Using machine learning to estimate the heterogeneous impact of Airbnb on house prices: Evidence from Corsica

Daniel Brunstein (), Georges Casamatta () and Sauveur Giannoni ()
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Daniel Brunstein: LISA - Laboratoire « Lieux, Identités, eSpaces, Activités » (UMR CNRS 6240 LISA) - CNRS - Centre National de la Recherche Scientifique - Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli]
Georges Casamatta: LISA - Laboratoire « Lieux, Identités, eSpaces, Activités » (UMR CNRS 6240 LISA) - CNRS - Centre National de la Recherche Scientifique - Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli]
Sauveur Giannoni: Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli], LISA - Laboratoire « Lieux, Identités, eSpaces, Activités » (UMR CNRS 6240 LISA) - CNRS - Centre National de la Recherche Scientifique - Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli]

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Abstract: This study investigates the influence of Airbnb on property prices in Corsica. Leveraging machine learning techniques, we obtain more robust results than those achieved with conventional methods and uncover heterogeneous effects of Airbnb on property values. Our analysis reveals that a 1% increase in Airbnb listings leads to an average 0.21% rise in house prices. Interestingly, this effect is more pronounced in economically less developed regions, such as inland municipalities and remote seaside resorts, compared to traditionally popular tourist destinations and urban areas.

Keywords: Short-term rental; Housing market; Machine learning; Heterogeneous effects (search for similar items in EconPapers)
Date: 2025
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
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Published in Journal of Housing Economics, 2025, 67, pp.102044. ⟨10.1016/j.jhe.2025.102044⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04934630

DOI: 10.1016/j.jhe.2025.102044

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