How sharing is the “sharing economy”? Evidence from 97 Airbnb markets

Abstract

Digital platforms such as Airbnb have become a major economic and political force in recent years, presenting themselves as a “sharing economy”–a new, more just way of organizing social and economic activity–while functioning as owners and managers of proprietary markets. These platforms have in recent years been subject to variegated but growing regulations, begging questions of how these affect their platform markets. This paper examines these claims by a large-scale international comparative analysis of the revenue distribution of Airbnb markets in 97 cities and regions, focusing on the level and evolution of revenue inequality, and estimating the racial and gender revenue gaps by using machine learning classification of host profile pictures. Examining 834,722 listings, 513,785 hosts, and 13,466,854 reviews, the paper finds an average Gini coefficient of 0.68, implying that a majority of the market revenue tends to go to about 10% of the hosts. The level of centralization varies significantly across cities, but is consistently growing over time, with government regulation appearing as a counteracting factor, which however only temporarily slows down the growing dominance of a small minority of large-scale hosts. The paper furthermore finds large gender and race revenue gaps, as Black hosts receive on average 22% less revenue for their listings, and women an average of 12% less. These findings contribute important data to ongoing academic and policy debates, as well as a starting point for further research on inequality in the sharing economy, and how it can be regulated.

Spatio‐Temporal Variation in the Bid–Rent Functions of Long‐Term and Short‐Term Rentals: Evidence from South‐East Queensland, Australia

“…Though in principle Airbnb's involvement in each stay is transactional, researchers have pointed to the fact that the company has taken an aggressive approach to lobbying regulators (Ferreri & Sanyal 2018; van Doorn 2020; Bosma 2022) to liberalise the STR market. For this reason, Airbnb has also become vilified in many research circles (Törnberg & Chiappini 2020; Törnberg 2022) as STRs are perceived to remove housing stock from the LTR market. This observation has led to research linking gentrification and Airbnb, in that the conversion of LTRs into STRs prices lower‐income residents out of neighbourhoods (Wachsmuth & Weisler 2018).…”

An enhanced decision-making framework for predicting future trends of sharing economy

“…If ε< p , the transfer in this case will also be accepted, otherwise the transfer fails to proceed to the next step of the cooling process, and so on. The cooling equation for the above cooling process is: where q denotes the cooling factor, the value of which is usually set to 0.99 [ 28 ]; T denotes the current temperature. When the temperature drops to the termination temperature Tend, the optimal value is output.…”

“…Wang et al [ 27 ] constructed a multi-relational network to analyze the value of cooperation in the sharing economy by using different machine learning classification models and feature sets to predict consumers’ purchase behavior on the platform. Tornberg et al [ 28 ] used a machine learning approach to classify picture profiles provided by landlords of shared rentals to assess the gap between race and gender income and to further explore the impact of sharing economy. Shokoohyar et al [ 29 ] use multiple machine learning models (support vector machines, plain Bayes and neural networks) to predict the highest return rental strategy for a specific property in Philadelphia in the context of sharing economy based on data from 2163 properties.…”

Fighting bias with bias: How same-race endorsements reduce racial discrimination on Airbnb

“…However, recent studies have documented an unintended consequence. Names and photos also reveal sociodemographic characteristics that can enable racial discrimination in users’ choices of individuals with whom to interact ( 1 – 3 , 5 ), thereby reinforcing racial barriers to participation in the sharing economy ( 5 – 9 ). These barriers are well documented.…”