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The Journal of Real Estate Finance and Economics

Erschienen in:

19.07.2022

The Effect of Regulatory Oversight on Nonbank Mortgage Subsidiaries

verfasst von: Eliana Balla, Raymond Brastow, Daniel Edgel, Morgan Rose

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 3/2024

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Abstract

In 2009, the Federal Reserve subjected nonbank mortgage-originating subsidiaries of bank holding companies (BHCs), but not independent nonbank (INB) mortgage originators, to consumer compliance supervision. We examine the effects of this regulatory change on the pricing and performance of nonbank originations using a sample of conventional, first-lien, amortizing mortgages originated between 2000 and 2015. We find that subsidiary nonbank (SNB) loans, which had a higher probability of default than INB mortgages prior to the policy change, had a lower probability of default following the change. In addition, we identify small but statistically significant decreases in loan interest rates and loan-to-value ratios for SNB mortgages relative to INB mortgages. When we split our sample into prime and subprime mortgages, we find those effects hold for prime mortgages. For subprime mortgages, after the policy change SNB originations had higher interest rates and lower LTV ratios than INB mortgages, with only weakly significant differences in probabilities of default. The findings are robust to several potential confounding effects, including those due to firm entries and exits. Our findings are consistent with BHCs reducing risk shifting in mortgage lending across subsidiaries following their heightened regulatory scrutiny.

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Federal Reserve Board Consumer Affairs Letter CA 09–8 (September 14, 2009). Consumer compliance is the adherence to all applicable laws that regulate the treatment of consumers in lending, collections, disclosures, and reporting. Regulators examine institutions for compliance with consumer protection provisions in several laws, including the Fair Housing Act, the Equal Credit Opportunity Act, the Home Mortgage Disclosure Act (HMDA), and the Community Reinvestment Act. Additionally, consumer compliance examinations monitor banks for unfair or deceptive practices and any practices that have the potential to harm consumers. We provide details about the 2009 consumer compliance policy change and the history of subsidiary nonbank supervision in the following section.

The online appendix is available at https://economics.umbc.edu/files/2021/04/Appendices_nonbank_mortgage_subsidiaries.docx

A summary of additional federal and state mortgage regulations enacted or proposed during our sample period is presented in Table B1 of the online appendix. Brief descriptions of the effect of each regulation and, where available, a citation of recent research analyzing the effect are contained in the table.

Demyanyk and Loutskina control for these varying loan and product types using indicator variables, but indicator variables are unlikely to be sufficient to capture performance and pricing differences. For example, specifications of performance regressions of adjustable-rate mortgages should account for changes in mortgage payment amounts as well as the specific timings of those changes (e.g., 2/28 versus 3/27, six-month versus twelve-month adjustments), none of which are relevant for fixed-rate mortgages.

Controlling for such loan characteristics may resolve the seeming contradiction between Demyanyk and Loutskina (2016), who find that bank originations have lower probabilities of default than nonbank originations, and Keys et al. (2009), who find that bank originations have higher probabilities of default than nonbank originations among mortgages in the same securitization pool. Mortgages within a securitization pool tend to have similar characteristics in order to appeal to investors seeking particular risk, return, and maturity profiles, such that Keys et al. (2009) may in effect control for loan characteristics in a way that Demyanyk and Loutskina (2016) do not.

Federal Reserve Board Joint Press Release, July 17, 2007: “Federal and State Agencies Announce Pilot Project to Improve Supervision of Subprime Mortgage Lenders”

Downs and Shi (2015) state that they derive similar results using either 2008 or 2009 as the year of the policy change.

The Federal Reserve is the primary supervisor for BHCs and for state-chartered banks that are members of the Federal Reserve System. The Office of the Comptroller of the Currency (OCC) is the primary supervisor for nationally chartered banks. The Office of Thrift Supervision (OTS) was the primary supervisor for thrifts until the OTS merged into the OCC in 2011. The Federal Deposit Insurance Corporation (FDIC) is the primary supervisor of state-chartered banks that are not members of the Federal Reserve System.

Public Law 84–511, Section 5(c). Title 12 of the US Code, Section 1813(q)(F) designates the Federal Reserve Board as the “appropriate federal banking agency” of “any bank holding company and any subsidiary (other than a depository institution) of a bank holding company;” Section 1844(c)(2)(A)(ii) states that “the Board may make examinations of a bank holding company and each subsidiary of a bank holding company in order to… monitor the compliance of the bank holding company and the subsidiary with” federal laws under the Federal Reserve’s jurisdiction (emphasis added).

Federal Reserve Board Consumer Affairs Letter CA 98–1.

Demyanyk and Loutskina (2016), page 334.

Despite examinations of the relevant statutes and regulations, and inquiries with OCC staff, we were unable to ascertain with certainty whether the OCC increased its consumer compliance scrutiny of the SNBs of nationally chartered banks (most of which operate within BHC structures) concurrently with the Federal Reserve in September 2009. If the OCC did not, and if the 2009 Federal Reserve policy change is a driver of our results, then our results are conservatively biased against finding differences between how SNB versus INB originations changed following the financial crisis.

