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The Importance of Originator-Servicer Affiliation in Loan Renegotiation

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Abstract

This paper presents evidence that affiliation between the mortgage servicer and the originator provides a mechanism to reduce information frictions inherent in debt renegotiation. We find that originator-servicer affiliation increases the likelihood of modification by 10–23% using a large sample of delinquent securitized non-agency mortgages. Post-modification, affiliated loans are also 7.3% more likely to not return to severe delinquency within 12 months. Further examination reveals that affiliation affords servicers lower-cost access to borrower and loan information, thus improving their ability to implement effective debt restructuring strategies. In the absence of standardized information transmission between originators and servicers, information critical for debt renegotiation will be lost as banks disintegrate origination and servicing.

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Notes

  1. As pointed out in Eggert (2007), loan modification is labor intensive and time consuming, essentially equivalent to re-underwriting the mortgages. The cost of modification is rather expensive and the compensation structure of servicers does not cover these modification costs. When dealing with defaults, the servicer can recover foreclosure cost but not modification cost. In the recent foreclosure crisis, record default rates caused servicers to favor cost-cutting through automated foreclosure processes rather than risking incurring modification costs with a low likelihood of success.

  2. Debt renegotiation is typically called loan modification in mortgage markets. We will use these two terms interchangeably throughout this paper.

  3. Generally, the lender has several options when dealing with delinquent loans: forebear on the delinquency, modify the loan terms, allow a short sale (whereby borrower can sell the property at a price lower than the remaining loan balance), or foreclose on the property.

  4. Focusing only on securitized loans ensures that the cash flow rights of the mortgages are sold to mortgage backed security (MBS) investors regardless of whether the originator and the servicer are affiliated entities. Thus, any principal-agent issues between the MBS investors (the principal) and the servicers (the agent) should be independent of originator-servicer affiliation.

  5. To be clear, we do not argue that these two pieces of information are absolutely critical to the debt renegotiation decision of the servicer. Instead, our primary purpose is to provide evidence that affiliated servicers have more information available to them than their unaffiliated counterparts.

  6. Adelino et al. (2013) and Mayer et al. (2014) are notable exceptions.

  7. Eggert (2007) discusses several frictions preventing debt renegotiation for securitized mortgages, including agency issues related to the mortgage servicer, securitization contracts that limit servicer discretion, and conflicting interests between investors in different tranches of securitizations.

  8. EPD require loan originators to repurchase any securitized loans that become delinquent typically within 90 days of being securitized.

  9. These studies acknowledge that their small samples have limited statistical power because principal forbearance is very rare.

  10. Lewtan markets its services primarily to non-agency MBS investors for valuation purposes. The bulk of the loans in the ABSNet database were originated after 1997. In addition to ABSNet Loan, the company provides periodic collateral valuations it markets as ABSNet HomeVal.

  11. Lewtan monitors changes in loan terms monthly in a separate data file within ABSNet Loan. The company receives monthly loan modification updates directly from servicers and trustees, but it also independently checks modifications against changes in loan characteristics recorded in the monthly loan snapshots for confirmation. Unreported modifications identified by the company are then recorded and flagged as “implied modifications.” We classify multiple reports of modifications within the same month as one modification.

  12. We excluded loans originated before 2000 in order to focus on the last housing boom.

  13. To limit the potential for reporting errors, we dropped loans with a balance less than $25,000, loans with original property appraised values missing or less than $25,000, loans with original LTV below 25% or above 150%, loans with less than 60 months of remaining life at the end of 2007, loans with initial maturity of more than 600 months, missing borrower locations (CBSA), and observations with missing loan characteristics included in Eq. 1.

  14. In a bank failure, the mortgage servicing assets are typically transferred to another servicing company. This is analogous to a servicing firm being acquired.

  15. Current LTV\(=\frac {balance_{t}}{value_{t}}\), where balancet is the current outstanding balance of the mortgage and valuet is the updated value of the house after adjusting for changes in the FHFA MSA-level house price index.

  16. We thank an anonymous referee for suggesting this proxy for servicer distress.

  17. Time-varying covariates in \(\mathbf {Z}_{i}^{\prime }\) are measured at the last date before an “event.” In Eq. 1 the event is modification, so they are measured as of the month prior to modification. For loans that are not modified, they are calculated at the end of our sample period.

  18. Agarwal et al. (2012a) show that some servicers have the organizational structure that is capable to handle large-volume renegotiation cases, while others do not have this capability. We therefore add servicer fixed effects to control for servicer-specific factors that may affect renegotiation rates.

