Theme：Costly Price Adjustment and Automated Pricing: The Case of Airbnb
Speaker：Qi Pan (University of Pennsylvania)
Address：Room 1008, Mingde Business Building
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.
Qi Pan is a Ph.D. candidate in economics at the University of Pennsylvania, and he is expected to receive his Ph.D. degree in May 2020. He received his master degree in economics from Renmin University and bachelor degree in Mathematics from Sun Yat-sen University. His interests lie in quantitative marketing, empirical industrial organization, econometric modeling, and big data analysis. His current research is focusing on dynamic pricing and sharing economy. In his job market paper, he develops an empirical framework to study the pricing dynamics on Airbnb and to evaluate the impact of automated pricing, a tool that allows hosts to update their prices without manual interference. His research explores the empirical dynamic pricing problem with competition and provides insight into automated pricing feature. He also studies the impact of Airbnb on the traditional rental market using dynamic exit and entry model. The modeling methods and result in his research have rich managerial implications. In addition to dynamic pricing and sharing economy, Qi Pan is also interested in Bayesian econometrics.
Your registration information has been received!
Please wait patiently for the organizing committee's SMS notification to register successfully. Thank you for your attention.
RMBS made the Top-50 list of MBA,
EMBA and EE programs——The Financial Times
@Business School, Renmin University of China 京ICP备05066828号-1