Understanding the effects of Covid-19 on P2P hospitality: Comparative classification analysis for Airbnb-Barcelona

Abstract

[EN] The Covid-19 pandemic has produced significant changes in tourism markets around the world. The large amount of data available on the Airbnb platform, one of the world's largest hosting services, makes it an ideal prospecting place to try to find out what the aftermath of this event has been. This paper explores the entire Airbnb housing stock in the city of Barcelona with the aim of identifying the key characteristics of the homes that have remained operational during the 2019-2021 period. We carried out this analysis by using two classification methods, the random forest and logistic regression with elastic net. The objective is to classify the houses that have remained on the platform against those that have not. Finally, we analyze the results obtained and compare both the general performance of the models and the individual information of each variable through partial dependence plots (PDP). We found a better performance of Random Forest over logistic regression, but not significant differences in the relevant variables chosen by each method. It is worth noting the importance of the geographical location, the number of amenities in the home or the price in the survival of the homes.