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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.

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”.

Source: RiskSpan’s Edge Platform

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.1 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.

Source: RiskSpan’s Edge Platform

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.

Cracking the Code for a Gender-Equal Future:  Strategies for addressing unconscious gender bias

Bias is real and we all have it – both men and women. It’s often hard to recognize, as bias is the result of cultural and societal norms that have existed for decades or more. Thus, the challenge for changing subtle behaviors requires intentional action. Earlier this week, RiskSpan co-hosted with the Structured Finance Association a lively panel discussion focused on taking intentional action towards achieving a gender-equal future.

The heart of the discussion focused on unconscious gender bias in the workplace and strategies to effect change. The group discussed the importance of intentionality to drive the mission forward and the required participation of men. Part of the dialogue addressed how women and men interact in the workplace (having lunch is completely appropriate), and the need to sometimes get comfortable being uncomfortable. The panel included a sole male representative, but the room was filled with roughly 25% men. One important take-away was that the goal of gender equality is simply unattainable without the commitment and sustained effort from both women and men, and a continued dialogue on the subject is essential. 

Further, to effect social change, economic incentives have to be sustainable and aligned with the social mission. With women graduating from college at higher rates than ever before, we are beginning to unleash half of our country’s brainpower into many fields, including business, finance and tech, that continue to be dominated by men. Women in these fields continue to face obstacles stemming from gender bias. These biases need to be addressed head-on with intentional strategies for change.

Common Gender Biases and Strategies for Change

Myth No. 1: There are plenty of women in the C-suite, so what’s the problem?

Although there has been progress with better representation of women in the C-suite, the pay gap continues to exist and is significant. Part of this relates to the fact that there are more women in C-level jobs that are considered “less risky.” These less-risky jobs include legal, compliance, or accounting, as opposed to jobs such as Chief Investment Officer, Head of Capital Markets or CEO.  

Earning potential increases not only with experience and qualification, but with a bold ambition that tends to be encouraged more often among men than women (think competitive sports). This may contribute to a more heightened fear of failure among women — the business world is no exception to that.   

Strategy for change:
Assure all members of your team, regardless of gender, that mistakes are inevitable and recoverable. What’s important is how you react to a mistake or challenge. Further, encourage women to challenge themselves – invite women to lead the next challenging project and client pitch. A favorite quote of mine serves as a reminder of this:

“I always go back to my grandmother’s advice the first time I fell and hurt myself. She said, ‘Honey, at least falling on your face is a forward movement.’ You have to be willing to be brave enough to risk falling on your face, to risk failing… Everything we do is about taking risks.”

Pat Mitchell

Myth No. 2: The workplace is (solely) a meritocracy

The fact is advancement happens not only via hard work and merit, but via personal relationships and connections. This is not to say that qualifications don’t matter – of course they do. But it is human nature to consider the people you know best, have met face-to-face, or have worked with. Others may be qualified, but if you don’t know them, you’re simply not going to consider them. This is particularly difficult for women who often expect to be recognized and rewarded for their great work. However, they are missing a big part of the equation – networking and relationship building and sponsorship.  

Strategy for change:
Find a sponsor that will advocate for you when you’re not in the room. For managers, reach out to the next generation of women and make the needed introduction to help expand her network (and invite her to coffee – it’s totally appropriate). Include women in regular networking events and create a networking practice that is naturally welcoming for women to participate in.

Myth No. 3: It is inappropriate for a man and woman to have an unchaperoned business lunch

A 2017 New York Times article reported that most people (women as well as men) thought it inappropriate for a person to have a drink with someone of the opposite sex other than their spouse.

Let’s face it — It can be awkward for a man to ask a female colleague (particularly a subordinate) to join him for coffee, lunch, or a drink. However, this significant social barrier disadvantages women and hinders their opportunity for advancement. It is virtually impossible to level the playing field if women and men can’t develop a professional relationship that includes socializing over coffee or a meal.

Business communities incorporate professional socializing to foster relationships and partnerships. This extends to advancement opportunities. You typically select the people to promote from a short list of people you know well and feel comfortable with. The people you are having lunch with are the ones who are most likely to make the list. 

Strategy for change:
Start with coffee – invite her for a 1:1 conversation. The only way to break with this social norm may be to get comfortable being uncomfortable. This means it’s ok for a woman to ask a man to coffee or lunch or visa-versa (particularly between a senior and a subordinate). Treat everyone with respect and professionalism, and never withhold an invitation simply on the basis of gender.

Myth No. 4: Women with children don’t want to travel

Managers sometimes make this assumption and withhold assignments from women that require travel – be it for a conference or a critical client engagement. Although the intention might be noble, the impact is detrimental. Doing so deprives women of the same opportunities as men to engage with important clients and others in the industry. It undermines her ability to make decisions and may adversely impact how she is viewed and valued by her peers. 

Strategy for change:
Always offer travel opportunities equally among team members regardless of gender, marital status, or motherhood/fatherhood and allow the choice to be theirs. Resist reinforcing stereotypes that consequentially keep mothers at home. 

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.1

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,2 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.3 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.

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.1 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.2 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.3 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 IDFactor DateCurrent FaceExtension (months)
FR QD76177/202220,070,7376
FR QD00061/202215,682,7755
FN CB336711/202214,839,9195
FR QD57367/202210,916,9596
FN BU05814/202210,164,0006
FR QD44926/20223,113,53216
FN BV20765/20223,165,50918
FR QD60137/20223,079,25022

Takeaways from SFVegas 2023

The most highly attended conference in recent years brought together leaders from government, capital markets, and tech institutions to discuss the current state and future of the securitization markets.

SFVegas remains the optimal environment for fostering healthy dialogue aimed at making markets more efficient and transparent by creating innovative, new solutions.  RiskSpan is delighted to be engaged in this dialogue.  

Here are our key takeaways from the conference.

Loan Innovation

Sticky inflation and high interest rates are creating a macroeconomic environment that is particularly conducive to bringing new residential mortgage products to market. Market demand for HELOCs and other second-lien products is driving innovation around these offerings and accelerating their acceptance. ARM production is growing rapidly and is at some of the highest levels in over a decade.

Product Innovation is moving forward with both consumers and investors in mind. Consumers are in search of access to better financing while investors seek new ways to participate in these markets.

Technology-Accelerated (R)evolution

Data is driving the dialogue. New scoring tools (FICO10T and Vantage Score 4.0), new ESG-related data and better disclosures are creating a much more transparent investment process

Cloud-native applications continue to make analytics processing cheaper and differentiate how investors and their counterparties seek relative value. Efficiency in data management and analytics separates winner and losers.

Accelerated adoption of AI-driven solutions will drive market operational efficiency in the coming years. The adoption and use cases are just beginning to be uncovered. 

New Investors, New Ideas

New investors are bringing fresh capital to the market with new ideas on how to maximize risk-adjusted returns. Investors backed by private equity are seeking new returns in virtually every category of structured markets: MSRs, BPLs and CLOs. Interest in these classes will only grow in the coming years as more investors seek to maximize returns in private assets.

The international investor community remains strong as global asset allocation is shifting towards the U.S. and fewer opportunities exist in overseas markets

RiskSpan sits at the intersection of all of these trends by helping structured finance investors of every type to leverage technology and data solutions that uncover market opportunities, mitigate risks and deliver new products

Great conference! Get in touch with us to learn more about how RiskSpan help clients simplify, scale, and transform their structured finance analytics!

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.1 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.

A Practical Approach to Climate Risk
Assessment for Mortgage Finance

Note: The following is the introduction from RiskSpan’s contribution to a series of essays on Climate Risk and the Housing Market published this month by the Mortgage Bankers Association’s Research Institute for Housing America.

Significant uncertainty exists about how climate change will occur, how all levels of government will intervene or react to chronic risks like sea level rise, and how households, companies, and financial markets will respond to various signals that will create movements in prices, demographics, and economic activity even before climate risk manifests. This paper lays out a pragmatic framework for assessing these risks from the perspective of a mortgage company. We evaluate available public and proprietary data sources and address data limitations, such as different sources providing a different view of risk for a particular property. We propose a sensitivity analysis approach to quantify risk and mitigate the uncertainties in measuring and responding to climate change.

Global temperatures will continue to increase over the next 50 years regardless of the actions people and governments take. The impacts of that warming are expected to accumulate and become more severe and frequent over time, causing stress throughout our economy. Regulators are clearly signaling that climate risk analysis will need to become a regular part of risk management activities. But detailed, industry-specific guidance has not been defined. FHFA and the regulated entities have yet to release a climate risk framework. They clearly recognize the threat to the housing finance system, however, and are actively working towards accounting for these risks.

Most executives and boards have become conceptually familiar with the physical and transition risks of climate change. But significant questions remain around how these concepts translate into specific, quantifiable business, asset, regulatory, legal, and reputation risks in the housing finance industry. Further complicating matters, climate science continues to evolve and there is limited historical data to understand how the effects of climate change will trickle into the housing market.

Sean Becketti1 describes the myriad ways climate change and natural hazard risk can permeate the housing and housing finance industries as well as some of the ways to mitigate its effects. However, quantifying these risks and inserting them into mortgage credit and prepayment models comes with significant challenges. No “best practices” have emerged for incorporating these into traditional model frameworks.

