Adverse Selection: Equity Prepayment Risk In Student Loans

Most good insurance companies and every failed insurance company are familiar with a phenomenon called “adverse selection.”  Adverse selection says that any situation in which the buyer and seller of insurance have asymmetric information will ultimately create a cycle whereby the low-risk individuals forego insurance that they perceive as too expensive while the high-risk individuals aggressively purchase insurance that they perceive as cheap.

Take the example of health insurance.  In the U.S., there are strict regulations about discriminating against individuals with hereditary pre-existing conditions: let’s take hereditary hypertension as an example.  Hereditary hypertension creates a higher risk for cardiovascular disease and, thus, a higher risk for a health insurer.  However, because the insurer cannot discriminate for this condition in its pricing model, the insurer is forced to ignore this predictive factor and price the insurance as if this information were unattainable.  However, most individuals who are purchasing insurance will know whether or not they have hereditary hypertension, and those individuals who do will have a great demand for insurance at a price that is ignoring that risk.  For those who do not have hypertension, however, this insurance will seem unduly expensive.  As a result, each year the “healthy” individuals will tend to opt against purchasing insurance that is unjustly expensive while the “sick” individuals will aggressively purchase insurance that is irrationally cheap.  The cost of insurance will increase each time a “healthy” individual leaves the insurance pool or an additional “sick” person joins at the price that ignores this risk.  As this cycle progresses, the risk in the underlying insurance pool becomes ever more highly concentrated, until eventually you are left with an insurance pool consisting entirely of sick individuals – which isn’t insurance at all, it’s just the original risk.

Lending is a lot like insurance: a lender creates a diversified portfolio of loans under the assumption that while some may fail, on average the portfolio will perform to some level of consistency.  Like insurance, a lender is trying to minimize an unpredictable risk (or difficult to predict risk) through diversification and the law of large numbers.  Unsurprisingly then, we find that adverse selection is also highly relevant for student loans.  In the previous post we noted that the top two causes of student loan default are the failure of the borrower to graduate and the failure of the borrower to attain gainful employment within six months of graduating.  We also previously noted that student loans, like mortgages, are freely prepayable – a unique attribute in the broader capital markets.  Further, we reviewed how the combination of these two qualities introduces meaningful equity prepayment risk into the system.  At its core, we can show that this equity prepayment risk becomes a textbook example of adverse selection.

The primary goal of a student lender is to predict default risk.  A lender who is more skilled at predicting the probability that a borrower will default on their obligation will experience smaller loan losses, which will allow it to offer lower rates than its less adept competitors.  Consequently, we would expect student lenders to expend great efforts attempting to predict graduation rates and post-graduate employment rates.  Although there are some lenders partially doing this, the vast majority of student lenders ignore these risks almost entirely or else price them very broadly: law students have higher average interest rates than medical students, but the interest rates between individual law students are largely ignorant of the individual’s academic prowess.  Most importantly, student loans by their very definition are priced at the time when the uncertainty of the borrower’s eventual graduation and employment is at its highest – that is, when the borrower is actually a student.  The result is that student lenders do a very poor job of predicting which borrowers are going to default; they only consistently predict how many borrowers are going to default.

This presents an obvious problem: lenders are pricing loans under the assumption that the risks of failure to graduate and unemployment are effectively unknowable to both the lender and the borrower.  More importantly, lenders are assuming that this fact will remain unknowable throughout the life of the loan.  This is obviously untrue: within six months of entering repayment a borrower will know with complete certainty whether they have graduated or attained a job.  In a rational market, we would expect these “healthy” borrowers to view their existing interest rate as unduly expensive while the “sick” borrowers would view their interest rate as far too cheap.  Further, given that student loans, like mortgages, are fully prepayable, then we would expect increasing numbers of healthy borrowers to exercise their prepayment option and opt out of their high-interest loans.  This is just adverse selection and will ultimately have the same consequences: student lenders will find that the risk in their loan pools are becoming increasingly concentrated. 

