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Article

Housing Affordability Risk and Tourism Gentrification in Kyoto City

by
Mikio Yoshida
and
Haruka Kato
*
Department of Living Environment Design, Graduate School of Human Life and Ecology, Osaka Metropolitan University, Osaka 5588585, Japan
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(1), 309; https://doi.org/10.3390/su16010309
Submission received: 10 November 2023 / Revised: 20 December 2023 / Accepted: 26 December 2023 / Published: 28 December 2023
(This article belongs to the Special Issue Sustainable Development of Hotels and Tourism)
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Abstract

:
Before the Coronavirus Disease pandemic of 2019, many tourist cities suffered from over-tourism, and tourism gentrification seriously impacted the living environment for residents. This study aimed to clarify the statistical relationship between the increase in the number of accommodations―hotels and simple accommodations―and housing prices in Kyoto City, one of the world’s most famous tourist cities. As a key result, this study clarified that the price change in houses for sale was significantly related to the number of hotels in the historical center of Kyoto City. Specifically, it was found that the average price of houses for sale increased by JPY 2,013,957/USD 18,382 per hotel in a neighborhood district. In addition, the average price of houses for sale increased by JPY 6,412,102/USD 58,526 from 2015 to 2019. Compared to previous studies, in the historical center of Kyoto City, the novelty of our finding is that the cause for increasing housing prices was not simple accommodations but hotels, and the effect of housing prices was not on houses for rent but those for sale. These results are significant because they indicate that tourism gentrification causes housing affordability risk for the local communities, including young households.

