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.
A curious policy development this year has been the stop-start approach towards the imposition of caps on the GSE’s regarding their purchases of loans backed by non-owner occupied (NOO) residences. In January the Treasury and FHFA amended the Enterprise’s Preferred Stock Purchase Agreements (PSPAs) to limit their acquisitions of single-family mortgage loans secured by second homes and investment properties to 7% of single-family acquisitions over the preceding 52-week period. In September these caps were suspended.
Below find charts of the shares of second homes and investment properties out of all purchase mortgage deliveries to Fannie Mae and Freddie Mac, along with supporting fundamental factors.
In both cases, there was a drop in the shares in the NOO categories after the initial policy announcement this year followed by a rebound in recent months. With regards to the fundamental factors, in the case of second homes, the share of purchase mortgages rose from about 6% to 8% following the onset of the pandemic as households sought refuge from densely populated areas. According to the National Association of Realtors, more than 50% of second homes are all-cash transactions, suggesting that the equity market is more important than earned income in driving these buying decisions. Of course, the data used here come from loans in Agency pools, but the performance of the equity market likely has a significant influence on buyer sentiment in this market segment.
The recent acceleration of consumer prices is likely supporting the sales of homes purchased for investment purposes, as real estate is widely seen as a hedge against inflation, in part because mortgage payments will not rise if the purchase is financed with a fixed-rate mortgage.
Once again, we have a case where optimal investment decisions are driven by detailed knowledge of a combination of policy and fundamental factors. Loan-level digital tools are essential in drilling down to the level needed to formulate successful strategies.
In a recent post, we looked at the share of the use of the cash window for bank and nonbank sellers. We found a reversal in the long-term upward trend in this share this year, correlated with the imposition of FHFA imposed lender-level caps on the use of the cash window. We next turn to performance.
We look below at prepayment speeds for the 2018, 2019 and 2020 cohorts broken down by bank and nonbank sellers.
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.
It’s always interesting to look at the underlying dynamics within the mortgage market to get a deeper handle on the forces behind recent trends and to gain insights into the market impact of policy changes. This time we will look at a breakdown of the market between the cash window and swaps. Simply, in a swaps transaction the lender sends loans to one of the Enterprises, Fannie Mae or Freddie Mac, and in return obtains a security which it can keep as an investment (mostly in the case of banks) or else sell into the market (both banks and nonbanks). The alternative is to sell the loans directly to the Agencies for cash. This is important to nonbanks in particular as this cash is used as a funding source for running their businesses.
As it turns out, neither GSE reports the path by which a loan is obtained in their loan-level disclosures. However, in the case of Freddie Mac, cash loans are placed in their own pools with distinct prefixes. As a result we can unpack these pools and perform a matching exercise with the loan tape and assign these accordingly. This allows us to perform queries on this characteristic across our loan-level querying tool Cohort Analyzer. Below find the share of deliveries made to Freddie Mac from the Cash Window by loan purpose.
With affordable housing for Low-Moderate Income (LMI) households at the top of the policy agenda, we take a look at loan data for manufactured housing (MH). In a recent report, the CFPB provided a comprehensive survey of this market based on enhancements to the HMDA data first made available in 2018. These include data on
Secured property type:
In their survey, the CFPB looked deeply into the data for 2019. In this note, we update some of their work with 2020 HMDA data. This is important because of the onset of Covid-19 that year. The site-built market performed strongly, but this cannot necessarily be presumed to carry over to MH as Covid is a supply shock, impacting labor markets and supply chains.
Another innovation in this note is that rather than looking at this market by state the way the CFPB does, as a policy guide we look at it bifurcated between rural and nonrural MSAs.
Below finds a chart of the progression of single-family manufactured housing origination volumes for personal loans (securitized by chattel) and mortgages (securitized by real property) from 2018-2020, along with the share of all single-family manufactured housing loans (personal loans plus mortgages) of the total single-family mortgages including those for site-built homes.
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:
In a recent post, we looked at the declining FHA share of the purchase mortgage market relative to the GSE’s across a variety of price points. Another way to look at this question is by a geographic breakdown focusing on those census tracts with a Low-Moderate Income (no greater than 80% of area median income) population greater than 51% (we will call these LMI areas).
To address this issue, we utilize the HMDA dataset, and then apply the LMI information to compute shares of originated purchase loans delivered to FHA vs GSE. This is done on both a loan count and loan balance basis.