The Covid-19 pandemic has resulted in a great economic shock that has been met with a tremendous policy reaction in the form of interest rate cuts and MBS purchases by the central bank. Prepayments have picked up substantially during the year. The question arises as to whether the magnitude of the response is unusual compared to previous episodes of rate declines, and whether they can rise further should borrowing costs fall further.

The magnitude of the relationship between rates and refis is complex and depends crucially on a number of factors. First, the relationship is path-dependent. That is, it doesn’t just matter if rates fall 1.0%, but whether this decline takes rates to new lows so that the biggest possible set of borrowers can profitably refinance.
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Great gobs of sophisticated statistics and modeling go into forecasting prepays on the part of lenders and investors. But a look at prepays over time shows three main waves of refis over the past 20 years. The first (A) is 2000-2003, (B) 2008-2013, and (C) 2019-2020. In all three instances, rates reached new lows. The dates correspond roughly to the times when the mortgage rate broke to a new low until a new trough was formed (or the present in the case of “C”)

​Here are the corresponding periods for prepays:

​Here are the corresponding periods for originations:

Here is a summary of the three periods:

Notably, the biggest jump in prepays occurred in the early 2000s, reaching a record high of 60 CPR. Rates had fallen substantially based on aggressive Fed ease in the wake of the bursting of the tech stock bubble. This passed right through to refis. There was also a substantial decline in rates with the Global Financial Crisis(GFC), but the refi response was more muted due to declining house price, but perhaps also more stretched out in time (off and on through 2013). The current episode with Covid-19 has resulted in record-low mortgage rates, and a substantial spike in refis, but still well below the experience of 2002-04.
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What might explain the differences? A clear place to look is at credit conditions. If rates drop the same amounts in time periods X and Y, but credit conditions are tighter in Y than X, we can reasonably expect a bigger refi impact in X than Y. Below shows the Urban Institute’s Housing GSE Credit Availability Index, which is used to evaluate lender’s risk tolerance:

Note:  Urban Institute’s Housing Credit Availability Index for GSE Chanel. Adapted from Urban Institute Housing Finance at a Glance (August 2020). (https://www.urban.org/research/publication/housing-finance-glance-monthly-chartbook-august-2020)

As can be seen, credit was extremely loose before the GFC, very tight thereafter, followed by a period of modest loosening until 2019. This correlates well with the magnitude of the response of prepays to interest charges in the three regimes. Too hot, too cold and just right? Maybe.

All of this is important not just for investors but for the central bank as the Fed attempts to steer the economy through this uniquely uncertain period. Recently, credit conditions appear to have started tightening. It’s unclear whether this is a “normal” market reaction as volumes rise and capacity is constrained (good credits are easier to process) or whether lenders are becoming more cautious based on a more pessimistic view of the economic outlook. There is a big difference between the two in terms of choosing a successful investment strategy or an optimal monetary policy. The answer is unlikely to come from attempts to model borrower and lender behavior in a nuanced way and more likely to be discerned by careful observation of emerging trends in big data sets.

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.

2019 HMDA data has been released and is loaded into Recursion’s HMDA Analyzer so clients can perform consistent queries back to 1990. As always, a vast wealth of information is available. Below are several high-level observations.

First, total originations rose by over $700 billion compared to 2018[1], a 13-year high. The bank share fell for the eighth consecutive year, reaching a record low of 37%. This was down 1 percentage point from 2018, the smallest decline posted for 8 years. Nonetheless, banks have suffered a remarkable 30-point drop in market share since 2008.

Overview

With the onset of the Covid-19 crisis, the role of the banking sector has once again risen to the forefront of concern. As noted in an earlier post[1], the sharp spike in unemployment is certain to lead to a surge in delinquencies. Banks play a significant role in the mortgage pipeline as originator, servicer and investor. In our previous post, we noted that the onset of the crisis has triggered a flood of cash flowing into bank deposits as households and others shed risky assets. As such, banks have more assets to invest, including in the mortgage market[2].
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Banks like mortgages as an investment, spurred by solid fundamentals related to firm labor markets and rising, but not overly stretched home prices. Banks are protected from credit and default risk by owning agency MBS instead of mortgage whole loans and enjoy favorable treatment from the capital rules set by the regulators. According to Federal Reserve data, in Q4 2019 banks held about 25% of the $9.6 trillion agency MBS market[3]. To understand the behavior of banks in this market it is important to probe its underlying structure.

Overview
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With the onset of the Covid-19 crisis, the role of the banking sector has once again risen to the forefront of concern. As noted in an earlier post[1] the sharp spike in unemployment is certain to lead to a surge in delinquencies. Substantial purchases by the Federal Reserve of Mortgage Backed Securities (MBS) have had a limited impact on rates facing borrowers[2] due in part to uncertainty around the magnitude of the losses and who will bear the costs. Policies regarding forbearance and liquidity provision to mortgage servicers are having an impact on lending standards and the availability of credit.
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Banks play a significant role in the mortgage pipeline as originator, servicer and investor. Most of the current focus is on the first two, but the importance of their role as investor is also crucial. According to Home Mortgage Disclosure Act (HMDA) data Recursion uploaded to the cloud, 3.1 million individual single family loans with a balance of $739.4 billion were originated in 2018 by the banks, of which 60.4% were held on their balance sheet. Each loan file in the data set contains many characteristics, including originator information. As banks originated about 43% of all mortgages that year, the implication is that about one quarter or all residential mortgage production was kept by the banks.

​On April 7, 2020 our CEO Li Chang was invited to speak as an industry expert at a graduate-level finance class at the Gabelli School of Business at Fordham University. Students were also given free access to the Recursion Analyzers to help them monitor the current mortgage market trend using big data tools.
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Students were introduced to the problem of understanding the role of new mortgage fintech lending based on the use of loan-level data on U.S. mortgage applications and originations reported to their regulators according to the Home Mortgage Disclosure Act (HMDA).