There is one key difference, however, between adverse selection in insurance and lending.  In insurance, the insurer has the opportunity each year to withdraw or modify its offer to sell insurance at the same price, that is: adverse selection in insurance drives up annual premiums, but it does not create long-term liabilities for the insurer.  In lending, the lender has no opportunity to withdraw or modify their initial loan terms, that is: adverse selection in lending reduces the value of the existing loan portfolio through the concentration of default risk, which creates an enormous long-term liability for the lender.  This creates major instabilities in today’s student loan market and, ultimately, makes the entire practice unsustainable.

Byline:

Derek Kaknes

The Evolution of Credit Quality: Student Loans ARE NOT Mortgages

In the last post we discussed the two types of prepayment risk: interest rate risk and equity prepayment risk.  In this post, we focus exclusively on how equity prepayment risk affects each mortgages and student loans.  We note that there are fundamental differences between the loan types that make using similar credit analyses impossible. 

Equity prepayment risk is derived entirely from the change in the credit quality (e.g. default risk) of the borrower.  A borrower will only be incentivized to refinance into a lower rate if their credit quality has improved significantly since the loan was originated.  Thus, in order to analyze equity prepayment risk, we need to review the evolution of credit quality in both mortgages and student loans:

The traditional mortgage is a secured loan that is collateralized by the real estate asset (the house) with a loan-to-value of no greater than 80% (20% down payment).  As a result, the vast majority of the credit quality of a mortgage is dependent upon the value of the underlying home as collateral: if the borrower defaults the bank can foreclose on the home and sell it for 80% of its original value without taking any losses.  Thus, the evolution of credit quality for a mortgage is almost entirely derived from the change in value of the underlying home.  The last decade notwithstanding, U.S. home prices have historically been very stable.  Consequently, the evolution of credit quality of mortgage borrowers is very gradual as well and thus we would expect the effect of equity prepayment risk to be minimal.  The exception to this rule is in instances where housing prices increase dramatically in a short period of time.  In this case, we would expect homeowners to rapidly and consistently refinance their mortgages as the value of the underlying collateral increased.  This is precisely what occurred from 2002 to 2007 during the housing bubble.

Student loans are not like mortgages.  Student loans are unsecured debt whose underlying credit quality is solely determined by the expected future income of the borrower (and cosigner).  The credit quality of a student loan will evolve with direct proportion to the certainty regarding the borrower’s ability to earn an income commensurate with their debt level.  This figure, however, is extremely volatile in the early years of a student loan.  Key binary metrics that determine credit quality evolve dramatically over the student’s educational period, most notably: the likelihood that the student will graduate and the likelihood that they will attain a high paying job post-graduation.  In fact, the failures to adequately meet these qualifications are the top two causes of student loan default.  As a result, the evolution of credit quality in student loans is extremely steep: 70% of defaults occur within the first two years of a loan entering repayment.  

This phenomenon reflects the huge level of uncertainty and variability in the value of higher education degrees.  More importantly, this phenomenon would lead us to believe that there is enormous equity prepayment risk in the student loan market, and particularly for private student loans held by Prime borrowers.  That is an assertion that we will look into further in the following posts.

   

Byline:

Derek Kaknes

Empowering the Student Borrower: Understanding Prepayment Risk

Since Louis Ranieri started touring the country peddling mortgage bonds to savings and loans institutions in the 1970’s, the largest concern in the amortizing consumer loan market has been the borrower’s ability to voluntarily refinance their loans at any time for no expense.  This problem is particularly important for longer term consumer loans such as mortgages and student loans: the consumer maintains a unique option contract on prepayment that no other market participant can efficiently access.  Lenders can hedge their interest rate risk through swaps and their default risk through credit default swaps, but they cannot hedge the prepayment risk because those contracts carry heavy prepayment penalties.  Lenders (or investors) can mitigate the risk by speculating on prepayment trends, but they can never fully hedge the prepayment risk.  As a result, there is a meaningful and unavoidable financial risk built into these consumer loans that is entirely dependent on the consumer’s behavior.  It is critical for student borrowers, or at least their financial advisors, to understand this structure because it creates a zero-sum game where any positive outcome for the borrower is an inherent negative outcome for the lender.  Under this scenario, students must be aware that lenders will be trying to persuade them to act against their own best interest and the consumer’s only recourse is to maximize the leverage of their prepayment option.  In order to do this, we need to understand prepayment risk.

