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What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features

Author

Listed:
  • Shunyuan Zhang

    (Harvard Business School, Harvard University, Cambridge, Massachusetts 02163)

  • Dokyun Lee

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

  • Param Vir Singh

    (Tepper School University, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Kannan Srinivasan

    (Tepper School University, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

We study how Airbnb property demand changed after the acquisition of verified images (taken by Airbnb’s photographers) and explore what makes a good image for an Airbnb property. Using deep learning and difference-in-difference analyses on an Airbnb panel data set spanning 7,423 properties over 16 months, we find that properties with verified images had 8.98% higher occupancy than properties without verified images (images taken by the host). To explore what constitutes a good image for an Airbnb property, we quantify 12 human-interpretable image attributes that pertain to three artistic aspects—composition, color, and the figure-ground relationship—and we find systematic differences between the verified and unverified images. We also predict the relationship between each of the 12 attributes and property demand, and we find that most of the correlations are significant and in the theorized direction. Our results provide actionable insights for both Airbnb photographers and amateur host photographers who wish to optimize their images. Our findings contribute to and bridge the literature on photography and marketing (e.g., staging), which often either ignores the demand side (photography) or does not systematically characterize the images (marketing).

Suggested Citation

  • Shunyuan Zhang & Dokyun Lee & Param Vir Singh & Kannan Srinivasan, 2022. "What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features," Management Science, INFORMS, vol. 68(8), pages 5644-5666, August.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:8:p:5644-5666
    DOI: 10.1287/mnsc.2021.4175
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    Cited by:

    1. Jinyang Zheng & Youwei Wang & Yong Tan, 2023. "Platform Refund Insurance or Being Cast Out: Quantifying the Signaling Effect of Refund Options in the Online Service Marketplace," Information Systems Research, INFORMS, vol. 34(3), pages 910-934, September.
    2. Zhao, Lu & Zhang, Mingli & Tu, Jianbo & Li, Jialing & Zhang, Yan, 2023. "Can users embed their user experience in user-generated images? Evidence from JD.com," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    3. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.

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