The Boston Fed – a rich source of high quality research – has released a paper by Ryan Bubb and Alex Kaufman titled Securitization and Moral Hazard: Evidence from a Lender Cutoff Rule:
Credit score cutoff rules result in very similar potential borrowers being treated differently by mortgage lenders. Recent research has used variation induced by these rules to investigate the connection between securitization and lender moral hazard in the recent financial crisis. However, the conclusions of such research depend crucially on understanding the origin of these cutoff rules. We offer an equilibrium model in which cutoff rules are a rational response of lenders to perapplicant fixed costs in screening. We then demonstrate that our theory fits the data better than the main alternative theory already in the literature, which supposes cutoff rules are exogenously used by securitizers. Furthermore, we use our theory to interpret the cutoff rule evidence and conclude that mortgage securitizers were in fact aware of and attempted to mitigate the moral hazard problem posed by securitization.
I am astounded that cut-off rules exist, but they do and they are step functions:
One promising research strategy for addressing this question is to use variation in the behavior of market participants induced by credit score cutoff rules. Credit scores are used by lenders as a summary measure of default risk, with higher credit scores indicating lower default risk. Examination of histograms of mortgage loan borrower credit scores, such as Figure 1, reveal that they are step-wise functions.
Using step functions to evaluate differences in complex systems is suspicious at the very least. Any time you hear a portfolio manager talk about a “screen” for instance, you should ensure that the screen is very coarse, throwing out only the most ridiculous of potential investments. For proper, verifiable, assessments of single entitites in a complex universe – whether it is a universe of government bonds, preferred shares, common equity, or mortgage applicants – you need a coherent system of continuous smooth functions.
The only rationale I can think of for using step functions at all is suggested by the authors: lenders must make a decision regarding whether or not to incur costs to collect additional data to feed into (a presumably rational) evaluation system and incurring such a cost – whether it’s a single charge, or a member of a sequence of possible charges – is a binary decision, implying a stepwise preliminary evaluation. But anyway, back to the paper:
It appears that borrowers with credit scores above certain thresholds are treated differently than borrowers just below, even though potential borrowers on either side of the threshold are very similar. These histograms suggest using a regression discontinuity design to learn about the effects of the change in behavior of market participants at these thresholds. But how and why does lender behavior change at these thresholds? In this paper we attempt to distinguish between two explanations for credit score cutoff rules, each with divergent implications for what they tell us about the relationship between securitization and lender moral hazard.
We refer to the explanation currently most accepted in the literature as the securitizer-first theory. First put forth by Keys, Mukherjee, Seru, and Vig (2008) (hereafter, KMSV), it posits that secondary-market mortgage purchasers employ rules of thumb whereby they are exogenously more willing to purchase loans made to borrowers with credit scores just above some cutoff. This difference in the ease of securitization induces mortgage lenders to adopt weaker screening standards for loan applicants above this cutoff, since lenders know they will be less likely to keep these loans on their books. In industry parlance, they will have less “skin in the game.” Because lenders screen applicants more intensely below the cutoff than above, loans below the cutoff are fewer but of higher quality (that is, lower default rate) than loans above the cutoff. We call this the “securitizer-first” theory because securitizers are thought to exogenously adopt a purchase cutoff rule, which causes lenders to adopt a screening cutoff rule in response. Under the securitizer-first theory, finding discontinuities in the default rate and securitization rate at the same credit score cutoff is evidence that securitization led to moral hazard in lender screening.
We offer an alternative rational theory for credit score cutoff rules and refer to our theory as the lender-first theory. When lenders face a fixed per-applicant cost to acquire additional information about each prospective borrower, cutoff rules in screening arise endogenously. Under the natural assumption that the benefit to lenders of collecting additional information is greater for higher default risk applicants, lenders will only collect additional information about applicants whose credit scores are below some cutoff (and hence the benefit of investigating outweighs the fixed cost). This additional information allows lenders to screen out more high-risk loan applicants. The lender-first theory thus predicts that the number of loans made and their default rate will be discontinuously lower for borrowers with credit scores just below the endogenous cutoff.
Such a cutoff rule in screening also results in a discontinuity in the amount of private information lenders have about loans.
We investigate these two theories of credit score cutoff rules using loan-level data and find that the lender-first theory of cutoff rules is substantially more consistent with the evidence than is the securitizer-first theory. We focus our investigation on the cutoff rule at the FICO score of 620. We do this for two reasons: of all the apparent credit score cutoff thresholds, the discontinuity in frequency at 620 is the largest in log point terms; also, 620 is the focus of inquiry in previous research. After reviewing institutional evidence that lenders adopted a cutoff rule in screening at 620 for reasons unrelated to the probability of securitization, we use a loan-level dataset to show that in several key mortgage subsamples there are discontinuities in the lending rate and the default rate at 620, but no discontinuity in the securitization rate. Without a securitization rate discontinuity at the cutoff, the securitizer-first theory is difficult to reconcile with the data.
Having established that the lender-first theory is the more likely explanation for the cutoff rules, we then interpret the evidence in light of the theory. We find that in the jumbo market of large loans, in which only private securitizers participate, the securitization rate is lower just below the screening threshold of 620. This suggests that private securitizers were aware of the moral hazard problem posed by loan purchases and sought to mitigate it.
However, in the conforming (non-jumbo) market dominated by Fannie Mae and Freddie Mac (the government sponsored enterprises, or GSEs), there is a substantial jump in the default rate but no jump in the securitization rate at the 620 threshold. One explanation for this is that the GSEs were unaware of the threat of moral hazard. An arguably more plausible explanation is that, as large repeat players in the industry, the GSEs had alternative incentive instruments to police lender moral hazard.
The authors conclude:
Interpreting the cutoff rule evidence in light of the lender-first theory, our evidence suggests that private mortgage securitizers adjusted their loan purchases around the lender screening threshold in order to maintain lender incentives to screen. Though our findings suggest that securitizers were more rational with regards to moral hazard than previous research has judged, the extent to which securitization contributed to the subprime mortgage crisis is still an open and pressing research question.
[…] repeatedly warns about “cliff effects” in the securitization market; such cliff effects are a sign of incompetent analysis; prohibitions and special regimes bring about cliff effects by their […]