Tag Archives: Ginnie Mae

Mortgage default risk drops in Q2 2020, despite pandemic, due to home price growth and refinance volume

Milliman today announced the second quarter (Q2) 2020 results of the Milliman Mortgage Default Index (MMDI), which shows the latest monthly estimate of the lifetime default risk of U.S.-backed mortgages.

During Q2 2020, the economic component of default risk for government-sponsored enterprise (GSE) acquisitions (purchased and refinanced loans backed by Freddie Mac and Fannie Mae) defied expectations, decreasing for the first time since at least Q3 2019 as a result of home price growth and robust refinance volume. In Q2, approximately 70% of mortgage volume was refinance loans, which are considered lower risk relative to purchase loans. Because of this, and an increased demand for housing, overall default risk for GSE loans decreased, from 1.99% in Q1 2020 to 1.74% in Q2.

Low interest rates have driven homeowners to refinance in record numbers, with 2020 refinance volume exceeding $1 trillion and totaling more than the volume of 2018 and 2019 combined. That, coupled with home price growth, has resulted in an improvement in mortgage default risk in Q2, despite the economic stressors from the COVID-19 pandemic.

For Ginnie Mae acquisitions, the MMDI rate increased from 10.33% in Q1 2020 to 10.61% in Q2 2020, driven mainly by increased refinance volume. Many of these loans were originated through streamlined refinance programs, where a credit score is not provided. A credit score of 600 is conservatively assigned, which increases borrower default risk during heavy refinance periods

The models used in Milliman’s MMDI analysis rely on home prices to forecast default rates, and do not rely on unemployment rates, nor do they have specific adjustments for special legislative actions or programs such as the Coronavirus Aid, Relief, and Economic Security (CARES) Act.

For more information, click here.

Increased economic risk from COVID-19 puts pressure on mortgage performance in Q1 2020, but losses not expected to rise to global financial crisis level

Milliman today announced the first quarter (Q1) 2020 results of the Milliman Mortgage Default Index (MMDI), which shows the latest monthly estimate of the lifetime default risk of U.S.-backed mortgages. Default risk is driven by various factors, including the risk of a borrower taking on too much debt, underwriting risk (such as loan term, loan purpose, and other influential mortgage features), and economic risk as measured by historical and forecast home prices. The goal of the MMDI is to provide a benchmark to understand trends in U.S. mortgage credit risk.

During Q1 2020, the economic component of default risk for government-sponsored enterprise (GSE) acquisitions (purchased and refinanced loans backed by Freddie Mac and Fannie Mae) climbed 20 basis points—a 40% increase—as a result of housing pressure from COVID-19. For Ginnie Mae loans, which have a higher level of borrower risk relative to GSEs, economic risk jumped 80 basis points—an increase of 33%.

Despite the increased economic risk in Q1, the MMDI for GSE loans decreased to an estimated average default rate of 2.02%, down from 2.06% in Q4 2019. This means that the average lifetime probability of default for all Freddie or Fannie mortgages originated in Q1 2020 was 2.02%. The lower quarterly default risk in the face of economic pressure is because, as interest rates continued to decline, less risky refinance loans offset an increase in default risk for purchase loans. For Ginnie Mae acquisitions, however, the MMDI rate increased from 10.29% in Q4 2019 to 10.48% in Q1 2020. Beginning in 2014, Ginnie Mae has experienced a credit score drift relative to GSE and increased economic risk from COVID-19.

While we anticipate that the large number of unemployment claims will translate to an increase in mortgage delinquency rates, default rates (i.e., the number of borrowers who lose their homes) will likely not be as severe as during the global financial crisis, thanks to the robust home price growth we saw over the past several years.

The models used in Milliman’s MMDI analysis rely on home prices to forecast default rates, and do not rely on unemployment rates, nor do they have specific adjustments for special legislative actions or programs such as the Coronavirus Aid, Relief, and Economic Security (CARES) Act.

For more information on the MMDI, click here.

Default risk for government-backed mortgages decreases in Q2 2019 as low interest rates spur borrowers to refinance

Milliman has announced the second quarter (Q2) 2019 results of the Milliman Mortgage Default Index (MMDI), which shows the latest monthly estimate of the lifetime default risk of U.S.-backed mortgages. The goal of the MMDI is to provide a benchmark to understand trends in U.S. mortgage risk.

As of July 1, 2019, the MMDI for government-sponsored enterprise (GSE) acquisitions (purchased and refinanced loans backed by Freddie Mac and Fannie Mae) decreased to an estimated average default rate of 1.99%, down from 2.01% in Q1. This means that, for the average Freddie or Fannie mortgage that originated in Q2 2019, there is a 1.99% probability the loan will become 180 days delinquent or worse. To put that in context, equivalent research from Freddie Mac shows the actual to-date default rate of GSE mortgages originated in 2007 (shortly before the financial crisis) was 13.8%.

