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.
We received complete GSE data for November late last week and as always there is a lot to churn on. Another record high of issuance was achieved, although this was entirely due to a surge in refi deliveries (+$16 Bln from October) while purchase deliveries declined slightly (-$7 Bln). Lack of supply and softer seasonal demand appear to be weighing on purchase volumes.
A long-term trend in these comments is the trend decline in the bank share of deliveries to the GSE’s. We have commented that the Covid-19 pandemic has played to the natural technological advantage of nonbanks, while eroding the value of the bank branch networks, particularly for purchase mortgages.
Interestingly, a little bit of a reverse trade can be seen the last couple of months, at least in purchase mortgages. The chart below looks at the bank share, graphed against the gap in the weighted average coupon between nonbanks and banks (“WAC Gap”).
There is a distinct correlation between these two series, although considerable noise is also apparent. Many factors drive market share including underwriting characteristics and product types, but the basic relationship comes across. In November, the gap in the WAC between Nonbanks and Banks increased by almost 4 basis points from October, which was attained by a 3 bp drop in the nonbank WAC being exceeded by a 7 bp drop in that of banks. In a market measured in tens of billions, a single bp has significance.
The question going forward is whether the decline in the rates of banks’ offerings is supported by efficiency enhancements or simply reflects reduced profitability. The answer to this is key in determining the question of their long-term role in this market.
 See, for example: https://www.recursionco.com/blog/besieged-banks
The millennial generation has reached peak home buying age, covering a range of about 25-40 years old. Just this year this cohort at 72.1 million passed the baby boom generation in sheer size although this is a bit less than the 78.8 million peak reached by boomers in the late 90’s. Millennial presence or lack of it in the housing market has been the subject of voluminous commentary, ranging from extreme optimism derived from the magnitude of the population bulge to caution related to affordability and impediments to building savings stemming from student loan debt and high rental and child care costs. What can big data tell us about this?
A 2018 study by the Federal Reserve showed that Millennials had lower incomes and assets and higher debt than previous cohorts at a similar age. Our data sets can provide some useful, but far from conclusive, insights into these trends since the onset of the Covid-19 pandemic. Covid-19 is both a cyclical and structural shock. On the cyclical side mortgage rates have fallen to record lows as the Federal Reserve responded quickly and decisively to the health crisis. On the structural side, residents of urban areas have picked up and fled to less dense locations, resulting in a sharp increase in house prices nationally. According to Redfin these price gains have been led by those in rural and suburban areas, rendering these areas less affordable, while urban areas remain out of reach for most.
We have posted numerous blogs about how Covid-19 has served to accelerate structural change in the mortgage market, particularly in the growing share of nonbank mortgage sales to the GSE’s. It’s natural in such an environment to look at deliveries by channel. As has been widely noted in the industry, the broker channel enjoyed a considerable increase in market share over the 2018-2019 period, as the broker community became better organized. Has this trend continued with the onset of the Covid-19 crisis?
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:
In a recent post we compared the average credit score at origination history from NSMO with that from the agency loan-level datasets on an apples-to-apples basis.
In today’s blog, we improved the matching by using the Analysis Weights that are provided in the NSMO dataset to compute the averages. NSMO Analysis Weights are the product of the sampling weight and a non-response adjustment, which can make the NSMO dataset more representative.
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.
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.
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.