Events

Dr. Weiming Zhu
Assistant Professor of Operations Management
IESE Business School
University of Navarra Barcelona

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 Resnet50, 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 the order of display on the listings’ webpages. 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 non-cover photos and that a high quality bedroom cover image leads to the largest increase in demand. Furthermore, we build a non-linear 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 the revenue by $500 to $1100.