Investigating Airbnb evolution in an urban tourism context: the application of mathematical modelling and spatial analysis

Abstract

ABSTRACT Short-term rentals (STRs) are affecting many tourist cities, and the growth of this phenomenon has been frequently illustrated so far through the number of listings progressively registered on platforms such as Airbnb. However, analysing additional variables (e.g. number of new listings opened/closed), as well as performing mathematical modelling and spatial analyses, could help better interpret the evolution of this reality and shed some light on the hospitality dynamics enabled by digital platforms. Taking the Italian city of Turin as an example, in this article we firstly describe the local STRs trends by the means of a linear regressive statistical model, highlighting that – after a speedy growth in 2014–2016 – the number of STRs has started to experience a stabilization phase. Then, through the analysis of geo-referenced data and the application of Exploratory Spatial Data Analysis techniques, we stress that – as in other European cities – also in Turin STRs have particularly interested the areas located in the proximity of the city centre. Finally, we advance that, in order to inform local policies and decision-making, future studies should include forecasting and the investigation of a larger variety of variables (e.g. real estate stock, Airbnb nightly rates, long-term rental patterns, etc.).