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The Economic Effects of Legal Restrictions on High-Cost Mortgages

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We analyze the effects of state predatory mortgage lending laws, which have been a model for recent changes in the United States federal legislation enacted to regulate the mortgage contract terms common in higher-risk mortgage market segments. Using the Rothschild-Stiglitz approach to model credit markets under asymmetric information, legal restrictions are shown to reduce the use and attractiveness of mortgage credit. Consistent with model predictions, empirical results indicate that originations of regulated high-cost mortgages were significantly less than predicted in states with more restrictive laws. The differences between predicted and actual originations of high-cost mortgages in states with less restrictive laws were not significant. These differences were also not significant for non-high-cost originations across all states. Thus, credit regulation was differentially associated with reduction in originations of high-cost mortgages, and non-high-cost lending did not consistently expand in areas where high-cost mortgages were restricted.

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  1. For a comprehensive analysis of Mortgage Related Provisions of the Dodd-Frank Wall Street Reform and Consumer Protection Act, see Mortgage Bankers Association (2010).

  2. This paper is only concerned with the effects of high-cost mortgage regulation. Sorting out the relative effect of high-cost mortgage regulation as opposed to other events causing the financial crisis of 2007-2010, such as the collapse of yield spread premiums, the secondary market and substantial numbers of both subprime lenders and secondary market investors is beyond the scope of this analysis.

  3. Ho and Pennington-Cross (2005) consider loans originated by lenders identified as subprime on the HUD list as subprime. Bostic et al. (2008) consider HMDA-reportable high-cost loans as subprime. Ho and Pennington Cross (2006a, b) and Bostic et al. (2008) limit observations to metropolitan areas that cover different states. These studies also consider effects of the state laws on interest rates using a different database.

  4. Before the Dodd-Frank Act became effective, in July 2008, the U.S. Board of Governors of the Federal Reserve System introduced similar restrictions on high-cost loans by amending HOEPA provisions. The amendments required lenders to verify borrowers’ ability to repay a loan, set more extensive minimal documentation requirements, and banned prepayment penalties on higher-priced loans.

  5. For a comprehensive summary of state predatory lending legislation affecting mortgage loan terms, see Ho and Pennington-Cross (2005).

  6. Ho and Pennington-Cross (2006b, p.211) argue that introducing predatory lending laws can be interpreted as a “natural experiment with well-defined control and treatment groups, […] because state boundaries reflect political and not economic regions.”

  7. See e.g. Ho and Pennington-Cross (2006c), Danis and Pennington-Cross (2008), and Bhardwaj and Sengupta (2008).

  8. Asymmetric information models have been applied to the analysis of contract features restricting prepayment (Dunn and Spatt 1985; Chari and Jagannathan 1989; Yang 1992; Brueckner 1994; LeRoy 1996; Stanton and Wallace 1998). However, these models focused on aspects such as borrowers’ mobility and interest rate reductions affecting prepayment in the prime market.

  9. As house prices continued rising rapidly through the early- 2000s, borrowers with relatively high credit scores became more common in the subprime. These borrowers were subprime (or Alt-A) because they were seeking high LTVs and could only service the debt with an ARM (Corbae and Quintin 2011). Nevertheless, lower credit-score borrowers, FRMs, and loans without prepayment penalties retained a significant, albeit smaller share of the subprime market toward the end of the house price boom (Chomsisengphet and Pennington-Cross 2006; Gerardi et al. 2008).

  10. This framework was also applied in mortgage market models by Yezer et al. (1994).

  11. Although the model only considers the effect of restrictions on prepayment penalties, it has broader applicability. Other provisions of subprime laws such as negative amortization, balloon payments, and hybrid teaser rates may also reflect lender reactions to adverse selection based on the borrowers’ private information (e.g., about tenure in house or ability to prepay). Therefore, the model’s predictions may generalize to other features of state predatory lending laws, which tend to restrict these provisions.

  12. Loan to value ratio thus is equal to the normalized size of the loan.

  13. Courchane et al. (2004) provide evidence consistent with our model’s hypothesis that many subprime borrowers are subsequently able to qualify for lower risk loans. Using data on mortgage transactions compiled from public records, they found that 40 percent of borrowers whose previous mortgage was subprime (defined as originated by a lender designated as subprime in the HUD subprime lender list) currently had a prime mortgage.

