Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning

by Huang Guofang, Hong Luo & Jing Xia

Overview — Dealers who need to price idiosyncratic products--like houses, artwork, and used cars--often struggle with a lack of information about the demand for their specific items. Analyzing sales data from the used-car retail market, the authors of this paper develop a model of dynamic pricing for idiosyncratic products, showing that seller learning has an impact on pricing dynamics through a rich set of mechanisms. Overall, findings suggest a potentially high return to taking a more serious information-based approach to pricing idiosyncratic products.

Author Abstract

Pricing idiosyncratic products is often challenging because the seller, ex ante, lacks information about the demand for individual items. This paper develops a model of dynamic pricing for idiosyncratic products that features the optimal stopping structure and a seller that learns about item-specific demand through the selling process. The model is estimated using novel panel data of a leading used-car dealership. Policy experiments are conducted to quantify the value of the demand information that the dealer obtains through the initial assessment and subsequent learning in the selling process. With the dealer's average net profit per car in the estimation sample being around $1150, the initial assessment is worth around $101, and the subsequent learning in the selling process helps improve the dealer's profit by at least $269. These estimates suggest a potentially high return to taking the “information-based" approach to pricing idiosyncratic products.