Why is Intermediating Houses so Difficult? Evidence from iBuyers
We study the frictions in dealer-intermediation in residential real estate through the lens of “iBuyers,” technology entrants, who purchase and sell residential real estate through online platforms. iBuyers supply liquidity to households by allowing them to avoid a lengthy sale process. They sell houses quickly and earn a 5% spread. Their prices are well explained by a simple hedonic model, consistent with their use of algorithmic pricing. iBuyers choose to intermediate in markets that are liquid and in which automated valuation models have low pricing error. These facts suggest that iBuyers’ speedy offers come at the cost of information loss concerning house attributes that are difficult to capture in an algorithm, resulting in adverse selection. We calibrate a dynamic structural search model with adverse selection to understand the economic forces underlying the tradeoffs of dealer intermediation in this market. The model reveals the central tradeoff to intermediating in residential real estate. To provide valuable liquidity service, transactions must be closed quickly. Yet, the intermediary must also be able to price houses precisely to avoid adverse selection, which is difficult to accomplish quickly. Low underlying liquidity exacerbates adverse selection. Our analysis suggests that iBuyers’ technology provides a middle ground: they can transact quickly limiting information loss. Even with this technology, intermediation is only profitable in the most liquid and easy to value houses. Therefore, iBuyers’ technology allows them to supply liquidity, but only in pockets where it is least valuable. We also find limited scope for dealer intermediation even with improved pricing technology, suggesting that underlying liquidity will be an impediment for intermediation in the future.
We thank Peter DeMarzo, Darrell Duffie, Tim McQuade, Adriano Rampini, Martin Schneider, Randall Wright, Jeff Zwiebel and participants at Kellogg, Stanford, Columbia Workshop in New Empirical Finance, Duke Fuqua, National University of Singapore, NBER Summer Institute, Search and Matching in Macro and Finance Virtual Seminar Series and Stanford Institute of Theoretical Economics for useful comments. Piskorski and Seru thank the National Science Foundation Award (1628895) for financial support. Buchak is at Stanford Graduate School of Business (GSB), Matvos is at Northwestern University and the National Bureau of Economic Research (NBER), Piskorski is at Columbia University and NBER, and Seru is at Stanford GSB, the Hoover Institution, the Stanford Institute for Economic Policy Research (SIEPR), and NBER. First Draft: November 2019 The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.