Estimating Willingness-to-Pay and Brand Market Power in Auctions: A Hedonic Approach

We model how to measure consumer willingness-to-pay (WTP) from an English or ascending first-price auction based on two general bidding premises: no bidder bids more than her WTP, and no bidder allows a rival bidder to win at a price that she is willing to beat. We thus impose no structural assumptions on rationality of bidders or sellers. The proposed model is general enough to include buy-it-now (BIN) auction mechanism, and to provide controls for competition among bidders including those who do not participate in an auction, and competition among various items up for sale concurrently. We use data of notebook auctions from one of the largest Internet auction sites in Korea. Our simulated second-highest WTP based on estimation results closely approximates the final selling price (the latter is 0.7% higher), pointing to an efficient and rational auction. Most product characteristics matter in the expected ways. Our other primary findings are as follows: (1) The magnitude of the estimate of the number of months the laptop has been used appears consistent with a lack of lemons problem; (2) Compared to neutral seller reputation, positive seller reputations matter more for WTP than to negative seller reputations; (3) More extensive site-surfing and bidding histories lead to lower WTP, implying that search costs and experience matter in bidding; (4) WTP declines as more similar items are concurrently listed with the focal item; there is an additional effect if these similar items also belong to the same brand. As specific substantive benefits, we demonstrate how sellers can calculate changes in WTP, and hence the expected revenue, as the auction setting varies. For items with BIN option, we show that how the probability of the option being exercised and the expected revenue vary with the level of BIN prices and product uniqueness.