Costly Price Adjustment and Automated Pricing: The Case of Airbnb
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.
The Impact of Airbnb on Housing Affordability, joint with Wen Wang (work in progress)
In recent years, peer-to-peer accommodation platform Airbnb has developed into a major player in the short-term rental market. While some local governments and community leaders blame Airbnb for driving up rent in the long-term rental market, Airbnb argues that the platform helps many families stay in their houses. Although the actual effect of Airbnb on housing affordability is still unclear, regulations on Airbnb in some cities have already been enacted. In this paper, we develop a model of hosts' exit and entry decisions. The model considers three types of hosts: share their apartments with others, rent out their apartments when they are away and rent out their apartments like small hotels. With the model estimates, we explore the impacts of two popular regulations: cap on rental nights and ban on certain apartment types.
Credit Rating Stability and Quality with Multiple Credit Rating Agencies, joint with Wei Tan
In this paper, we model the credit rating firm's rating decision in a dynamic discrete choice framework. The rating firm decides when to revise the rating of a bond in order to minimize the reputation cost. And we allow serially correlated unobservables in our model -- the underlying rating of a bond is known by the firm but not by the researcher. We empirically estimate the parameters of the structural model using the mortgage-backed security bond data from 2004-2007. In the counterfactual analysis, we evaluate the effects of competition on the accuracy of credit ratings.
“Testing Volatility Persistence on Markov Switching Stochastic Volatility Models,” with Yong Li, Economic Modelling, Vol 35, Issue 1, 45-50.
Dynamic Pricing, Sharing Economy, Quantitative Marketing, Econometric Modeling, Industrial Organization
Department of Economics
The Ronald O. Perelman Center for Political Science and Economics
133 South 36th Street
Philadelphia, PA 19104
Eric T. Bradlow (Committee member)
K.P. Chao Professor, Professor of Marketing, Statistics, Education and Economics
The Wharton School, University of Pennsylvania
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