1. Introduction

Before the Coronavirus Disease pandemic of 2019 (COVID-19), many tourist cities suffered from over-tourism, negatively impacting the local economies and living environments [1,2,3,4]. Tourism gentrification seriously impacts the living environment for residents. Gotham [5] defines tourism gentrification as transforming a middle-class neighborhood into a relatively affluent and exclusive enclave marked by a proliferation of corporate entertainment and tourism venues. In recent years, the driving forces of tourism gentrification is not the global rent gap caused by the middle-class migration, but the new forms of tourism provided via P2P digital platforms, such as Airbnb [6,7,8,9]. Therefore, tourism gentrification has been a social problem that needs to be solved toward sustainable tourism [10]. The social issue was immigration, population decline, and the displacement of local communities [11,12,13,14,15]. It has been found that the social effects of tourism gentrification are related to the economic effects, such as increasing housing prices in the real estate market [16,17,18]. The economic effects of tourism gentrification contribute to urban revitalization in tourist cities [19,20]. However, they also cause housing affordability risks for the local communities.
This study’s research question concerns the relationship between the increase in the number of accommodations and housing affordability in tourist cities. Housing affordability refers to the cost of housing, including services for renters and homeowners, relative to the disposable income of a given individual or household [21]. Therefore, changes in housing prices have a direct impact on housing affordability [22]. The issue of housing affordability extends beyond the financial cost of housing to broader issues of urban sustainability [23]. This means that policymakers need to control housing prices to maintain housing affordability in tourist cities. Regarding the increase in housing prices due to the increase in accommodations, Lestegás et al. [6] found that most Airbnb accommodations in Lisbon were concentrated in neighborhoods that experienced a sharp rise in housing prices. In addition, Ardura et al. [16] found that there was a simultaneous increase in rental house prices and short-term rentals in Madrid. It was also found that the increase in accommodations widened the rent gap, especially in low-income neighborhoods in tourist cities such as New Orleans [8]. Regarding its statistical relationship, Gonzales et al. [24] found that increasing the number of accommodations increased residential rental prices using the panel data on the fifty Spanish provinces. In addition, Cunha et al. [25] found that the transfer of real estate asset use from housing to tourism led to an increase in housing prices. To make policy decisions, it is necessary to understand the statistical relationship between different types of accommodations and housing at the neighborhood district scale. This is because it has been reported that in some cases, gentrification has affected specific local neighborhoods but not on a city-wide scale [26]. In addition, gentrification has been discussed emotionally [27]. Overall, there is a research gap regarding how much housing prices increase when accommodations increase according to the type of accommodations and houses.
This study aimed to clarify the relationship between the increase in the number of accommodations and housing prices in Kyoto City. The analysis period was from 1 January 2015 to 31 December 2019, before the COVID-19 pandemic. Figure 1 shows the location of Kyoto City in East Asia. Kyoto City is a famous tourist city, with a history of over 1200 years [28]. In the historical center of Kyoto City, many famous shrines and temples are registered as UNESCO World Heritage Sites [29]. To protect the traditional townscape, the Kyoto City government developed the strict city-wide building height regulation [30]. In addition, the Japanese government eased visa requirements to attract foreign tourists actively in 2013 [31]. Therefore, many tourists have visited Kyoto City since the 2010s. The rapid tourism growth in Kyoto City has led to an increased number of accommodations and the displacement of residents from houses, resulting in tourism gentrification. Regarding tourism gentrification in the historical center of Kyoto City, Kato et al. [32] stated that neighborhoods with simple accommodations (SAs) had significantly lower populations than those without SAs. The SAs are residential-type accommodations mainly supplied via P2P digital platforms such as Airbnb [32]. This population decline was caused by the displacement of residents from houses [10]. Local communities negatively evaluate this displacement due to the increase in accommodations [33], and some have tried to cope with it [34]. Therefore, this study fills the research gap on the relationship between accommodation increases and housing prices in the historical center of Kyoto City. The results provide valuable insights into the coexistence of accommodations and housing to achieve sustainable tourism.
This study analyzed the causal relationships using the situations like geographic natural experiments, so the causes in 2015 were analyzed for the results in 2019. In other words, this study analyzed the temporal precedence of the impact of increased accommodations on the increase in housing prices from 2015 to 2019. The analysis period for this causal inference was from 2015 to 2019 because this was when it was reported that tourism gentrification occurred in Kyoto City [10,32]. Regarding the causal inference, the outcomes were set as the housing price changes in houses for sale and rent from 2015 to 2019, and the predictors were set as the numbers of hotel and SAs from 2015 to 2019. The confounding factors were stratified into historical and outside centers of Kyoto City into geographical conditions, referring to previous studies [12,13,35,36]. The analysis employed the neighborhood district (ND) scale. NDs are neighborhoods governed by communities, regarding aspects such as education, welfare, and disaster prevention based on primary schools [37]. In Kyoto City, the NDs are the foundation upon which communities make decisions about urban planning in Japan. Therefore, the ND scale was appropriate for this study.

2. Material and Method

2.1. Analysis Flow

This study consists of five steps. The first step was to identify the accommodation locations using the accommodation list. As such, this study identified the accommodations that opened for business between 2015 and 2019. These accommodations were categorized as hotels and SAs. These hotels and SAs were plotted on a map.
The second step was to identify the locations of houses traded between 2015 and 2019 using a real estate dataset. The houses were categorized as for sale or rent. These houses for sale and rent were plotted on a map.
The third step calculated the number of accommodations and houses for each ND. Regarding accommodations, this study calculated the total number of hotels and SAs, which were identified in the first step. Regarding houses, this study calculated the average price and gross floor area for each ND. The total numbers were analyzed separately for houses for sale and houses for rent, which were identified in the second step. The houses were calculated as the change from 2015 to 2019.
The fourth step analyzed the changes in the average price and the average gross floor area of houses between 2015 and 2019. The prices and gross floor areas were analyzed separately for houses for sale and rent. In addition, the houses for rent and sale were analyzed between the historical and outside centers of Kyoto City. A paired-sample t-test was used to analyze the changes between 2015 and 2019. The significance levels were set at 0.05 and 0.01. The t-test examined the significant differences between the historical and outside centers separately for houses for sale and rent. In addition, this step examined the relationship between the change in price and the gross floor area.
The fifth step analyzed the relationship between the changes in accommodations and housing prices. The change in housing prices was the difference between the average prices in 2015 and 2019, calculated in the fourth step. The relationship was analyzed using regression analysis with ordinary least squares estimation (OLS). The regression analysis set the number of accommodations as the explanatory variable and the change in house prices as the objective variable. For the confounding factor, the regression analysis was stratified into the historical and outside centers of Kyoto City. The analysis elucidated the statistical relationship between the increase in accommodations and housing prices.

