On June 25, 2021, Ginnie Mae announced the creation of a new pool type C-ET that consists of modified loans with original terms greater than 361 months and less than or equal to 480 months. The Custom pool design implies that each pool is created by a single issuer. Other custom pools are limited to 360-month maturities, so this structure is designed to enhance liquidity for these borrowers. 7 such pools were issued in December 2021, and 1 in January 2022 so far. The 8 pools have only 13 loans, from 3 issuers. 8 out the 13 loans are Rural loans, 5 are VA.
Once again, Ginnie Mae has provided the market with new investment opportunities, and analysts with the opportunity to learn about how markets behave under long-term timeframes.
As we approach year-end and the beginning of the process of phasing out forbearance programs, the natural question market participants are asking is which indicators should they be watching to gain a sense of the mortgage landscape in 2022. Along these lines, there is a significant difference between the Ginnie Mae programs and the GSE’s. In particular, for conforming loans, it is the Agencies themselves that buy nonperforming loans out of pools, while for FHA and VA, this function is performed by servicers. As the timeframe for buyouts on the part of the GSE’s was extended to 24 months earlier this year, we won’t see much activity prior to April 2022 on this front. So in this post, we focus on the Ginnie Mae programs.
As we have written previously, it is challenging to follow the path of a loan once it has been purchased out of a pool. At the aggregate level, we can view the activity of individual lenders using the FHA Neighborhood Watch data. In terms of the process, a nonperforming loan is bought out of a pool, and one of three actions can be taken. First, the borrower can be taken into foreclosure. Second, the borrower can become current and roll the unpaid balance into a second lien, in a process known as a partial claim. Third, the borrower can accept a loan modification.
In terms of the scale of buyouts, after an early spurt of activity in 2020 on the part of some parties, notably banks, the involuntary prepayment rate, measured by CDR(constant default rate), has settled down in recent months. FHA nonbank servicers have been more active in this space than other categories over the past year. As forbearance plans begin to expire towards the end of the year, these numbers may start to rise.
Financial support for rural communities has been a feature of US economic policy since the Farm Credit System was established as the first GSE in 1916, sixteen years before the Federal Home Loan Banks were established in 1932. This support continues to this day and has expanded to encompass additional programs.
Ginnie Mae Program
While the best-known collateral for Ginnie Mae securities is loans underwritten through the FHA and VA programs, another form is loans underwritten by the US Department of Agriculture Rural Development (RD) Program, launched in 1990 as part of the Farm Bill passed that year. The Single-Family Direct Home Loan Program in particular is designed to provide payment assistance to low- and very-low-income households in rural communities. Of course, FHA and VA provide loans in rural areas under the terms of their programs as well.
GSE Rural Lending
Single-family lending at Fannie Mae and Freddie Mac was handed a mandate to provide liquidity to rural communities through the adoption of the Duty-to-Serve provision of the HERA Act enacted in 2008. This program requires the GSE’s to engage in activities to facilitate liquidity in three underserved markets:
A key feature of this regulation is that FHFA has provided new datasets and tools to enhance the analysis of these markets. In particular,
FHFA's Duty to Serve regulation defines "rural area" as: (1) a census tract outside of a metropolitan statistical area, as designated by the Office of Management and Budget; or (2) a census tract in a metropolitan statistical area, as designated by the Office of Management and Budget, that is outside of the metropolitan statistical area's Urbanized Areas as designated by the U.S. Department of Agriculture's Rural-Urban Commuting Area Code #1, and outside of tracts with a housing density of over 64 housing units per square mile for USDA's RUCA Code #2. Below is a link to the specific geographies which meet the Rural Areas definition.
Using this segmentation, we are now in a position to load their definition into our databases and look at trends in this market segment. Using HMDA data as a base, we produce the following chart:
In a recent post we looked at the evolution of the FHA purchase mortgage market share broken down between areas with a high percentage of Low-Moderate Income (LMI) households and those without. While the overall FHA share has generally declined since the onset of the pandemic, its share has held up in areas with a preponderance of LMI households. There are many factors behind these trends, but a natural consideration is underwriting standards.
To examine this factor, we use the Recursion Matched Dataset, where we create a large sample of loans with characteristics from both HMDA and the Agency disclosure data. A very high share of mortgages can be matched using our proprietary algorithm over the years 2018-2020. The coverage ratio from the Matched Dataset is provided in a previous post.
We proceed by looking at three major underwriting characteristics for LMI and non-LMI areas for FHA and the GSE’s: Credit Score (CS), Loan-to-Value (LTV) and Debt-to-Income (DTI),
Most interesting is Credit Scores:
One of the many recurring themes of these posts is that the shock of the Covid-19 Pandemic and subsequent policy response has resulted in structural changes in behavior that cause loan performance metrics to shift compared to the pre-crisis world. An interesting example of this can be found in the performance of modified loans in Ginnie Mae programs.
Modified loans in these programs are those that have been purchased out of pools by servicers that are past due that subsequently have features such as rate and term adjusted in order to bring households back to a current status. These are then often resecuritized into a new GNM pool.
In a previous post, we mentioned the Recursion Matched data set, which uses a proprietary algorithm to match the loans provided in the monthly Agency loan tapes, with HMDA data. This allows for a broad analysis of loan performance (delinquency and prepayment rates) in terms of both underwriting standards (credit score, DTI, LTV) with demographic and household economic characteristics (income, race, gender, etc). We are always working to improve our algorithm, below find the match rates for Ginnie Mae loans over the 2013-2020 period. HMDA has released more characteristics in recent years, allowing for a greater matching rate.