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The market for private student loans: an analysis of credit union exposure, risk, and returns

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Abstract

Beginning in 2011, credit unions in the United States have been required to report in their quarterly call reports their holdings of private student loans. Since this time, private student loans have been the fastest growing loan product among credit unions. The empirical results here indicate credit unions respond to external market forces and internal exposure to interest rate risk in their decision to hold private student loans. The effect of which, to date, has led to lower returns on their assets and no effect on overall risk. Credit unions looking to diversify their loan portfolio should do so with caution. Private student loans being in deferral reduce both delinquency and charge-off rates, which will rise over time with their seasoning and as interest rates rise.

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Notes

  1. Sheila Bair, former FDIC Chairman (2006–2011), has been vocal in this comparison, which is noteworthy given she warned in a September 2006 public speech of issues with relaxed underwriting standards in non-traditional loan products (see Nasiripour 2016).

  2. As of 2011 there were 510 credit unions with PSL in their loan portfolio. Fourteen credit unions had PSL concentrations greater than 10% of loans, with the largest concentration at 34%.

  3. Commercial banks are less dependent than are credit unions on traditional intermediation activities. The mean ratio of non-interest income to operating income for credit unions is 11.68% (Goddard et al. 2008) over the period 1993–2004, whereas for commercial banks DeYoung and Rice (2004) show the ratio increases from 24.6% in 1984 to 42.2% in 2001.

  4. Returns and risk adjusted returns rise with the increase in the share of non-interest income to income up to 30% and then risk and return begin to decrease. The average assets of the international banks in Gambacorta et al. (2014) sample is 439 billion, compared to 57 million dollars for the credit unions in Goddard et al. (2008).

  5. Private student loans originated in academic year 1995–1996 were 1.7 billion dollars and in 2005–2006 were 19 billion. (College Board 2014). Between 1990 and 2010 the real price of college (tuition, fee, and room and board) at a 4-year public institution grew by 83%, whereas real median household income grew by 1.8% over the entire period. Calculations using Digest of Education Statistics 2013 and Federal Reserve data.

  6. In academic year 2005–2006 67 billion dollars of loans were made under the federal Stafford and Parent Plus loan programs (College Board 2014). These were loans originated by depository institutions, yet guaranteed by the federal government.

  7. The CFPB (2012) report Private Student Loans was mandated pursuant to Section 1077 of The Dodd–Frank Wall Street Reform and Consumer Protection Act. The sample of loan level PSL data was obtained from nine large lenders, which did not include any credit unions.

  8. The r-squared from regressing the inverse Mills ratio on our covariates is 0.9977. The corresponding variance inflation factor for the inverse Mills ratio is 435, which indicates (Madden 2008) problems with a selection model. These estimates are available upon request.

  9. Another alternative would be to use a zero inflated model, which is another two part model. In the second part a beta distribution is used in place of the generalized linear model used here. Readers may be more familiar with other two part models, such as the bivariate probit model (Cotei and Farhat 2011) which has a binary outcome in each stage.

  10. QMLE are found using Stata’s GLM command with the logit link and binomial family specified, in addition to heteroscedasticity robust standard errors.

  11. One could use different controls for each if there were strong reasons a priori to suggest why there might be factors that only influence one or the other. Ramalho and da Silva (2009) and Oberhofer and Pfaffermayr (2012) also use the same controls for each part.

  12. National Center for Educational Statistics IPEDS data is used to identify the set of schools and their latitude and longitude. The latitude and longitude of branch addresses was found using Texas A&M’s free geocoding software: http://geoservices.tamu.edu/Services/Geocode/ The great-circle distance between branches and schools was determined using the haversine formula.

  13. Quarterly call report data for the period examined is publicly available for download from the National Credit Union Agency at https://www.ncua.gov/analysis/Pages/call-report-data/quarterly-data.aspx.

  14. Real financial data are required to calculate financial ratios that combine income statement and balance sheet items because these ratios use values from multiple periods. For example, the return on assets for a credit union in period (t) is equal to their net income reported on the income statement in period (t) divided by the average of their assets from the current (t) and previous period’s (t − 1) balance sheets. A credit union’s size is also measured in real terms and is equal to the natural log of real total assets.

  15. Two million dollars is the cutoff the NCUA use in their Financial Performance Reports to identify the smallest of credit union peers. This restriction results in the elimination of 800 credit unions from the sample. Goddard et al. (2008, p. 1842) apply several criteria to eliminate credit unions with “extreme” or “nonsensical” financial data values.

  16. The measure uses FDIC summary of deposits data to construct a Herfindahl–Hirschman Index (HHI) of deposit concentration in the county or metropolitan statistical area, when applicable, where the credit union is headquartered. The index was scaled to range between 0 and 1, by dividing it by 10,000.

  17. Ely (2014) examines the loan shares of auto loans and the aggregate share of credit card and unsecured loans, with real estate and other loans as the omitted share, whereas Frame et al. 2002 use the share of auto loans, unsecured loans, and real estate loans, with other loans omitted.

  18. See Palia and Porter (2004)for an analysis of the performance of US bank holding companies that uses variation in stock returns and Tobin’s Q.

  19. Similar to the other financial formulas, the return on assets is calculated using the NCUA formula. Due to the previous year’s assets appearing in the formula, data from 2010 is used to calculate the value for 2011.

  20. The net-worth ratio is excluded from the specification of the z-score given its use in the score’s calculation.

  21. What is unclear from the data and potentially a much larger concern is the vintage of the PSL held and whether credit unions understand the effects of seasoning beyond the deferral period. Without cohort level data this cannot be addressed.

  22. A variable indicating the number of previous years the credit union held PSL was also examined, but did not have an observed effect either by itself or interacted with the portfolio variables. We presume they are fresher, but in fact a credit union could potentially enter into a participating agreement with a pool of loans long in repayment.

  23. Call reports only provide data on loans sold with recourse at the aggregate level of loans. Typically it is left to the originator to determine when to account for the charge-off in a participation loan.

  24. The 3 year cohort default rate (CDR) on federal student loans for the fiscal year 2011 cohort was 7% for private nonprofit 4-year institutions and 8.9% for public institutions (see Snyder et al. 2016, Digest of Education Statistics 2014, Table 332.50). Average student loan borrowing for full-time, first-time undergraduate students who borrowed averaged $6326 at public 4-year institutions and 7493 at private institutions (See Snyder et al. 2016, Digest of Education Statistics 2014, Table 331.20).

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Acknowledgements

The author would like to thank Melissa Sagness and Jessica Jensen for providing valuable research assistance along with the Editor and two anonymous referees for their thoughtful comments.

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  1. Department of Economics & Finance, University of North Dakota, 293 Centennial Drive Stop 8369, Grand Forks, ND, 58202, USA

    Cullen F. Goenner

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Goenner, C.F. The market for private student loans: an analysis of credit union exposure, risk, and returns. Rev Quant Finan Acc 50, 1227–1251 (2018). https://doi.org/10.1007/s11156-017-0660-y

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