Agency MBS Archives

RiskSpan Launches MBS Loan Level Historical Data on Snowflake Marketplace

ARLINGTON, Va., June 18, 2024 – RiskSpan, a leading provider of data analytics and risk management solutions for the mortgage industry, announced today that it has launched MBS Loan Level Historical Data on Snowflake Marketplace. RiskSpan’s MBS Loan Level Historical Data on Snowflake Marketplace enables joint customers to access RiskSpan’s normalized and enriched loan-level data for Fannie Mae, Freddie Mac, and Ginnie Mae mortgage-backed securities.

“We are thrilled to join the Snowflake Marketplace and offer our loan-level MBS data to a wider audience of Snowflake users,” said Janet Jozwik, Senior Managing Director at RiskSpan. “This is a first step in what we believe will ultimately become a cloud-based analytical hub for MBS investors everywhere.”

RiskSpan and Snowflake, the AI Data Cloud company, are working together to help joint customers inform business decisions and drive innovations by enabling them to query the data using SQL, join it with other data sources, and scale up or down as needed. RiskSpan also provides sample code and calculations to help users get started with common metrics such as CPR, aging curves, and S-curves.

“RiskSpan’s launch of a unique blend of enriched data onto Snowflake Marketplace represents a major opportunity for Snowflake customers to unlock new value through data on their business journey,” said Kieran Kennedy, Head of Marketplace at Snowflake. “We welcome RiskSpan to the ecosystem and look forward to exploring how we can support our customers as they look to leverage the breadth of the Snowflake platform more effectively.”

Joint customers can now leverage Loan-Level MBS Data on Snowflake Marketplace, allowing them to access RiskSpan data enhancements, including servicer normalization, refinements, mark-to-market LTV calculations, current coupon. These and other enhancements make it easier and faster for users to perform analysis and modeling.

Snowflake Marketplace is powered by Snowflake’s ground-breaking cross-cloud technology, Snowgrid, allowing companies direct access to raw data products and the ability to leverage data, data services, and applications quickly, securely, and cost-effectively. Snowflake Marketplace simplifies discovery, access, and the commercialization of data products, enabling companies to unlock entirely new revenue streams and extended insights across the AI Data Cloud. To learn more about Snowflake Marketplace and how to find, try and buy the data, data services, and applications needed for innovative business solutions, click here.

About RiskSpan, Inc.

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions. Learn more at www.riskspan.com.

The newest, fastest and easiest way to access and analyze Agency MBS data

TL;DR Summary of Benefits

Data normalization and enhancement: RiskSpan’s MBS data on Snowflake normalizes Fannie, Freddie, and Ginnie loan-level data, consolidating everything into one set of field names. It also offers enhanced loan level-data fields, including current coupon, spec pool category, and mark-to-market LTV, which are not available in the raw data from the agencies. The data also includes pool-level factors like pool prefix and pool age, as well as full loan histories not available from the GSEs directly.
Data access and querying: Users access the data in Snowflake using SQL or Python connectors. Snowflake functions essentially as a cloud SQL server that allows for instantaneous data sharing across entities. In just a few clicks, users can start analyzing MBS data using their preferred coding language—no data, ETL, or IT Teams required.
Data merging and analytics: Users can merge the data in Snowflake with other available loan level or macroeconomic data, including interest rates, home prices, and unemployment, for advanced analytics. Users can also project performance, monitor portfolios, and create spec pools, among other features.

CHECK OUT RISKSPAN’S OFFERING ON SNOWFLAKE MARKETPLACE

The Problem

Even though Fannie, Freddie and Ginnie have been making MBS performance data publicly available for years, working with the raw data can be challenging for traders and back-office analysts.

Traders and analysts already have many of the tools they need to write powerful queries that can reveal hidden patterns and insights across different markets – patterns that can reveal lucrative trading opportunities based on prepayment analysis. But one big obstacle often stands in the way of getting the most out of these tools: the data from the agencies is large and unwieldy and is not formatted in a consistent way, making it hard to compare and combine.

What’s more, the Agencies do not maintain full history of published data on the websites for download. Only recent history is available.

The Solution: RiskSpan’s new MBS loan-level historical offering on Snowflake Marketplace

Using RiskSpan’s new MBS Loan-Level Historical Data Offering, MBS traders and analysts can now leverage the power of Snowflake, the leading cloud data platform, to perform complex queries and merge data from multiple sources like never before.

This comprehensive data offering provides a fully normalized view of the entire history of loan-level performance data across Agencies – allowing users to interact with the full $9T Agency MBS market in unprecedented ways.

A list of normalized Fannie and Freddie fields can be found at the end of this post.

In addition to being able to easily compare different segments of the market using a single set of standardized data fields, MBS traders and analysts also benefit from derived and enhanced data, such as current coupon, refinance incentive, current loan-to-value ratio, original specified pool designation, and normalized seller and servicer names.

The use cases are practically limitless.

MBS traders and analystscan track historical prepayment speeds, find trading opportunities that offer relative value, and build, improve, or calibrate prepayment models. They can see how prepayment rates vary by loan size, credit score, geographic location, or other factors. They can also identify pools that have faster or slower prepayments than expected and exploit the differences in price.

Loan originators can see how their loans perform compared to similar loans issued by other originators, servicers, or agencies, allowing them to showcase their ability to originate high-quality loans that command premium pricing.

Enhanced fields provide users with more comprehensive insights and analysis capabilities. They include a range of derived and enhanced data attributes beyond the standard dataset: derived fields useful for calculations, additional macroeconomic data, and normalized field names and enumerations. These fields give users the flexibility to customize their analyses by incorporating additional data elements tailored to their specific needs or research objectives.

Enhanced loan-level fields include:

Refi Incentive: The extent to which a borrower’s interest rate exceeds current prevailing market rates
Spread at Origination (SATO): a representation of the total opportunities for refinancing within a mortgage servicing portfolio. SATO encompasses all potential refinance candidates based on prevailing market conditions, borrower eligibility, and loan characteristics
Servicer Normalization: A standardization of servicer names to ensure consistency and accuracy in reporting and analysis
Scheduled Balance: A helper field necessary to easily calculate CPR and other performance metrics
Spec Pool Type: A designation of the type of spec story on the loan’s pool at origination
Current LTV: a walked forward LTV based on FHFA’s HPI and the current balance of the loan

Not available in the raw data from the agencies, these fields allow MBS traders and analysts to seamlessly project loan and pool performance, monitor portfolios, create and evaluate spec pools, and more.

Access the Data on Your Terms

Traders and analysts can access the data in Snowflake using SQL or Python connectors. Alternatively, they can also access the data through the Edge UI, our well-established product for ad hoc querying and visualization. RiskSpan’s Snowflake listing provides sample queries and a data dictionary for reference. Data can be merged with macroeconomic data from other sources – rates, HPI data, unemployment – for deeper insights and analytics.

The listing is available for a 15-day free trial and can be purchased on a monthly or annual basis. Users don’t need to have a Snowflake account to try it out. Learn more and get started at the Snowflake Marketplace or contact us to schedule a demo or discussion.

