Airbnb listings’ performance: determinants and predictive models

Abstract

The present study analyzes Airbnb listings’ performance in terms of occupancy rate, number of bookings and revenue, by employing data mining methodologies. The research objective is twofold, to highlight the strongest determinants that influence customer’s purchase intentions and to propose reliable models capable of predicting the listings’ performance. The data set refers to the Airbnb market of Thessaloniki, Greece and contains explanatory variables about the hosts, lodgings, rules and quests’ ratings. Elaborated inducers derived from Artificial Intelligence are used as analytical tools. The interpretable models, sensitivity analysis and a proposed complex wrapper estimator provide evidence about the significance of specific explanatory variables and highlight the central role of the host. Random Forest outperforms its competitors and is proposed as the suitable classifier for the specific domain. The results and conclusions can be useful to individual hosts, professional listings’ managers, as well as legislative and taxation authorities.