This post provides an update on delinquency rate trends observed in the Non-Agency mortgage market with a deep dive on different vintages and credit segments of the Non-QM market. All of the figures in this post are based on queries of historical CoreLogic Non-Agency data from the most recent factor date (December, 2025) via our proprietary RiskSpan Edge Historical Performance module.

December delinquency rates continue to decline from their post-Covid highs in May 2025:

Figure 1.

Figure 2.

Figures 3 through 5 show the relative delinquency performance of mortgages across 4 segments of the Non-QM population, which comprises the largest portion of the PLS 2.0 market. While loans with full documentation represent the largest segment of this market from a total outstanding balance perspective, originations have been shifting towards DSCR/Investor and Bank statement loans since 2022.

Figure 3.

Figure 4.

Figure 5.

Non-QM delinquency rates are highly differentiated by credit quality, but performance is still highly differentiated by documentation type when controlling for credit quality:

Figure 6.

Figure 7.

Figures 8 and 9 show the relative delinquency performance of Non-QM mortgages by year of origination. For these charts, vintages prior to 2021 are excluded to avoid the distorting impact of the COVID delinquency shock.

Figure 8.

Figure 9.

Given the elevated delinquency rates of Non-QM mortgages relative to Agency and Prime Jumbo mortgages, particularly in the Bank Statement and DSCR/Investor and segments and in the lower FICO ranges, it is important for investors to monitor their portfolios that have Non-QM exposure. Our credit models at RiskSpan model these delinquency roll rates directly, and our modeling team calibrates our suite of models to capture both the overall trends and the differentiated performance across loan and product types. These models are just one component of our scaled analytics solutions to help our clients evaluate risk and make investment decisions.

By David Andrukonis & Thomas Pappalardo

Advances haven’t changed much. But the data behind them has. 

For decades, the Federal Home Loan Bank System (FHLBanks) has provided reliable, collateralized liquidity to its member institutions, which include banks, credit unions, insurance companies, and CDFIs through FHLBank advances. The model’s value has been proven through multiple credit cycles: members pledge eligible collateral, receive funding, and FHLBanks monitor that collateral to ensure adequate coverage throughout the advance term. In 2024, FHLBanks extended $737 billion to member institutions, with collateral pledged across the system securing advances and other credit products totaling approximately $4.45 trillion. 

While the fundamental approach and underwriting of the FHLBank advance program remain sound, the environment has transformed. The collateral backing today’s advances—primarily residential mortgage loans—now generates unprecedented volumes of performance data. Property values can be revalued continuously, payment histories update in real time, geographic risk concentrations can be mapped and stress-tested instantly, and predictive analytics can forecast delinquency probability months in advance. 

The Evolution of Collateral Risk Management 

Historically, the advance business was built during an era when loan-level data was expensive to collect and difficult to analyze at scale. FHLBanks developed robust monitoring and risk management processes suited to those constraints: periodic reviews, manual sampling, and conservative haircuts compensated for limited visibility between monitoring cycles. These approaches have served the System well for over 90 years, with minimal credit losses even through severe market stress events. 

However, the technological landscape has changed significantly. Data processing and management capabilities have advanced at a rapid pace. Transfers that once required manual translation now move through AI-driven smart-mapping tools that provide quality control and transparency. Loan-level data spanning hundreds of fields per loan, including payment status, property values, borrower characteristics, and modification history, is now easily ingested into analytics-ready formats and can be updated monthly. 

Analytical tools have advanced and are more accessible and cost-effective. Cloud-based platforms deliver sophisticated analytics such as updated valuations, loan-level forecasts, machine learning-based predictions, and comprehensive stress testing. 

FHLBank members and regulatory expectations have also evolved. Members expect data-driven insights and transparency; regulators emphasize quantitative rigor and proactive risk management. Both expect FHLBanks to leverage available tools to enhance risk oversight and delivery safely on its core liquidity mission. 