CHECK OUT RISKSPAN’S OFFERING ON SNOWFLAKE MARKETPLACE

Fannie/Freddie Normalized Fields

NAME	TYPE	DESCRIPTION
AGE	Number	Loan Age in Months
AGENCY	Varchar	FN [Fannie Mae], FH [Freddie Mac]
ALTDQRESOLUTION	Varchar	Payment deferral type: CovidPaymentDeferral,DisasterPaymentDeferral,PaymentDeferral,Other/NA
BORROWERASSISTPLAN	Varchar	Type of Assistance: Forbearance, Repayment, TrialPeriod, OtherWorkOut, NoWorkOut, NotApplicable, NotAvailable
BUSINESSDAYS	Number	Business Day in Factor Period
COMBINEDLTV	Float	Original Combined LTV
CONTRIBUTION	Float	Contribution of Loan to the Pool, to be used to correctly attribution Freddie Mirror Pools
COUPON	Float	Net Coupon or NWAC in %
CURRBALANCE	Float	Current Balance Amount
CURRENTCOUPON	Float	Primary rate in the market (PMMS)
CURRENTLTV	Float	Current Loan to Value Ratio based on rolled-forward home value calculated by RiskSpan based on FHFA All-Transaction data
CURTAILAMOUNT	Float	Dollar amount curtailed in the period
DEFERRALAMOUNT	Float	Dollar amount deferred
DQSTRING	Varchar	Delinquency History String, left most field in the current period
DTI	Float	Debt to Income Ratio %
FACTORDATE	Date	Performance Period
FICO	Number	Borrower FICO Score [300,850]
FIRSTTIMEBUYER	Varchar	First time home buyer flag Y,N,NA
ISSUEDATE	Date	Loan Origination Date
LOANPURPOSE	Varchar	Loan Purpose: REFI,PURCHASE,NA
LTV	Float	Original Loan to Value Ratio in %
MATURITYDATE	Date	Loan Maturity Date
MICOVERAGE	Float	Mortgage Insurance Coverage %
MOSDELINQ	Varchar	Delinquency Status: Current, DQ_30_Day, DQ_60_Day, DQ_90_Day, DQ_120_Day, DQ_150_Day, DQ_180_Day, DQ_210_Day, DQ_240_Day, DQ_270_Day, DQ_300_Day, DQ_330_Day, DQ_360_Day, DQ_390_Day, DQ_420_Day, DQ_450_Day, DQ_480_Day, DQ_510_Day, DQ_540_Day, DQ_570_Day, DQ_600_Day, DQ_630_Day, DQ_660_Day, DQ_690_Day, DQ_720pls_Day
MSA	Varchar	Metropolitian Statistical Area
NUMBEROFBORROWERS	Number	Number of Borrowers
NUMBEROFUNITS	Varchar	Number of Units
OCCUPANCYTYPE	Varchar	Occupancy Type: NA,INVESTOR,OWNER,SECOND
ORIGBALANCE	Float	Original Loan Balance
ORIGSPECPOOLTYPE	Varchar	Spec Story of the pool that the loan is a part of. Please see Spec Pool Logic in our linked documentation
PERCENTDEFERRAL	Float	Percentage of the loan balance that is deferred
PIW	Varchar	Property Inspection Waiver Type: Appraisal,Waiver,OnsiteDataCollection, GSETargetedRefi, Other,NotAvailable
POOLAGE	Number	Age of the Pool
POOLID	Varchar	Pool ID

What Do 2024 Origination Trends Mean for MSRs?

While mortgage rates remain stubbornly high by recent historical standards, accurately forecasting MSR performance and valuations requires a thoughtful evaluation of loan characteristics that go beyond the standard “refi incentive” measure.

As we pointed out in 2023, these characteristics are particularly important when it comes to predicting involuntary prepayments.

This post updates our mortgage origination trends for the first quarter of 2024 and takes a look at what they could be telling us.

Average credit scores, which were markedly higher than normal during the pandemic years, have returned and stayed near the averages observed during the latter half of the 2010s.

The most credible explanation for this most recent reversion to the mean is the fact that the Covid years were accompanied by an historically strong refinance market. Refis traditionally have higher FICO scores than purchase mortgages, and this is apparent in the recent trend.

Purchase markets are also associated with higher average LTV ratios than are refi markets, which accounts for their sharp rise during the same period.

Consequently, in 2023 and 2024, with high home prices persisting despite extremely high interest rates, new first-time homebuyers with good credit continue to be approved for loans, but with higher LTV and DTI ratios.

Between rates and home prices, borrowers simply need to borrow more now than they would have just a few years ago to buy a comparable house. This is reflected not just in the average DTI and LTV, but also the average loan size (below) which, unsurprisingly, continues to trend higher as well.

Recent large increases to the conforming loan limit are clearly also contributing to the higher average loan size.

What, then, do these origination trends mean for the MSR market?

The very high rates associated with newer originations clearly translate to higher risk of prepayments. We have seen significant spikes in actual speeds when rates have taken a leg down — even though the loans are still very new. FICO/LTV/DTI trends also potentially portend higher delinquencies down the line, which would negatively impact MSR valuations.

Nevertheless, today’s MSR trading market remains healthy, and demand is starting to catch up with the high supply as more money is being raised and put to work by investors in this space. Supply remains high due to the need for mortgage originators to monetize the value of MSR to balance out the impact from declining originations.

