At the market highs, banks gorged themselves on assets, lending and loading their balance sheets in an era of cheap money and robust valuations. As asset prices drop, these same companies find their balance sheets functionally impaired and in some cases insolvent. They are able to stay alive with substantial help from the central bank but require ongoing support. This support and an unhealthy balance sheet preclude them from fulfilling their role in the economy.

We are describing, of course, the situation in Japan in the late 1980s and early 1990s, when banks lent freely, and companies purchased both real estate and equity at the market highs. When the central bank tightened monetary policy and the stock market tanked, many firms became distressed and had to rely on support from the central bank to stay afloat. But with sclerotic balance sheets, they were unable to thrive, leading to the “lost decade” (or two or three) of anemic growth.

While there are substantial parallels between the U.S. today and Japan of three decades ago, there are differences as well. Firstly, the U.S. has a dynamic non-bank sector that can fill typical roles of lending and financial intermediation. And second, much of the bank impairment comes from Agency MBS, which slowly, but surely, will prepay and relieve pressure on their HTM assets.

How fast will these passthroughs pay off? It will vary greatly from bank to bank and depends on their mix of passthroughs and their loan rates relative to current market rates, what MBS traders call “refi incentive” or “moniness.” It is helpful to remember that incentive also matters to housing turnover, which is a form of mortgage prepayment. For example, a borrower with a note rate that is 100bp below prevailing rates is much more likely to move to a new house than a borrower with a note rate that is 200bp out of the money, a trait that mortgage practitioners call “lock-in”.

As a proxy for the aggregate bank’s balance sheet, we look at the universe of conventional and GNMA passthroughs and remove the MBS held by the Federal Reserve.

The Fed’s most substantial purchases flowed from their balance sheet expansion during COVID, when mortgage rates were at all-time lows. Consequently, the Fed owns a skew of the MBS market. Two-thirds of the Fed’s position of 30yr MBS have a note rate of 3.25% or lower. In contrast, the market ex Fed has just under 50% of the same note rates.

From here, we can estimate prepayments on the remaining universe. Prepay estimates from dealers and analytics providers like RiskSpan vary, but generally fall in the 4 to 6 CPR range for out-of-the-money coupons. This, coupled with scheduled principal amortization of roughly 2-3% per annum means that for this level in rates, runoff in HTM MBS should occur around 8% per annum — slow, but not zero. After five years, approximately 1/3 of the MBS should pay off. Naturally, the pace of runoff can change as both mortgage rates and home sales change.

While the current crisis contains echoes of the Japanese zombie bank crisis of the 1990s, there are notable differences. U.S. banks may be hamstrung over the next few years, with reduced capacity to make new loans as MBS in their HTM balance sheets run off over the next few years. But they will run off — slowly but surely.

In a previous blog, we highlighted large curtailments on loans, behavior that was driving a prepayment spike on some new-issue pools. Any large curtailment should also result in shortening the remaining term of the loan because the mortgage payment is nearly always “level-pay” for loans in a conventional pool. And we see that behavior for all mortgages experiencing large curtailments.

However, we noted that nearly half of these loans showed a subsequent extension of their remaining term back to where it would have been without the curtailment.

This extension occurred anywhere between zero and sixteen months after the curtailment, with a median of one month after the large payment. We presume these maturity extensions are a loan “recast,” which is explained well in a recent FAQ from Rocket Mortgage. In summary, a recast allows the borrower to lower their monthly payment after making a curtailment above some threshold, typically at least $10,000 extra principal.

Some investors may not be aware that a recast loan may remain in the trust, especially since the terms of the loan are being changed without a buyout.

Further, since the extension lowers the monthly payment, the trust will receive principal more slowly ex curtailment than under the original terms of the loan. This could possibly affect buyers of the pool after the curtailment and before the recast.

While the number of recast loans is small, we found it interesting that the loan terms are changed without removing the loans from the pool. We identified nearly 7,800 loans that were issued between 2021 Q4 and 2022 Q1 and had both a curtailment greater than $10,000 and a subsequent re-extension of loan term.

Of these loans, the typical time to term-recast is zero to two months, with 1% of the loans recasting a year or more after the curtailment.

Some of these loans reported multiple curtailments and recasts, with loan 9991188863 in FR QD1252 extending on three separate occasions after three large curtailments. It seems the door is always open to extension.

