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United States: Since 2008, Airbnb has grown exponentially as a vacation rental platform into the world’s second most valuable hospitality provider after Marriott.
It now counts over 3,000,000 listings in more than 65,000 cities across the world, however critics argue its rapid growth has been accelerated by a lack of regulation. While this has led to political and regulatory debates on how best to regulate Airbnb, there is little evidence on which factors explain adoption by consumer.
A hospitality survey was conducted by EPJ Data Science to discover the relationship between Airbnb’s penetration in a variety of cities and each city’s geographic, demographic, and socio-economic characteristics. These cities were Austin, Los Angeles, Manhattan (New York City), New Orleans, Oakland, San Diego, San Francisco, and Seattle because they vary in size, population composition, wealth, and cost of living.
The main research question focused on which factors are associated with Airbnb adoption in cities. Traditional economic models would suggest that the most important factor is the proximity to the city centre: the closer a neighbourhood is to the centre, the higher Airbnb’s penetration. However, the survey also highlights other factors.
The most important of these factors is the Bohemian index of a neighbourhood, which reflects the presence of what urbanist Richard Florida calls ‘the creative class’. This includes 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 from a hospitality and adoption perspective.
A predictive model for Airbnb’s spatial penetration that generalises results across cities has now been created which researchers can use to revise existing theoretical models of adoption and, more practically, by municipalities to proactively apply policies that direct growth in selected areas based on the model’s estimates of adoption. The survey can be found at the link below:
https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0156-6