Public Law 111–203, Section 605(a).

In the robustness checks described in Robustness Checks, we repeat our analyses after dropping 2000–2002 originations from the sample and find no substantive changes to our main results.

An alternative to our matching and verification process for identifying SNBs and INBs is to use data fields available in HMDA. Due to requirements from the proprietary data vendors, we are contractually unable to merge HMDA data with our other data sources. In addition, given that institutions must meet a specified size threshold to be captured by HMDA, we would lose mortgages originated by the smallest institutions, which comprise a large share of our INB sample.

We are able to identify lender types in the HMDA data by matching the HMDA respondent IDs to those in the lender panel that was created and generously shared by Robert Avery of the FHFA.

Figures 2–4 are based on all first-lien, for-purchase originations in the Black Knight McDash dataset. Using Black Knight McDash allows us to show data from the start of our sample period, rather than starting with 2004 as in Fig. 1a and b. Ownership structure cannot be identified using the Black Knight McDash data alone, so we do not break these figures out between banks, INBs, and SNBs as in Fig. 1a and b. Due to the time-intensive nature of the identification process, we only classify our Maryland and Virginia sample loans by ownership structure.

Our dataset for this paper includes only conventional mortgages because the interest rate/LTV ratio trade-off is quite different for federally-insured mortgages, which often do not require substantial downpayments. In untabulated regressions we performed our main analyses on FHA and VA loans. Those results indicate that following the 2009 policy action, the SNB probabilities of both default and prepayment fell, SNB interest rates rose (by fewer than ten basis points), and SNB LTV ratios fell, relative to INB values. All of those findings are statistically significant.

According to HMDA, the SNB share of ARMs originated by nonbanks is slightly higher than that of FRMs in the 2000–2015 period (46% vs. 44%) and more notably higher in the 2009–2015 period (50% vs. 37%). Most of this difference, however, appears to be due to INBs decreasing their ARM originations more than either banks or SNBs in the post-policy period. The SNB share of mortgages originated by either banks or nonbanks was 32% for both ARMs and FRMs in the 2000–2015 period and 29% vs 26%, respectively, in 2009–2015. Total ARM originations by banks and nonbanks in Maryland and Virginia in the HMDA sample drop from 9,948 per year in 2000–2008 to 2,297 per year in 2009–2015.

During our sample period, banks and BHCs often purchased independent nonbanks. A nonbank therefore could switch between being an INB and a SNB. The NIC tables provide the exact dates during which a given originator was owned by another entity, allowing us to determine whether that lender was an SNB or an INB when a particular loan was originated.

www.nmlsconsumeraccess.org

To honor the requirements from the proprietary data vendors, after the lender classification process, the lenders were anonymized so that we cannot link sample loans to individual originators. We only know whether the originator of a given loan is an INB, a SNB whose direct parent company is a depository institution (which may or may not be within a BHC structure), or a SNB whose direct parent company is a BHC. We thank Ross Podbielski and Cooper Killen for assistance assembling and anonymizing the sample.

In the robustness checks described in Robustness Checks, we repeat our analyses after dropping the approximately six percent of sample loans that exit the sample due to a servicer transfer. We also repeat our analyses with control variables for the owner of each loan in a given month, and on only loans that were owned by a government-sponsored enterprise at some point in their performance history. In each case, the results were consistent with our main results.

In Determination of treatment date, we discuss the rationale for using this date to represent our policy date.

For examples, see Clapp et al., 2006, Pennington-Cross and Ho (2010), Rose (2012, 2013).

As a robustness check, in untabulated regressions we performed our main performance and pricing analyses defining FICO score in buckets as given in Fig. 2a and b, instead of as a continuous variable. Our results are substantively unchanged.

We use state-level house price indices for HPI to avoid correlation with CLTV, which uses county-level indices as described above.

As a robustness check, in untabulated regressions we performed our main performance analysis using the natural log of LoanAge instead of LoanAge and its square. Our results are substantively unchanged, with the exception that RefiPenalty becomes positively associated with the probability of default.

In untabulated regressions we performed our main performance analysis with standard errors clustered by lender instead of by loan. Subsidiary is no longer statistically significant in the default equation, but beyond that our results in both equations hold.

Each mortgage property’s 2000 and 2010 census tracts are identified using the geographic coordinates provided in the CoreLogic Solutions data and census tract shapefiles for Maryland and Virginia, which are publicly available on the U.S. Census Bureau website. For originations from 2007 to 2015, age and house value distribution data are taken from the five-year American Community Survey (ACS), based on the midpoint of the five-year range. For example, a 2009 origination is assigned census tract data based on the 2007–2011 ACS. The earliest ACS five-year data range is 2005–2009. For 2000 originations, we used data from the 2000 decennial census. For 2001–2006 originations, we used an interpolation between the 2000 decennial census data and the 2005–2009 ACS data.