2.2. Accommodation List

This study analyzed hotels and SAs as accommodations. According to the Ryokan Business law, there are hotels and SAs in Japan [38]. Hotels are businesses that operate facilities, receive fees, and accommodate people [38]. Under the Ryokan Business Law, Ryokans are Japanese-style hotels. SAs are businesses that operate facilities consisting of common areas and equipment or furnishings shared by many people, receive fees, and accommodate people [38]. In Japan, many SAs are provided via P2P accommodation-matching digital platforms such as Airbnb. SAs have been analyzed as a factor of tourism gentrification in Kyoto [10,32]. Figure 1 shows images of a hotel, Ryokan, and SA.
In addition to the accommodation registered to the Ryokan Business law, there is the short-term rentals business in Japan, as defined by the Private Lodging Business Act [39]. According to the Private Lodging Business Act, short-term rentals are operated facilities, receive fees, and accommodate people in residences. In addition, regarding the short-term rentals, the number of operating days is limited to no more than 180 days in a year. However, their number is only one-fifth that of SAs [40]. In addition, short-term rentals are also considered residences and not accommodation in accordance with the restrictions on the number of operating days. Therefore, this study excluded the short-term rentals from the analysis.
This study used the List of Licensed Accommodations under the Ryokan Business Law provided by Kyoto City as open data, published at the end of March 2020 [41]. In Japan, all accommodations are required to apply for a permit from the municipality [42]. Therefore, the list covers all accommodations operated in Kyoto City. This list has been used as reliable data in academic studies in Kyoto City [10,32].
The variables in the list include name, address, applicant, type of accommodation, and business license date. The accommodation types are hotels and SAs. Based on the address variables, the latitude and longitude were calculated using an address-matching service [43]. In Kyoto City, there were some confusing addresses, including street names. Therefore, this study corrected these addresses so the locations of NDs could be identified. Furthermore, this study identified accommodations that started operating between 1 January 2015 and 31 December 2019, using the business license date variable.

2.3. Real Estate Dataset

This study used the At Home dataset as the real estate dataset. The At Home dataset comprises the data registered with the real estate information network operated by At Home Corporation [44]. It is used by more than 60,000 real estate companies nationwide [45]. Therefore, the dataset covers most of Japan’s private real estate market. The dataset has been used as reliable data in academic studies on real estate in Japan [22,46]. However, it should be noted that the properties mainly include used real estate, not new real estate.
The authors accessed the dataset from 2015 to 2019 via the IDR dataset service of the National Institute of Informatics (no. ATHM-21-04). The dataset included land for sale, detached houses for sale, detached houses for rent, apartments for sale, apartments for rent, and mixed-use properties for rent. The data of apartments were in units of room number. The data variables included listed date/year, property ID, building ID, address, housing price, gross floor area, location floor, layout, latitude, and longitude. The housing prices of rental housing were standardized per month. The prices were expressed in JPY, and this study presents the USD prices converted at the JPY/USD rate on 31 December 2019, the last day of the analysis period for this study [47].
This study used the variable of listed date/year and extracted the real estate data registered from 1 January 2015 to 31 December 2015 and from 1 January 2019 to 31 December 2019. They were categorized into houses for sale and houses for rent. Houses for sale included detached houses for sale and apartments for sale, and houses for rent included detached houses for rent and apartments for rent. The total number of extracted data was approximately 640,000 for 2015 and 2019. However, these data contained duplicates, such as the same real estate listed repeatedly each month. Therefore, this study used approximately 280,000 data, which excluded duplicates. The method of removing duplicates followed the National Institute of Informatics’s specifications. For detached houses, this study removed duplicate data with the same building ID. For apartments, this study removed duplicate data with the property ID, building ID, location floor, layout, and gross floor area. This process extracted 155,879 data samples in 2015 and 126,974 in 2019.