  14. It will be seen later in the text that the default option is never exercised in this model.

  15. See Kau and Keenan (1995) for details.

  16. See Steinbuks (2008) to verify that prepayment condition (4) is optimal.

  17. The borrower is thus left with positive equity and the loan is repaid. If the value of the collateral is predetermined, this can be a plausible outcome, given the relatively poor credit history of borrowers, the loan to value ratio in the subprime market is typically lower than on the prime market. For details, see Calomiris and Mason (1998).

  18. This assumption is necessary to ensure that the model is analytically solvable and empirically tractable without the use of complicated option pricing techniques. The main results of the model will not be changed if this assumption is relaxed and the housing prices are allowed to fall. Deng et al. (2000) have found empirically that competing risks of prepayment and default in the mortgage market are correlated. Thus, the borrowers with lower prepayment costs will be more likely to end up with negative equity and default if housing prices are falling. The resulting equilibria in this scenario are similar to those discussed below, as illustrated in Brueckner’s (2000) model.

  19. Y may also capture the utility the household gets from consuming the housing services.

  20. This cost is smaller if the loan is subject to a prepayment penalty. However, as it will become clear later, loans carrying prepayment penalties are never prepaid in this model.

  21. These costs are the same for the lender regardless of whether prepayment occurs or not, so they have no effect on subsequent results.

  22. See Steinbuks (2008) for a formal proof.

  23. This assumption is necessary for the model to be well behaved.

  24. Recalling that both indifference and zero-profit curves are convex, a tangency will be optimal only if the zero-profit curve is ”more convex” than the indifference curves, e.g. \(\left \vert \frac {\partial ^{2}U}{\partial B_{1}^{2}}\right \vert <\left \vert \frac {\partial ^{2}\pi }{\partial B_{1}^{2} }\right \vert .\)

  25. The inequality \(C^{B}>\) \(C^{A}\) represents the differences between type A and type B borrowers in the cost of improving their credit history and in their ability to qualify for low cost refinancing.

  26. Elliehausen et al. (2008) found that borrowers’ choice of prepayment penalty can be predicted on the basis of variables hypothesized to affect their propensity to prepay.

  27. We tested this hypothesis on loan-level data from the FSRP database by running a regression of the amount of points and fees on the dummy variable for loans with a prepayment penalty, the dummy variable for states regulating prepayment penalties, the interaction of these two terms, and the time fixed effects. We found that on average, subprime borrowers could achieve a 2.5 percent reduction in points by accepting the prepayment penalty. We also found that the trade-off between points and prepayment penalties was about 10 times smaller in the states regulating prepayment penalties.

  28. Trans Union’s TrenData database provides quarterly county-level data on credit use and payment performance, based on information from a series of large random samples of U.S. consumer credit histories.

  29. See Harvey and Nigro (2004), Elliehausen and Staten (2004), Calem et al. (2004), and Ho and Pennington-Cross (2006a) for further discussion of explanatory variables.

  30. For a discussion of event study methodology, see MacKinlay (1997).

  31. For a more detailed description of the FSRP dataset, see Elliehausen and Staten (2001).

  32. Because the errors for high-cost and non-high-cost originations are likely to be correlated, we also estimated seemingly unrelated regression (SUR) representation of (12). The results from SUR were not statistically different from those reported here.

  33. Estimated equations explained 55 percent of the variation in all originations, 20 percent of the variation in high-cost originations, and 60 percent of the variation in non-high-cost originations.


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The authors are indebted to Anthony Yezer for his invaluable contribution. They would like to thank Kenneth Brevoort, Jan Bruekner, Mike Staten, George Wallace, the anonymous reviewer, members of FSRP advisory board, and seminar participants at the Bank of England, Bocconi University, the George Washington University, Miami University, and the University of Guelph for helpful comments. The views expressed in this paper are those of the authors and do not represent the views of the Board of Governors or its staff. All remaining errors are ours.

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Correspondence to Jevgenijs Steinbuks.

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Steinbuks, J., Elliehausen, G. The Economic Effects of Legal Restrictions on High-Cost Mortgages. J Real Estate Finan Econ 49, 47–72 (2014).

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