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Dynamic pricing and revenues of Airbnb listings: estimating heterogeneous causal effects

Author

Listed:
  • Veronica Leoni

    (Universitat de les Illes Balears)

  • Jan Olof William Nilsson

    (Universitat de les Illes Balears)

Abstract

This paper investigates the extent to which the implementation of intertemporal price discrimination affects Airbnb listings’ revenue. We found that on average, a price surge (i.e., increasing the price as we approach the date of service consumption) has an adverse effect on revenue. However, the magnitude of such effect exhibits significant heterogeneity among listings. Through the application of generalized random forests, a causal machine learning technique, we identify exacerbating and moderating treatment modifiers and shed light on the listing dimensions that cause price surges to be particularly detrimental for hosts’ revenues.

Suggested Citation

  • Veronica Leoni & Jan Olof William Nilsson, 2020. "Dynamic pricing and revenues of Airbnb listings: estimating heterogeneous causal effects," DEA Working Papers 92, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    • Handle: RePEc:ubi:deawps:92
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    More about this item

    Keywords

    Airbnb; dynamic pricing; heterogeneous causal effects; generalized random forest.;
    All these keywords.

    JEL classification:

    • Z30 - Other Special Topics - - Tourism Economics - - - General
    • Z31 - Other Special Topics - - Tourism Economics - - - Industry Studies
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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