Pricing Frictions and Platform Remedies: The Case of Airbnb

53 Pages Posted: 18 Feb 2021 Last revised: 16 May 2024

See all articles by Yufeng Huang

Yufeng Huang

University of Rochester - Simon Business School

Date Written: May 8, 2024


I document the prevalence of pricing frictions among sellers on Airbnb and quantify the effect of platform policies designed to ameliorate such frictions. Optimal Airbnb prices should reflect varying demand across nights of stay and changes in the opportunity costs over time of booking. However, I demonstrate that, compared to the optimum, sellers' observed prices are much more uniform across nights and rigid over time. I further show that the degree of simplicity is the most prevalent among single-listing sellers, is not captured by several alternative mechanisms, and is most plausibly explained by sellers' cognitive constraints—a limit on the complexity of sellers’ pricing strategies. Estimating a structural equilibrium model where sellers set constrained-optimal prices, I find significant frictions amounting to 14% loss in the average consumer surplus and 0-15% profit loss across sellers. Lastly, I demonstrate that an effective remedy is a modified version of the current pricing algorithm that simplifies, but does not completely take away, sellers' pricing decisions.

Keywords: Pricing frictions, Algorithmic pricing, Platform design, Airbnb

Suggested Citation

Huang, Yufeng, Pricing Frictions and Platform Remedies: The Case of Airbnb (May 8, 2024). Available at SSRN: or

Yufeng Huang (Contact Author)

University of Rochester - Simon Business School ( email )

Rochester, NY 14627
United States

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