Our regular readers will be aware that an ongoing theme is the collapsing bank share of mortgage deliveries to the Agencies[1]. Our recent monthly download shows that the bank share of deliveries to the GSEs fell sharply again in December, collapsing by over 7.0%(!) from November to a record low 22.3%. A year earlier this figure stood at 41.7%. The plunge witnessed over the past year marked an acceleration in a long-term trend, as banks face a heavier regulatory burden relative to nonbanks, and as nonbanks have made inroads into the market through their development of superior technology interfaces with their clients. Covid-19 has served to accelerate this trend by pulling customers out of bank branches and putting them in front of their laptops and smart phones.

The latest drop incentivized us to dig a little deeper; we didn’t have to peer too deep to find an interesting result. Below finds a bank of the bank share of GSE deliveries, and the same chart excluding Wells Fargo and JP Morgan Chase.

For some time, we have been writing about the surge in the share of nonbanks in mortgage underwriting and servicing[1]. We are just getting GSE data for October and the results are striking:

One of our ongoing themes in this blog is that we are entering a period of unremitting structural change. We’ve noted previously that the combination of Covid-19 and technological innovation is leading to a surge in the nonbank share of purchase mortgages to the GSE’s[1]. Of course, there are others, notably climate change. As the technology leader among states and also the one suffering severe damage from wildfires, California is at the nexus of these transformations.
A survey conducted by the University of California at Berkeley in 2019 revealed that more than half of the residents of the state had given “some” or “serious” thought to leaving the state[2].  Has this in fact occurred? Such a desire may be offset by the traditional role of the state in attracting immigrants and young people looking for careers in technology and media. One way to look at this is to pull data for the count of new purchase mortgages sold to the GSEs in the state as a share of the US total:

We have commented previously on the rising share of nonbank deliveries to the GSE’s in the wake of the Covid-19 crisis[1], but the data just released for the month of July shows this trend to be picking up at an astonishing pace. This time, let’s break the market up into two pieces: Purchase and Refi:

In our third look at 2019 HMDA[1] characteristics we look at mortgage originations by income bracket. Lending to low- and moderate-income households is an important regulatory requirement of banks[2]. The definition of “low” and “moderate” depends on the local area in which the bank operates. HMDA data is well-suited to regulators looking to track the performance of the institutions they oversee and allows banks to benchmark their performance against their competition. If banks need to add low- to moderate-income loans to their portfolio to meet requirements, HMDA can provide direction regarding which institutions might be a source of product that meets needed characteristics.

Below we present a quick high-level example. HMDA data operates down to the census tract level, but for our purposes here let’s look at two distinct states: California and Oklahoma. In 2018, median income in the two states was $70,500 and $54,400, respectively[3]. According to Zillow data[4], the median house prices in California and Oklahoma that year were $550,000 and $122,000 respectively. Clearly housing is relatively unaffordable for households at or below median income in California compared to Oklahoma. So it is not surprising that the homeownership rate in Q2 2018 for California, at 54.3%, is substantially below that of Oklahoma, at 69.1%[5].
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Confirming this, the following table from 2018 and 2019 HMDA show that there is a substantially greater share of lower- and moderate- income loans available in Oklahoma than in California. Interestingly this share declined in 2019 relative to 2018, particularly for Oklahoma. It is not clear whether this is due to fundamental factors or technical issues related to an increase in the share of “N/A” responses between the two years.

​Finally, to be consistent with prior posts we look at the share of conforming loans originated by banks that are sold to the GSEs, broken down by income brackets:

​A few interesting observations pop up. First, in California the loans that banks keep on their book are almost entirely made to the highest-income households. For Oklahoma, it’s a mixture of highest income and lowest income. This suggests that policy requirements regarding serving poorer communities plays a relatively greater role in Oklahoma than California.

In a recent post we looked at the differences in bank underwriting characteristics between those conforming loans held on book compared to those delivered to the GSEs using data pulled from Recursion HMDA Analyzer[1]. We now extend this into another dimension via the addition of LTV.
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Below find the difference in share of such deliveries between sold loans and those held on book:

With the release of 2019 HMDA data, we now have two years of loan-level information that contains both demographic and credit characteristics. Demographic information in HMDA includes income, race, and geography down to the census tract level, while credit characteristics include DTI. Our agency loan level databases contain a richer set of information regarding lending characteristics, but limited data on geography and demographics. For institutions looking to benchmark their performance in affordable and minority lending for regulatory purposes, 2019 HMDA, with data on thousands of lenders, is an invaluable tool. If you are interested in finding out more, please reach out.
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There are of course policy uses for this data as well. A significant difference between HMDA and the agency pool loan-level data is that HMDA contains data for loans held on book, the so-called “Unsold” category. This allows a comparison of loans that banks originate and keep and those they deliver. We can break this down in any number of ways, but let’s look at it for conforming loans broken down by DTI.

In the table above, we can readily observe that banks tend to keep higher-quality loans (as measured by DTI<=43) compared to those they deliver to the Enterprises. Of course, this is not a complete picture of this issue; there are many other ways to slice the data (credit score, LTV, loan size, geography). Moreover, as there is a correlation between low LTV and desirable loan characteristics for regulatory purposes (minority status, low income), we cannot simply conclude that it’s a matter of keeping the best for themselves.
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A second interesting question is: did behavior in this regard change between 2018 and 2019? Below you can find a chart of the change in the distribution between unsold and delivered loans between these two years.

It appears that banks kept more of the loans associated with very low levels of indebtedness (DTI<35) in 2019 compared to 2018, while they distributed a small share of higher-risk loans across the spectrum of DTIs above that level.
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Explanations for such behavior are the subject of future research.

In a recent post[1], we pointed out an acceleration in the trend towards an increasing share of deliveries of purchase mortgages to the GSEs. This trend continued with the release of June data earlier this month, so a deeper dive is called for. First, the trend is far more evident for deliveries to Fannie Mae than Freddie Mac: