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Determinants of short-term rental prices in the sharing economy: The case of Airbnb in Moscow

Author

Listed:
  • Bobrovskaya, Ekaterina

    (RANEPA, Moscow, Russian Federation)

  • Polbin, Andrey

    (RANEPA; Gaidar Institute, Moscow, Russian Federation)

Abstract

In this paper we analyze pricing on a large online platform for short-term rental housing Airbnb based on Moscow dataset in January 2021. We build a multiple regression model based on a hedonic price function. We identify the main price determinants and the features typical for the specified market. In addition, the results demonstrate the importance of applying quantile regression and geographically weighted regression for more detailed analysis of the determinants of short-term rental prices.

Suggested Citation

  • Bobrovskaya, Ekaterina & Polbin, Andrey, 2022. "Determinants of short-term rental prices in the sharing economy: The case of Airbnb in Moscow," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 5-28.
    • Handle: RePEc:ris:apltrx:0436
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    References listed on IDEAS

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    1. Gleb Goncharov & Timur Natkhov, 2020. "Textual Analysis of Pricing in the Moscow Residential Real Estate Market," HSE Economic Journal, National Research University Higher School of Economics, vol. 24(1), pages 101-116.
    2. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    3. Catalina Juaneda & Josep Maria Raya & Francesc Sastre, 2011. "Pricing the Time and Location of a Stay at a Hotel or Apartment," Tourism Economics, , vol. 17(2), pages 321-338, April.
    4. Ozhegov, Evgeniy & Kosolapov, Nikita & Pozolotina, Iuliia, 2017. "On dependence between housing value and school characteristics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 47, pages 28-48.
    5. Zhihua Zhang & Rachel J. C. Chen & Lee D. Han & Lu Yang, 2017. "Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach," Sustainability, MDPI, vol. 9(9), pages 1-13, September.
    6. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    7. Sidorovykh, Aleksandra, 2015. "Estimation of effects of transport accessibility on housing prices," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 37(1), pages 43-56.
    8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    9. Insu Hong & Changsok Yoo, 2020. "Analyzing Spatial Variance of Airbnb Pricing Determinants Using Multiscale GWR Approach," Sustainability, MDPI, vol. 12(11), pages 1-18, June.
    10. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    11. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74(2), pages 132-132.
    12. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    13. Georgios Zervas & Davide Proserpio & John W. Byers, 2021. "A first look at online reputation on Airbnb, where every stay is above average," Marketing Letters, Springer, vol. 32(1), pages 1-16, March.
    14. Katyshev, Pavel & Khakimova, Yulia, 2012. "Ecological factors and the price of Moscow apartments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 28(4), pages 113-123.
    15. Ching-Fu Chen & R. Rothschild, 2010. "An Application of Hedonic Pricing Analysis to the Case of Hotel Rooms in Taipei," Tourism Economics, , vol. 16(3), pages 685-694, September.
    16. Magnus, Jan & Peresetsky, Anatoly, 2010. "The price of Moscow apartments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 17(1), pages 89-105.
    17. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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    1. 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.

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    More about this item

    Keywords

    sharing economy; hedonic pricing; Airbnb; short-term rental housing; quantile regression; geographically weighted regression.;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • Z39 - Other Special Topics - - Tourism Economics - - - Other

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