Transnational displacement by short‐term rentals in Japan’s depopulating society: Dilemma between rent gap emergence and inbound tourist accommodations

Abstract

The influence of the so-called "Airbnb effect" on local housing markets has grown into a global concern for affordability crisis, gentrification, and displacement. We analyzed the impacts of short-term rental (STR) on the rental housing market and explored the relative factors for the rent gaps in Tokyo and Osaka: two major metropolitan areas in Japan. This research builds upon the rent gap theory by Neil Smith and clarifies the rent gaps by following previous work. Our results demonstrate that landlords in Osaka, more depopulated with unoccupied housings, have already removed a significant number of residential rental units from the housing market, and those of Tokyo, less depopulated with less unoccupied housings, is likely to evict their tenants to shift more profitable touristic short-term rentals when their long-term rentals' rent revenue decreases. We concluded that the rent gaps affected by the depopulated societal context require tougher limits on touristic short-term rentals to mitigate "transnational displacement." The future of community livability depends upon municipalities' ordinance as well as their strategies: how to leverage unoccupied housing to survive an era of shrinking economy.

Touristification and Conflicts of Interest in Cruise Destinations: The Case of Main Cultural Tourism Cities on the Spanish Mediterranean Coast

“…When the spatial contextual factors of short-term rentals are identified, Airbnb studies use the distance to the city centre as an explanatory descriptor (e.g., [35]). Furthermore, sociodemographic variables-such as inhabitants [36,37] and population change (e.g., [38]); together with housing [39,40] and long-term rental [41,42]-are taken into account in the spatial distribution of Airbnb listings. As regards the socio-economic variables, rental price [43], revenue [44,45] and rent gap [46] are the indicators also used to analyse the Airbnb phenomenon.…”

“…Indicator systems have been extensively analysed using density maps and, to a lesser extent, cluster maps [9,22,28,30,34,36,37]. In addition, the considered variables in each study have been likewise analysed using correlations [23,25,38,39,42,43,47] or regression models [8,9,28,30,34,35,40,43,48], and even both statistics used for the same study [12,36,37]. It should be noted that univariate global spatial autocorrelations (Global Moran's Index)-rather than the bivariate one [9,28,30,49]-have been extensively performed in the studies [16,[34][35][36][37]41].…”