Amit Seru

Steven and Roberta Denning Professor of Finance
Senior Fellow, Hoover Institution
Senior Fellow, Stanford Institute for Economic Policy Research

Recent Research

(with G Buchak, G Matvos and T Piskorski), 2020 (R&R, Journal of Political Economy)

Bank balance sheet lending is commonly viewed as the predominant form of lending. We document and study two margins of adjustment that are usually absent from this view using microdata in the $10 trillion U.S. residential mortgage market. We first document the limits of the shadow bank substitution margin: shadow banks substitute for traditional—deposit-taking—banks in loans which are easily sold, but are limited from activities requiring on-balance-sheet financing. We then document the balance sheet retention margin: banks switch between traditional balance sheet lending and selling loans based on their balance sheet strength, behaving more like shadow banks following negative shocks. Motivated by this evidence, we build and estimate a workhorse structural model of the financial intermediation sector. Banks and shadow banks compete for borrowers. Banks face regulatory constraints but benefit from the ability to engage in balance sheet lending. Critically, departing from prior literature, banks can also choose to access the securitization market like shadow banks. To evaluate distributional consequences, we model a rich demand system with income and house price differences across borrowers. The model is identified using spatial pricing policies of government-sponsored entities and bunching at the regulatory threshold. We study the quantitative consequences of several policies on lending volume and pricing, bank stability, and the distribution of consumer surplus across rich and poor households. Both margins we identify significantly shape policy responses, accounting for more than $500 billion in lending volume across counterfactuals. Secondary market disruptions such as quantitative easing have significantly larger impacts on lending and redistribution than capital requirement changes once we account for these margins. We conclude that a regulatory policy analysis of the intermediation sector must incorporate the intricate industrial organization of the credit market and the equilibrium interaction of banks and shadow banks.

(with S Agarwal, J Grigsby, A Hortacsu, G Matvos and V Yao), 2020. (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), 2020. (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 G Buchak, G Matvos and T Piskorski), 2020

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), 2020

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.

Selected Data