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Agent Pricing in the Sharing Economy: Evidence from Airbnb

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Sharing Economy

Part of the book series: Springer Series in Supply Chain Management ((SSSCM,volume 6))

Abstract

One of the major differences between markets that follow a “sharing economy” paradigm and traditional two-sided markets is that the supply side in the sharing economy often includes individual nonprofessional decision makers, in addition to firms and professional agents. Using a data set of prices and availability of listings on Airbnb, we find that there exist substantial differences in the operational and financial performance of professional and nonprofessional hosts. In particular, properties managed by professional hosts earn 16.9% more in daily revenue, have 15.5% higher occupancy rates, and are 13.6% less likely to exit the market compared with properties owned by nonprofessional hosts, while controlling for property and market characteristics. We demonstrate that these performance differences between professionals and nonprofessionals can be partly explained by pricing inefficiencies. Specifically, we provide empirical evidence that nonprofessional hosts are less likely to offer different rates across stay dates based on the underlying demand patterns, such as those created by major holidays and conventions.

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Notes

  1. 1.

    Airbnb will soon be booking more rooms than the world’s largest hotel chains. Quartz. January 20, 2015.

  2. 2.

    Airbnb in the city. New York State Office of General Attorney. October, 2014.

  3. 3.

    We restrict our attention to properties offered as entire apartments or houses and exclude those properties where the hosts also reside, so that we focus on a relatively homogeneous group of hosts with similar levels of mobility.

  4. 4.

    “Airbnb, New York State Spar Over Legality Of Rentals.” NPR. October 16, 2014.

  5. 5.

    In Sect. 20.5, we focus on the subset of hosts who make their properties available more than four days per week (50% of the time). Because of the high availability of their properties, it is less likely that these hosts will cancel availability for other reasons. We do not find any qualitative differences in our results by focusing on this subsample.

  6. 6.

    Ideally, we would like to use a fixed-effect model to control for a listing’s specific characteristics. However, since our independent variable of interest (i.e., whether a property is managed by a professional or a nonprofessional host) is time-invariant, including fixed effects in our model would absorb the effect of the variable of interest.

  7. 7.

    We did not find a significant effect of average rating due to its lack of variation. Moreover, average rating is missing when there is no review available, which will limit the number of observations when included. Therefore, we decide to drop average rating in our analyses.

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Author information

Authors and Affiliations

  1. Ross School of Business, University of Michigan, Ann Arbor, MI, USA

    Jun Li

  2. Harvard Business School, Boston, MA, USA

    Antonio Moreno

  3. Olin Business School, Washington University in St. Louis, St. Louis, MO, USA

    Dennis J. Zhang

Authors
  1. Jun Li
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  2. Antonio Moreno
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  3. Dennis J. Zhang
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Corresponding author

Correspondence to Jun Li .

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Editors and Affiliations

  1. Rotman School of Management, University of Toronto, Toronto, ON, Canada

    Ming Hu

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Li, J., Moreno, A., Zhang, D.J. (2019). Agent Pricing in the Sharing Economy: Evidence from Airbnb. In: Hu, M. (eds) Sharing Economy. Springer Series in Supply Chain Management, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-01863-4_20

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