Searching for Approval

Abstract

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.

Keywords: Search, Screening, Mortgages, Credit Markets, Approval, Rejection, Fintech, AI, CRA

JEL Classification: G21, G5, G51, G53, L00