Proceedings of the 5th International Conference on Tourism Management and Hospitality
Year: 2024
DOI:
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The Spread of Airbnb and its Impact on the Italian Real Estate Market: Evidence from Italy
Chiara Agnoletti, Chiara Bocci, Claudia Ferretti, Francesco Viviani
ABSTRACT:
The growing expansion of short-term rentals and Airbnb listings has had significant impacts on urban environments, raising concerns about conflicts with long-term residents, both in qualitative terms (i.e. insecurity, quality of life) and in quantitative terms (i.e., house prices). This study seeks to explore the influence of Airbnb proliferation on the Italian real estate market. In particular, by collecting data from Idealista.it (one of the prominent Italian real estate listing website) and Inside Airbnb.com, we constructed a panel dataset covering ten Italian cities with high tourist appeal over the period spanning from 2012 to 2022. In order to capture both city-level and neighborhood-level variations, the choice of the model was that of a multilevel regression model with random effect on the intercept. In particular, two model specifications were employed: one incorporating interaction effects to examine varying influences on house prices, and another using the Cronbach’s approach to distinguish “within” and “between” effects across neighborhoods and cities. Our findings reveal that Airbnb density correlates positively with house prices, with particularly strong effects observed in historic city centers. Additionally, the impact is amplified in cities attracting both cultural and business tourism (compared to cities with predominantly cultural tourism) and in cities which are characterized by a more organized supply of short-term rentals (hosts managing multiple properties). These findings underscore the substantial influence of short-term rentals on the real estate market and suggest that increased regulatory measures, similar to those in cities like Barcelona and Florence, may be needed to mitigate the effects of Airbnb on the real estate market itself.
keywords: Airbnb impact, Multilevel models, Panel regression, Tourism management