Obama Policy Impact On Portfolio Values
Several hundred thousand struggling borrowers have applied for assistance under President Obama's Making Home Affordable program and 9 million may ultimately benefit. But not all who apply will be eligible, and not all eligible borrowers will apply. For mortgage investors, sorting out the ultimate impact of the program on the value of their portfolios can be a daunting task.
This article illustrates a methodology that can help investors predict, and even influence, how these policies may affect cash flows in their loan portfolio.
The Making Home Affordable initiative offers two main options to prevent foreclosure: refinancing and loan modification. Each of these options comes with a detailed list of criteria that borrowers must satisfy. Estimating the program's impact involves estimating losses of the current portfolio, filtering the portfolio for eligible loans, filtering the eligible loans for likely participants, and re-estimating losses on the portfolio after taking into consideration loan modifications and refinances. To the extent that investors or servicers can influence borrower participation, they can also influence the impact of the programs on cash flows in their loan portfolios.
The refinancing portion of the program was created to help as many as 5 million qualifying borrowers get lower interest rates, or change from balloon-payment or adjustable-rate mortgages to more affordable 15-year or 30-year fixed-payment fixed-rate mortgages. These borrowers are in good standing, but their property values have depreciated substantially relative to the size of the loan, leading to a high current loan-to-value ratio. They typically have some equity in the home, but tightening credit has made it difficult to refinance.
Finding the intrinsic value of this program for a particular portfolio involves estimating the portfolio's total cash flow losses, i.e., future interest shortfalls and principal writedowns, with and without the program, and calculating the difference. The intrinsic value of the program is the added benefit an investor can expect to receive that is directly attributable to these two Obama housing initiatives. Databases with individual loan-level data typically provide the key variables for analysts to estimate the portfolio value without the program. Analysts can estimate the likelihood of default for individual borrowers and roll these up to the portfolio level to develop an estimated loss for the entire portfolio.
The portfolio value with the program depends on which loans are eligible, and analysts must first filter the portfolio according to program criteria. Some of the qualification criteria include that the loan must be owned by a government-sponsored enterprise, i.e., Freddie Mac or Fannie Mae, that it is no more than 30 days delinquent in the last 12 months, and that it has a CLTV ratio between 80% and 105% on a first mortgage.
Borrowers with interest rates close to the current rates, or who face prepayment penalties, have less of an incentive to refinance even if they are eligible. Therefore, another filter must be applied to eliminate these borrowers, using a threshold interest rate on fixed-rate mortgages and assuming all non-fixed-rate mortgages will refinance. The portfolio remaining after all these filters consists of all loans that are eligible and have an incentive to refinance under the plan.
Lastly, not all eligible borrowers who could benefit will actually choose to refinance. Borrowers may be unaware of the program, may prefer their current monthly payment under a balloon-payment or ARM loan, or may not have the proper documentation to refinance under the plan. Servicer data can be used to estimate the percentage of remaining borrowers who will go through with the plan.
To illustrate with a simplified example, consider a hypothetical portfolio of 1,000 loans with estimated cash-flow losses of 10% of the original balance. Suppose that, using the filtering approach, we find that 150 of these loans are eligible and have an incentive to refinance under the plan. After looking at the individual characteristics of these 150 loans, we determine that 80% of them (or 120 loans) are likely to refinance under the plan.
We then remove these 120 loans from the original portfolio to create a filtered portfolio of 880 loans, and re-estimate the losses on the filtered portfolio. If these losses total 8% of the original balance, then the estimated value of the refinancing program is 2% of the original balance (the difference between the original portfolio's 10% expected losses and the 8% expected losses on the filtered portfolio).
This approach gives the investor some room to optimize the portfolio value in response to the plan by increasing the percentage of qualifying borrowers who participate. If our hypothetical portfolio includes 15 ARM loans on schedule to reset in the next six months, the investor can encourage these 15 borrowers to take advantage of the program, thereby reducing their probability of default and the potential losses on the portfolio.
The loan modification plan is a $75 billion initiative designed to facilitate modifications for up to 4 million at-risk borrowers, and share the cost with loan servicers, whose participation is voluntary. The servicer determines if a borrower is at-risk-either at or near default, or experiencing extraordinary hardship - and if a reasonable reduction in their monthly payment could prevent the borrower from going into foreclosure.
Estimating the intrinsic value of this program requires consideration of the borrower's income and debt level in addition to other characteristics of the property and the loan. Investors must also consider the degree of modification that each loan may require, as well as the expected cash-flow losses on modified loans. However, the methodology is similar: estimate losses on the original portfolio, apply participation filters and re-estimate losses on the filtered portfolio.
The value of this program comes from avoiding defaults on delinquent loans. So estimating portfolio losses without the program involves calculating the expected losses from delinquencies. Delinquency status is used along with other variables to estimate the probability of default (e.g., a loan 30 days delinquent is less likely to default than a loan 60 days delinquent). Multiplying the default probability by the severity of the loss gives the expected loan-level losses, which are rolled up to the portfolio level to give an estimate of future losses.