For loans that recast their maturities after a curtailment, 85% had extensions between 10 and 25 years.

Large curtailments are uncommon and term-recasts comprise roughly half of loans in our sample with large curtailments, so term recasts will typically have only a small effect on pool cash flows, extending the time of principal receipt ex curtailment and possibly changing borrower behavior.

For large pools, any effect will be typically exceeded by prepayments due to turnover.

However, for some smaller pools the WAM extension due to recast is noticeable. We identified dozens of pools whose WAM extended after a recast of underlying loan(s). The table below shows just a few examples. All of these pools are comparatively small, which is to be expected since just one or two individual loan recasts can have an outsized effect on a small pool’s statistics.

As any mortgage market veteran will attest, the distribution and structure of the mortgage market is constantly in flux. When rates fall, at-the-money coupons become premiums, staffing at originators rises, the volume of refis increase, and the distribution and seasoning of coupons change.

And then the cycle turns. Rates rise. Premiums become discounts. Originators cut staff and prepay speeds plummet. But this too changes, and longtime participants will recognize echoes of 1994 or 1999-2000 in today’s washout.

The brief video animation below tracks the evolution of the mortgage market since 2006, with an eye on distribution and seasoning of borrowers.

Mortgage rates have risen nearly 200bp from the final quarter of 2021, squelching the most recent refinancing wave and leaving the majority of mortgage holders with rates below the prevailing rate of roughly 5% (see chart below). For most homeowners, it no longer makes sense to refinance an existing 30yr mortgage into another 30yr mortgage.

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But, as we noted back in February, the rapid rise in home prices has left nearly all households with significant, untapped gains in their household balance sheets. For homeowners with consumer debt at significantly higher rates than today’s mortgage rates, it can make economic sense to consolidate debt using a cash-out refi loan against their primary residence. As we saw during 2002-2003, cash-out refinancing can drive speeds on discount mortgages significantly higher than turnover alone. Homeowners can also become “serial cash-out refinancers,” tapping additional equity multiple times.  

In this analysis, we review prepayment speeds on cash-out refis, focusing on discount MBS, i.e., mortgages whose note rates are equal to or below today’s prevailing rates. 

The volume of cash-out refis has grown steadily but modestly since the start of the pandemic, whereas rate/term refis surged and fell dramatically in response to changing interest rates. Despite rising rates, the substantial run-up in home prices and increased staffing at originators from the recent refi boom has left the market ripe for stronger cash-out activity. 

The pivot to cash-out issuance is evidenced by the chart below, illustrating how the issuance of cash-out refi loans (the black line below) in the first quarter of this year was comparable with issuance in the summer of 2021, when rates near historic lows, while rate/term refis (blue line) have plunged over the same period. 

With cash-out activity set to account for a larger share of the mortgage market, we thought it worthwhile to compare some recent cash-out activity trends. For this analysis, the graphs consist of truncated S-curves, showing only the left-hand (out-of-the-money) side of the curve to focus on discount mortgage behavior in a rising rate environment where activity is more likely to be influenced by serial cash-out activity. 

This first chart compares recent performance of out-of-the money mortgages by loan purpose, comparing speeds for purchase loans (black) with both cash-out refis (blue) and rate/term refis (green). Notably, cash-out refis offer 1-2 CPR upside over rate/term refis, only converging to no cash out refis when 100bp out of the money.[1] 

Next, we compare cash-out speeds by servicer type, grouping mortgages that are serviced by banks (blue) versus mortgages serviced by non-bank servicers (green). Non-bank servicers produce significantly faster prepay speeds, an advantage over bank-serviced loans for MBS priced at a discount. 

Finally, we drill deeper into the faster non-bank-serviced discount speeds for cash-out refis. This chart isolates Quicken (red) from other non-bank servicers (green). While Quicken’s speeds converge with those of other non-banks at the money, Quicken-serviced cash-out refis are substantially faster when out of the money than both their non-bank counterparts and the cash-out universe as a whole.[2]

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We suspect the faster out-of-the-money speeds are being driven by serial cash-out behavior, with one servicer in particular (Quicken) encouraging current mortgage holders to tap home equity as housing prices continue to rise. 

This analysis illustrates how pools with the highest concentration of Quicken-serviced cash-out loans may produce substantially higher out-of-the-money speeds relative to the universe of non-spec pools. To find such pools, users can enter a list of pools into the Edge platform and simultaneously filter for both Quicken and cash-out refi. The resultant query will show each pool’s UPB for this combination of characteristics. 

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[1] For this analysis, we focused mortgages aged 18 to 48 months, with FICO above 700, LTV less than or equal to 80, and loan size greater than or equal to 250k. Other cuts are available. Please see us for details. 

[2] For this analysis, we removed points with less than 1000 observations to reduce statistical noise. 

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In early 2021, GNMA began issuing a new class of custom pools with prefix “RG.” These pools are re-securitizations of previously delinquent loans which were repurchased from pools during the pandemic.[1] Loans in these pools are unmodified, keeping the original rate and term of the mortgage note. In the analysis below, we review the recent performance of these pools at loan-level detail. The first RG pools were issued in February 2021, growing steadily to an average rate of $2B per month from Q2 onward, with a total outstanding of $21 billion. 

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The majority of RG issuance has included loans that are two to seven years seasoned and represent a consistent 2-3% of the total GNMA market for those vintages, dashed line below.

Coupons of RG pools are primarily concentrated between 3.0s through 4.5s, with the top-10 Issuers of RG pools account for nearly 90% of the issuance.

Below, we compare speeds on GNMA RG pools under various conditions. First, we compare speeds on loans in RG pools (black) versus same-age multi-lender pools (red) over the last twelve months. When out of the money, RG pools are 4-5 CPR slower than comparably aged multi-lender pools but provide a significantly flatter S-curve when in-the-money.

Next, we plot the S-curve for all GNMA RG loans with overlays for loans that are serviced by banks (green) and non-banks (blue). Bank-serviced RG loans prepay significantly slower than non-banks by an average of 9 CPR weighted across all incentives. Further, this difference is caused by voluntary prepays, with buyouts averaging a steady 4% CBR, plus or minus 1 CBR, for both banks and non-banks with no discernable difference between the two (second graph).

Finally, we analyzed the loan-level transition matrix by following each RG loan through its various delinquency states over the past year. We note that the transition rate from Current to 30-day delinquent for RG loans is 1.6%, only marginally worse than that of the entire universe of GNMA loans at 1.1%. RG loans transitioned back from 30->Current at similar rates to the wider Ginnie universe (32.3%) and the 30->60 transition rate for RG loans was marginally worse than the Ginnie universe, 30.8% versus  24.0%.[2]

Monthly Transition Rates for Loans in GNMA RG Pools: In summary, loans in RG pools have shown a substantial level of voluntary prepayments and comparatively low buyouts, somewhat unexpected especially in light of their recent delinquency. Further, their overall transition rates to higher delinquency states, while greater than the GNMA universe, is markedly better than that of reperforming loans just prior to the outbreak of COVID.

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[1] For a more detailed description of the RG pool criteria, GNMA’s original memorandum can be found here.

[2] These transition rates have been stable over different time periods. The dollar-weighted average transition rates over the last year closely match the rates over the prior six months.

With the Fed starting their tightening cycle and reducing balance sheet, mortgage rates have begun rising. Since late summer, 30-year conforming rates have risen more than 100bp, with 75bp of that occurring since the end of December. The recent flight-to-quality rally has temporarily eased that, but the overall trend remains in place for higher mortgage rates.

With this pivot, mortgage investors have switched from focusing on prepayment protection to mitigating extension risk. In this post, we offer analysis on extension risk and turnover speeds for various out-of-the-money Fannie and Freddie cohorts.[1]

In the chart below, we first focus on out-of-the-money prepays on lower loan balance loans. For this analysis, we analyzed speeds on loans that were 24 to 48 months seasoned. We further grouped the loan balance stories into meta-groups, as the traditional groupings of “85k-Max”, etc, showed little difference in out-of-the-money speeds. When compared to loans with balances above 250k, speeds on lower loan balance loans were a scant 1-2 CPR faster than borrowers with larger loan balances, when prevailing rates were 25bp to 100bp higher than the borrower’s note rate.

We next compare borrowers in low FICO pools, high LTV pools, and 100% investor pools. Speeds on low-FICO pools (blue) offer some extension protection due to higher involuntary speeds. At the other end, loans in 100% investor pools were dramatically slower than non-spec pools when out-of-the money.

Finally, we look at the behavior of borrowers in non-spec pools segregated by loan purpose, again controlling for loan age. Borrowers with refi loans pay significantly faster than purchase loans when only slightly out-of-the money. As rates continue to rise, refi speeds converge to purchase loans at 75bp out of the money and pay slower when 75-100bp out of the money, presumably due to a stronger lock-in effect.

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We also separated these non-spec borrowers by originators, grouping the largest banks and non-bank originators together. Out-of-the-money speeds on refi loans were significantly faster for loans originated by non-bank originators (blue and green) versus those originated by banks (red and orange). Speeds on purchase loans were only 1-2 CPR faster for non-banks versus banks and were omitted from this graph for readability.

In the current geopolitical climate, rates may continue to drop over the short term. But given the Fed’s tightening bias, it’s prudent to consider extension risk when looking at MBS pools, in both specified and non-specified pools.

[1] For investors interested in GNMA analysis, please contact RiskSpan

Since the Great Financial Crisis of 2008, the Federal Reserve Bank of New York has been the largest and most influential participant in the mortgage-backed securities market. In the past 14 years, the Fed’s holdings of conventional and GNMA pools has grown from zero to $2.7 trillion, representing roughly a third of the outstanding market. With inflation spiking, the Fed has announced an end to MBS purchases and will shift into balance-sheet-reduction mode. In this short post, we review the Fed’s holdings, their distribution across coupon and vintage, and their potential paydowns as rates rise.

The New York Fed publishes its pool holdings here. The pools are updated weekly and have been loaded into RiskSpan’s Edge Platform. The chart below summarizes the Fed’s 30yr Fannie/Freddie holdings by vintage and net coupon.

We further categorize the Fed’s holdings by vintage and borrower note rate (gross WAC) at the loan level. Using loan-level data (rather than weighted-average statistics published on Fed-held Supers or their constituent pools [1]) provides a more accurate view of the Fed’s distribution of note rates and hence prepayment exposure.

Not surprisingly, the recent and largest quantitative easing has left the Fed holding MBS with gross WACs below the current mortgage rate. Roughly 85% of the mortgages held by the Fed are out-of-the-money, and the remaining in-the-money mortgages are several years seasoned. These older pools are beginning to exhibit burnout, with the sizable refinancing wave over the last two years having limited these moderately seasoned loans mainly to borrowers who are less reactive to savings from refinancing.

With most of the Fed’s portfolio at below-market rates and the remaining MBS moderately burned out, market participants expect the Fed’s MBS runoff to continue to slow. At current rates, we estimate that Fed paydowns will continue to decline and stabilize around $25B per month in the second quarter, just shy of 1% of its current MBS holdings.

With these low levels of paydowns, we anticipate the Fed will need to sell MBS if they want to make any sizable reduction in their balance sheet. Whether the Fed feels compelled to do this, or in what manner sales will occur, is an unsettled question. But paydowns alone will not significantly reduce the Fed’s holdings of MBS over the near term.

[1] FNMA publishes loan-level data for pools securitized in 2013 onward. For Fed holdings that were securitized before 2013, we used FNMA pool data.  

With significant home price gains over the last two years, U.S. homeowners are sitting on vast, mostly untapped wealth. Nationally, home prices are up an aggregate of 28% over the last two years, with some regions performing even better. But unlike other periods of strong home price gains, cash-out refinancings lagged overall refinancings during the pandemic rate-rally. In this short article, we look at cash-out refinancings over time, and their potential impact on prepayments, especially on discount cohorts.

A historical perspective

In the early 2000s, mortgage rates fell nearly 200bp, triggering a massive refinancing wave as well as a rally in home prices that lasted well into 2005.

During this early millennium rally, the market saw significant cash-out refi activity with homeowners borrowing at then-historically low rates to free up cash. The market even saw refinancing activity in mortgages with note rates below the prevailing market rate. In 2002, CPRs on some discount cohorts hit the low to middle teens, which many participants attributed to cash-out refinancing. Resetting a mortgage 50 basis points higher can nevertheless often lead to overall lower debt servicing when borrowers use cash-out refis to consolidate auto loans, credit cards and other higher-rate unsecured borrowings.[1] In the early 2000s, this cash-out refinancing activity led to overall faster speeds and a higher S-curve for out-of-the-money cohorts. How does 2002-03 cash-out refi activity compare to today? In the early 2000s, issuance of cash-out mortgages, as a percentage of the total market, varied between 1% and 2.5% of the outstanding mortgage universe each month.

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Since the onset of the pandemic, that figure has not experienced the same kind of spike, hovering around just 0.9%.[2]

In 2002-03, most of these cash-out borrowers refinanced into lower rates, but a sufficient number took out mortgages at same or higher rates to drive prepayments on discount MBS into the low teens CPR (see black s-curve below). By comparison, out-of-the money speeds today (the blue s-curve) are approximately 4 CPR slower.

The nearly 30% rally in home prices during the pandemic has further strengthened a solid housing market. Today’s borrowers have substantial equity in their homes, leaving many homeowners with untapped borrowing power, shown in the market-implied LTVs below. From an origination standpoint, mortgage lenders have sufficient capacity to support any uptick in cash-out refinancing as rate-term refinancing volumes decline.

Any growth in cash-out refi issuance is likely to come on loans with note rates close to the prevailing mortgage rate. If a homeowner needs to generate cash for a large purchase, it can make economic sense to refinance an existing loan into a new loan with rates as much as 25bp or 50bp higher, rather than incur even higher (and shorter-term) interest rates on credit cards or personal loans. Therefore, any uptick in cash-out refinancing will likely have a larger effect on prepayment speeds for MBS that are either at-the-money or slightly out-of-the-money. This uptick may mitigate some of the extension risk in near-discount mortgages, especially in non-spec cohorts where refinancing frictions are lower. While the past two years have seen substantial changes, positive and negative, in overall refinancings, cash-out refis have largely not followed suit. But a significant home price rally, coupled with strong economic activity and excess originator capacity, could change that trend in the upcoming year.

[1] See RiskSpan for examples.

[2] To compute this statistic, we summed the originations of cash-out refi mortgages and divided by the total size of mortgages extant.

In June, we highlighted Fannie Mae’s and Freddie Mac’s new “expanded delinquency” states. The Enterprises are now reporting delinquency states from 1 to 24 months to better account for loans that are seriously delinquent and not repurchased under the extended timeframe for repurchase of delinquent loans announced in 2020.

This new data reveals a strong correlation between loan balance and “chronically delinquent” loans. In the graph below, we chart loan balance on the x-axis and 180+Day delinquency on the y-axis, for 2017-18 production 30yr 3.5s through 4.5 “generic” borrowers.[1]

As the graph shows, within a given coupon, loans with larger original balances also tended to have higher “chronic delinquencies.”

The graph above also illustrates a clear correlation between higher chronic delinquencies and higher coupons. This phenomenon is most likely due to SATO. While each of these queries excluded low-FICO, high-LTV, and NY loans, the 2017-18 30yr 3.5 cohort was mostly at-the-money origination, whereas 4.0s and 4.5s had an average SATO of 30bp and 67bp respectively. The higher SATO indicates a residual credit quality issue. As one would expect, and we demonstrated in our June analysis, lower-credit-quality loans tend also to have higher chronic delinquencies.

The first effect – higher chronic delinquencies among larger loans within a coupon – is more challenging to understand. We posit that this effect is likely due to survivor bias. The large refi wave over the last 18 months has factored-down higher-balance cohorts significantly more than lower-balance cohorts.

Higher-credit-quality borrowers tend to refinance more readily than lower-credit-quality borrowers, and because the larger-loan-balance cohorts have seen higher total prepayments, these same cohorts are left with a larger residue of lower-quality credits. The impact of natural credit migration (which is observed in all cohorts) tends to leave behind a larger proportion of credit-impaired borrowers in faster-paying cohorts versus the slower-paying, lower-loan-balance cohorts.

The higher chronic delinquencies in larger-loan-balance cohorts may ultimately lead to higher buyouts, depending on the resolution path taken. As loan balance decreases, the lower balance cohorts will have reduced risk to these potential buyouts, leaving them better protected to any uptick in involuntary speeds.

Contact us if you are interested in seeing variations on this theme. Using Edge, we can examine any loan characteristic and generate a S-curve, aging curve, or time series.

[1] We filtered for borrowers with LTV<=80, FICO>=700, and ex-NY. We chose 2017-18 production to analyze, to give sufficient time for loans to go chronically delinquent. We see a similar relationship in 2019 production, see RiskSpan for details.