Learning and Trust in Auction Markets

TL;DR

This study examines bidder behavior and trust in a new auction platform by Zillow, revealing that many bidders distrust platform recommendations, leading to suboptimal bidding and increased regret, especially in early market stages.

Contribution

It provides empirical insights into bidder decision-making and trust issues in auction platforms, highlighting the impact of recommendation tools on bidder behavior and market dynamics.

Findings

Half of the bidders do not follow recommendations, increasing their regret.

Trust issues cause many bidders to experiment, reducing their net value.

Trust in platform tools improves as markets mature, but remains significant.

Abstract

In this paper, we study behavior of bidders in an experimental launch of a new advertising auction platform by Zillow, as Zillow switched from negotiated contracts to using auctions in several geographically isolated markets. A unique feature of this experiment is that the bidders in this market are real estate agents that bid on their own behalf, not using third-party intermediaries. To help bidders, Zillow also provided a recommendation tool that suggested a bid for each bidder. Our main focus in this paper is on the decisions of bidders whether or not to adopt the platform-provided bid recommendation. We observe that a significant proportion of bidders do not use the recommended bid. Using the bid history of the agents we infer their value, and compare the agents' regret with their actual bidding history with results they would have obtained following the recommendation. We find that for half of the agents not following the recommendation, the increased effort of experimenting with alternate bids results in increased regret, i.e., they get decreased net value out of the system. The proportion of agents not following the recommendation slowly declines as markets mature, but it remains large in most markets that we observe. We argue that the main reason for this phenomenon is the lack of trust in the platform-provided tool. Our work provides an empirical insight into possible design choices for auction-based online advertising platforms. While search advertising platforms (such as Google or Bing) allow bidders to submit bids on their own, many display advertising platforms (such as Facebook) optimize bids on bidders' behalf and eliminate the need for bids. Our empirical analysis shows that the latter approach is preferred for markets where bidders are individuals, who don't have access to third party tools, and who may question the fairness of platform-provided suggestions.