The Era to Modernize Data and Technology for the System 

Each FHLBank’s board establishes its own collateral policy, creating significant variability across the eleven-bank system. These differences reflect variations in member risk characteristics, individual risk tolerances, geographic market differences, and diverse methods and vendors for determining collateral lendable values. Key distinctions include eligible collateral types, collateral discounts (“haircuts”), and conditions for collateral delivery. Each FHLBank discounts the reported market or par value of pledged collateral to ensure liquidation value exceeds the value of products being secured, with haircuts depending on collateral type, member credit quality, security method, financial condition, and asset value trends under adverse conditions. 

This decentralized approach creates opportunities for advanced technology platforms to standardize risk assessment, manage arbitrage through sophisticated pricing models, enhance collateral valuation precision, and provide comprehensive data analytics that modernize collateral management and advance pricing practices across the system. 

What Modern Collateral Analytics Enable 

Platforms like RiskSpan’s transform collateral monitoring from periodic assessment to continuous risk management. For FHLBanks, this translates into several powerful capabilities: 

Real-Time Collateral Visibility 

RiskSpan provides continuous monitoring of pledged collateral across multiple dimensions: 

This visibility enables proactive conversations with members about their collateral profiles and borrowing capacity. 

The chart and table below illustrate how the RiskSpan Platform can immediately summarize geographic concentration and performance data across one FHLBank region (Atlanta’s in this example). The charts below reflect public Agency (Fannie and Freddie) data. But the same analysis can easily and immediately be performed on loan collateral pledged to a FHLBank once the data service is established to maintain that data in the Platform. This is accomplished through an AI-enabled data collection and normalization process. 

Exhibit 1: Performance by State – FHLBank Atlanta Region – Agency Data Extracted from RiskSpan Platform – Historical Performance Module 

Predictive Risk Assessment 

Modern analytics can forecast where risks are heading: 

These predictive capabilities allow FHLBanks to move from reactive problem-solving to proactive risk management, enabling earlier intervention and more real-time reporting to regulators. 

Granular Analytics for Better Decisions 

RiskSpan’s Platform enables analysis at multiple levels—from system-wide exposure down to individual loan characteristics. Credit officers can: 

This granularity supports both risk assessment and member relationship management. 

Innovation Opportunities for Managing Advances  

Enhanced collateral analytics create opportunities to fundamentally reimagine FHLBank member advance products: 

Risk-Based Pricing and Terms 

With precise, objective measures of collateral quality, FHLBanks can move toward pricing and structuring advances that reflect actual risk levels: 

Enhanced Member Value 

Modern analytics deliver more value to members: 

Collateral Transparency and System Resilience in Times of Stress 

The Federal Home Loan Bank system is a critical liquidity tool for the national banking system in times of distress. A recent Urban Institute report outlines how significant a role FHLBanks play in reducing the risk of financial crises.  

The March 2023 regional bank liquidity events also highlighted the systemic importance of FHLBank liquidity provision. During peak stress, the FHLBank System’s advances outstanding increased by over $300 billion—demonstrating its role as a critical stabilizing force. But this massive, rapid deployment of liquidity required FHLBanks to quickly assess collateral from institutions they might not have previously served extensively, while coordinating with other FHLBanks and government agencies supporting the same institutions. As regional banks sought emergency funding from multiple sources, it exposed challenges in collateral coordination across government regulators and FHLBanks that were proactively intervening. Determining available collateral capacity, avoiding double-pledging, and coordinating lien positions becomes complex when speed is essential. 

Enhanced collateral analytics and data management can dramatically improve coordination: 

Real-time collateral position visibility allows FHLBanks to instantly see what collateral a member has pledged, its current valuation, and remaining borrowing capacity. When regulators, the Federal Reserve, or other FHLBanks need to understand a troubled institution’s collateral position, RiskSpan can generate comprehensive reports in minutes rather than days. 

The examples below (shown for illustrative purposes using public data) address exposure at geographic and servicer level. FHLBanks can run analogous queries on the platform at the member level using their own proprietary data. 

Exhibit 2: Query Screenshot: RiskSpan AI MBS Agent Module 

Exhibit 3: Performance by Servicer – FHLBank San Francisco – Agency Data Extracted from RiskSpan Platform – Historical Performance Module (via AI MBS Agent) 

AI tools can also help identify trends in performance data: 

Standardized collateral data management facilitates communication across the FHLBank System and with other government entities. If an institution operates across multiple FHLBank districts and has pledged collateral to different Banks, consistent data standards and analytical frameworks enable those Banks to quickly share information and coordinate responses. Rather than reconciling different valuation methodologies or collateral categorizations during a crisis, all parties work from common data foundations. 