There are two types of prepayment risk that arise because of a consumer borrower’s opportunity to refinance: interest rate risk and equity prepayment risk. 

Interest rate risk is very straightforward and arises from the consumer demand for fixed rate loans and the market’s willingness to lend on fixed terms.  From the consumer perspective, the logic is simple: if interest rates decline, the borrower can refinance their existing loans into a new and lower fixed rate.  This transaction has little or no cost to the borrower, so the outcome is purely beneficial to the consumer and purely detrimental to the existing lender.  Notably, this prepayment trend has nothing to do with credit risk and is entirely derived from interest rate risk.  Thus, interest rate risk can be quantified by forecasting changes in interest rates: falling rates increase prepayment while rising rates decrease prepayment.  Broadly speaking, the value of interest rate risk is derived from the volatility in prevailing interest rates over time. 

Equity prepayment risk is a little more nuanced and arises from changes in the borrower’s credit quality.  The logic is fairly simple: if a borrower’s credit quality improves materially over the term of the loan, then the borrower should be able to refinance their loans at lower rate – at a “tighter spread” to be technical.  Thus, equity prepayment risk can be quantified by forecasting changes in the borrower’s credit quality: increasing quality increases prepayments, decreasing quality decreases prepayments.  Broadly speaking, the value of equity prepayment risk is derived from the volatility in the borrower’s credit quality over time.  In contrast to interest rate risk, equity prepayment risk is entirely derived from credit quality and has nothing to do with prevailing interest rate risk. 

Mortgages and student loans each contain both interest rate risk and equity prepayment risk, but their relative significance varies widely between the two loan types.  For mortgages, prepayment risk is almost entirely concentrated in interest rate risk.  For student loans, prepayment risk is almost entirely concentrated in equity prepayment risk.  This fact has major consequences on the most effective repayment strategies for each loan type, a topic we will discuss further in the next post.

Byline:

Derek Kaknes

A Framework for Analyzing Student Lending: How the U.S. Mortgage Market Influences EVERYTHING

Going forward in this blog, we will be using some conventional and some more complex financial analytics to evaluate the current student lending market.  We use this financial framework because we believe it the best and only way to rigorously analyze the state of student loans and to empower student borrowers in their personal finances.  With that in mind, we put forth the following framework for viewing the student loan market: the student loan market is an offshoot of the much larger U.S. mortgage market – the same people are using the same models to value similar securities.  We use this framework because it helps explain why the student loan market works the way it does, why that model makes little logical sense, and how the flaws in the model will likely unravel.  In this post, we will give a brief introduction to the mortgage market, describe one way in which it has influenced student loans and uphold the comparison as a framework for further analysis.

At ~$10 trillion, the U.S. mortgage market is the single largest capital market in the history of the civilization.  However, roughly half of these mortgages are guaranteed by the U.S. government through Fannie Mae, Freddie Mac and Ginnie Mae.  As a result, interest rates in the mortgage market largely track the rate of U.S. Treasury securities with slight adjustments for prepayment risk.  This guarantee program is exactly analogous to the federal student loan program in the student loan market (in fact, Sallie Mae was created specifically to replicate the mortgage market).  The $1.0 trillion student loan market is even more heavily influenced by this government guarantee: ~95% of the student loan market is government guaranteed.  While there are many consequences of this involvement, we focus here on how that government intervention affects the burden of credit diligence for the private lending market in both mortgages and student loans.

