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

Author

Listed:
  • 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])

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.

Suggested Citation

  • Daniel Brunstein & Georges Casamatta & Sauveur Giannoni, 2025. "Using machine learning to estimate the heterogeneous impact of Airbnb on house prices: Evidence from Corsica," Working Papers hal-04934630, HAL.
    • Handle: RePEc:hal:wpaper:hal-04934630
    Note: View the original document on HAL open archive server: https://hal.science/hal-04934630v1
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