3. Results

3.1. Locations of Accommodations and Houses

Figure 2 shows the locations of hotels and SAs that opened for business between 1 January 2015 and 31 December 2019. Figure 2 shows there were 199 hotels in Kyoto City and 122 in the historical center of Kyoto City. In the historical center, there was an average of 1.94 hotels per ND. These results indicate that most hotels were located in the historical center, especially in the east area. In the historical center, Kaichi-ND and Rissei-ND had the highest number of hotels. In the outside center, Sanno-ND had the highest number of hotels.
In addition, Figure 2 shows that there were 2820 SAs in Kyoto City. This result indicates that there were 15 times more SAs than hotels. The SAs were located in the historical center and outside the center, including some mountainous areas. There were 1393 SAs in the historical center, where SAs were located randomly throughout the city and not clustered in particular areas. In the historical center, Kikuhama-ND had the highest number of SAs. In the outside center, Rokuhara-ND had the highest number of SAs.
Figure 3 shows the locations of houses for sale and rent in 2019. There were 5012 houses for sale in Kyoto City. Among the houses for sale, 2168 were located in the historical center of Kyoto City and were clustered in the eastern area.
In addition, Figure 3 shows there were 121,962 houses for rent in Kyoto City. The number of houses for rent was 60 times higher than the number of those for sale. They were located throughout the city, except for some in mountainous areas. A total of 38,094 were located in the historical center of Kyoto City.
The results show that accommodations and houses were densely located in the historical center. Furthermore, it was found that both hotels and houses for sale were clustered in the east part of the historical center. The eastern part of the historical center would be at risk in terms of housing for sale due to tourism gentrification. The areas have been a mixture of residential and commercial areas prior to 2015. Meanwhile, only a few houses for sale or rent were located outside the center of Kyoto City, such as the peripheries of mountain areas.

