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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