Pricing Frictions and Platform Remedies: The Case of Airbnb

Pricing in a complex environment is difficult for individual sellers. Whereas the platform tries to aid seller pricing, its different objectives might steer seller behavior towards the platform’s goal. This paper empirically studies pricing frictions on Airbnb and explores the equilibrium consequence of different platform designs. I first show that pricing frictions are prevalent. Then, leveraging natural variation in the platform’s interface design, I demonstrate that sellers’ price-setting costs and cognitive constraints are plausible drivers of the frictions. I then estimate a structural equilibrium model and find that pricing frictions lead to a 14% consumer welfare loss and a 0–15% seller-profit loss. Finally, I ask: How to ameliorate these frictions? The platform’s revenue-maximizing algorithm does not lead to market-clearing prices because it fails to internalize sellers’ high opportunity costs of time. However, a simple platform design, where the platform sets price variation but gives sellers the final decision right to determine the price levels, will eliminate almost all frictions.