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.
Paper Information
- Full Working Paper Text
- Working Paper Publication Date: September 2015
- HBS Working Paper Number: 16-027
- Faculty Unit(s): Strategy
