The cherry blossoms are blooming, which means it’s time for the HMDA preliminary data set to be released. The dataset provides a social underpinning to the nation’s mortgage market and enhances our understanding of the behavior of borrowers and lenders. The 2022 dataset has been particularly eagerly awaited, as we get our view on the new world of high inflation and mortgage rates for the first time in decades. We start with origination volumes and get not just confirmation of the onset of mortgage winter, but some breakdown of its characteristics.

With all eyes on the turmoil in the banking sector, it’s good to see that policymakers continue to innovate to help borrowers. Earlier this month, HUD published Mortgagee Letter 2023-06 “Establishment of the 40-Year Loan Modification Loss Mitigation Option”, which establishes the 40-year standalone Loan Modification into FHA’s COVID-19 Loss Mitigation policies[1]. The standalone 40-yr mod is scheduled to be implemented by May 8. This follows the establishment of a 40-yr modification with a partial claim in April 2022[2]. The introduction of standalone 40-yr mods reminded us that we haven’t focused on the progress of the 40-year mod with a partial claim identified by pool prefix “ET”. Below find a chart of issuance by program:

In recent posts, we introduced the phrase “Mortgage Winter” to describe the current environment where high-interest rates and elevated home prices lead to a severe drop in transaction volumes[1]. Subsequently, we looked at the impact of this situation on individual market participants[2]. The bulk of market participants across the mortgage ecosystem is experiencing year/year revenue declines of two-thirds or more. These entities are having to adjust their business models to this situation and develop strategies to navigate the uncertain environment ahead.
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Spring will come, but whether the ensuing rebound will be sufficient to return the sector to a state of financial health is a question that remains far from assured. There is also another factor to consider besides revenue, and that is the potential for increased servicing costs associated with delinquent borrowers.

In a recent post[1], we spoke about how the current market environment of high interest and home prices is leading to downward pressure on both supply and demand in the housing market, a situation we call "Mortgage Winter". While this environment is unlikely to result in a severe recession such as the Global Financial Crisis, there is the potential for broad fallout associated with distress in the lender and broker markets.

First, we look at the originations.
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The count of loans that were delivered to the three agencies dropped by 68% from Q4 2021 to Q4 2022:

With 2022 at a close, we can begin to assess the impact of the extraordinary shocks of the past few years on the mortgage market landscape. The boom-bust nature of the housing market since the onset of the Covid-19 pandemic has resulted in an unprecedented degree of uncertainty about the outlook. Most commentary in this regard is understandably focused on home prices, but in the end, real estate is a transactional business, and we focus on that market aspect here:

Recursion has undertaken an intensive effort to compute the size of the Agency CMO market back to 2000. The size of the Agency CMO market is calculated by building up from the loan level. This data is provided by agency disclosure of the portfolio of each collateral group and collected from text files, pdfs, and other formats across single-family and multifamily CMOs. The formats of the disclosure files differed across agencies and changed over time, presenting a challenge to unify.

The inconsistent data quality posed another challenge. The single metric we used to assess quality was assets = liabilities. The existence of Re-Remics and IOs introduced overcounting, which we eliminated using an algorithm that closed the asset-liability gap, with the remaining portion largely explained by over-collateralization. In the end, we were able to construct a direct relationship with all single-family and multifamily CMOs and the loans backing them up via the “exploded method”.
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We performed these calculations by agency for both single-family and multifamily loans on a monthly basis. Below find bar charts of the progression of the single and multifamily CMO markets back to 2000 on a year-end basis. The single-family CMOs for the three agencies are fairly homogenous. For multifamily CMOs, we include the CMOs collateralized by Ginnie Mae multifamily pools backed by Ginnie construction loans and project loans. For Fannie Mae, we include Fannie Mae GeMS (CMO deals backed by Fannie DUS pools), and for Freddie Mac, we include all Freddie K deals-- classifying them as 100% CMO due to their structure.

We’ve written before about curtailments, which are particularly interesting during times of rising interest rates when refinancings are at low levels[1]. We believe that investors and modelers would benefit from examining this aspect of borrower behavior. A good way to demonstrate this is to look at the home payment patterns of repeat homebuyers. In the recent environment of skyrocketing home prices, buyers of new homes have been confident about their ability to sell their current residence and have been more likely to purchase their new home before they pay off their old mortgage. If this story is true, we would expect to see significant curtailment activity within a few months of the purchase of a residence on the part of repeat buyers. In our previous note, we introduced the concept of a “Constant Curtailment Rate”, and implemented the calculation in Cohort Analyzer to quantify this effect:

In a recent post, we discussed the attributes of manufactured housing data that came with the final 2021 HMDA release in July[1], which were not available in the preliminary release. Another important data point from this final release is the conforming flag, identifying which loans satisfy the requirements for delivery to Fannie Mae and Freddie Mac. Obviously, any loan sold to a GSE is conforming, so its main use is to enable analysts to examine these loans which are held on bank balance sheets.
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The lag between the preliminary and final releases of HMDA data can be four months or longer, so it would be useful to be able to identify these loans right after the preliminary release. One way to approach this is to flag non-government loans with balances above the conforming limits as “jumbo”. How does Recursion’s “Jumbo” flag compared with HMDA’s Conforming flag? If the information is perfect, “Jumbo” loans should be all the loans that are not “Conforming”.  However, the exact original balance of a mortgage is not provided by HMDA to protect privacy. For those loans close to the conforming boundary, our program can misjudge which category to assign.  Given all that, going back to 2018, there is still a very strong negative connection between the two measures: