We received the first loan-level performance data for the GSE’s a few months ago, so it’s about time to see what tentative observations can be drawn from this new data set. As a popular theme for this blog is the bank/nonbank share this seems a good place to start. In general, we have noticed that nonbank DQ’s tend to be higher than those for banks, and that this distinction is correlated with the relatively more generous credit terms available in the nonbank sector. Below find a table that demonstrates this for 2018 and 2019 vintage mortgages:

In a recent Liberty Street Economics blog post, researchers at the New York Federal Reserve discussed the background and landscape of mortgages qualifying for Covid forbearance under the CARES Act[1].

​We received the monthly GSE data download for the June book of business over the weekend and prepayment speeds dropped for the second consecutive month, with the 1-month CPR printed 22.4, the low posted since 17.1% was reached in February 2020 just before the onset of the pandemic.

Mortgage rates are of course the key driver here, but other issues matter as well, notably lending capacity. With the onset of the pandemic and the associated loosening of monetary policy and spike in demand for housing away from dense locations, the mortgage industry became overwhelmed. Originators were busy hiring and increased their capacity over the past 18 month to deal with the long period of refinancing activity.  However, as prepayment speeds slow down, it appears that the capacity building may be overshooting. In response, originators have started to lower their underwriting standards to create enough volume to fully utilize the capacity.

What we can see is that purchase demand remains strong, with the swing product being refinance mortgages. It is evident that lenders are trying to smooth out refinance production with countercyclical credit tightening and loosening. As credit scores are higher than was the case in the pre-pandemic period there is room to ease further, but the ultimate extent is highly uncertain.

Recently, the Federal Reserve released its May 2021 Financial Stability Report[1], with a particular emphasis on asset valuations. Valuations are raised as a concern as “Prices of risky assets have generally increased since November with improving fundamentals, and, in some markets, prices are high compared with expected cash flows”. While not cited as a matter of high alarm the report commented that “House price growth continued to increase, and valuations appear high relative to history.”
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On May 25, FHFA released the purchase-only house price index for March, showing a record-high growth rate of 13.9%, far above the bubble-era peak of 10.7% attained in 2005[2]. Housing fundamentals are of course supportive with mortgage rates below 3% and economic activity rebounding as vaccine optimism spreads. The unique factor now in housing is the impact of the pandemic on preferences for housing away from density and towards suburban and smaller-urban centers. This new fundamental can easily be seen via booming housing demand during the pandemic as measured here by purchase mortgage deliveries to Freddie Mac[3].

In a recent post we discussed trends in the conforming purchase market by occupancy type[1]. In this note we look at performance metrics.
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To begin, we look at prepayment speeds. It’s important to note that certain fees (which Fannie Mae calls LLPA’s[2] and Freddie Mac calls Credit Fees[3]) vary by occupancy type, particularly for those with high LTV’s.

While market commentary is focused on developments such as inflation and house price increases, the key housing policy issues in the post-Covid world are financial inclusion and climate change. Our agency loan-level data provide us with many insights into market trends, but these do not contain demographic or geological details that are necessary to perform in-depth analysis in these areas.
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On the topic of financial inclusion, the key supplemental data set is the Home Mortgage Disclosure Act (HMDA) dataset, an annual disclosure made by lenders in support of fair lending. HMDA data contains relevant data points such as income, gender, and race. Any assessment of fair lending practices requires an analysis of how these factors influence the availability of credit. To accomplish this, Recursion has applied a proprietary matching algorithm to create a robust dataset consisting of loans with both underwriting and demographic characteristics. Over the period 2008 – 2020 the data set consists of about 20 million loans. Below finds a chart of average credit scores by race (as measured by race of the first borrower) over the 2008 – 2020 period from this matched data set:

​Recursion’s Chief Research Officer Richard Koss published an article in Housing Wire Magazine on agency mortgage forbearance and the capital markets. When the CARES act was originally passed on March 27, 2020, there were notable concerns that these measures would merely postpone an inevitable correction in the housing market once the programs expired. However, home price appreciation came to the rescue. But the mortgage market continues to face the prospect of involuntary buyouts of loans from agency pools. Check out the details at:
https://www.housingwire.com/articles/the-end-of-forbearance-and-the-capital-markets/

With house prices soaring to new highs on the back of pandemic-related household relocations and sub 3% mortgage rates, the natural question is how far these trends can continue. While we have no crystal ball for calling market tops and bottoms, we feel we can gain some insight into market dynamics by looking into the composition of demand.
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The GSE’s provide us with the ability to peer into this composition through the “Occupancy Type” flag, which consists of three types: owner-occupied, second homes and investor properties. Here we look at the share of all purchase mortgages from the last two types: