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Determinants of Airbnb demand in Vienna and their implications for the traditional accommodation industry

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  • Ulrich Gunter

    (MODUL University Vienna, Austria)

  • Irem Önder

    (MODUL University Vienna, Austria)

Abstract

This study identifies key determinants of Airbnb demand and quantifies their marginal contributions in terms of demand elasticities. A comprehensive cross-sectional data set of all Viennese Airbnb listings that were active between July 2015 and June 2016 is examined. Estimation results, which are obtained by cluster-robust ordinary least squares, show that Airbnb demand in Vienna is price-inelastic. Significant positive drivers include listing size, number of photos, and responsiveness of the host. Significant negative drivers include listing price, distance from the city center, and response time of the host. Implications for the traditional accommodation industry are that, on the one hand, it should better communicate its sought-after advantages (e.g. lower average minimum duration of stay). On the other hand, it should increase its offer of bigger and better equipped hotel rooms since hosting more than two guests at a time is one of the major benefits of Airbnb.

Suggested Citation

  • Ulrich Gunter & Irem Önder, 2018. "Determinants of Airbnb demand in Vienna and their implications for the traditional accommodation industry," Tourism Economics, , vol. 24(3), pages 270-293, May.
    • Handle: RePEc:sae:toueco:v:24:y:2018:i:3:p:270-293
    DOI: 10.1177/1354816617731196
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    Cited by:

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    4. Dolnicar, Sara, 2019. "A review of research into paid online peer-to-peer accommodation," Annals of Tourism Research, Elsevier, vol. 75(C), pages 248-264.
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    6. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    7. Matthias Wrede, 2022. "How Short-Term Rentals are Changing the Neighborhood," International Regional Science Review, , vol. 45(4), pages 417-443, July.
    8. Bozana Zekan & Irem Önder & Ulrich Gunter, 2019. "Benchmarking of Airbnb listings: How competitive is the sharing economy sector of European cities?," Tourism Economics, , vol. 25(7), pages 1029-1046, November.
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    11. Martin Thomas Falk & Yang Yang, 2021. "Hotels benefit from stricter regulations on short-term rentals in European cities," Tourism Economics, , vol. 27(7), pages 1526-1539, November.
    12. Bobrovskaya, EV. & Polbin, A., 2023. "Econometric modeling of the demand for short-term rental housing: The case of Airbnb in Moscow," Journal of the New Economic Association, New Economic Association, vol. 59(2), pages 64-84.
    13. Augusto Voltes-Dorta & Federico Inchausti-Sintes, 2021. "The spatial and quality dimensions of Airbnb markets," Tourism Economics, , vol. 27(4), pages 688-702, June.
    14. Türk, Umut & Östh, John & Kourtit, Karima & Nijkamp, Peter, 2021. "The path of least resistance explaining tourist mobility patterns in destination areas using Airbnb data," Journal of Transport Geography, Elsevier, vol. 94(C).
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    16. Juan L Eugenio-Martin & José M Cazorla-Artiles & Christian González-Martel, 2019. "On the determinants of Airbnb location and its spatial distribution," Tourism Economics, , vol. 25(8), pages 1224-1244, December.
    17. Bozana Zekan & Ulrich Gunter, 2022. "Zooming into Airbnb listings of European cities: Further investigation of the sector’s competitiveness," Tourism Economics, , vol. 28(3), pages 772-794, May.
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