Abstract

This paper examines challenges with algorithmic intermediation on the real estate market and evaluates strategies to mitigate adverse selection when private information about product quality is intertwined with private information about preferences. I examine these issues in the context of iBuyers—firms that offer instant home purchases using big-data-driven pricing models—and analyze why they have struggled to achieve sustainable profitability. I develop a model in which home sellers choose between selling to an iBuyer and listing on the open market based on two dimensions of private information: unobserved house quality and the hassle costs of traditional selling. Sellers may select an iBuyer either to avoid the time and effort of listing or because the iBuyer’s offer exceeds their expected market price, with the latter case generating adverse selection against the iBuyer. Using detailed transaction and listing data, I estimate the joint distribution of these factors, identified from repeated sales and seller choice following iBuyer entry. Counterfactual analyses show that a revenue-sharing contract mitigates adverse selection by improving selection incentives, while incorporating a fine-tuned LLM-based text score derived from past unstructured listing data further reduces informational frictions by providing a signal of unobserved house quality. Together, these mechanisms enhance the viability of algorithmic intermediation in the housing market.