Who Benefits from the "Sharing" Economy of Airbnb? (1602.02238v1)

Abstract: Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of "algorithmic regulation".

• The paper demonstrates that Airbnb listings concentrate in central areas with high transport accessibility and in tech-savvy, diverse neighborhoods.
• The paper distinguishes between entire home and private room listings, linking private rooms to lower-income, renter-dominated areas.
• The paper reveals temporal dynamics with Airbnb expanding from affluent centers to suburban areas, and advocates transferable sharing rights for effective regulation.

An Analytical Perspective on Airbnb's Influence in London

The paper, "Who Benefits from the 'Sharing' Economy of Airbnb?" by Giovanni Quattrone et al., provides a comprehensive examination of Airbnb's socio-economic impact within the city of London. This paper offers a data-driven approach to understanding how Airbnb has spread across different neighborhoods of London and which areas benefit from this platform, using data collected from Airbnb, UK Census, Ordnance Survey, and Foursquare.

Methodological Approach

The authors have employed a longitudinal paper design, drawing on data from 2012 to 2015, to capture the temporal adoption and geographical spread of Airbnb listings. The analysis primarily utilizes Ordinary Least Squares (OLS) regression models to correlate the Airbnb offering and demand with various socio-economic metrics collected at the ward level. These metrics include demographic data such as employment rates, income levels, and the mix of ethnicities, indicators of neighborhood attractiveness like transport accessibility and presence of amenities, and housing market statistics including the ratio of owned versus rented dwellings, all contributing to a nuanced understanding of Airbnb's integration into the urban fabric.

Key Findings

1. Spatial Distribution: The research reveals that Airbnb listings are not uniformly spread across London. Instead, they concentrate in central and attractive areas with high transport accessibility. This spatial heterogeneity suggests that Airbnb’s market penetration first occurred in tech-savvy neighborhoods characterized by younger, ethnically diverse, and often transient populations.
2. Listings and Socio-Economic Correlates: The analysis distinguishes between entire home and private room listings, noting that the latter are predominantly found in areas with lower income yet with a high proportion of renters. This differentiation is crucial as it underscores varied motivations of hosts, with room rentals often indicating supplementary income for tenants, while entire homes are linked to wealthier homeowners.
3. Temporal Dynamics: Over the observed period, Airbnb's presence expanded from central, affluent areas to more suburban neighborhoods. This suggests a maturation and democratization of the Airbnb market, reflecting broader urban socio-economic shifts. Interestingly, the demand measured by review frequency remained concentrated in touristic and central areas throughout the paper period.
4. Policy Implications and Recommendations: The paper advocates for "transferable sharing rights" as a means to regulate Airbnb more effectively. By proposing a framework akin to that used in ticket sales platforms like StubHub, the authors suggest that a market-driven allocation of short-term rental rights could mitigate negative externalities while capitalizing on decentralization opportunities. This regulatory suggestion is part of a broader call for 'algorithmic regulation'—a concept of using real-time data for dynamic policy adjustments.

Broader Implications

This research contributes to the ongoing debate on the regulation of sharing economy platforms, particularly in urban settings. The detailed socio-economic profiling of Airbnb’s impacts provides policymakers with a granular understanding necessary for drafting informed regulatory frameworks. It challenges existing regulatory practices, advocating for a flexibility that aligns with the spatial and temporal dynamics observed in data.

The paper's recommendations reflect a growing need to balance the manifold effects of the sharing economy—economic, social, and cultural—while preserving urban integrity and community cohesion. It calls for municipalities to leverage data science to navigate the complexities of modern digital platforms themselves being catalysts of change in urban ecosystems.

The applicability of these findings extends beyond London, directly informing any global city grappling with similar sharing economy phenomena. The envisioned algorithmic regulatory framework holds potential for broader application not just across similar platforms, but in any civic issue where data-informed policies could enhance governance and urban life quality.

In conclusion, the paper extends an invitation to researchers and policymakers alike to engage deeply with empirical data analyses for more nuanced and responsive regulatory mechanisms, setting a benchmark for future interdisciplinary studies in urban policy and the sharing economy.