3.2. Changes in Houses from 2015 to 2019

In this section, the analysis examined 196 NDs among the total 227 NDs in Kyoto City. This study excluded 31 NDs that did not contain houses for sale or rent in 2015 or 2019 because the price changes in the 31 NDs could not be analyzed. The 31 NDs were located in the peripheries of the mountains outside the center of Kyoto City. The 196 remaining NDs were classified into the historical and outside centers. The average prices and gross floor area were calculated for each ND. A paired-sample t-test analyzed the changes between 2015 and 2019. The significance levels were set at 0.05 and 0.01.
Figure 4 shows the boxplot diagram of the average price change from 2015 to 2019. The point data in the boxplot diagram represents outliers. Figure 4a shows the average price change in houses for sale from 2015 to 2019. Figure 4a demonstrates that the average price significantly increased in the historical center of Kyoto City. Specifically, the average price was JPY 23,778,837/USD 217,039 in 2015 and JPY 30,190,939/USD 275,565 in 2019, indicating that the average price increased by JPY 6,412,102/USD 58,526 over the five years in the historical center of Kyoto City. The average increase rate of each ND from 2015 to 2019 was 55.36% in the historical center of Kyoto City. This result suggests that the sharp price rise caused housing affordability risk in the historical center of Kyoto City. In the historical center of Kyoto City, the average price of houses for sale increased the most in Kaichi-ND, shown in Figure 3. This suggests that Kaichi-ND had the worst affordability risk. In addition, Kaichi-ND had the highest number of hotels in the historical center of Kyoto City, as found in Section 3.1. The increase in the price of houses for sale in Kaichi-ND may be related to the increase in the number of hotels from 2015 to 2019.
In addition, Figure 4a shows that the average price significantly increased in the outside center of Kyoto City. Specifically, the average price was JPY 20,401,002/USD 186,208 in 2015 and JPY 24,545,730/USD 224,039 in 2019, indicating that the average price increased by JPY 4,144,728/USD 37,831 over the five years in the outside center of Kyoto City. The rate of each ND from 2015 to 2019 was 35.95% in the outside center of Kyoto City. This result suggests that the sharp price rise also caused housing affordability risk in the outside center of Kyoto City, which is smaller than the historical center of Kyoto City. In the outside center of Kyoto City, the average price of houses for sale increased the most in Awata-ND, shown in Figure 3. However, Awata-ND was not the district with the highest number of accommodations.
Next, Figure 4b shows the average price change in houses for rent from 2015 to 2019. Figure 4b shows that the average price did not significantly change in the historical center of Kyoto City. The average price was JPY 64,097/USD 585 in 2015 and JPY 63,172/USD 577 in 2019. On the other hand, the average price in the outside center of Kyoto City significantly decreased. Specifically, the average price was JPY 56,078/USD 512 in 2015 and JPY 54,543/USD 498 in 2019. However, its decrease rate of each ND from 2015 to 2019 was 1.50% in the historical center of Kyoto City. The price change from 2015 to 2019 was small, calculated as JPY 1535/USD 14.
Concerning the price relationship between houses for sale and rent, these two prices are correlated with each other. For example, increasing the attractiveness of the area may lead to increased interest in buying or renting the houses. Therefore, this study conducted a correlation analysis between houses for sale and houses for rent. According to the analysis, the correlation coefficient is 0.14 (p = 0.12) in the historical center of Kyoto City. In addition, the correlation coefficient is 0.18 (p = 0.15) in the outside center of Kyoto City. Both results indicate that there is no price relationship between houses for sale and rent. The results suggest that the price increase mechanism is different between houses for sale and houses for rent.
In general, the average housing price changes relate to the changes in housing quality regarding gross floor area. Therefore, Figure 5 shows the changes in the average gross floor area. Figure 5a shows the average gross floor area of houses for sale from 2015 to 2019. Figure 5a shows that the average gross floor area did not significantly change in the historical center of Kyoto City, where there was a significant price increase, as shown in Figure 4a. On the other hand, houses for sale in the outside center of Kyoto City, where there was also a significant price increase, demonstrated a significant increase in gross floor area. Meanwhile, Figure 5b demonstrates that the houses for rent in the outside center of Kyoto City, where there was a significant price decrease, did not show a significant change in total floor area.
These results suggest that the prices of houses for sale in the outside center of Kyoto City increased due to the increase in the gross floor area of houses for sale in circulation. However, the results suggest that the prices of houses for sale in the historical center may have increased due to factors other than changes in gross floor area. Similarly, the data suggest that the prices of houses for rent in the outside center of Kyoto City may have decreased due to factors other than changes in gross floor area. One factor might be the increase in the number of accommodations that opened for business between 2015 and 2019 depicted in Figure 2.

