Online Auction Demand

Abstract

With $40 billion in annual gross merchandise volume, electronic auctions comprise a substantial and growing sector of the retail economy. Using unique data on Celtic coins, we estimate a structural model of buyer and seller behavior via Markov chain Monte Carlo (MCMC) with data augmentation. Results indicate that buyer valuations are affected by item, seller, and auction characteristics; buyer costs are affected by bidding behavior; and seller costs are affected by item characteristics and the number of listings. The model enables us to compute fee elasticities even though there is no variation in fees in our data. We find that commission elasticities exceed per item fee elasticities because they target high-value sellers and enhance their likelihood of listing. By targeting commission reductions to high-value sellers, auction house revenues can be increased by 3.9%. Computing customer value, we find that attrition of the largest seller would decrease fees paid to the auction house by $97. Given the seller paid $127 in fees, competitive effects offset only 24% of those fees. In contrast, competition offsets 81% of the buyer attrition effect. In both events, the auction house would overvalue its customers by neglecting competitive effects.