The ribbons are thecorresponding 95% confidence intervals. Standard errors are clustered at the street (cho-cho) level.

48

Figure A5: The number of complaints for the new flight paths of Haneda Airport
Note: The line represents the number of complaints related to Haneda Airport’s new flight paths, as published by MLIT,
aggregated every two months. Complaints for March 2020 are combined with those for April and May because the countfor March only includes complaints from after the 29th.

Figure A6: The effect of the new landing flight paths on population size
Note: The panel shows the results for the effects of the new landing flight paths on the logarithmic population of
directly below streets (cho-cho). The blue points are the estimated coeﬀicients on the variable cross-term between adummy variable representing distance bands and a dummy variable identifying streets in each year. The ribbons are
the corresponding 95% confidence intervals. To align with the timing of the new flight path introduction in our main
analysis, the statistics recorded on 2018 are considered as 2017 data. Standard errors are clustered at the municipality
level.

49

(a) ln(f loorarea)(b) ln(buildingage)(c) ln(f loorlevel)

Figure A7: The effect of the new landing flight paths on property characteristics
Note: The left panel shows the results for the effects of the new landing flight paths on floor area of directly below
sales properties. The central panel is the estimation results for the building age of the sales properties. The right panel
displays the effects of the new landing flight paths of Haneda Airport on floor level of sales properties. Each explained
variable is logarithmically transformed. The blue points are the estimated coeﬀicients on the variable cross-term between
a dummy variable representing distance bands and a dummy variable identifying properties transacted in each year forthe property characteristics. The ribbons are the corresponding 95% confidence intervals. Standard errors are clusteredat the street (cho-cho) level.

50Appendix tables
Table A1: Estimation for noise level
(1)

(2)

Average noise (dB)

Lden (dB)Distance from the Haneda airport terminal building-0.0005∗∗∗(0.0000)-0.0004∗∗∗(0.0000)

Flight path A side

-1.2457∗∗∗(0.1797)

0.9493∗∗(0.4778)

Distance from the landing flight path A × Flight path A side

-0.0061∗∗∗(0.0002)-0.0057∗∗∗(0.0006)

Distance from the landing flight path C × Flight path C side

-0.0094∗∗∗(0.0004)-0.0090∗∗∗(0.0009)transacted year=2021-0.2534∗(0.1417)1.2446∗∗∗(0.4032)transacted year=2022-0.0574(0.1418)

1.8606∗∗∗(0.4026)transacted year=2023

0.3664∗∗∗(0.1417)5.1672∗∗∗(0.4086)transacted year=2024

0.7283∗(0.3963)transacted month=20.0159(0.2672)

6.8000∗∗∗(0.7763)transacted month=30.1884(0.2509)9.7845∗∗∗(0.7596)transacted month=40.1391(0.2516)

11.1842∗∗∗(0.7260)transacted month=50.0475(0.2503)11.3149∗∗∗(0.7172)transacted month=6-0.1116(0.2508)

11.4252∗∗∗(0.7213)transacted month=7-0.1500(0.2517)12.0366∗∗∗(0.7004)

transacted month=8-0.0630(0.2513)10.4082∗∗∗(0.6756)

transacted month=9-0.0619(0.2503)8.4633∗∗∗(0.7172)transacted month=10-0.0174(0.2517)7.6977∗∗∗(0.7098)transacted month=11

0.1998(0.2508)8.7854∗∗∗(0.7213)transacted month=12

0.0267(0.2512)6.4015∗∗∗(0.6802)# of observations
Adj R-sq

1,330
0.68

372
0.71

Dependent variable:

The table shows the coeﬀicients and standard errors of the results of estimation for noise level directly below
areas of the new landing flight paths. Standard errors clustered at the building level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

51

Table A2: Results of Robustness Checks(1)
Sample:Dependent variable:ln price(2)(3)

Transactions for saleln priceln price(4)
ln price/m2

ul95

Exclude built after NFP

Exclude reformed units

After new flight paths started-0.0154(0.0221)-0.0091(0.0217)-0.0055(0.0221)-0.0214(0.0210)

0 − 500m from new flight paths

0.0099(0.0386)

0.0022(0.0445)-0.0029(0.0447)-0.0136(0.0308)

500 − 1000m from new flight paths

0.0163(0.0345)

0.0074(0.0417)0.0035(0.0416)-0.0084(0.0282)

1000 − 1500m from new flight paths

0.0098(0.0262)

0.0261(0.0327)

0.0238(0.0333)

0.0024(0.0204)

