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
Date Written: May 25, 2017
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
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