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 a previous blog, we pointed out the importance of overseas investors to the mortgage market.[1] We looked at the share of holdings from this sector, as well as on the country breakdown within the sector. These shares tend to move somewhat smoothly over time as they represent very large portfolios of assets. There is, however, a second data set released in the TIC system every month, namely net capital flows. These data are purchases (or sales) and can be quite volatile in the short term. But they may provide a picture of emerging trends that will not show up in the holdings data for some time.

The same important caveat that applies to the holdings data applies as well to the flow data: the data on purchases and sales of assets reported for a country may be on behalf of agents domiciled in a different location, even the US.

As we have been in a declining rate environment since the start of 2019, it is not surprising that purchases of long-term agency securities have been generally strong from the point of view of asset replacement over that time. But Q2 2020 marked a break in the pattern with the onset of the Covid-19 crisis. Flows were smaller than in Q1, notably so for China, but markedly so for Hong Kong which engaged in outright sales of assets. Interestingly, Taiwan bucked the trend and added substantially to its holdings in Q2 compared to the prior quarter.
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In the background, the economic and financial environment remains highly uncertain. Added to that is the increasing factor of rising geopolitical uncertainty as we head into the US election. Following these flows may provide useful insights into how different agents assess these considerations.

​The drop in the 30-yr mortgage rate to close to 3.0% has worked to keep purchase mortgage securitizations relatively steady while refinancing activities have soared to record highs. Refis in Q2 2020 for the GSE’s came in at more than twice the next highest quarterly figure reported over the past seven years:

Besides the sharp drop in rates, another factor in this sudden surge compared to previous years is the increasing use of Property Appraisal Waivers. With the transaction costs of refinancing going down as a result, it appears that refis accelerated compared to prior episodes of falling rates. To look at this a bit deeper, below find 1-month prepayment speeds of both PIW loans and PIW eligible purchase loans for the two agencies[1]:

​For both agencies, it appears that loans with appraisal waivers prepay faster than eligible loans without, but the impact diminishes over time in part because there are fewer such loans with a PIW left that haven’t prepaid. In fact, for non-cashout refis the impact is even more pronounced:

​In this case not only have prepayment speeds for this category of loans with appraisal waivers slowed relative to others, but in the last couple of months they have actually become slower compared to other eligible loans.
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A combination of unprecedented policy moves and market developments have served to upend conventional mortgage market analysis, underscoring the need for new thinking, and new tools.

In a recent post[1] we noted that prepayment speeds jumped in June, led by a startling 12.4% rise in 1-month FHA speeds to 36.1, likely due to a change in pool rules that prohibited loan buyouts from being re-securitized beginning on July 1. This bears looking into. If this jump is due to such a policy change then one would expect that prepayment speeds would rise more for banks than thinly capitalized nonbanks. Indeed, this is the case, and remarkably so.

A reverse mortgage is a mortgage loan backed by a residential property, that allows the borrower to access the unencumbered equity in their home without making monthly payments. The loans are usually offered to senior homeowners. Currently, FHA has endorsed reverse mortgage loans an outstanding balance of 54 billion USD and these are securitized in Ginnie Mae’s HECM pools. This program is available for people age 62 and over.
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There are many differences between reverse mortgages and regular (forward) mortgages, particularly that the balance of reverse mortgages tends to grow over time as interest accrues and sometimes payments are made to the homeowner. But in both cases investors face prepayment risk. The HECM loan tape disclosed monthly by Ginnie Mae provides data by many characteristics, including reason for prepayment and the age of the borrower.