Airbnb is a hospitality platform for booking stays in other people’s houses. Founded in 2008, it has grown exponentially in the past few years, and it now has over 3,000,000 listings in more than 65,000 cities across the globe. The platform has already become the world’s second most valuable hospitality provider after Marriott.
Critics say that the rapid growth of Airbnb has been accelerated by a lack of regulation. These concerns have given rise to political and regulatory debates about how to best regulate Airbnb, but there is little evidence on which factors explain adoption by consumer and, as such, public policy cannot shape adoption in desirable ways.
What determines the spatial penetration of Airbnb
To support evidence-based policy making, we studied the relationship between Airbnb’s penetration in a variety of cities and each city’s geographic, demographic, and socio-economic characteristics. We considered Austin, Los Angeles, Manhattan (New York City), New Orleans, Oakland, San Diego, San Francisco, and Seattle. We chose these cities because they vary in size, population composition, wealth, and cost of living.
Our main research question was: which factors are associated with Airbnb adoption in a city? Traditional economic models would suggest that the most important factor is the distance from the city center: the closer a neighborhood is to the center, the more touristy the neighborhood is, and the higher Airbnb’s penetration. By contrast, our analysis found a surprising result: there are other factors that better explain adoption than the distance from the center.
The bohemian factor
Despite being very different in size and socio-economic conditions, all the cities – no matter how wealthy they are – benefit from the presence of the creative class
The most important of these factors is the Bohemian index of a neighborhood, which reflects the presence of what urbanist Richard Florida calls “the creative class:” individuals such as artists, musicians, writers, designers, and entertainers who work in the creative industry. Interestingly, this finding is consistent across all the cities under study. Despite being very different in size and socio-economic conditions, all the cities – no matter how wealthy they are – benefit from the presence of the creative class.
Based on these consistent results, we were able to build a predictive model for Airbnb’s spatial penetration that generalizes across cities. Our model can be used by researchers to revise existing theoretical models of adoption and, more practically, by municipalities to proactively deploy policies that direct growth in selected areas based on the model’s estimates of adoption.