The influence of neighbourhood environment on Airbnb: a geographically weighed regression analysis

Sharing accommodation has emerged recently as a new business model in the accommodation sector. Due to the potential gentrification Airbnb might bring to an area, it is critical to understand the spatial patterns of sharing economy and its possible determinants. The neighbourhood environment has proven to be an important factor in the traditional hotel business, and whether it is the same for sharing accommodation is worth investigating. In this study, location data of 29,780 houses/apartments on Airbnb.com in London was collected. Using Ordinal Least Square and Geography Weighed Regression analysis, the spatial distribution features of Airbnb and its relationship with neighbourhood environment in London were explored. The results show that sharing accommodation is mainly located in the city centre and around tourist attractions. Neighbourhood elements such as Water, Vegetation Coverage, Art & Human Landscape, Travel & Transport, University, Nightlife Spot emerged as important factors influencing Airbnb. In addition, the distribution of Airbnb in London is spatially non-stationary, in some areas high Airbnb is associated with higher transportation accessibility, in other areas, high Airbnb is associated with more attractions or nightlife spots, suggesting that the role of different factors varies in different regions, proving Tobler's first law of geography.