Analyzing and predicting the spatial penetration of Airbnb in U.S. cities

Quattrone, Giovanni ORCID: https://orcid.org/0000-0001-9219-8437, Greatorex, Andrew, Quercia, Daniele, Capra, Licia and Musolesi, Mirco (2018) Analyzing and predicting the spatial penetration of Airbnb in U.S. cities. EPJ Data Science, 7 (1) , 31. pp. 1-24. ISSN 2193-1127 [Article] (doi:10.1140/epjds/s13688-018-0156-6)

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Abstract

In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb's spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb's spatial distribution in eight U.S. urban areas, in relation to both geographic, socio-demographic, and economic information. We find that, despite being very different in terms of population composition, size, and wealth, all eight cities exhibit the same pattern: that is, areas of high Airbnb presence are those occupied by the \newpart{``talented and creative''} classes, and those that are close to city centers. This result is consistent so much so that the accuracy of predicting Airbnb's spatial penetration is as high as 0.725.

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