3.3. The Relationship between Accommodation and Housing Price

In this section, the regression analysis was conducted separately for the historical and outside centers of Kyoto City as the confounder regarding 196 NDs, which were analyzed in Section 3.2. The objective variable was the change in average housing prices between 2015 and 2019 for houses for sale and rent as the outcomes. Furthermore, the explanatory variables were the total number of hotels and SAs that opened for business between 2015 and 2019 as the predictors. The regression analysis used the OLS method. The significance levels were set at 0.05 and 0.01.
Table 1 shows the results of the regression analysis, indicating that the price change in houses for sale was significantly related to the number of hotels in the historical center of Kyoto City. The prices of the houses for sale in the historical center of Kyoto City significantly increased and caused housing affordability risks. The findings verified the result as shown in Section 3.2. This means that the prices of the houses for sale in the historical center of Kyoto City increased due to the increasing number of hotels not due to the increase in the gross floor area. This result is important because it indicates that the increasing number of hotels caused housing affordability risks. The regression coefficient indicates that the operation of one hotel increased the housing price by JPY 2,013,957/USD 18,382. In the historical center of Kyoto City, the average number of hotels was 1.94 in each ND. Figure 4a shows that the housing price in the historical center increased by JPY 6,412,102/USD 58,526 from 2015 to 2019. Therefore, it is reasonable that the prices increased due to hotel operations.
Furthermore, the price change in the houses for rent in the outside center of Kyoto City was significantly related to the total number of SAs. Figure 4 and Figure 5 indicate that the price significantly decreased, while the gross floor area did not significantly change. These results are interesting because the decrease in housing prices was due to the increasing number of SAs. However, the regression coefficient indicates that the operation of one SA increased the housing price by JPY 40/USD 0.36. These two results are contradictory. However, the price change is so small that it is not a meaningful result. Instead, the results suggest the possibility of another factor.
Finally, Table 1 shows that the price change in houses for rent was not significantly related to the total number of hotels or SAs in the historical center of Kyoto City. In Section 3.2, it was found that the prices of houses for rent did not significantly change in the historical center. Furthermore, in the outside center of Kyoto City, the prices of houses for sale were not significantly related to the total number of hotels or SAs. This is likely because housing prices increased due to increases in gross floor area. Therefore, the lack of a statistically significant relationship in the regression analysis is reasonable.

