Estimating and Exploiting the Impact of Photo Layout: A Structural Approach

Hanwei Li (), David Simchi-Levi (), Michelle Xiao Wu () and Weiming Zhu ()
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Hanwei Li: Department of Management Sciences, College of Business, City University of Hong Kong, Hong Kong, China
David Simchi-Levi: Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Michelle Xiao Wu: Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Weiming Zhu: Faculty of Business and Economics, The University of Hong Kong, Hong Kong, China

Management Science, 2023, vol. 69, issue 9, 5209-5233

Abstract: Host-generated property images as a visual channel reveal substantial information about properties. Selecting proper images to display can lead to higher demand and increased rental revenue. In this paper, we define, estimate, and optimize the impacts of Airbnb photos on customers’ renting decisions. We apply ResNet-50, a convolutional neural network model, to build two separate, supervised learning models to evaluate the image quality and room types posted by Airbnb hosts. Then, we characterize the overall impacts of photo layout by the room type featured in the photo, photo quality, and order of display on the listings’ web pages. To address two estimation challenges in the Airbnb setting, namely, censored demand and changing consideration sets, we propose a novel pairwise comparison model that utilizes customers’ booking sequence data to consistently estimate the impact of photo layout on customers’ renting decisions. Our estimation results suggest that the cover image has a significantly larger impact than noncover photos and a high-quality bedroom cover image leads to the largest increase in demand. Furthermore, we build a nonlinear integer programming optimization problem and develop an algorithm to determine the optimal photo layout. Our counterfactual analysis suggests that a listing’s unilateral adoption of optimal photo layout leads to 11.0% more bookings on average. Moreover, depending on the neighborhood and market size, when listings simultaneously switch to the optimal photo layout, they get booked for two to five additional days in a year on average, which boosts revenue by $500 to $1,100.

Keywords: sharing economy; empirical analysis; structural estimation; choice model; computer vision (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:9:p:5209-5233

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