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

Quattrone, Giovanni, 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 . ISSN 2193-1127 (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.

Item Type: Article
Additional Information: Article number = 31
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 25516
Notes on copyright: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Depositing User: Giovanni Quattrone
Date Deposited: 05 Nov 2018 11:25
Last Modified: 04 Apr 2019 15:58
URI: https://eprints.mdx.ac.uk/id/eprint/25516

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