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

Articolo
Data di Pubblicazione:
2018
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 “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.
Tipologia CRIS:
03A-Articolo su Rivista
Keywords:
Airbnb; Quantitative analysis; Sharing economy; Spatial data mining
Elenco autori:
Quattrone G.; Greatorex A.; Quercia D.; Capra L.; Musolesi M.
Autori di Ateneo:
QUATTRONE Giovanni
Link alla scheda completa:
https://iris.unito.it/handle/2318/1730489
Link al Full Text:
https://iris.unito.it/retrieve/handle/2318/1730489/836055/s13688-018-0156-6.pdf
Pubblicato in:
EPJ DATA SCIENCE
Journal
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