Short‐Term Rental Platform in the Urban Tourism Context: A Geographically Weighted Regression (GWR) and a Multiscale GWR (MGWR) Approaches

Abstract

This article contributes to advancing the knowledge on the phenomenon of the most popular short-term rental platforms, Airbnb. By implementing a geographically weighted regression (GWR) and its multiscale form, MGWR, we examine the relationship between Airbnb locations and the core elements of urban tourism including hotels, food and beverages (F&B) venues, as well as access to public transport. This article's contributions are twofold: methodological and empirical. First, the results show that incorporating localities improve overall model performance. It allows us to account for the nuance of each area of interest as the MGWR performs slightly better than the GWR in the case of spatially sparse data. Second, both models show that Airbnbs collocate with hotels supported by various amenities, but Airbnbs also go beyond traditional hotel zones. This analysis highlights and extends the latter where Airbnb concentrations are those for which there are strong associations with F&B establishments and access to public transports. This suggests that Airbnbs might benefit local businesses outside the reach of major tourist zones. However, there is further work to be done to understand whether the economic benefit to the local economy is worth the associated social costs raised by previous studies.

Spatial Variability of the ‘Airbnb Effect’: A Spatially Explicit Analysis of Airbnb’s Impact on Housing Prices in Sydney

“…Table 3(b) summarises the distribution of coefficient parameter estimates for the GWR. It must be noted that GWR is foremost a descriptive spatial tool rather than a detailed method for causal inference (Shabrina et al, 2020). Therefore, the magnitudes of coefficient estimates are not the primary concern of the GWR analysis, rather the variation of the directional association (i.e., positive/negative) of the Airbnb density term and property prices.…”

“…However, when there is spatial non-stationarity present in the data, spatial variations in different regions will be lost due to global coefficient estimates for each variable (Brunsdon et al, 1996). The Geographically Weighted Regression (GWR) provides a method to incorporate spatial heterogeneity in modelling, by fitting a linear regression for each individual point locally to allow for spatial variation in local parameter estimates (Fotheringham et al, 2003;Shabrina et al, 2020). GWR has been used in housing research to analyse market segmentation (Manganelli et al, 2014) and model spatial variability in rent (Zhang et al, 2019).…”

“…GWR has been used in housing research to analyse market segmentation (Manganelli et al, 2014) and model spatial variability in rent (Zhang et al, 2019). It has also been applied to Airbnb to model Airbnb price determinants (Suarez-Vega et al, 2020) and examine its spatial relationship to core elements of urban tourism such as hotels, restaurants, and transport (Shabrina et al, 2020). However, the use of GWR to examine the impact of Airbnb on property prices is absent from the literature.…”

“…The core specifications in conducting a GWR are the choice of optimum bandwidth and kernels (Shabrina et al 2020). The bandwidth determines the radius around each individual point within which observations are considered in that point's local regression.…”

Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China

“…How-ever, these methods do not take geographical attributes into consideration and may not work well for regression analysis of geoscience information. A geographically Weighted Regression (GWR) model, in view of spatial characteristics, has successfully revealed the actual relationships of geoscience attributes [31][32][33].…”