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Middlesex-led research finds distribution of Airbnbs in city neighbourhoods may be due to who lives there, not just distance to city centre

Study of eight US cities yields a model that could predict the location of Airbnbs in urban areas

New research led by a Middlesex academic has enabled the authors to build a model that could predict where Airbnbs are likely to be located within a city.

The researchers from Middlesex, UCL and Nokia Bell Labs investigated the distribution of Airbnb rental properties in relation to geographic, social and economic conditions of neighbourhoods across eight US cities: Austin, Los Angeles, Manhattan, New Orleans, Oakland, San Diego, San Francisco and Seattle. They found several factors, including the number of residents who work in the creative industries, may determine their location, according to an article published in EPJ Data Science.

Computer science lecturer Dr Giovanni Quattrone, corresponding author at Middlesex University said: “Previous economic models have overly emphasized the importance of the distance from the city centre. Yet, we find that other factors may be just as important, for example the presence of educated, creative workers and what we call bohemian people, which some scholars refer to as the 'creative class’.”

The consistent pattern of Airbnb location allowed the authors to build a model,  validated by predicting the Airbnb distribution in eight cities in the study, which the authors were able to do with a high degree of accuracy. The model may be useful for regulators aiming to create policies to help prevent an excessive number of short-term rentals in the same neighbourhood, while encouraging the growth of Airbnb in areas where the economy would benefit from more guests.

"The consistency we have observed suggests that our model could be applied to cities that have not been previously analysed"
Dr Giovanni Quattrone, Lecturer in Computer Science

Dr Quattrone explains: “One of the key findings was the striking consistency of the results across the US cities we investigated. We specifically selected these cities because they vary in size, population composition, wealth, and cost of living. Given these differences, the consistency we have observed suggests that, to a certain degree, our model could be applied to cities that have not been previously analysed, predict the spread of Airbnb properties across that city, and suggest reasons for Airbnb distribution.”

“This study makes clearer the areas benefiting or suffering from Airbnb, shows how to regulate – indicating which areas are hotspots and which aren't - and could help planning by the hotel industry”.

To build their model, the researchers downloaded all of the Airbnb listings in each location and noted distance to the city centre, along with other factors including transport links, number of hotels, population density, average household income, unemployment, residents who work in the creative industries and distances to points of interest.

The authors caution that although the eight cities studied capture a variety of socioeconomic conditions, all are located in the US, so it may not be possible to generalise the findings to cities in other countries.

Dr Quattrone has previously studied Airbnb offerings and demand in London, to assess who benefits from the sharing economy of Airbnb, and is conducting other research, on what people talk about in Airbnb reviews (with academics from UCL and Nokia Bell Labs), on how guests select a host and host-guest relationships (with UCL academics) and on trust in Airbnb networks (with HU University of Applied Science, Utrecht).

An enthusiast for the platform himself, he is optimistic about the potential of Airbnb to boost urban economies. “If Airbnb were properly regulated, it could drive tourism in the way we want,” he says.

Learn more about Computer Science at Middlesex University

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