However, the nature of the MSR trade has evolved from the investor’s perspective. When rates were at historic lows for an extended period, the MSR trade was relatively straightforward as there was a broader secular rate play in motion. Now, however, bidders are scrutinizing available deals more closely — evaluating how speeds may differ from historical trends or from what the models would typically forecast.

These more granular reviews are necessarily beginning to focus on how much lower today’s already very low turnover speeds can actually go and the extent of lock-in effects for out-of-the-money loans at differing levels of negative refi incentive. Investors’ differing views on prepays across various pools in the market will often be the determining factor on who wins the bid.

Investor preference may also be driven by the diversity of an investor’s other holdings. Some investors are looking for steady yield on low-WAC MSRs that have very small prepayment risk while other investors are seeking the higher negative convexity risk of higher-WAC MSRs — for example, if their broader portfolio has very limited negative convexity risk.

In sum, investors have remained patient and selective — seeking opportunities that best fit their needs and preferences.

So what else do MSR holders need to focus on that may may impact MSR valuations going forward?

The impact from changes in HPI is one key area of focus.

While year-over-year HPI remains positive nationally, servicers and other investors really need to look at housing values region by region. The real risk comes in the tails of local home price moves that are often divorced from national trends.

For example, HPIs in Phoenix, Austin, and Boise (to name three particularly volatile MSAs) behaved quite differently from the nation as a whole as HPIs in these three areas in particular first got a boost from mass in-migration during the pandemic and have since come down to earth.

Geographic concentrations within MSR books will be a key driver of credit events. To that end, we are seeing clients beginning to examine their portfolio concentration as granularly as zipcode level.

Declining home values will impact most MSR valuation models in two offsetting ways: slower refi speeds will result in higher MSR values, while the increase in defaults will push MSRs back downward. Of these two factors, the slower speeds typically take precedence. In today’s environment of slow speeds driven primarily by turnover, however, lower home prices are going to blunt the impact of speeds, leaving MSR values more exposed to the impact of higher defaults.

Impact of Mr. Cooper’s Cyber Security Incident on Agency Prepayment Reporting

Amid the fallout of the cyberattack against Mr. Cooper on October 31st was an inability on the large servicer’s part to report prepayment activity to investors.

According to Freddie Mac, the incident “resulted in [Mr. Cooper’s] shutting down certain systems as a precautionary measure. As a result, Freddie Mac did not receive loan activity reporting, which includes loan payoffs and payment corrections, from Mr. Cooper during the last few days of the reporting period related to October loan activity.”

Owing to Mr. Cooper’s size, were curious to measure what (if any) impact its missing days of reporting might have on overall agency speeds.

Not a whole lot, it turns out.

This came as little surprise given the very low prepayment environment in which we find ourselves, but we wanted to run the numbers to be sure. Here is what we found.

We do not know precisely how much reporting was missed and assumed “the last few days of the reporting period” to mean 3 days.

Assuming 3 days means that Mr. Cooper’s reported speeds of 4.5 CPR to Freddie and 4.6 CPR to Fannie likely should have been 5.2 CPR and 5.4 CPR, respectively. While these differences are relatively small for to Mr. Cooper’s portfolio (less than 1 CPR) the impact on overall Agency speeds is downright trivial — less than 0.05 CPR.

	Fannie MBS	Freddie MBS
Sch. Bal.	195,221,550,383	168,711,346,228
CPR (reported)	4.6	4.5
*CPR (estimated)**	5.4	5.2

*assumes three days of unreported loan activity and constant daily prepayments for the month

Fannie Mae and Freddie Mac will distribute scheduled principal and interest when servicers do not report the loan activity. Prepayments that were not reported “will be distributed to MBS certificateholders on the first distribution date that follows our receipt and reconciliation of the required prepayment information from Mr. Cooper.”

What Do 2023 Origination Trends Mean for MSRs?

When it comes to forecasting MSR performance and valuations, much is made of the interest rate environment, and rightly so. But other loan characteristics also play a role, particularly when it comes to predicting involuntary prepayments.

So let’s take a look at what 2023 mortgage originations might be telling us.

Average credit scores, which were markedly higher than normal during the pandemic years, have returned during the first part of 2023 to averages observed during the latter half of the 2010s.