Stress scenario analysis becomes critical when evaluating whether to extend emergency liquidity. During March 2023, FHLBanks needed to rapidly assess: How would this institution’s pledged collateral perform if deposit outflows continue? What if property values in their markets decline by 20%? Is the current haircut adequate if market conditions deteriorate further? RiskSpan’s AI-driven MBS Data Agent tool has stress testing capabilities that enable making these assessments in real-time, supporting confident decision-making when hours matter. 

Lien priority and collateral allocation transparency helps coordinate among multiple creditors. When an institution has borrowed from both an FHLBank and the Federal Reserve, clear documentation of which specific assets secure which facilities, lien positions, and remaining unencumbered assets is essential. Modern collateral management systems maintain this documentation systematically, reducing confusion and potential disputes during already stressful periods. 

Rapid collateral substitution and revaluation capabilities allow FHLBanks to respond dynamically as conditions evolve. If an institution’s collateral quality deteriorates, the technology platform can immediately model how much additional collateral would be needed to maintain existing advance levels, or conversely, whether advance reductions are necessary. This agility protects FHLBank credit quality while maintaining maximum possible support for the troubled institution. 

Enhanced collateral analytics don’t just improve routine risk management but serve to strengthen the FHLBank System’s ability to fulfill its countercyclical liquidity role during the moments when that role matters most. Clear collateral visibility, rapid assessment capabilities, and standardized data management transform the FHLBank System’s crisis response from a challenge requiring heroic manual efforts into a systematic capability supported by robust infrastructure. 

For policymakers and regulators evaluating the FHLBank System’s role in financial stability, this enhanced capability is crucial. It demonstrates that FHLBanks can rapidly deploy substantial liquidity during stress periods while maintaining strong risk management and coordinating effectively with other parts of the financial safety net. This combination of mission-critical liquidity provision backed by sophisticated risk assessment directly serves the System’s purpose while protecting its safety and soundness. In this age of advanced data and analytics, and with the AI tools available the promise of modernizing FHLBank Advances is tangible and timely. 

The Path Forward 

Modernizing advance management doesn’t require abandoning proven approaches or taking excessive risk. It means enhancing what works by deploying the technology and data tools that provide deeper insight, earlier warning, and more precise calibration of terms to risk. The journey typically begins with integrating member collateral data into a modern analytics platform, establishing baseline metrics, and developing staff capabilities to interpret and act on enhanced analytics. From there, individual FHLBanks can pilot specific innovations—risk-based pricing, dynamic monitoring with automated alerts, before expanding successful approaches system-wide. 

A Strategic Imperative 

The Federal Home Loan Bank System faces an evolving competitive and regulatory landscape. Mission scrutiny has intensified, member needs have become more sophisticated, and the technology and data landscape is far more robust. Regulatory expectations emphasize quantitative rigor. In this environment, advances that leverage modern data and analytics ensure FHLBanks remain relevant, competitive, and mission focused. 

The technology exists. The data is available. The analytical techniques are proven. What’s required is vision to see beyond traditional approaches and commitment to enhancing a business line that has served the FHLBank System well for generations. Advances and the critical liquidity purpose they serve haven’t changed much. But as data and technology have evolved, the opportunity to enhance them has never been greater. FHLBanks that embrace modern collateral analytics can deliver superior risk management, stronger member relationships, and sustainable competitive advantage—all while staying true to their mission of supporting housing finance and community development. 

The data revolution in collateral-backed lending has arrived.  

About RiskSpan 

RiskSpan delivers a single, intelligent analytics solution for structured finance public and private asset-backed finance 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.  

RiskSpan thanks Alanna McCargo of iAM Housing Advisors for her advisory services and contributions to this report. 

Private ABF managers are facing a critical infrastructure decision as they scale: build proprietary technology systems in-house, or partner with an established platform?

