Selling Fast or Selling Junk: Is iBuying Sustainable?
◈ 주 제 : Selling Fast or Selling Junk: Is iBuying Sustainable?
◈ 발표자 : 윤소혜 (Princeton University)
◈ 일 시 : 2026년 6월 24일 수요일 16:30 ~ 17:45
◈ 장 소 : 16동 654호
◈ 주 관 : 경제학부, 경제연구소 한국경제혁신센터, SSK, BK21
세미나 이전에 연사님과 개인면담을 원하시는 분은 아래 구글 스프레드시트에서 원하시는 시간에 성함을 기입해주시기 바랍니다. 개인면담 스프레드시트는 23일 오후 12시에 마감하도록 하겠습니다.
https://docs.google.com/spreadsheets/d/1fwKtAIRI2G8B6nq94EOYRHyDE-p_r6tJ57vqmKF_6G4/edit?usp=sharing
- 본 세미나는 경제학부 BK21 관련 세미나 참석으로 인정됩니다.
경제학부 대학원생 중 세미나 참석 인정을 받기를 원하시는 학생은 세미나 종료 후 세미나실 내에 위치한 참석 명단을 기재해 주시기 바랍니다.
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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.