4. Discussion and Conclusions

This study examined the relationship between the increase in the number of accommodations and housing prices in Kyoto City from 2015 to 2019. In the analysis, this study investigated the causal relationships using the situation like geographic natural experiments, in which the causes were analyzed for the results from 2015 to 2019. As a result, this study clarified that the number of hotels was significantly related to the increase in the average price of houses for sale in the historical center of Kyoto City. Specifically, it was found that the average price of houses for sale increased by JPY 2,013,957/USD 18,382 per hotel in NDs. This means the increasing number of hotels caused housing affordability risk. In addition, the average price of houses for sale increased by JPY 6,412,102/USD 58,526 over the five years and its average increase rate of each ND was 55.36% in the historical center of Kyoto City, while the average gross floor area did not significantly change. Therefore, the price change in houses for sale was not affected by the increasing gross floor area but by the increasing number of hotels. This result indicates that tourism gentrification occurred in Kyoto City, based on Gotham’s definition [5]. This finding adds new insights to the previous studies on the impact of increasing accommodation in the historical center of Kyoto City [10,32,33,34]. This result is significant because it indicates that housing prices increase when accommodation increases according to the type of accommodation and houses. This insight suggests that tourist cities will no longer fulfill the role of living environments due to the expanding tourism development. A balanced coexistence between tourism and the residential environments within a tourist city is necessary for sustainable tourism.
This study’s findings about hotels are interesting compared with previous studies on SAs, which cause tourism gentrification. Before the pandemic, tourism gentrification was mainly discussed concerning P2P digital platforms such as Airbnb [7,8,9,17,18]. In Spain and Portugal, housing prices have increased in relation to accommodation using Airbnb [6,16]. In Japan, many Airbnb accommodations are the same as SAs under the Ryokan Business Law. In the historical center of Kyoto City, SAs have caused tourism gentrification, resulting in population decline [10,32]. Compared with other studies on the historical center of Kyoto City, the novelty of this study was to clarify that the cause of increasing housing prices was not SAs but hotels. In addition, this study clarified that the effect of housing prices was not on houses for rent but on those for sale. These findings are valuable as a unique phenomenon in Kyoto City, because, in Japan, the price of houses for rent is protected by the Act on Land and Building Leases [48]. Therefore, instead of houses for rent, increasing the price of houses for sale imposes significant difficulties on the life courses of young households in the form of housing affordability risk. Those with higher incomes obviously do not face housing affordability risk even if house prices spike in the specific NDs. However, young households have low incomes in Japan, where many companies have a seniority pay system [49]. The Japanese housing system is based on homeownership for young households, and many family households ascend the housing ladder towards residential property ownership [50,51]. In Japan, the JPY 2,013,957/USD 18,382 that a hotel raise is approximately half the average salary [52]. Local young households have preserved and promoted the traditional culture, such as the Gion Festival [53]. Therefore, a decline in the number of young households will lead to a decline in culture.
This study also found a significant relationship between the price change in houses for rent and the number of SAs in the outside center of Kyoto City. The estimated price change was only JPY 40/USD 0.36. Therefore, the impact of the increase in SAs on housing prices was small. In 2019, the average price was JPY 54,543/USD 498. In addition, the price change from 2015 to 2019 was JPY 1,535/USD 14, while the average gross floor area did not significantly change. These findings indicate the possibility of factor other than accommodations and gross floor area. Meanwhile, in the outside center of Kyoto City, the price of houses for sale increased significantly from 2015 to 2019. However, this price change was not related to the number of accommodations; instead, it was likely because of the increase in gross floor area. This means that increasing accommodations did not affect the prices of houses for sale in the outside center of Kyoto City. In addition, the prices of houses for rent in the historical center of Kyoto City did not change significantly from 2015 to 2019. The prices were also not related to the number of accommodations. Therefore, the prices of houses for rent were not affected by tourism gentrification from 2015 to 2019. Our insights are valuable because they highlight the necessity to focus on the houses for sale from the perspective of housing affordability risk.
This study’s findings indicate the need for urban policy to approach the number of hotels and housing acquisitions of local residents in order to prevent housing affordability risks. The Kyoto City government has relaxed building height limit regulations in some parts of the outside center of Kyoto City [54]. This deregulation will increase the total amount of housing and decrease housing prices in the outside center. However, our findings indicate the need for additional urban planning for both hotels and houses for sale in the historical center of Kyoto City. First, we need to reduce the increase in hotels in the historical center of Kyoto City. For example, in Spain, there are regulations on accommodations through urban zoning [55]. Regulations on accommodations have been reported to be effective in many countries, particularly for short-term rentals [56]. However, this study’s findings indicate the need to regulate hotels in the historical center of Kyoto City. Second, it would be effective to provide housing purchase subsidies for local residents, including young households with low incomes, in the historical center of Kyoto City. The revenue from the accommodation tax—which Kyoto City has introduced, but its use does not include housing assistance—could be used to finance this [57]. The study’s insights provided justification for using the accommodation tax to subsidize the housing purchase. For sustainable tourism, it is necessary to share the economic benefits from tourism with local people directly impacted by development [58]. Therefore, housing subsidies financed by tax from tourism are needed to ensure the affordability of housing for local people.
There are two limitations of this study. First is the lack of a defined mechanism by which hotels increase housing prices. This study approached the causal inference based on the temporal precedence of the impact of increased accommodations on the increase in housing prices from 2015 to 2019. However, to understand causality in the urban planning field, it is also necessary to understand the mechanisms of land transactions. Condominium and hotel companies purchased large parcels of land at competitive prices during the pandemic. That competitive mechanism may have reduced the total amount of housing for sale and increased those of hotels. Second, it was not possible to calculate housing affordability by ND scale using household income data. Housing affordability risk depends not only on housing prices but also on household income. Therefore, the use of household income data would provide a more detailed picture of the relationship between accommodation and housing affordability. However, regarding the household income data, we could not use it publicly due to difficulties in privacy protection. Future research should investigate land transaction mechanisms and household income.
Despite these limitations, this study concluded that the number of hotels was significantly related to the increase in the average price of houses for sale in the historical center of Kyoto City. In addition, the average price of houses for sale increased by JPY 2,013,957/USD 18,382 per hotel in NDs in the historical center of Kyoto City. The average price of houses for sale increased by JPY 6,412,102/USD 58,526 over the five years and its average increase rate of each ND was 55.36% from 2015 to 2019. In Japan, the housing system is based on homeownership for young households [50]. Therefore, the increasing price of houses for sale imposed significant difficulties on the life courses of young households in the form of housing affordability risk. This conclusion is significant because it indicates that tourism gentrification caused housing affordability risk for the local residents, including young households, in tourist cities. We need to look at the impact of accommodations on local residents to achieve sustainable tourism and find ways to maintain their proper housing affordability through urban policies for both hotels and houses for sale in the historical center of Kyoto City.