For Figs. 6 and 7, we map the vintage year indicators such that all vintage years end on September 14. For example, we assign a loan originated on October 1, 2015 to vintage year 2016. Our sample includes loans originated through the end of calendar year 2015, so our vintage year 2016 only contains three months of originations. This explains the wide confidence intervals for vintage year 2016 in Figs. 6 and 7.

In Figures A1 through A4 of the online appendix to this paper, we show the results of the same analyses in Figs. 6 and 7 but separated into prime and subprime loans. The results for prime loans do not meaningfully differ from those for full sample. In the subprime loan results, the difference in the probability of default becomes significantly negative slightly later than in Fig. 6, and the difference in the probability of prepayment is significantly positive for part of the post-policy period.

The percentage change in the probability of default or prepayment, relative to the probability of remaining current, associated with a one-unit change in a given variable, is calculated as e^(coefficient estimate) – 1. For example, the coefficient estimate of 0.0692 for Subsidiary in model 3 of Table 4 indicates an increase in the probability of default, relative to the probability of remaining current, of e^(0.0692) – 1 = 0.0717 or 7.17 percent.

A caveat to our results is that we do not control for the presence of prepayment penalty provisions. This is most relevant for our results using the subprime subsample, because prepayment penalties are much more common in subprime mortgages than prime mortgages, especially in the pre-policy period. The Black Knight McDash data includes a flag for prepayment penalties, but it is too sparsely populated for nonbank originations to be included in our analyses.

The negative effect of house price appreciation on the probability of prepayment may be due to the dramatic and simultaneous movement of both house prices and unemployment rates during the financial crisis. There may also be asymmetric effects from negative versus positive changes to these variables on the probability of prepayment, with heterogeneous impacts on different borrowers. See the results for subprime mortgages below.

The joint effect of a post-policy SNB mortgage (–0.0293 + 0.0751 + 0.0264 = 0.0722) is slightly lower than the effect of a post-policy INB mortgage (0.0751), but the difference is not statistically significant.

See Tables A1 and A2 of the online appendix.

In Table 7, the difference between the joint effect on the probability of default of a post-policy SNB subprime mortgage (–0.0494 – 0.438 – 0.177 = –0.6644) and the effect of a post-policy INB subprime mortgage (–0.438) is –0.2264.

In the online appendix, we show results from repeating the analysis in Table 5 (and related tests in Tables A1 and A2) using our prime mortgage subsample (Tables A5-A7) and our subprime mortgage sample (Tables A8-A10). The results for prime mortgages generally match those form Table 5. For subprime mortgages, Post and its interaction with Subsidiary are sometimes negatively related to the probability of default, and the post-policy probability of default is significantly lower for SNBs than for INBs only in Table A10. The results for the probability of prepayment for subprime mortgages generally match those in Table 7.

This result is potentially consistent with Agarwal et al. (2016), who present evidence from mortgages originated in 1998–2006 that lenders within a BHC structure steered certain mortgage applicants to affiliated lenders within the same BHC that offered the applicants mortgages with less favorable terms for the applicants, including higher interest rates. Agarwal et al. (2016) do not distinguish between bank and nonbank lenders, but it plausible that some banks steered borrowers to affiliated, less-regulated subsidiary nonbank lenders, at least prior to the Federal Reserve subjecting the SNBs to closer consumer compliance supervision.

This is consistent with the first two sets of columns in Table 3, which show statistically significant but economically small differences in loan characteristics between SNB and INB originations, both before and after the policy change.

These potential issues with the LTV equation results for our subprime mortgage sample reinforce our decision, described in Pricing Analysis Methodology, to use the equation-by-equation. 2SLS analysis, which confines misspecification problems to one equation, rather than a simultaneous equation model in which misspecification in one equation would affect the results of both equations.

In Tables A3 and A4 of the online appendix, we restrict the sample to lenders that were active in both the pre- and post-policy periods, as we did for the performance regressions in Tables A1 and A2. The pre-policy difference is no longer significant, suggesting that the 2009 policy change had no meaningful differential effect on initial interest rates based on originator type.

Tables A15 and A16 of the online appendix, we repeat the robustness checks presented in Table 11 using subsamples of prime-only and subprime-only loans, respectively. The prime results in Table A15 closely mirror the results presented in Table 11. The subprime results are generally consistent with the main subprime results in Tables 7 and 10, with some variations in statistical significances. The signs and significances of the post-policy differences in all four dependent variables are consistent in nearly all of the models in Table A16.

See footnote 40.

Agarwal, S., Amromin, G, Ben-David, I. & Evanoff, D. (2016). Loan product steering in mortgage markets. National Bureau of Economic Research, w22696.

Berndt, A., Hollifield, B., & Sandas, P. (2010). The role of mortgage brokers in the subprime crisis. National Bureau of Economic Research, w16175.

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Titel: The Effect of Regulatory Oversight on Nonbank Mortgage Subsidiaries
verfasst von: Eliana Balla
Raymond Brastow
Daniel Edgel
Morgan Rose
Publikationsdatum: 19.07.2022
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 3/2024
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-022-09906-z

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