The government guarantee in both the mortgage and student loan markets has the effect of driving down the yield that investors receive on the loans they invest in.  If we assume that the risk of a U.S. default is zero, then the yield in excess of the treasury rate (the “spread”) represents the maximum value that an investor can extract through their own research and credit diligence: if the spread is zero, then the investor would be much better off just investing in risk-free treasuries.  For guaranteed mortgages and student loans, this spread has been extremely low – less than 0.50%.  This small spread means that investors cannot afford to perform extensive diligence on the credit quality of the loans or the market before making an investment decision.  As a result, the amount of credit diligence performed in an industry is inversely proportional to the amount of government guarantee in the market.  In the mortgage market, this disparity implies that the market is “under-diligenced” by close to 50%.  In the student loan market, this disparity implies that the market is “under-diligenced” by over 90%.  This is critical because it affects the market as a whole and not just the specific investments: 50% of the capital going into the housing market is not performing diligence; 95% of the capital going into higher education is not performing diligence.

Investors are highly rational, so the fact that the student loan industry is under-diligenced would not in-and-of-itself dissuade investment.  However, it does necessitate a larger burden on the private segment of the market: the private student loan market is responsible for completing 100% of the diligence but only reaps 5% of the reward.  Ultimately, this translates to higher borrowing costs on private loans in order to offset the disproportionately large diligence expense.  It also makes it tantalizingly attractive to take short-cuts.

The sub-prime mortgage meltdown and the private student loan mass defaults in 2008 were examples of the private sector taking short-cuts.  In 2012, the short-cut in student lending is co-signers: 90% of new private student loans are cosigned by a parent.  This effectively shifts the burden of diligence away from evaluating higher education (which has a 90% diligence deficit) to the broader consumer lending market – which is already thoroughly diligence for mortgages, credit cards, auto loans and every other consumer credit instrument.  Lenders are able to leverage existing credit models, existing data sets and existing credit metrics (like FICO) in order to make decisions about student lending, and largely avoid tackling questions about value in the higher education market as a whole.  The consequence is that much of the analyses used to evaluate student loans are techniques used to evaluate mortgages that have been hastily and rudimentarily converted to evaluate student loans.  Thus, it is often helpful to use mortgage valuation techniques when reviewing student loans, which is why we will continually return to this comparison throughout this dialogue.

Byline:

Derek Kaknes

The Prime Student Loan Blog – A Financially Rigorous View of Higher Education and Student Lending

The higher education system in the United States of America is ominously broken.  The cost of higher education has outpaced inflation for the past 20 years while the earnings power of a degree has failed to keep pace.  Graduation rates at four-year institutions continue to slump: currently 40% of students fail to achieve their degree within six years.  For those who do graduate, the near-term labor markets continue to be challenging with over 10% unemployment and much higher underemployment.  All the while student loan debt has continued to mount: student loan debt has risen to over $1 trillion, surpassing auto loans and credit cards combined.  The culmination of these factors (runaway costs, declining value and exploding debt) has created a highly unstable system that is on the brink of a major restructuring.  At the Prime Student Loan Blog, we hope to highlight these instabilities, and to provide an intellectually and financially rigorous thesis on how the restructuring of the higher education market will occur.  Fundamentally, we hope to highlight the path to reverse the higher education bubble while causing the least amount of harm to past, current and future students.

The Prime Student Loan Blog is an outreach platform for Prime Student Loan, LLC (referred to herein as “Prime Student Loan” or “PSL”).  Prime Student Loan was founded by the authors of this blog in order to help graduates reduce the burden of existing student loan by accounting for the evolution of credit quality, and, ultimately, help future students more appropriately finance their educational investments.  Many of the topics and assertions presented in this blog directly underpin the mission statement of PSL, so we encourage anyone who is intrigued by these posts, who feels a personal resonance with any of the predicaments, or who is simply additionally curious about our enterprise to reach out to us directly.

 
Stay tuned for additional (approximately) weekly posts.

Derek Kaknes