Abstract
We find that private-securitized loans perform worse than observably similar, nonsecuritized loans, which provides evidence for adverse selection. The effect of securitization is strongest for prime mortgages, which have not been studied widely in the previous literature and, in particular, prime adjustable-rate mortgages (ARMs): These become delinquent at a 30 % higher rate when privately securitized. By contrast, our baseline estimates for subprime mortgages show that private-securitized loans default at lower rates. We demonstrate, however, that “early defaulting loans” account for this: those that were so risky that they defaulted before they could be securitized.
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Notes
Source: Inside Mortgage Finance
Another reason why portfolio and securitized loans may perform differently is monitoring. This is discussed further below.
We do control for the interest rates on the individual loans.
See also Calem et al. (2010).
For example, 7.4 million first mortgage originations were recorded in LPS in 2005, compared with 10.5 million in the Home Mortgage Disclosure Act (HMDA) data, and 6.4 million in 2006, compared with 8.6 million in HMDA.
In particular, approximately 25 % entered the database more than twelve months following origination, and 15 % were either non-owner-occupied properties, or had an unknown occupancy type. The restrictions on the initial fixed period for ARMs eliminated 10 % of subprime ARMs and 1/3 of prime ARMs.
The government-sponsored enterprises (GSEs) are restricted to guaranteeing loans with balances no higher than the conforming loan limit. Such loans are termed “conforming;” loans with balances above this are known as jumbo loans. In 2005, the conforming loan limit for single-family homes was $359,650, and in 2006, it was $417,000.
As in Gross and Souleles (2002), we use a fifth-order polynomial in loan age to model the associated hazard function. We also include state, quarter, and origination quarter dummy variables. In a previous version of this paper, we obtained similar baseline results with a Cox proportional hazard model.
We use the Mortgage Bankers Association (MBA) definition of delinquency: A loan increases its delinquency status if a monthly payment is not received by the end of the day immediately preceding the loan’s next payment due date.
Many papers have studied the effect of these state laws on foreclosure outcomes; for example, Ghent and Kudlyak (2011) use the LPS data to address laws that restrict deficiency judgments.
But see Foote et al. (2009) for an opposing view.
The investor type is even more likely to change in later stages of default.
This definition is also used by Bubb and Kaufman (2014). In an earlier version of this paper, we considered a different definition of the investor type and obtained nearly identical estimation results.
Our procedure is similar to that described in Haughwout, Mayer, and Tracy (2009). Mortgages were matched based on the zip code of the property, the date when the mortgage originated (within 5 days), the origination amount (within $500), the purpose of the loan (purchase, refinance, or other), the type of loan (conventional, VA guaranteed, FHA guaranteed, or other), occupancy type (owner-occupied or nonowner-occupied), and lien status (first lien or other). The match rate was approximately 50 %.
The anonymity is due to restrictions imposed by the data provider.
In particular, the variables used in this first stage are interest rate, FICO score, loan source, initial LTV, refinancing, property type, loan size, documentation type, interest-only flag, and origination year. We keep only those matched pairs with a propensity score above 0.5. We drop FHA loans and conduct the analysis separately for GSE-securitized and private-securitized loans.
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Acknowledgments
The author thanks Mitchell Berlin, Philip Bond, Paul Calem, Larry Cordell, Scott Frame, Will Goetzmann, Robert Hunt, David Musto, Leonard Nakamura, Richard Rosen, Amit Seru, Anthony Sanders, Nicholas Souleles, and Paul Willen, as well as participants at the Wharton Macro Finance Lunch, the FDIC Mortgage Default Symposium, the Yale Financial Crisis Conference, the Mid-Atlantic Research Conference, Ben-Gurion University, Tel-Aviv University, and the Conference on Enhancing Prudential Standards in Financial Regulations. I am particularly indebted to Mathan Glezer, Bob O’Loughlin, and Ted Wiles for outstanding research support.
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The views expressed in this paper are those of the author and do not necessarily represent policies or positions of the Federal Reserve Bank of Philadelphia or the Federal Reserve System.
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Elul, R. Securitization and Mortgage Default. J Financ Serv Res 49, 281–309 (2016). https://doi.org/10.1007/s10693-015-0220-3
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DOI: https://doi.org/10.1007/s10693-015-0220-3