Inside Airbnb's performance and adaptive strategies in Barcelona using artificial neural networks: A longitudinal, spatial, and multi-host perspective

Abstract

This research explores the Airbnb platform's performance and adaptive strategies by analysing its spatial, temporal, and multi-host patterns. A three-layer model based on machine learning and neural networks, compared with a multiple linear regression, Random Forest Regression (RFR), and Support Vector Regression (SVR) methods, is used to conduct a longitudinal analysis of three representative months for tourism each year from 2016 to 2022. The study reveals the importance of “minimum nights”, active price management and professionalization, coupled with the potential transfer of accommodations in the medium- and long-term residential markets, as the platform's adaptive strategies. The findings also suggest a shift towards more professional host profiles and the consolidation of new tourist hubs in the city in post-Covid period. The study contributes to the understanding of Airbnb's performance and impact on global urban dynamics and demonstrates an application of machine learning to tourism and hospitality research. Theoretical and practical implications are discussed.