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

Abstract

Pricing products such as used cars, houses, and artwork is often challenging, because each item is unique, and the seller, ex ante, lacks information about the demand for individual items. This paper develops a dynamic pricing model for products with significant item-specific demand uncertainty, in which a forward-looking seller learns about the item-specific demand through an initial assessment, as well as during the selling process. The model demonstrates how seller learning, through several mechanisms, can lead to the commonly observed downward trend in the prices of individual items. These mechanisms include the seller’s optimal adjustment of prices over time to account for the dynamic adverse selection of unsold items and the diminishing option value in future learning. The model is estimated using novel panel data of a leading used-car dealership. Counterfactual experiments show that the value of learning in the selling process is $203 per car. Conditional on subsequent learning in the selling process, the initial assessment further improves profit per car by $139. With the dealer’s net profit per car being about $1,150, these estimates suggest a potentially high return to taking an information-based approach toward pricing products with item-specific demand uncertainty. This paper was accepted by Juanjuan Zhang, marketing.