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Crowding Out Effects of Refinancing on New Purchase Mortgages

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Abstract

We present evidence that binding mortgage processing capacity constraints reduce mortgage originations to borrowers with low-to-modest credit scores. Mortgage processing capacity constraints typically bind when the demand for mortgage refinancing rises far above average levels, such as when mortgage interest rates drop to record low levels. As a result, high capacity utilization leads mortgage lenders to ration mortgage credit by focusing on mortgage applications that require less underwriting resources. This is hypothesized to have a particularly adverse impact on relatively higher credit-risk borrowers’ ability to obtain mortgages, particularly for purchasing borrowers with low-to-modest credit scores.

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Notes

  1. Hancock and Passmore (2015) show that Federal Reserve purchases reduced mortgage rates, while Stroebel and Taylor (2012) show that these purchases had little effect on mortgage rates. Fuster and Willen (2010) and Fuster et al. (2014), using data on the menu of mortgage rates and points available to borrowers, found substantial effects of the Federal Reserve’s asset purchase programs on MBS yields and mortgage rates.

  2. The credit scores referenced throughout this article are the so-called “FICO” scores that were developed by the FICO company (formerly Fair, Isaac and Co.). They have a range from 300 to 850, with a higher score indicating greater borrower creditworthiness.

  3. As the HMDA data are currently available only through the end of 2013, we use data from the Mortgage Bankers Association’s Weekly Applications Survey to estimate the total number of HMDA applications for October 2013–September 2014 and the total number of HMDA originations for January 2014–September 2014.

  4. The HMDA data cover 80–90 % of the mortgage market and are described in detail in Bhutta and Ringo (2014), among others.

  5. We partition the LPS/Black Knight data into GSE (Fannie Mae and Freddie Mac), Federal Housing Administration (FHA) and Veterans Affairs (VA), “private-label” (i.e., non-government-(or GSE)-insured securitized (jumbo, alt-A, and subprime mortgages), and bank portfolio loans and adjust for differential data coverage of these market segments.

  6. The real estate credit and mortgage and non-mortgage loan broker employment components that we use from BLS likely overstate the number of employees who solely process mortgage applications. We do not delineate between different types of mortgage employees (such as loan officer versus loan servicing), as these data do not separate among the types cleanly.

  7. Because the number of mortgages for refinancing is generally much larger than the number of purchase mortgages, particularly since 2009, excluding purchase applications from the numerator does not have a large effect on our capacity utilization measure; that is, measures that include and exclude purchase applications are highly correlated.

  8. In addition, lenders might have some ability to recoup higher marginal costs that are imposed by higher credit risk borrowers by charging higher mortgage rates, up-front points, or fees.

  9. In particular, Andersen et al. (2015) describe two types of households: (1) “woodheads” that refinance at a constant rate irrespective of refinance incentives and (2) “levelheads” that respond solely to refinancing incentives. As a refinancing wave progresses, the levelheads that exercise their refinancing option no longer have an incentive to refinance, i.e., those borrowers “burn out.”

  10. We de-mean the independent variables in these regressions in order to interpret the constant as the mean value of the 10th-percentile credit score over each period.

  11. The Purchase-Refinance results are provided for ease of interpretation, as the coefficients themselves are a linear combination of the Purchase and Refinance results.

  12. It bears noting, however, that these effects pale in comparison to the drastic pull-back in lending that followed the financial crisis, when 10th-percentile credit scores jumped 60–80 points—likely as a result of lenders’ and potential borrowers’ reevaluating their tolerance for risk.

  13. These data are from Optimal Blue and report mortgage and borrower characteristics for mortgage applications that have “locked in” on their mortgage rate.

  14. Indeed, as reported below, Chow tests for the hypothesis of no break in the coefficient estimates across the pre-crisis and post-crisis periods are strongly rejected.

  15. The large standard errors for the 620-or-lower credit-score group in the post-crisis period are a result of a large standard deviation in the month-to-month originations for this group during the post-crisis period. The standard deviation during the post-crisis period is 9 times larger than during the pre-crisis period.

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Acknowledgments

We thank Neil Bhutta, Robin Prager, Michael Palumbo, Lawrence White, conference participants at the 2014 Federal Reserve Bank of Chicago’s 50th Annual Conference on Bank Structure and Competition, and seminar participants at the Federal Reserve Board for helpful comments and suggestions. Brett McCully and Madura Watanagase provided excellent research assistance.

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Authors and Affiliations

  1. Board of Governors of the Federal Reserve System, 20th & Constitution Ave. NW, MS K1-144, Washington, DC, 20551, USA

    Steve A. Sharpe

  2. Board of Governors of the Federal Reserve System, 20th & Constitution Ave. NW, MS K1-149, Washington, DC, 20551, USA

    Shane M. Sherlund

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  1. Steve A. Sharpe
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Correspondence to Shane M. Sherlund.

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The analysis and conclusions contained herein are those of the authors and do not necessarily reflect the views of the Board of Governors of the Federal Reserve System, its members, or its staff.

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Sharpe, S.A., Sherlund, S.M. Crowding Out Effects of Refinancing on New Purchase Mortgages. Rev Ind Organ 48, 209–239 (2016). https://doi.org/10.1007/s11151-016-9500-9

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