Purchase markets are also associated with higher average LTV ratios than are refi markets, which accounts for their sharp rise during the same period

Consequently, in 2023, with high home prices persisting despite extremely high interest rates, new first-time homebuyers with good credit continue to be approved for loans, but with higher LTV and DTI ratios.

Recent large increases to the conforming loan limit are clearly also contributing to the higher average loan size.

What, then, do these origination trends mean for the MSR market?

The very high rates associated with newer originations clearly translate to higher risk of prepayments. We have seen significant spikes in actual speeds when rates have taken a leg down — even though the loans are still very new. FICO/LTV/DTI trends also potentially portend higher delinquencies down the line, which would negatively impact MSR valuations.

Nevertheless, today’s MSR trading market remains healthy, and demand is starting to catch up with the high supply as more money is being raised and put to work by investors in this space. Supply remains high due to the need for mortgage originators to monetize the value of MSR to balance out the impact from declining originations.

However, the nature of the MSR trade has evolved from the investor’s perspective. When rates were at historic lows for an extended period, the MSR trade was relatively straightforward as there was a broader secular rate play in motion. Now, however, bidders are scrutinizing available deals more closely — evaluating how speeds may differ from historical trends or from what the models would typically forecast.

These more granular reviews are necessarily beginning to focus on how much lower today’s already very low turnover speeds can actually go and the extent of lock-in effects for out-of-the-money loans at differing levels of negative refi incentive. Investors’ differing views on prepays across various pools in the market will often be the determining factor on who wins the bid.

Investor preference may also be driven by the diversity of an investor’s other holdings. Some investors are looking for steady yield on low-WAC MSRs that have very small prepayment risk while other investors are seeking the higher negative convexity risk of higher-WAC MSRs — for example, if their broader portfolio has very limited negative convexity risk.

In sum, investors have remained patient and selective — seeking opportunities that best fit their needs and preferences.

So what else do MSR holders need to focus on that may may impact MSR valuations going forward?

The impact from changes in HPI is one key area of focus.

While year-over-year HPI remains positive nationally, servicers and other investors really need to look at housing values region by region. The real risk comes in the tails of local home price moves that are often divorced from national trends.

For example, HPIs in Phoenix, Austin, and Boise (to name three particularly volatile MSAs) behaved quite differently from the nation as a whole as HPIs in these three areas in particular first got a boost from mass in-migration during the pandemic and have since come down to earth.

Geographic concentrations within MSR books will be a key driver of credit events. To that end, we are seeing clients beginning to examine their portfolio concentration as granularly as zipcode level.

Declining home values will impact most MSR valuation models in two offsetting ways: slower refi speeds will result in higher MSR values, while the increase in defaults will push MSRs back downward. Of these two factors, the slower speeds typically take precedence. In today’s environment of slow speeds driven primarily by turnover, however, lower home prices are going to blunt the impact of speeds, leaving MSR values more exposed to the impact of higher defaults.

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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Duration Risk: Daily Interest Rate Risk Management and Hedging Now Indispensable

The rapid decline of Silicon Valley Bank and Signature Bank affirms the strong need for daily interest rate risk measurement and hedging. All financial institutions should have well documented management and board limits on these exposures.

Measuring risk on complex mortgage-backed securities and loan portfolios that have embedded prepayment and credit risk is challenging. RiskSpan has a one-stop risk measurement solution for all mortgage-backed securities, structured product, loan and other related assets including data management, proprietary models and risk reporting.

Our bank clients enjoy the benefit of daily risk measurement to ensure they are well-hedged in this volatile market environment.

For a limited time, under full non-disclosure, RiskSpan will offer a one-time analysis on your securities portfolio.

Please reach out if we can help your institution more fully understand the market risk in your portfolios.

There are many lessons to learn through the SVB failure. While technology (the internet) enabled the fastest run on a bank in US history, technology can also be the solution. As we just saw US Government securities are risk-free for credit but not interest rate movements. When rates rose, security prices on the balance sheet of SVB declined in lock-step. All financial institutions (of all sizes) need to act now and deploy modern tech to manage modern risks – this means managing duration risk on a daily basis. It’s no longer acceptable for banks to review this risk monthly or weekly. Solutions exists that are practical, reliable and affordable.

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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