“Costly Price Adjustment and Automated Pricing: The Case of Airbnb” (Job Market Paper)
On many e-commerce platforms such as Airbnb, StubHub and TURO, where each seller sells a fixed inventory over a finite horizon, the pricing problems are intrinsically dynamic. However, many sellers on these platforms do not update prices frequently. In this paper, I develop a dynamic pricing model to study the revenue and welfare implication of automated pricing which allows sellers to update their prices without manual interference. The model focuses on three factors through which automated pricing influences sellers: price adjustment cost, buyer’s varying willingness to pay and inventory structure. In the model, I also take into account competition among sellers. Utilizing a unique data set of detailed Airbnb rental history and price trajectory in New York City, I find that the price rigidity observed in the data can be rationalized by a price adjustment cost ranging from 0.9% to 2.2% of the listed price. Moreover, automated pricing can increase the platform’s revenue by 4.8% and the hosts’ (sellers’) by 3.9%. The renters (buyers) could be either better off or worse off depending on the length of their stays.
Working in Progress
1. “The Impact of Airbnb on Housing Affordability”, joint with Wen Wang
2. “Credit Rating Stability and Quality with Multiple Credit Rating Agencies”, joint with Wei Tan
“Testing Volatility Persistence on Markov Switching Stochastic Volatility Models,” with Yong Li, Economic Modelling, Vol 35, Issue 1, 45-50.