Low interest rates in Q2 spurred more borrowers to refinance, which typically reduces credit risk for underlying mortgages. But while the default rated dipped slightly in the second quarter of this year, we’re also starting to see increased economic risk from slower home price growth, which may elevate mortgage default risk in the future.

For Ginnie Mae loans, the Q2 2019 MMDI rate increased from 8.09% in Q1 to 8.15% in Q2. This uptick is consistent with the overall trend for these loans, as default risk for Ginnie Mae acquisitions has been rising since 2014. Default risk is driven by various factors, including the risk of a borrower taking on too much debt, underwriting risk such as certain mortgage features, and economic risk such as a recession, which can put pressure on home prices.

For more information on the MMDI, click here.

Milliman launches new index to measure the risk of default for government-backed mortgages

Milliman today is launching the first-ever Milliman Mortgage Default Index (MMDI), a quarterly publication that shows the latest monthly estimate of the lifetime default risk of U.S.-backed mortgages. The goal of the MMDI is to provide a benchmark to understand trends in U.S. mortgage risk.

After the subprime mortgage crisis of 2008, the financial services industry instituted various risk mitigation efforts to help guard against a similar rise in mortgage credit risk and its associated effects on the global economy. As part of this effort, Milliman is launching the MMDI, a lifetime default rate estimate calculated at the loan level for a portfolio of single-family mortgages delivered to Freddie Mac, Fannie Mae, and Ginnie Mae. The MMDI rate is an index benchmarking the probability that mortgages in a given portfolio will become 180 days delinquent or worse over the lifetime of the loan, with historical data dating back to 2014.

As of March 31, 2019, the MMDI for government-sponsored enterprise (GSE) acquisitions (purchased and refinanced loans backed by Freddie Mac and Fannie Mae) increased to an estimated average default rate of 2.19%, up from 1.83% the year prior. For Ginnie Mae loans, the Q1 2019 MMDI rate stands at 8.77%, up from 7.09% the year prior.

For comparison, the actual to-date default rate of GSE mortgages originated in 2007 (shortly before the financial crisis) was 13.8%, according to Freddie Mac data. The actual to-date default rate for Federal Housing Administration (FHA) loans (which are the majority of Ginnie Mae loans) originated in 2007 was approximately 26.5%, according to FHA’s Single Family Loan Performance Trends report as of February 2019. While this data is not directly comparable, these numbers provide an equivalent comparison of the magnitude of defaults during the crisis relative to the current expected mortgage default risk for new originations in 2019.

Default risk is driven by various factors including the risk of a borrower taking on too much debt, underwriting risk such as certain mortgage features, and economic risk such as a recession, which can put pressure on home prices. In the first quarter of 2019, we’ve seen default risk creep up for both GSE and Ginnie Mae loans as a result of an increase in borrower debt-to-income ratios, credit score drift, and the anticipated increased risk of an economic downturn.

For more information on the MMDI and to view detailed, granular data, click here.

Predictive analytics for the mortgage industry

How can predictive analytics help Government National Mortgage Association (GNMA/Ginnie Mae) issuers decide whether they want to buy out a nonperforming loan or not? In their article “Enhanced vision,” Milliman’s Jonathan Glowacki and Makho Mashoba provide perspective on an algorithm used to analyze loans that are likely to bounce back in order to reissue them as a mortgage-backed security.

Here is an excerpt:

The XGBoost model, like similar algorithms, is easy to implement. Once the mechanics of the technique are understood, and the parameters are tuned correctly, the model can be turned on a data set to produce accompanying predictions. The model can be updated continuously each month based on new data feeds. Pointing an XGBoost program toward a new data set and running it again is virtually all that is needed to refresh the results. It is also possible to retune the parameters for the update to further enhance the effects.

A use case of this type of model would be to pursue early buyouts for mortgages that have a high probability of re-performing and potentially not pursue early buyouts for mortgages that have a low probability of re-performing, as long as this policy is consistent with GNMA servicing guidelines.

This same technique can be used on a variety of data for alternative purposes. Predictive analytics can capture predictive power from internal data, whether that involves established and go-to data sets or whether that involves bringing together data from across an organization to make predictions. Predictive analytics can also help a firm leverage industry data and other outside sources to forecast trends or improve decisions. This case is a concrete example of how using the tool should result in higher return on investment on GNMA early buyouts.

Considering the growing amounts of data available, the mortgage industry should pay attention to predictive analytics tools. Investing in the technology has proven to generate significant returns. GNMA issuers is just one group to which predictive analytics can be applied. Predictive analytics can be applied to many other techniques and tools to increase efficiencies within the mortgage industry. The future depends on it.