Who Gets to Share in the “ Sharing Economy ” : Understanding the Patterns of Participation and Exchange in Airbnb

Recently, Airbnb has released a number of studies that claim that the company is having a positive impact in the regions it operates in by driving tourists and revenue to areas underserved by traditional hotels, providing lower-income residents with a revenue stream and building up interpersonal trust (Airbnb, 2015). However, these claims have yet to be validated by independent research and they are further complicated by recent findings showing that racial discrimination against guests is a real concern in the platform (BG Edelman, Luca, & Dan, 2015). Issues of inequality in access and use go beyond the Airbnb platform and have been a consistent concern in both popular and academic literature on the “sharing economy. ” Yet we still do not fully understand how these new institutions interact with the existing socio-economic cleavages, at least partly because large scale data on the “sharing economy” has not been easily available. In this paper, we overcome this lack of data using a unique dataset containing information about roughly 125,000 Airbnb listings from 104 metropolitan statistical areas in the US. We match the information found on the Airbnb platform to the US Census data on the census-tracts in which listings are located. This allows us to study how income, race and education impact the economic activity on the platform. We show that while the platform appears to favor lower income and more racially diverse areas at first glance, the true patterns of participation and exchange are much more complicated. Despite the fact that census tracts with higher income and a greater proportion of white people have less listings and fewer reviews, these still have better ratings and demand higher prices. More importantly, we show that education is a heavily influential factor in all of these patterns, suggesting that these new institutions might be benefitting individuals with higher cultural capital disproportionately. While our data has some inherent limitations, because we only study the hosts on the platform and do not have complete information on the exchanges, we nonetheless believe that our findings are a significant contribution to the debate on the sharing economy and its future. 1 Preliminary draft, please do not cite or disseminate without contacting the authors. Introduction: A recent report, publicized by Airbnb proclaimed that Airbnb offers “middle-class families facing the squeeze of stagnant incomes help to make up the difference” (Sperling, 2015). Similar claims about the online accommodation sharing company are abundant in various media, ranging from guides on how to maximize earnings (Kessler, 2013) to discussions on the social benefits of the service (Lott, 2014). However, coverage about Airbnb, like many other “sharing economy” companies, has not been all positive. Criticism of the platform has mostly been focused on units that are exclusively being used as a rental properties while bypassing regulations and taxes (Streitfeld, 2014), with several local authorities taking action or threatening to do so (Berton & Wecker, 2015). In response Airbnb has released a number of studies that claim that the company is having a positive impact in the regions it operates in by driving tourists and revenue to areas underserved by traditional hotels, providing lower-income residents with a revenue stream and building up interpersonal trust (Airbnb, 2015). These claims have yet to be validated by independent research and they are further complicated by recent findings showing that racial discrimination against guests is a real concern in the platform (BG Edelman et al., 2015). The biggest impediment to answering these complex questions about the patterns of participation in the sharing economy and their consequences has been the lack of data. In this paper, using a unique dataset containing information about roughly 125,000 Airbnb listings from 104 metropolitan statistical areas in the US, we attempt to map the patterns of participation and exchange on Airbnb. We match the information found on the Airbnb platform to the census-tracts in which listings are located, and show that while the platform appears to favor lower income and more racially diverse areas at first glance, the true patterns are much more complicated. Despite the fact that census tracts with higher income and a greater proportion of white people have less listings and fewer reviews, these still have better ratings and generate more revenue. More importantly, we show that education is a heavily influential factor in all of these patterns, driving participation, exchange and revenue. Literature Review: Airbnb is a web-based marketplace in which people can list their accommodations for (usually) short-term rental. The listings range between a futon in a studio apartment to entire medieval castles in Europe. The company, founded in 2007, makes money by charging a commission on any rentals that take place on their platform. Currently it is active worldwide, and has more than 2,000,000 unique accommodation options listed for rent (Airbnb, 2016). However most of what we know about its functioning and impact is based on the company’s own reports. These reports focus on the experiences of hosts, guests and the impact of the company on twelve major cities around the world (Airbnb, 2015). The summary findings on hosts highlight the fact that about half of all hosts are low to moderate income, and report using the income they make through the platform for essential expenses. On the guest side, the reports highlight the fact that guests prefer to experience their destinations “like a local” and explore specific neighborhoods, 2 The results presented here are based on preliminary analysis, we are currently still adding listings to the data and improving our analytical methods, as a result of which the findings might change. The details of how the data was collected, cleaned and analyzed are below. spending more than double the time and money compared to the average visitor. This goes handin-hand with their findings that almost three quarters of the listings are outside “the main hotel districts” and about half of the guest spending stays in these areas. However, the reports are very brief, the findings not elaborated, and the methods and data they use is not presented in any detail. Perhaps more importantly, some important aspects of their findings are contradicted by other sources. For example, there are serious claims that “professional hosts” with two or more properties on the site, account for a very large portion of the revenue (Kang, 2016). In New York, these claims have come to a head, with the Attorney-General of the state arguing that these practices are illegal (Streitfeld, 2014). The company was forced to share its user data in November 2015, after a lengthy court battle (Isaac & Mike, 2015). However, there is evidence to suspect that the data might have been manipulated by a purge of the worst offenders from the Airbnb platform in the weeks leading up to the data release (Cox & Slee, 2016). While we partially adress this controversy in the data section below, we believe that the debate over professional hosts should not take our focus away from the larger question of who gets to benefit from the opportunity presented by Airbnb. There is a wide-ranging “neighborhood effects” literature on the inequalities produced and re-produced by housing patterns, and Airbnb has the potential to be influencing these dynamics because it allows people to monetize their housing. Researchers have focused on crime (Krivo & Peterson, 2000; M. R. Lee, 2000), poverty (Clampet‐ Lundquist & Massey, 2008; Ludwig et al., 2008; Parisi, Lichter, & Taquino, 2011; Quillian, 2012; Sampson, 2008; Swisher, Kuhl, & Chavez, 2013) and psychological well-being (Ross, Reynolds, & Geis, 2000) in conjunction with race and racial segregation to understand how housing patterns impact a number of social outcomes. In this literature the causal mechanism through which housing patterns impact these outcomes pivots around the social ties within neighborhoods. These ties are theorized to provide and restrict access to material and psychological resources as well as economic opportunities. While a similar social connection might be driving Airbnb listings –with membership and utilization of the platform spreading through the community with neighbors imitating one anotherwe are much more interested in the patterns of participation in Airbnb and what they can reveal about social cleavages. There is reason to expect that participation of hosts in Airbnb might be predicated upon factors such as race, class and education. First, participation not only requires access to capital (having a place to rent, living in a location desireable for travelers, furnishing it, maintaining an online presence) but also requires the cultural competency to navigate the often complex dynamics of making and managing bookings. Thus the location of individuals across the cleavages of class, race, and education has a relatively direct relationship with their participation. Perhaps more interestingly, there are theoretical reasons to focus on these cleavages as well. The relationship between class (or at least income) and sharing is theorized in a relatively straightforward manner in the field of economics. Individuals are understood to enage in sharing so long as the marginal utility they draw from it is greater than the marginal costs it imposes on them. Thus, in simplified terms, sharing is expected to be more prevalent among the poor, where the marginal benefits it brings (access to goods and services) are high than among the rich where they are lower (since they already have access through ownership or through the market). Recently, Salcedo, Schoellman, and Terlitt (2012) have used this approach to explain the reduction in household sizes between 1850 and 2000, as an outcome of income growth. However, Benkler (2004)