Abstract
Sharing economy platforms have rapidly disrupted and transformed many traditional markets. Companies such as AirBnB, in the housing market, and Uber, in the ride-sharing space, have thrived by creating opportunities for so-called “micro-entrepreneurs”, allowing them to leverage existing personal assets, such as a spare room or car, to generate additional income. While often heralded as an opportunity to reduce income inequality, opening opportunities through technology to a much larger segment of the population, there is however a latent concern that these platforms are in practice not as inclusive as advertised. In this paper we study the AirBnB listings in Chicago and examine a number of different dimensions regarding the hosts, their property and the environment within which they operate. Specifically we examine who the hosts are by detecting hosts’ ethnicity, gender and age using images posted publicly on the site. Leveraging this information and socio-economic metrics from the Census, we examine the properties different hosts offer and what is received in return. Finally we study how these hosts present their properties by measuring the aesthetic score of the main listing photographs using a deep learning algorithm. Our results suggest an ethnical discrepancy that affects minorities from lower socio-economic backgrounds, even when taking into account location and other attributes such as price of AirBnB listings. The findings also suggest that a wider range of factors, such as poorer pictures of listings, maybe affecting the inclusion and that could be corrected with internal policies and assistance of the platform owners.
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Notes
- 1.
http://insideairbnb.com/ last retrieved on Jan 2018.
- 2.
Super-hosts in the AirBnB platform are those who hosted at least 10 trips, maintaining at least a 90% response rate and received a 5-star for at least 80% of the time they have been reviewed.
- 3.
Census tracts are geographic areas defined by the U.S. census and generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people.
- 4.
Face++. http://en.faceplusplus.com/, 2013.
- 5.
Note that Face++ does not identify hispanic as a separate ethnic classification.
- 6.
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Mashhadi, A., Chapman, C. (2018). Who Gets the Lion’s Share in the Sharing Economy: A Case Study of Social Inequality in AirBnB. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11185. Springer, Cham. https://doi.org/10.1007/978-3-030-01129-1_23
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