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:
This can be summarized:
The release of loan-level dq data by the GSEs opens the door for much new analysis. In today’s blog we will look at servicer type. Below find a table of average DQ’s for each available type, along with average levels of underwriting characteristics:
It’s interesting to note that banks tend to service loans with a modestly higher total DQ rate than the “Nonbank Other” category. The table also shows that banks have a tighter credit box with respect to credit score (higher) and DTI (lower) than nonbanks but have a more generous appetite for higher LTV loans.
The data also presents financial analysts and strategists with a great deal of information about the performance of individual institutions. As an example, we look at the 100 largest servicers from the bank and “nonbank other” category (known as “nonbanks” from now on). There are 43 banks and 57 nonbanks in this group. The charts below plot total DQ’s vs credit score and DTI for each servicer type. Comparing different points or a single point vs trend lines can provide useful insights regarding the competitive landscape.
Of course, these charts just scratch the surface of what is possible here.
In prior posts, we commented on trends in the distribution of risk in the mortgage markets of single-family residential and multifamily markets from the Federal Reserve Z.1 data. This note takes a look at the commercial mortgage market in a similar fashion. Other than the multi-family category, commercial mortgage for properties such as office, retail, hospitality etc is not normally in our wheelhouse at Recursion, but insofar as it is a substitute as an investment vehicle for the residential markets it is useful to take a look at trends here. Commercial mortgages (excluding multifamily) are exposed to unique risk due to COVID-19, as it is very likely offices, hotels and shopping malls will never to able to achieve the same occupancy rate as before the pandemic.
The commercial mortgages(excluding multifamily) outstanding crossed the $3 trillion threshold in Q1 2020. Of interest is that the dominant holder of this risk is banks and thrifts. Their share has been in a narrow 1% range between 61.4% and 62.5% over the past four years. This observation leads naturally to the question of bank holdings across the residential and commercial categories:
What is interesting is the steady drop in the share of single-family mortgages held on bank balance sheets over the past dozen years by about 1% per year (currently 52.1%). The impact of Covid-19 on this trend appears to be quite small. Both multifamily and commercial mortgages have gained shares. It’s worth noting that banks hold sizable amounts of single-family MBS in addition to loans, on the order of $2.9 trillion  in Q3 2020. In this case, the agencies hold the credit risk, not the banks. There is ample room for banks to boost their holdings of residential loans, one more item to watch in the transition year 2021.
 Also called “nonfarm, nonresidential” in the Federal Reserve Z.1 data
 From Q4 2016 – Q3 2020
 The denominator of calculation only banks’ holdings of single-family mortgages, multifamily mortgages, and commercial (non-farm, non-residential) mortgages; It does not include the “farm” sector on the Federal Reserve Z.1 data
 According to L.211 Agency-and GSE-Backed securities outstandings from the Federal Reserve Z.1 data
Our regular readers will be aware that an ongoing theme is the collapsing bank share of mortgage deliveries to the Agencies. 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.
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. 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. 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, 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 characteristics we look at mortgage originations by income bracket. Lending to low- and moderate-income households is an important regulatory requirement of banks. 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. According to Zillow data, 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%.
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.
 The first two 2019 HMDA blogs are available at
 See for example, https://www.fdic.gov/regulations/resources/director/virtual/cra.pdf
 Data from 1984 – 2018 can be found https://www2.census.gov/programs-surveys/cps/tables/time-series/historical-income-households/h08.xls
 Taken from June 2018 data at https://www.zillow.com/ok/home-values/ and https://www.zillow.com/ca/home-values/