0 − 500m from new flight paths × After new flight paths started-0.0269∗(0.0157)-0.0452∗∗∗(0.0157)-0.0509∗∗∗(0.0160)-0.0327∗∗(0.0138)

500 − 1000m from new flight paths × After new flight paths started-0.0058(0.0131)-0.0136(0.0131)-0.0201(0.0132)-0.0071(0.0113)1000 − 1500m from new flight paths × After new flight paths started

0.0003(0.0158)-0.0157(0.0166)-0.0121(0.0172)

0.0022(0.0130)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of streets
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
13,933
361
0.93

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,469
365
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,033
363
0.95

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544365
0.88

The table shows the coeﬀicients and standard errors of the results of robustness checks. The sample is limited to the properties transacted between March 29, 2017
and March 28, 2023, and located within 10km from Haneda airport and 5km from the new landing flight paths A and C of Haneda airport. The omitted category
regarding the distance from new flight paths represents the properties located within 1.5-5km from the new landing flight paths A or C of Haneda airport. Standard
errors clustered at the building level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

52

Table A3: Using Different Distance Bands from New Landing Flight Paths(1)
Sample:Dependent variable:

(2)

(3)

(4)

(5)

Transactions for saleln price

Distance bands Xm:

400m

450m

500m

550m

600m

After new flight paths started-0.0203(0.0207)-0.0162(0.0213)-0.0089(0.0217)-0.0109(0.0215)-0.0140(0.0214)0 − 1Xm from new flight paths-0.0454(0.0408)

0.0732∗(0.0396)-0.0008(0.0443)0.0186(0.0455)

0.0092(0.0440)

1X − 2Xm from new flight paths-0.0348(0.0344)0.0809∗∗(0.0366)

0.0040(0.0416)0.0243(0.0425)

0.0167(0.0396)

2X − 3Xm from new flight paths-0.0240(0.0293)

0.0461∗(0.0278)0.0251(0.0328)0.0220(0.0285)0.0480∗∗(0.0239)0 − 1Xm from new flight paths × After new flight paths started-0.0251(0.0159)-0.0287∗(0.0158)-0.0454∗∗∗(0.0157)-0.0407∗∗∗(0.0156)-0.0277∗(0.0157)

1X − 2Xm from new flight paths × After new flight paths started

0.0028(0.0132)-0.0047(0.0125)-0.0143(0.0132)-0.0050(0.0136)0.0023(0.0146)

2X − 3Xm from new flight paths × After new flight paths started

0.0070(0.0173)

0.0048(0.0137)-0.0135(0.0163)-0.0214(0.0175)-0.0189(0.0193)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544
365
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544
365
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544
365
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544
365
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544365
0.94

The table shows the coeﬀicients and standard errors of the results of robustness checks. The sample is limited to the properties transacted
between March 29, 2017 and March 28, 2023, and located within 10km from Haneda airport and 5km from the new landing flight paths
A and C of Haneda airport. The omitted category regarding the distance from new flight paths represents the properties located within
3X − 5km from the new landing flight paths A or C of Haneda airport. Standard errors clustered at the building level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

53

Table A4: Using Different Distance Limitation from New Landing Flight Paths(1)
Sample:Dependent variable:

(2)

(3)

(4)

(5)

ln price

Transactions for saleln priceln priceln priceln price

Distance limitation from NFP:

3000m

4000m

5000m

6000m

7000m

After new flight paths started

0.0121(0.0274)

0.0072(0.0228)-0.0089(0.0217)-0.0068(0.0167)-0.0108(0.0156)

0 − 500m from new flight paths

0.0038(0.0464)0.0132(0.0457)-0.0008(0.0443)-0.0106(0.0439)-0.0164(0.0450)500 − 1000m from new flight paths

0.0086(0.0439)0.0170(0.0429)0.0040(0.0416)-0.0058(0.0410)-0.0156(0.0419)

1000 − 1500m from new flight paths

0.0299(0.0321)0.0338(0.0322)0.0251(0.0328)0.0178(0.0336)0.0102(0.0351)

0 − 500m from new flight paths × After new flight paths started-0.0538∗∗∗(0.0199)-0.0568∗∗∗(0.0175)-0.0454∗∗∗(0.0157)-0.0417∗∗∗(0.0145)-0.0383∗∗∗(0.0137)

500 − 1000m from new flight paths × After new flight paths started-0.0231(0.0182)-0.0249(0.0153)-0.0143(0.0132)-0.0105(0.0118)-0.0063(0.0111)1000 − 1500m from new flight paths × After new flight paths started-0.0180(0.0188)-0.0205(0.0172)-0.0135(0.0163)-0.0131(0.0158)-0.0116(0.0157)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
10,658
238
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
12,337
297
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
15,544
365
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
19,199
430
0.95

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
22,714477
0.95

The table shows the coeﬀicients and standard errors of the results of robustness checks. The sample is limited to the properties transacted between
March 29, 2017 and March 28, 2023, and located within 10km from Haneda airport and 5km from the new landing flight paths A and C of Haneda
airport. The omitted category regarding the distance from new flight paths represents the properties located within 1.5-3km, 1.5-4km, 1.5-5km,
1.5-6km, and 1.5-7km from the new landing flight paths A or C of Haneda airport. Standard errors clustered at the building level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

54

Table A5: Using Different Distance Limitation from New Landing Flight Paths
(1)

(2)

(3)

(4)

Transactions for saleln priceln price(5)

Sample:Dependent variable:ln priceln price

Distance limitation from Haneda airport:

0 − 6000m

6000 − 8000m

8000 − 10000m

10000 − 12000m

12000 − 14000m

After new flight paths started

0.0045(0.0449)-0.0274(0.0353)-0.0116(0.0237)-0.1618∗∗(0.0671)-0.0926∗(0.0547)

0 − 500m from new flight paths

0.0023(0.0689)0.0415(0.0775)-0.0238(0.0464)0.1181∗(0.0673)-0.0823(0.0947)500 − 1000m from new flight paths

0.0131(0.0644)0.0533(0.0686)-0.0162(0.0435)

0.1192∗(0.0603)-0.0813(0.0566)

1000 − 1500m from new flight paths-0.0029(0.0529)

0.0927∗∗(0.0438)-0.0091(0.0362)0.1380∗∗(0.0539)-0.0795∗(0.0404)

0 − 500m from new flight paths × After new flight paths started-0.0655∗∗(0.0315)-0.0205(0.0208)-0.0403∗(0.0235)

0.0379(0.0379)

0.0413(0.0363)500 − 1000m from new flight paths × After new flight paths started-0.0356(0.0307)-0.0149(0.0174)-0.0061(0.0211)0.0133(0.0297)0.0879∗∗(0.0385)

1000 − 1500m from new flight paths × After new flight paths started-0.0217(0.0320)0.0054(0.0221)-0.0064(0.0259)

0.0581∗(0.0323)0.0254(0.0311)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of buildings
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
4,246
125
0.95

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
4,785
103
0.95

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
6,443
157
0.94

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
4,230
127
0.93

Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
4,465
161
0.93

ln price

The table shows the coeﬀicients and standard errors of the results of robustness checks. The sample is limited to the properties transacted between March 29, 2017 andMarch 28, 2023, and located within 10km from Haneda airport and 5km from the new landing flight paths A and C of Haneda airport. The omitted category regarding the
distance from new flight paths represents the properties located within 1.5-5km from the new landing flight paths A or C of Haneda airport. Standard errors clustered at
the building level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

55

Table A6: Full result for estimations for time elapsed effect(1)
Model:

Time elapsed

Dependent variable:ln price

Year2017

0.0108(0.0346)

Year2018

0.0497∗∗(0.0235)

Year2019

0.0000(.)

Year2020-0.0094(0.0218)

Year2021-0.0349(0.0314)

Year2022-0.0271(0.0382)

0 − 500m from new flight paths-0.0080(0.0444)500 − 1000m from new flight paths

0.0034(0.0416)1000 − 1500m from new flight paths

0.0469(0.0349)

0 − 500m from new flight paths × Year2017

0.0242(0.0219)500 − 1000m from new flight paths × Year2017

0.0201(0.0206)

1000 − 1500m from new flight paths × Year2017-0.0346(0.0217)

0 − 500m from new flight paths × Year2018

0.0150(0.0152)

500 − 1000m from new flight paths × Year2018

0.0041(0.0177)

1000 − 1500m from new flight paths × Year2018-0.0251(0.0191)

0 − 500m from new flight paths × Year2019

0.0000(.)

500 − 1000m from new flight paths × Year2019

0.0000(.)

1000 − 1500m from new flight paths × Year2019

0.0000(.)

0 − 500m from new flight paths × Year2020-0.0491∗∗∗(0.0165)500 − 1000m from new flight paths × Year2020-0.0082(0.0140)1000 − 1500m from new flight paths × Year2020-0.0200(0.0176)

0 − 500m from new flight paths × Year2021-0.0576∗∗∗(0.0208)

500 − 1000m from new flight paths × Year2021-0.0137(0.0168)

1000 − 1500m from new flight paths × Year2021-0.0281(0.0221)0 − 500m from new flight paths × Year2022-0.0244(0.0251)

500 − 1000m from new flight paths × Year2022-0.0383(0.0233)

1000 − 1500m from new flight paths × Year2022-0.0633∗∗∗(0.0243)

Property characteristics control
Distance from amenities control
Other noise information
Floor-plan FE# of rooms FE
Building construction FE
Located floor FE# of stories FE
Street FE
Year FE
Month FEStreet × Year FE# of observations# of streets
Adj R-sq

Y
Y
N
Y
Y
Y
Y
Y
Y
N
Y
Y
15,544365
0.94

The table shows the coeﬀicients and standard errors of the estimation
for time-elapsed effects of the new landing paths of Haneda Airport. The
sample is limited to the properties transacted between March 29, 2017 andMarch 28, 2023, and located 5km from the new landing flight paths A and
C of Haneda airport. The omitted category regarding the distance fromnew flight paths represents the properties located within 1.5-5km from the
new landing flight paths A or C of Haneda airport. The dummy variables
for the transaction year used in the analysis are adjusted to the timing of
the new flight path’s introduction. For an instance, Y ear2017 represents
properties that were transacted between March 29, 2017, and March 28,
2018. Standard errors clustered at the building level are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

56

Table A7: Population size and ratio by age and years of residence range
Population size
% of population in the relevant age range
Total

From birth

0 − 5 years

5 − 10 years

10 − 20 years

More than 20 years

Unknown

Total

3,187,117

214,500
6.7

718,51222.5

379,24311.9

446,646
14.0

574,09418.0

854,12226.8

0 − 4 years old

127,31754,49642.8

36,925
29.0

0
0.0

0
0.0

0
0.0

35,89628.2

5 − 9 years old

125,019

30,67324.5

34,80827.8

25,635
20.5

0
0.0

0
0.0

33,90327.1

10 − 14 years old

113,604

22,33919.7

19,99317.6

25,28822.3

17,80015.7

00.0

28,18424.8

15 − 19 years old

110,464

16,386
14.8

20,681
18.7

15,35813.9

32,86029.7

00.0

25,17922.8

20 − 24 years old

171,441

10,956
6.4

60,70635.4

11,340
6.6

25,412
14.8

10,798
6.3

52,22930.5

25 − 29 years old

220,113

6,876
3.1

95,932
43.6

13,545
6.2

12,083
5.5

12,897
5.9

78,78035.8

30 − 34 years old

227,4446,228
2.7

99,18643.6

23,452
10.3

9,269
4.1

11,098
4.9

78,211
34.4

35 − 39 years old

245,2606,394
2.6

87,47735.7

42,70917.4

15,762
6.4

11,754
4.8

81,16433.1

40 − 44 years old

256,071

6,627
2.6

66,901
26.1

52,12620.4

36,881
14.4

14,576
5.7

78,96030.8

45 − 49 years old

275,7227,655
2.8

52,45519.0

49,86518.1

65,38923.7

25,6699.3

74,68927.1

50 − 54 years old

243,1397,017
2.9

37,86015.6

35,13814.5

65,19726.8

39,016
16.0

58,911
24.2

55 − 59 years old

198,488

6,902
3.5

27,31813.8

23,82412.0

49,84525.1

50,45425.4

40,14520.2

60 − 64 years old

152,346

7,152
4.7

18,043
11.8

15,68910.3

32,08521.1

55,35336.3

24,02415.8

65 − 69 years old

141,483

7,347
5.2

13,547
9.6

12,6979.0

24,80917.5

65,25646.1

17,827
12.6

70 − 74 years old

164,414

7,310
4.4

13,6498.3

12,202
7.4

24,75715.1

87,481
53.2

19,015
11.6

75 − 79 years old

127,102

4,580
3.6

9,757
7.7

8,284
6.5

15,81912.4

74,12858.3

14,53411.4

80 − 84 years old

96,145

2,933
3.1

8,344
8.7

5,316
5.5

9,981
10.4

56,63658.9

12,935
13.5

85 − 89 years old

68,120

1,866
2.7

7,711
11.3

3,701
5.4

5,762
8.5

37,85755.6

11,223
16.5

90 − 94 years old

32,296

627
1.9

5,075
15.7

2,135
6.6

2,231
6.9

16,362
50.7

5,866
18.2

more 95 years old

10,228

128
1.3

2,099
20.5

935
9.1

700
6.8

4,745
46.4

1,621
15.8

57