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/
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. We now extend this into another dimension via the addition of LTV.
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.
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.
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.
Explanations for such behavior are the subject of future research.
In a recent post, 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:
With the release of the GSE delivery data for May late last week we can start to see the impact of the Covid-19 crisis on the spectrum of loans delivered to Freddie Mac and Fannie Mae. First, deliveries of purchase mortgages have so far held up, with May deliveries up 3.5% from a year earlier. The notable development, however is the discrepancy between bank and nonbank deliveries, with Nonbank lenders in May delivering 30% more loans compared to a year earlier, while banks delivered 24% less.
In general, mortgage production has held up because mortgage rates are at record lows in the face of the economic crisis. The question is why they hold up better for nonbanks than banks. The bank data are more complicated to analyze than nonbank because banks have the option of holding loans on their balance sheets so a decline in deliveries may be due to an increase in loans retained rather than a drop in originations. Such a decline seems unlikely at present because banks have an incentive to sell loans that might go into forbearance because the two agencies charge the lenders substantially for such purchases. We have commented previously that banks are reducing loan balances but adding MBS to their balance sheets to reduce these risks. Another possibility is that banks are tightening lending standards due to concerns about rep and warrant issues if loans become delinquent. It is also possible that the virus has accelerated the trend to fintech lending, much like it has online shopping. There leaves many paths to investigate in future posts.
Mortgage lenders obtain loans through three channels 1) The retail channel through which they originate loans, 2) The wholesale channel through which they purchase loans that are originated by other financial institutions, and 3) the broker channel through which they acquire loans that are originated by the lender through an independent mortgage banker not affiliated with the originating institution. Channels from outside the selling institution are called Third Party Originations or TPO’s. Every month Fannie Mae and Freddie Mac report the selling institution of every loan delivered to them, and the channel by which the loan was obtained. Over the last couple of years there has been a notable rise in the share of the broker channel. This note looks at recent trends and looks for market segments in which these are most pronounced, with an emphasis on the broker channel. Table 1 shows the market shares of sales to the GSE’s by channel.
We previously noted that the recent surge in bank deposits, that is related to rising risk aversion associated with the onset of the Covid-19 crisis, serves to support bank investment in agency Mortgage Backed Securities (MBS). A look at recent Federal Reserve Board data reveals that growing MBS demand is not just the result of greater deposits, but also is due to a desire on the part of depository institutions to reduce risk in the mortgage space.