This paper puts forth a practical framework to incorporate climate risk into existing enterprise risk management practices for the housing finance industry. The framework incorporates suggestions to prepare for coming regulatory requirements on climate risk and, more importantly, proactively managing and mitigating this risk. Our approach is based on over two years of research and field work RiskSpan has conducted with its clients, and the resulting models RiskSpan has developed to deliver insights into these risks.

The paper is organized into two main sections:

  1. Prescribed Climate Scenarios and Emerging Regulatory Requirements
  2. A Practical Approach to Climate Risk Assessment for Mortgage Finance

Layering climate risk into enterprise risk management is likely to be a multiyear process. This paper focuses on steps to take in the initial one to two years after climate risk has been prioritized for investment of time and resources by corporate leadership. As explained in an MBA white paper from June 2022,2 “Existing risk management practices, structures, and relationships are already capturing potential risks from climate change.” The aim of this paper is to investigate specific ways in which existing credit, operational, and market risk frameworks can be leveraged to address this challenge, rather than seeking to reinvent the wheel.

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.

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Agency Social Indices & Prepay Speeds

Do borrowers in “socially rich” pools respond to refinance incentives differently than other borrowers? 

The decision by Fannie and Freddie to release social index disclosure data in November 2022 makes it possible for investors to direct their capital in support of first-time homebuyers, historically underserved borrowers, and people who purchase homes in traditionally underserved areas. Because socially conscious investors likely also have interest in understanding how these social pools are likely to perform, we were curious to examine and learn whether mortgage pools with higher social ratings behaved differently than pools with lower social ratings (and if a difference existed, how significant it was). To the extent that pools rich in social factors perform better (i.e., prepay more slowly) than pools generally, we expect investors to put an even higher premium on them. This in turn should result in lower rates for the borrowers whose loans contribute to pools with higher social scores. 

The data is new and we are still learning things, but we are beginning to discern some differences in prepay speeds.


First, a quick refresher on Fannie’s and Freddie’s social index terminology: 

  • Social Criteria Share (SCS): The percentage of loans in a given pool that meet at least one of the “social” criteria. The criteria are low-income, minority, and first-time homebuyers; homes in low-income areas, minority tracts, high-needs rural areas; homes in designated disaster areas and manufactured housing. As of December 2022, 42.12 percent of loans in the average pool satisfy at least one of these criteria. 
  • Social Density Score (SDS): A measure of how many criteria the average loan in a given pool satisfies. For simplicity, the index consolidates the criteria into three categories – those pertaining to income, those pertaining to the borrower, and those pertaining to the property. A pool’s SDS can be zero, 1, 2, or 3 depending on the number of categories within which the loan satisfies at least one criterion. The average SDS as of December 2022 is 0.62 (out of 3). 

Do social index scores impact prepay speeds? 

While it remains too early to answer this question with a great deal of certainty, historical performance data appears to show that pools with below-average social index scores prepay faster than more “social” bonds. 

We first looked at a high-level, simplistic relationship between prepayments and Social Density Score. In Figure 1, below, pools with below-average Social Density Scores (blue line) prepay faster than both pools with above-average SDS (black line) and pools with the very highest SDS (green line) when they are incentivized by interest rates to do so. (Note that very little difference exists among the curves when borrowers are out of the money to refi.)  

Fig. 1: Speeds by Prepay Incentive and Social Density Score 

See how easy RiskSpan’s Edge Platform makes it for you to do these analyses yourself.

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We note a similar trend when it comes to Social Criteria Share (see Fig. 2, below).  

Fig. 2: Speeds by Prepay Incentive and Social Criteria Share 

Social Pool Performance Relative to Spec Pools 

Investors pay up for mortgage pools with specified characteristics. We thought it worthwhile to compare how certain types of spec pools perform relative to socially rich pools with no other specified characteristics. 

Figure 3, below, compares the performance of non-spec pools with above-average Social Criteria Share (orange line) vs. spec pools for low-FICO (blue line), high-LTV (black line) and max $250k (green line) loans. 

Note that, notwithstanding a lack of any other specific characteristics that investors pay up for, the high-SCS pools exhibit a somewhat better convexity profile than the max-700 FICO and min-95 LTV pools and slightly worse convexity (in most refi incentive buckets) than max-250k pools. 

Fig. 3: Speeds by Prepay Incentive and Social Criteria Share: Socially Rich (Non-Spec) Pools vs. Selected Spec Pools

We observe a similar effect when we compare non-spec pools with an above-average Social Density Score to the same spec pools (Fig. 4, below).   

Fig. 4: Speeds by Prepay Incentive and Social Density Score: Socially Rich (Non-Spec) Pools vs. Selected Spec Pools 

See how social index scores affect speeds relative to other spec pools.

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