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:

​The Federal Reserve recently released its quarterly Z.1 report: The Financial Accounts of the US (formerly known as the Flow of Funds) for Q2 2020. This voluminous dataset contains very detailed information describing financial flows and stocks across all major segments of the economy. For our purposes the key chart is the distribution of holdings of Total Long-Term Agency Debt (Agency MBS + Agency notes and bonds):

The clear takeaway for Q2 is the central bank gained share as it took unprecedented actions to stabilize the financial system in the wake of the Covid-19 crisis. The Fed’s share of the stock of Long-Term Agency Debt jumped by about 4.9% from Q1 to Q2, a record high increase. The biggest losing category was “Others” which fell by 3.6%. This loss came largely from the household subsector within this category. The “Commercial Banks” share rose by 0.9%, but this does not necessarily imply greater appetite for mortgage risk as depositories are increasing their securitization rate by swapping whole loans for securities[1]. Finally, the “Foreign Investor” category lost 0.5% to a 7-quarter low.

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.

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:

As is the case for residential mortgages, every month Ginnie Mae publishes data on loan-level delinquencies for its commercial real estate programs. The structure is a bit different than for single family, with different categories (the dominant one being FHA multifamily, but also hospitals and nursing homes). In this short post we look at recent performance for FHA multifamily and nursing homes.
​
Traditionally, multifamily DQs for FHA are low because these loans are concentrated in affordable housing, where there is a persistent condition of excess demand. The costs of eviction are low and new tenants are ready to move in. But this is not necessarily the case in the COVID-19 era as the economic impact falls most heavily on the lower income working class, so there are fewer people who can afford affordable housing without support from government income programs such as jobless benefits.

One ongoing theme from these notes is that the COVID-19 crisis is resulting in policy actions that impact behaviors across the MBS production pipeline. One of these has to do with a change in the loan buyout policy from pools issued by the GSEs. Previously, general policy was to purchase loans out of pools that had become four months delinquent[1]. However, with the onset of the crisis, delinquent loans in forbearance programs remain in pools as long as this status is maintained[2]. Consequently, last week’s release of pool data by Freddie Mac gives us the opportunity to look at the share of loans that are 120D+DQ (as April was the first month in which the impact of COVID-19 became significant). 

Besides allowing us to track the magnitude of loans that have been delinquent for an extended period, this data allows us to make inferences about shifts in the composition of the burden of covering P&I costs between sectors for loans in forbearance. As we have written previously[3], this cost burden shifts from the servicers to the GSEs after four months of missed payments. Consequently, starting next month servicers will see costs related to loans that have missed payments for such a period move off their plates and onto those of Fannie Mae and Freddie Mac.
​
Insofar as the volume of loans beginning to miss payments is less than those which have missed more than four months payments, the aggregate cost burden on servicers may fall. Consequently, upward pressure on the mortgage spread over Treasuries may have scope to ease.

Of course, these costs don’t disappear; they merely get transferred to the taxpayers. Another potential flashpoint in our volatile age.

Cyclical and secular factors are coming together to boost agency mortgage production. On the cyclical front, record low mortgage rates are the key driver of surging refi activity. Purchase activity is supported as well by low rates, but there are also indications that secular changes surrounding lifestyle choices sparked by the Covid-19 pandemic are leading homeowners to change residences away from the largest urban centers[1].

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: