Divas Sanwal, Don Brown, Author at RiskSpan

Is Your Prepay Analysis Ready for the Rate Cut?

The forthcoming Federal Reserve interest rate cuts loom large in minds of mortgage traders and originators. The only remaining question is by how much rates will be cut. As the economy cools and unemployment rises, recent remarks by the Fed Chair have made the expectation of rate cuts essentially universal, with the market quickly repricing to a 50bp ease in September. This anticipated move by the Fed is already influencing mortgage rates, which have already experienced a notable decline.

Understanding the Lock-in Effect

One of the key factors influencing prepayments in the current environment is the lock-in effect, where borrowers are deterred from selling their current home due to the large difference between their current mortgage rate and prevailing market rates (which they would incur when purchasing their next home). As rates decrease, the gap narrows, reducing the lock-in effect and freeing more borrowers to sell and move.

As Chart 1 illustrates, a significant share of borrowers continues to hold mortgages between 2 and 3 percent. These borrowers clearly still have no incentive to refinance. But historical data suggests that the sizeable lock-in effect, which is currently depressing turnover, diminishes as the magnitude of their out-of-the-moneyness comes down. In other words, even a 100-basis point reduction can significantly increase housing turnover, as borrowers who were previously 300 basis points out of the money move to 200 basis points, making selling their old home and buying a new one, despite the higher interest rate, more palatable.

*CHART 1: Distribution of Note Rates for 30-Year Conventional Mortgages: July 2024*

Current Market Dynamics

Recent data from Mortgage News Daily indicates that mortgage rates have dropped over the past four weeks from around 6.8% to nearly 6.4%. This decrease is expected to continue, potentially bringing rates below 6% by the end of the year. This will likely have a profound impact on mortgage prepayments, particularly in the Agency MBS market.

Most outstanding mortgages, particularly those in Fannie and Freddie securities, currently have low prepayment speeds, with many loans sitting at 2% to 3% coupons. While a drop in mortgage rates to 6% (or lower) will still leave most of these mortgages out of the money for traditional rate-and-term refinances, it may bring a growing number of them into play for cash-out refinances, given significant home price appreciation and equity buildup over last 4 years. It will also loosen the grip of the lock-in effect for a growing number of homeowners currently paying below-market interest rates.

Implications for Prepayment Speeds

Factoring in the potential increase in turnover and cash-out refis, the impact of rate cuts on prepayment speeds could be substantial. For instance, with a 100-bp drop in rates, loans that are deeply out of the money could see their prepayment speeds increase by 1 to 2 CPR based on the turnover effect alone. Loans that are just at the money or slightly out of the money will see a more pronounced effect, with prepayment speeds potentially doubling. Chart 2, below, illustrates both the huge volume of loans deep out of the money to refinance as well as the small (but significant) uptick in CPR that a 100-bp shift in interest rates can have on CPR even for loans as much as 300 bps out of the money.

CHART 2: CPR by Refinance Incentive (dotted line reflects UPB of each bucket)

Historical data suggests that if mortgage rates move to 6.4%, the volume of loans moving into the money to refinance could increase up to eightfold — from $39 billion to $247 billion (see chart 3, below.) This surge in refinance activity will significantly influence prepays — impacting both turnover and refi volumes.

CHART 3: Volume and CPR by Coupon (dotted line reflects UPB of each bucket)

The Broader Housing Market

Beyond prepayments, the broader housing market may also feel the effects of rate cuts, but perhaps in a nuanced way. A reduction in rates generally improves affordability, potentially sustaining or even increasing home prices despite the increased supply from unlocked homes. However, this dynamic is complex. While lower rates make homes more affordable, the release of previously locked-in homes could counterintuitively depress home prices due to increased supply. With housing affordability at multi-decade lows, an uptick in housing supply could swamp any effect of somewhat lower rates.

While a modest rate cut may primarily boost turnover, a more significant cut could trigger a wave of refinancing. Additionally, cash-out refinances may become more attractive, offering a cheaper alternative to HELOCs and other more expensive options.

Conclusion

The forthcoming Fed interest rate cuts are poised to have a significant impact on mortgage prepayments. As rates decline, the lock-in effect will ease, encouraging more refinancing and increasing prepayment speeds. The broader housing market will also feel the effects, with potential implications for home prices and overall market dynamics. Monitoring these trends closely will be crucial for market participants, particularly those in the agency MBS market, as they navigate the changing landscape.

Contact us to staying informed and prepared and learn more about how RiskSpan can help you make strategic decisions that align with evolving market conditions.

Edge: Zombie Banks

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.

Chart — Source: *The Wall Street Journal*

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.

Quantifying Mortgage Risk — Best Practices in the Wake of SVB

Much has been made of the Silicon Valley Bank saga, from the need for basic risk management (was there any, other than a trivial nod?) to the possibility of re-extending the Dodd-Frank rules to cover all banks. Rather than adding our voice to that noise, this post makes a pitch for best practices in MBS and whole loan risk, regardless of whether existing regulation covers your institution.

“Best practices” in mortgage risk is a broad term meaning different things to different people. For our purposes, it refers to using sophisticated risk management tools to quantify both first- and second-order risk of various factors. It also refers to using scenario analysis to capture projected P/L under combinations of risks, for example twists in the interest rate curve combined with spread changes and changes in implied volatility.

Before these risks can be offset using rate and option hedges, our first step is quantifying what the risks are.

In the simplest case, good risk management analysis should quantify projected P/L of a rate-sensitive mortgage or MBS position for shifts in the rate curve — not just local rate shifts of 25 to 50bp, but much larger shifts in rates. It’s helpful to remember that MBS and their underlying mortgages have embedded calls, which lead to significant changes in both projected durations and projected convexity as rates move. Running scenarios with large rate shifts can help highlight the sizable second-order risks in MBS, which are typically negative but turn positive under large enough shifts. In turn, this extended analysis highlights a non-trivial third-order rate effect in MBS.

In the following chart, we show P/L on a position of TBA passthroughs, securities similar to SVB’s held-to-maturity portfolio. We project price movements under parallel rate shifts as of January 3, 2022, which roughly corresponds to the start of the tightening cycle. For this analysis, we use RiskSpan’s prepayment and interest-rate models, which are available in the Edge interface or via overnight batch.

In this analysis, the model projected prices of FNCL 2.0 to 3.0 within 2.5% of actual observed prices on March 8, 2023, shown by the diamonds on the chart, the Wednesday before the SVB crisis began to unfold. While not exact, this analysis illustrates the power of a straightforward rate curve to help a bank’s risk management team project actual, realized prices over very large rate moves.

In the next chart, we show a P/L chart that is duration-neutral at outset. This chart shows the losses from negative convexity,

driven by the homeowner’s option to refinance moving from at-the-money to significantly out-of-the-money. As rates continue to rise (moving right on the chart), underperformance from convexity continues to increase, but only to a point. This is where the homeowner’s call option is offset by the natural, positive convexity of discounting. Beyond that point, MBS become mildly positively convex as the call options become less relevant.

What does this change in convexity look like? In the final chart, we show convexity at various rate shifts for a par-priced passthrough.

This highlights convexity changes over large moves (and a non-trivial third derivative with respect to changes in rates) and underscores the importance of a quantitative approach to risk management for MBS.

From these straightforward scenarios, banks and other institutions can overlay combinations of other risk shocks, for example curve flatteners and steepeners, OAS changes, and changes in implied volatility. These mixed scenarios can quantify risk from cross-partial derivatives and inform potential hedges under multiple changes in inputs. All these simple and more complex user-defined scenarios are available in RiskSpan’s Edge platform, giving small and mid-sized banks the ability to quantify risk on high-quality MBS, which is the first fundamental in a rigorous risk management framework. Recent events have highlighted the tradeoff between cost savings generated by taking a light approach to rate risk management and the existential risk of insolvency. Yes, small and mid-sized banks can save costs while remaining within the current regulatory framework. But, as SVB has taught us, to do so can be tantamount to unwittingly betting the entire enterprise. Laying out a few basis points to ensure you’ve quantified the interest rate risk properly has never looked like a more worthwhile investment.

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Are Recast Loans Skewing Agency Speeds?

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.

Pool ID	Factor Date	Current Face	Extension (months)
FR QD7617	7/2022	20,070,737	6
FR QD0006	1/2022	15,682,775	5
FN CB3367	11/2022	14,839,919	5
FR QD5736	7/2022	10,916,959	6
FN BU0581	4/2022	10,164,000	6
FR QD4492	6/2022	3,113,532	16
FN BV2076	5/2022	3,165,509	18
FR QD6013	7/2022	3,079,250	22

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The Curious Case of Curtailments

With more than 90% of mortgages out-of-the-money from a refinancing standpoint, the MBS market has rightly focused on activities that affect discounts, including turnover and to a much lesser extent cash-out refinancings. In this analysis we examine the source of fast speeds on new issue loans and pools.

As we dig deeper on turnover, we notice a curious behavior related to curtailments that has existed for several years but gone largely ignored in recent refi-dominated environments. Curtailment activity, especially higher-than-expected curtailments on new-production mortgages, has steadily gotten stronger in more recent vintages.

For this analysis we define a curtailment as any principal payment that is larger than the contractual monthly payment but smaller than the remaining balance of the loan, which is more typically classified as payoff due to either a refinancing or house sale. In the first graph, we show curtailment speeds for new loans with note rates that were not refinanceable on a rate/term basis.

As you can see, the 2022 vintage shows a significant uptick in curtailments in the second month. Other recent vintages show lower but still significant early-month curtailments, whereas pre-2018 vintages show very little early curtailment activity.

Digging deeper, we separate the loans by purpose: purchase vs. refi. Curtailment speeds are significantly higher among purchase loans than among refis in the first six months, with a noticeable spike at months two and three.

Focusing on purchase loans, we notice that the behavior is most noticeable for non-first-time homebuyers (non-FTHB) and relatively absent with FTHBs. The 2022-vintage non-FTHB paid nearly 6 CPR in their second month of borrowing.

What drives this behavior? While it’s impossible to say for certain, we believe that homeowners purchasing new homes are using proceeds from the sale of the previous home to partially pay off their new loan, with the sale of the previous loan coming a month or so after the close of the first loan.

How pervasive is this behavior? We looked at purchase loans originated in 2022 where the borrower was not a first-time home buyer and noted that 0.5% of the loans account for nearly 75% of the total curtailment activity on a dollar basis. That means these comparatively high, early speeds (6 CPR and higher on some pools) are driven by a small number of loans, with that vast majority of loans showing no significant curtailments in the early months.

High-curtailment loans show large payments relative to their original balances, ranging from 5% to 85% of the unpaid balance with a median value of 25%. We found no pattern with regard to either geography or seller/servicer. Looking at mortgage note rates, 80% of these high-curtailment loans were at 3.5% or lower and only 10% of these borrowers had a positive refinancing incentive at all. Only 1.5% had incentives above 25bp, with a maximum incentive of just 47bp. These curtailments are clearly not explained by rate incentive.

The relatively rarity of these curtailments means that, while in aggregate non-FTHBs are paying nearly 6 CPR in the early months, actual results within pools may vary greatly. In the chart below, we show pool speeds for 2022-vintage majors/multi-lenders, plotted against the percentage of the pool’s balance associated with non-FTHB purchases. We controlled for refi incentive by looking at pools that were out of the money by 0bp to 125bp. As the percentage of non-FTHBs in a pool increases, so does early prepayment speed, albeit with noise around the trend.

We observe that a very small percentage of non-FTHB borrowers are making large curtailment payments in the first few months after closing and that these large payments translate into a short-term pop in speeds on new production at- or out-of-the-money pools. Investors looking to take advantage of this behavior on discount MBS should focus on pools with high non-FTHB borrowers.

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Video: Mortgage Market Evolution

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.

EDGE: Cash-Out Refi Speeds

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.

[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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EDGE: Recent Performance of GNMA RG Pools

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.

EDGE: Extension Protection in a Rising Rate Environment

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

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Is Your Prepay Analysis Ready for the Rate Cut?

Understanding the Lock-in Effect

Current Market Dynamics

Implications for Prepayment Speeds

The Broader Housing Market

Conclusion

Edge: Zombie Banks

Quantifying Mortgage Risk — Best Practices in the Wake of SVB

Are Recast Loans Skewing Agency Speeds?

The Curious Case of Curtailments

Video: Mortgage Market Evolution

EDGE: Cash-Out Refi Speeds

RiskSpan Technology

EDGE: Recent Performance of GNMA RG Pools

EDGE: Extension Protection in a Rising Rate Environment

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