Author Contributions

Conceptualization, M.Y.; methodology, M.Y.; software, M.Y.; validation, H.K.; formal analysis, M.Y.; investigation, M.Y.; resources, M.Y. and H.K.; data curation, M.Y.; writing—original draft preparation, M.Y.; writing—review and editing, H.K.; visualization, M.Y.; supervision, M.Y.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the JSPS KAKENHI (grant number 21K14318).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The accommodation list data are available in [41]. The list is available on request from the corresponding author, H.K. The “At Home Dataset” is provided by At Home Co., Ltd., via the IDR Dataset Service of the National Institute of Informatics [44]. Restrictions apply to the availability of these data, which were used under a license for the current study and are not publicly available. The data application and license are noted in the At Home Dataset repository.

Acknowledgments

We appreciate the support of At Home Co., Ltd., and the IDR Dataset Service of the National Institute of Informatics.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of Kyoto City in East Asia.
Figure 1. Location of Kyoto City in East Asia.
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Figure 2. Locations of accommodations.
Figure 2. Locations of accommodations.
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Figure 3. Locations of houses in 2019.
Figure 3. Locations of houses in 2019.
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Figure 4. Average housing price changes from 2015 to 2019.
Figure 4. Average housing price changes from 2015 to 2019.
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Figure 5. Average gross floor area changes from 2015 to 2019.
Figure 5. Average gross floor area changes from 2015 to 2019.
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Table 1. Accommodations and housing prices.
Table 1. Accommodations and housing prices.
BSEtVIFp
Historical center of Kyoto CityHouses for sale(Content)3,358,036.603,072,848.001.09 0.28
SA−30,551.73112,231.60−0.271.040.79
Hotel2,013,957.40795,062.302.531.040.01 *
Houses for rent(Content)−1099.081037.52−1.06 0.29
SA−32.0337.89−0.851.040.40
Hotel479.15268.451.781.040.08
Outside center of Kyoto CityHouses for sale(Content)3,611,673.70896,414.404.03 <0.01 **
SA90,074.0551,850.861.741.510.08
Hotel−618,761.40630,803.70−0.981.510.33
Houses for rent(Content)−2040.82341.31−5.98 <0.01 **
SA39.6219.742.011.510.05 *
Hotel254.39240.181.061.510.29
Note: SA is simple accommodation; B is regression coefficient; SE is standard error; VIF is variance inflation factor; * is p-value < 0.05; ** is p-value < 0.01.
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Yoshida, M.; Kato, H. Housing Affordability Risk and Tourism Gentrification in Kyoto City. Sustainability 2024, 16, 309. https://doi.org/10.3390/su16010309

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Yoshida M, Kato H. Housing Affordability Risk and Tourism Gentrification in Kyoto City. Sustainability. 2024; 16(1):309. https://doi.org/10.3390/su16010309

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Yoshida, Mikio, and Haruka Kato. 2024. "Housing Affordability Risk and Tourism Gentrification in Kyoto City" Sustainability 16, no. 1: 309. https://doi.org/10.3390/su16010309

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