Working Paper: NBER ID: w28252
Authors: Greg Buchak; Gregor Matvos; Tomasz Piskorski; Amit Seru
Abstract: 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 may 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 and quantify 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 accurately to avoid adverse selection, which is difficult to accomplish quickly. We find that 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 relatively 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.
Keywords: No keywords provided
JEL Codes: G0; G2; G5; L0; R20
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| ibuyer technology (L81) | efficiency of dealer intermediation (L81) |
| transaction speed (E41) | adverse selection (D82) |
| liquidity (E41) | efficiency of dealer intermediation (L81) |
| low liquidity (G19) | adverse selection (D82) |
| pricing errors (D49) | operational success of ibuyers (L81) |
| transaction speed (E41) | market share (L17) |
| high liquidity (E41) | operational success of ibuyers (L81) |