Tag Archives: Jonathan Glowacki

Lender credit risk transfer considerations for government-sponsored enterprises

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.

Credit risk sharing transactions considerations for insurers

Credit risk sharing transactions offered by Fannie Mae and Freddie Mac present a new business opportunity for insurance companies seeking to invest capital. Milliman’s Jonathan Glowacki and Michael Jacobson say insurers must first understand the risks associated with these transactions before investing in or insuring them. Their Contingencies article “The trillion-dollar marketplace” provides some perspective.

Here’s an excerpt from the article:

Given FHFA’s focus on de-risking the GSEs, mortgage credit risk offerings are likely to continue to become more prevalent in the marketplace as the GSEs seek to meet their annual conservatorship scorecard requirements and reduce capital. According to FHFA’s 2015 conservatorship scorecard, Fannie Mae and Freddie Mac were instructed to collectively transact credit risk transfers on reference pools of mortgages of at least $270 billion for the year. In actuality, the GSEs’ transactions covered reference pools exceeding $400 billion of the nearly $1 trillion of mortgages acquired by the GSEs in 2015. The 2016 scorecard requires the GSEs to transfer the credit risk on at least 90 percent of the unpaid principal balance of targeted groups of newly acquired mortgages, which represents the majority of expected acquisitions. Thus, it can be assumed that there will be a similar level or greater amount of credit risk transferred in 2016, assuming GSE mortgage acquisition levels remain consistent with 2015 acquisitions.

Insurance companies will have the opportunity to participate in this marketplace in 2016 through investment opportunities in the STACR and CAS debt structures as well as by writing credit insurance through anticipated ACIS and CIRT transactions. While the debt offerings require principal outlays equal to 100 percent of the notional amount of the securities, the credit insurance transactions to date have typically only required collateral between 15 and 20 percent of the credit risk assumed. The collateral requirements for the credit insurance transactions vary based on the rating of the insurance entities assuming the risk and the type of participation. For context, the $2.8 billion of credit insurance risk placed through the 10 Freddie Mac ACIS transactions in 2015 required minimum collateral of approximately $440 million (or approximately 16 percent of the risk assumed).

These debt securities and insurance opportunities may offer attractive risk/return profiles to strategic companies in the insurance sector. However, before entering into such agreements, it is important to understand the risk profile of the underlying collateral and the performance volatility inherent in the structure of the transactions. With data being published by the GSEs, it is now easier than ever before to evaluate the risk profiles of these exposures.

Student loan debt at for-profit colleges

For-profit colleges attract students through innovative scheduling and online educational opportunities. However, 44% of defaults on federal loans come from students at for-profit colleges. In his latest Insight article, Milliman’s Leighton Hunley examines some of the possible causes of these for-profit defaults as he revisits the issue of student loan debt. The article also highlights student loan debt and delinquency trends.

For more analysis on this issue read Leighton and Jonathan Glowacki’s article “The student loan debt crisis in perspective.” The authors also offer some reform ideas in the article.

Estimating credit losses for the lifetime of a loan

On December 20, 2012, the Financial Accounting Standards Board (FASB) issued a proposed Accounting Standards Update (ASU) that discusses changes to the ways banks recognize and account for potential credit losses (the ASU is “Financial Instruments—Credit Losses,” Subtopic 825-15). A simple summary of the update is that the FASB proposes that banks and other financial institutions modify recognition of impairment from a “probable loss” to a “lifetime of loss” estimate.

For mortgages, this means changing the base of the impairment provision from a provision for losses arising from the current delinquency inventory to a provision for all mortgages, recognized at origination. Impairment provisions for delinquent loans are typically estimated using a roll-rate model based on recent experience.

Milliman’s Eric Wunder and Jonathan Glowacki provide a methodology to estimate credit losses (including losses on loan repurchases) for the lifetime of a loan in this article.

Leveraging predictive analytics to lower quality control costs for mortgage originators

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

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.