  19. This is calculated as the marginal effect divided by the 12-month mean modification rate in our sample 2.8%/12.1%.

  20. Adding originator fixed effects does not materially change our findings. To the contrary, it pushes the magnitude of the affiliation effect back to that of column 1. The effect of affiliation on the likelihood of loan modification also remains statistically significant when we cluster standard errors by year of loan origination, originator, or location (CBSA).

  21. An alternative estimation strategy to account for this possibility is to include loan originator-origination year fixed effects. Our results are not materially affected when we use this alternative specification. We thank an anonymous referee for this suggestion.

  22. Although the summary statistics reported in Table 3 do not suggest any economically discernible differences between the two groups of loans, we cannot rule out that they differ in characteristics unobservable to econometricians.

  23. We implement the matching using psmatch2 in Stata (Leuven and Sianesi 2018). The matching considerably reduces differences in characteristics between affiliated and unaffiliated loans in our PSM sample compared to the unmatched sample. However, following a common practice in the literature, we also include the matching variables in our treatment estimation model to control for remaining minor differences in characteristics between affiliated and unaffiliated loans.

  24. This is calculated as 0.025/0.040 = 63%.

  25. Since the redefault decision is under the purview of the borrower, rather than the mortgage servicer, we exclude the servicer distress proxy in this regression. However, the results are materially unchanged when we include the servicer distress proxy.

  26. This is 1.95% divided by 1 minus the average 12-month redefault rate of 73.2%.

  27. Bourgeon and Dionne (2013) theoretically examine some of these same issues to explain bank refusal to renegotiate debt.

  28. In fact, in a recent contribution (Goodstein et al. 2017) find evidence for a contagion effect in strategic mortgage defaults.

  29. Available at http://bordencommunications.com/Loan%20Boarding%20at%20FCMC.pdf.

  30. Within servicer variation in boarded information is likely if the servicer works with multiple loan originators. Technically, we do not fully observe the information available to servicers; we have access only to information reported by the servicers to the MBS trustees.

  31. A growing body of literature examines the role of fraud in the recent financial crisis (Jiang et al. 2014, Agarwal et al. 2015, Ambrose et al. 2016, Ben-David 2011, and Mian and Sufi 2015). Our information hypothesis, however, does not depend on fraudulent behavior by the originator.

  32. LTV and CLTV at origination are included as control variables in column (1) of Panel B in Table 9. Columns (2) through (4) do not include LTV and CLTV as controls because the dependent variable of interest in each of those columns is created based on these measures.

  33. To further verify if the missing DTIs are likely due to data aggregation problems by ABSNet, we checked if DTI information is available in the prospectuses (we thank an anonymous referee for this suggestion). We collected the prospectuses (and Free Writing Prospectuses, if any) of a random sample of 190 affiliated and unaffiliated deals. We find that for the majority of these deals (67%), there is absolutely no information on DTI in the prospectus. In addition, consistent with our information hypothesis, loan-level DTI information was available for a smaller share of the unaffiliated deals compared with affiliated deals (14% versus 19%).

  34. In fact, the “hardness” of information is probably best thought of as a continuum (Petersen 2004).

  35. In Table 10 we combined low-doc and no-doc loans into one group. But the vast majority of these loans are low-doc (Table 3).

  36. The Secure and Fair Enforcement for Mortgage Licensing Act of 2008 (SAFE Act) and the “Ability to Repay” rule are examples.

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Acknowledgements

We thank Brent Ambrose, Itzak Ben-David, Scott Frame, Ben Keys, Michael LaCour-Little, Adam Levitin, C.F. Sirmans, an anonymous referee, and participants at the 2016 American Real Estate and Urban Economics Association National meeting for helpful comments. We also thank Dennis McWeeny and James Stevens for outstanding research assistance. All errors and omissions are our own. Funding support for the data used in this study was from University of Wisconsin-Madison Graduate School. Conklin received research support from the University of Georgia’s Terry-Sanford research award.

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Authors and Affiliations

  1. Terry School of Business, University of Georgia, Athens, USA

    James N. Conklin

  2. Wisconsin School of Business, University of Wisconsin-Madison, Madison, USA

    Moussa Diop

  3. J. Mack Robinson College of Business, Georgia State University, Atlanta, USA

    Thao Le

  4. Old Dominion University, Norfolk, USA

    Walter D’Lima

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  1. James N. Conklin
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Correspondence to James N. Conklin.

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Conklin, J.N., Diop, M., Le, T. et al. The Importance of Originator-Servicer Affiliation in Loan Renegotiation. J Real Estate Finan Econ 59, 56–89 (2019). https://doi.org/10.1007/s11146-018-9671-2

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