How Much Is an Image Worth? Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics

32 Pages Posted: 31 May 2017 Last revised: 17 Jun 2018

See all articles by Shunyuan Zhang

Shunyuan Zhang

Carnegie Mellon University - David A. Tepper School of Business

Dokyun Lee

Carnegie Mellon University - David A. Tepper School of Business

Param Vir Singh

Carnegie Mellon University - David A. Tepper School of Business

Kannan Srinivasan

Carnegie Mellon University - David A. Tepper School of Business

Date Written: May 25, 2017

Abstract

We investigate the economic impact of images and lower-level image factors that influence property demand in Airbnb. Using Difference-in-Difference analyses on a 16-month Airbnb panel dataset spanning 7,711 properties, we find that units with verified photos (taken by Airbnb’s photographers) generate additional revenue of $2,521 per year on average. For an average Airbnb property (booked for 21.057% of the days per month), this corresponds to 17.51% increase in demand due to verified photos. Leveraging computer vision techniques to classify the image quality of more than 510,000 photos, we show that 58.83% of this effect comes from the high image quality of verified photos. Next, we identify 12 interpretable image attributes from photography and marketing literature relevant for real estate photography that capture image quality as well as consumer taste. We quantify (using computer vision algorithms) and characterize unit images to evaluate the economic impact of these human-interpretable attributes. The results reveal that verified images not only differ significantly from low-quality unverified photos, but also from high-quality unverified photos on most of these features. The treatment effect of verified photos becomes insignificant once we control for these 12 attributes, indicating that Airbnb’s photographers not only improve the quality of the image but also align it with the taste of potential consumers. This suggests there is significant value in optimizing images in e-commerce settings on these attributes. From an academic standpoint, we provide one of the first large-scale empirical evidence that directly connects systematic lower-level and interpretable image attributes to demand. This contributes to, and bridges, the photography and marketing (e.g., staging) literature, which has traditionally ignored the demand side (photography) or did not implement systematic characterization of images (marketing). Lastly, these results provide immediate insights for housing and lodging e-commerce managers (of Airbnb, hotels, realtors, etc.) to optimize product images for increased demand.

Keywords: sharing economy, Airbnb, economic impact of images, photography, computer vision, deep learning, image quality classification, image feature extraction, treatment effect, image attribute analysis

JEL Classification: M3

Suggested Citation

Zhang, Shunyuan and Lee, Dokyun and Singh, Param Vir and Srinivasan, Kannan, How Much Is an Image Worth? Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics (May 25, 2017). Available at SSRN: https://ssrn.com/abstract=2976021 or http://dx.doi.org/10.2139/ssrn.2976021

Shunyuan Zhang

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Dokyun Lee

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Param Vir Singh (Contact Author)

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
412-268-3585 (Phone)

Kannan Srinivasan

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

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