In a recent post[1], we discussed using Recursion’s proprietary tools to unravel the Federal Reserve’s MBS holdings of Fannie Mae and Freddie Mac loans. The Fed’s holdings, however, are part of a bigger picture issue regarding the notion of “float” in the MBS market, that is, the amount of securities outstanding that are available to trade. The holdings of the central bank serve to reduce the float as the Fed is a buy-and-hold investor. These loans are said to be “locked up”. Besides the Fed, loans can be locked up in structured products, notably Collateralized Mortgage Obligations (CMOs). The first CMOs were launched by Freddie Mac as Real Estate Mortgage Investment Conduits (REMICs) in 1988 and allow cash flows to be tranched to meet the needs of different investors[2]. Pools in CMOs’ collateral groups are also locked up.

In a recent paper, researchers at the Philadelphia Fed (Liu, Song and Vickery, May 2021) discussed the history of the differential between the pricing and the trading volume differential between Fannie Mae and Freddie Mac securities[3]. Historically, Fannie Mae securities had traded ten times as often as those of Freddie Mac, with the consequence that trading costs for Freddie Mac could be twice those of Fannie Mae. In this paper they comment that Freddie Mac compensated for this by raising its g-fee, and by locking up its securities in CMOs.
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Using the same recursive algorithms as in our prior blog, we can back out the CMO lockup, FED lockup and Float by agency:

​With talk of taper at the top of the monetary policy discussion, it is worthwhile to dig a bit into the role of the Federal Reserve in the functioning of the MBS market. As is well known, the onset of the Covid-19 pandemic resulted in a resurgence of central bank purchases of Agency mortgage-backed securities (MBS).

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.

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.
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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.

One of our major rules at Recursion is that we are a fintech data and analytics company and that we don’t give investment advice. So spoiler alert: the answer to the question is that anything is possible.
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But we noticed in the most recent weekly Freddie Mac survey that the 30-year mortgage rate edged up to 3.01% from a record-low 2.98% the prior week, the first sub-3.0% level ever recorded. Market lore says that at a certain level, rates give lenders sticker shock and mark a point below which they are reluctant to venture. In an early blog post we noted that mortgage rates were at record lows, but that Treasury yields were deeper into record-low territory, so mortgage spreads were actually quite wide[1].

​Mortgage rates are set in the market reflecting offsetting pressures including: downward pressure from Federal Reserve purchases, upward pressure from record demand, and the costs of forbearance borne by servicers that they seek to recoup with higher margins on new business. Below is an update to the March chart with a new variable added: the inelegantly named OPUC: