(with G Buchak, G Matvos and T Piskorski), 2022
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.
(with E Jiang, G Matvos and T Piskorski), 2022
Is bank capital structure designed to extract deposit subsidies? We address this question by studying capital structure decisions of shadow banks: intermediaries that provide banking services but are not funded by deposits. We assemble, for the first time, call report data for shadow banks which originate one quarter of all US household debt. We document five facts.
(1) Shadow banks use twice as much equity capital as equivalent banks, but are substantially more leveraged than non-financial firms.
(2) Leverage across shadow banks is substantially more dispersed than leverage across banks.
(3) Like banks, shadow banks finance themselves primarily with short-term debt and originate long-term loans. However, shadow bank debt is provided primarily by informed and concentrated lenders.
(4) Shadow bank leverage increases substantially with size, and the capitalization of the largest shadow banks is similar to banks of comparable size.
(5) Uninsured leverage, defined as uninsured debt funding to assets, increases with size and average interest rates on uninsured debt decline with size for both banks and shadow banks.
Modern shadow bank capital structure choices resemble those of pre-deposit-insurance banks both in the U.S. and Germany, suggesting that the differences in capital structure with modern banks are likely due to banks’ ability to access insured deposits. Our results suggest that banks’ level of capitalization is pinned down by deposit subsidies and capital regulation at the margin, with small banks likely to be largest recipients of deposit subsidies. Models of financial intermediary capital structure then have to simultaneously explain high (uninsured) leverage, which increases with the size of the intermediary, and allow for substantial heterogeneity across capital structures of firms engaged in similar activities. Such models also need to explain high reliance on short-term debt of financial intermediaries.
(with S Agarwal, J Grigsby, A Hortacsu, G Matvos and V Yao), 2022. (R&R, Econometrica)
We study the interaction of search and application approval in credit markets. We combine a unique dataset, which details search behavior for a large sample of mortgage borrowers, with loan application and rejection decisions. Our data reveal substantial dispersion in mortgage rates and search intensity, conditional on observables. However, in contrast to predictions of standard search models, we find a novel non-monotonic relationship between search and realized prices: borrowers, who search a lot, obtain more expensive mortgages than borrowers’ with less frequent search. The evidence suggests that this occurs because lenders screen borrowers’ creditworthiness, rejecting unworthy borrowers, which differentiates consumer credit markets from other search markets. Based on these insights, we build a model that combines search and screening in presence of asymmetric information. Risky borrowers internalize the probability that their application is rejected, and behave as if they had higher search costs. The model rationalizes the relationship between search, interest rates, defaults, and application rejections, and highlights the tight link between credit standards and pricing. We estimate the parameters of the model and study several counterfactuals. The model suggests that overpayment may be a poor proxy for consumer unsophistication since it partly represents rational search in presence of rejections. Moreover, the development of improved screening technologies from AI and big data (i.e., fintech lending) could endogenously lead to more severe adverse selection in credit markets. Finally, place based policies, such as the Community Reinvestment Act, may affect equilibrium prices through endogenous search responses rather than increased credit risk.
(with M Egan and G Matvos), 2022. (R&R, Review of Economic Studies)
This paper studies the impact of the arbitrator selection process on consumer outcomes. Using data from consumer arbitration cases in the securities industry over the past two decades, where we observe detailed information on case characteristics, the randomly generated list of potential arbitrators presented to both parties, the selected arbitrator, and case outcomes, we establish several motivating facts. These facts suggest that firms hold an informational advantage over consumers in selecting arbitrators, resulting in industry-friendly arbitration outcomes. We then develop and calibrate a quantitative model of arbitrator selection in which firms hold an informational advantage in selecting arbitrators. Arbitrators, who are compensated only if chosen, compete with each other to be selected. The model allows us to decompose the firms’ advantage into two components: the advantage of choosing pro-industry arbitrators from a given pool, and the equilibrium pro-industry tilt in the arbitration pool that arises because of arbitrator competition. Selecting arbitrators without the input of firms and consumers would increase consumer awards by $60,000 on average relative to the current system. Forty percent of this effect arises because the pool of arbitrators skews pro-industry due to competition. Even an informed consumer cannot avoid this pro-industry equilibrium effect. Counterfactuals suggest that redesigning the arbitrator selection mechanism for the benefit of consumers hinges on whether consumers are informed. Policies intended to benefit consumers, such as increasing arbitrator compensation or giving parties more choice would benefit informed consumers but hurt the uninformed.
(with J Liberti and V Vig), 2017. (R&R Journal of Financial Economics)
This paper investigates the effect of a change in informational environment of borrowers on the organizational design of bank lending. We use micro-data from a large multinational bank and exploit the sudden introduction of a credit registry, an information-sharing mechanism across banks, for a subset of borrowers. Using within borrower and loan officer variation in a difference-in-difference empirical design, we show that expansion of credit registry led to an improvement in allocation of credit to affected borrowers. There was a concurrent change in the organizational structure of the bank that involved a dramatic increase in delegation of lending decisions of affected borrowers to loan officers. We also find a significant expansion in scope of activities of loan officers who deal primarily with affected borrowers, as well as of their superiors. There is suggestive evidence that larger banks in the economy were better able to implement similar changes as our bank. We argue that these patterns can be understood within the framework of incentive-based and information cost processing theories. Our findings could help rationalize why improvements in the information environment of borrowers may be altering the landscape of lending by moving decisions outside the boundaries of financial intermediaries.
(with S Agarwal, E Benmelech and N Bergman), 2012. (R&R Journal of Political Economy)
Yes, it did. We use exogenous variation in banks’ incentives to conform to the standards of the Community Reinvestment Act (CRA) around regulatory exam dates to trace out the effect of the CRA on lending activity. Our empirical strategy compares lending behavior of banks undergoing CRA exams within a given census tract in a given month to the behavior of banks operating in the same census tract-month that do not face these exams. We find that adherence to the act led to riskier lending by banks: in the six quarters surrounding the CRA exams lending is elevated on average by about 5 percent every quarter and loans in these quarters default by about 15 percent more often. These patterns are accentuated in CRA-eligible census tracts and are concentrated among large banks. The effects are strongest during the time period when the market for private securitization was booming.