Tag Archives: government-sponsored enterprise

CRT transactions offer diversification options

Javier SanabriaInsurance, , , , , , ,

The market for credit risk transfer (CRT) transactions continues to grow and diversify, providing institutional investors with valuable options to consider as part of their investment portfolios. In this article, Milliman’s Jonathan Glowacki and Rehan Siddique, explain CRT bonds and provide a risk profile for them. The authors also discuss how CRT bonds offer investors diversification to corporate bonds, comparing the two securities.

Here is an excerpt from the article:

Institutional investors are large investors in the corporate bond space. CRT bonds could offer diversification to corporate bonds. The table in Figure 4 compares corporate bonds with CRT bonds:

Spreading the underlying exposures for CRT bonds across many geographic areas allows for a diversification of exposures (i.e., thousands of individual borrowers) as compared with corporate bonds, which is a single entity. In addition, for a portfolio of 1,000 corporate bonds, an investor might expect 50 to default, for a default rate of 5%. With CRT bonds, you may have an expected default rate of 5% weighted across all economic scenarios, but in most of the scenarios you would experience a 0% default rate.

In terms of performance, as of February 2017, most of CRT bonds have shown positive movement in their credit ratings since issuance with none showing an unfavorable movement.

The table in Figure 6 provides a comparison of the average initial spread with corporate option-adjusted spread (OAS) over the last five years as of May 31, 2017. We can see that, historically, CRT bonds tend to have higher comparable spreads than corporate bonds. As the market for CRT bonds continues to grow and interest rates continue to rise in the short term, we can expect similar trends in spread.

Lender credit risk transfer considerations for government-sponsored enterprises

Javier SanabriaInsurance, , , , ,

One of the roadblocks for lender credit risk transfer (CRT) has been a lack of knowledge and understanding of the risk/reward profile of a potential lender CRT transaction. This article by Milliman’s Madeline Johnson and Jonathan Glowacki provides an overview of lender CRT and uses public information to demonstrate the expected premium and loss rates for a potential lender CRT transaction.

This article was originally published in the March/April 2017 issue of Secondary Marketing Executive.

Leveraging predictive analytics to lower quality control costs for mortgage originators

Javier SanabriaInsurtech, Risk, , , , ,

Financial institutions that sell loans to Freddie Mac and Fannie Mae collect data that can help them efficiently target loans to cure defects before they become problems using predictive analytics. In this article, Edem Togbey and Jonathan Glowacki provide an example showing how lenders can employ predictive analytics to reduce their quality control expense.

Assume lender “XYZ Mortgage Company” developed a scoring algorithm that segments its production into three levels of defect risk: low, medium, and high. The table in Figure 1 demonstrates how the process described above can reduce XYZ’s repurchase risk on 1,000 loans delivered to the GSEs. We assume 40% of potential defects are cured through a pre-funding quality control review.

In the above hypothetical example, XYZ would be able to significantly reduce its repurchase exposure by targeting high-risk loans pre-funding. Specifically, a random pre-funding review would correct 12 defects while a targeted approach would correct 25 defects while reviewing the same level of 10% of the loans. Assuming an average loan balance of $200,000 and a severity of 30% for a repurchase, this would result in a reduced repurchase exposure of $780,000 for 1,000 loans originated by XYZ for a savings around $780 per loan (see Figure 2 below).

Leveraging quality control sampling for your business

Javier SanabriaRisk, , , , ,

The Federal Home Loan Mortgage Corporation (FHLMC, known as Freddie Mac) and the Federal National Mortgage Association (FNMA, known as Fannie Mae) are government-sponsored enterprises (GSEs) that have issued guidance beginning in September 2012 concerning changes in their respective representations and warranty frameworks. The changes, effective for loans acquired by the GSEs on or after January 1, 2013, require lenders to report defects on various samples of loans delivered to the GSEs. These reports should be leveraged by lenders to monitor and mitigate their own risks of future repurchases.

In their article “Leveraging quality control sampling for your business,” Milliman’s Eric Wunder and Jonathan Glowacki offer perspective on the three types of required samples to monitor defect rates: Random sampling, discretionary sampling, and targeted sampling.