The Right Auction at the Right Price

Abstract:     
Research literature to date shows few attempts to formalize a successful method for bidding in online auctions. We have collected and analyzed data from approximately 5,000 eBay auctions of laptop computers, in order to develop a system that could guide an auction participant to winning without overpaying. As the foundation for our work, we construct three different models of final auction prices, one of which uses solely mean normalized price levels to make predictions. Next, we describe a strategy for using any one of our models in a live auction setting. The strategy is based on plausible assumptions about the general behavior and motivations of online auction participants. In the context of this strategy, we examine the performance of each of our models on test data. For the subset of test auctions that each model wins, we also examine the performance of the two alternative models, in order to quantify a user's incentive to switch from one model to another. Results for the model of mean normalized price levels are very promising, and our paper concludes with some discussion of this finding.

Keywords: online auctions, statistical models, price prediction, strategic bidding

Working Paper Series