This decision has major implications for growth, risk, investor perception, operational efficiency, and long-term competitiveness. And as highlighted at the 2025 Private Credit Technology Summit earlier this year, the industry’s rapid evolution makes this decision more consequential than ever.

Below, I break down the key considerations to help private ABF investors evaluate which approach – or combination of approaches best aligns with your strategy, resources, and ambitions.

The Market Context: Why this Decision Matters More Than Ever 

A DLA Piper article summarizing the Summit made one theme clear: 
Private credit’s competitive edge is increasingly defined by technology, not just origination and underwriting. 

Several market forces are reshaping how firms should think about technology infrastructure: 

In this environment, the build-versus-buy decision isn’t about technology preferences – it’s about strategic positioning and where you allocate your firm’s limited resources and attention. 

When Building In-House Makes Strategic Sense?

Building in-house can be the right choice if it aligns with your core strengths and long-term strategy and you have the resources to execute well. 

Building might be right for your firm if: 

1. You expect to have ongoing capacity to maintain and evolve systems as your business grows. This includes deep expertise in private ABF workflows, data engineering, collateral management, and performance analytics. 

2. Your investment strategy requires highly specialized, proprietary workflows.
For example, if your collateral type or loan structure is so differentiated that no third-party platform can support it without major customization. 

3. You have a long time horizon and can absorb slower time-to-value.
Building can take quarters (or years) and often requires multiple rebuilds as the business grows.  

4. You’re prepared to shoulder the full cost of development and ongoing maintenance.
This includes engineering headcount, version control, data pipelines, cloud infrastructure, documentation, cybersecurity, and ongoing regulatory adaptation – costs that often exceed initial projections. 

Even so, “build” is often harder than it looks. 

The Hidden Complexity of Building 

Even when building makes strategic sense, firms often underestimate the challenge.  ABF data is messy, siloed, and heterogeneous. Legacy spreadsheets and bolt-on tools don’t scale, and homegrown systems tend to break as soon as asset volume or collateral diversity increases. The engineering talent required understands both capital markets and modern data architecture – a rare and expensive combination. 

If technology infrastructure isn’t a core competitive differentiator – if your edge is in sourcing, underwriting, or structuring – building can divert critical resources from your highest value activities. 

When Partnering Accelerates Your Strategy 

For most firms, especially those entering ABF or scaling rapidly, buying and partnering is often the more strategic path. 

Partnering might be right for your firm if: 

1. You want fast, predictable time-to-value.
A platform built for ABF lets you launch monitoring, reporting, and analytics in weeks instead of quarters – allowing you to deploy capital and focus on deals rather than infrastructure. 

2. Your team’s primary value is in origination, structuring, underwriting, or asset management.
Your highest-value people should focus on making credit decisions, not on building and debugging software. 

3. You expect rapid AUM growth or expanding asset classes.
A third-party platform offers built-in scalability, flexible data ingestion, and the ability to support new deal types without major reinvestment. 

4. You have limited internal engineering infrastructure.
Most private credit firms simply aren’t structured like fintech companies. And they don’t need to be. 

5. LPs expect institutional-grade reporting and data transparency.
LPs are now benchmarking managers on data architecture and workflow maturity alongside investment performance. Good technology is no longer a “nice-to-have” – it’s table stakes for institutional capital. 

A Framework for Your Decision 

Step back and reflect on four key questions: 

1. What is your competitive advantage? 

If your edge is underwriting, structuring, servicing, or sourcing (i.e., things other than software development) then partnering usually aligns better with your strategy. 

2. How quickly do you need to scale? 

If speed matters, buying provides immediate infrastructure and eliminates long build cycles. 

3. How complex are your investments? 

If you’re dealing with multiple asset classes, specialty finance platforms, or varied servicers, you’ll need a system that can evolve faster than most internal builds can. 

4. What do your investors expect? 

Institutional LPs increasingly demand transparency, data fidelity, and reporting consistency. Technology plays a central role in meeting those expectations. 

The Hybrid Approach: Buy the Foundation, Build the Differentiation

We see the same themes across the clients we advise and the ABF platforms we support: 

Most private credit and ABF firms benefit from buying and partnering early. 
This preserves organizational focus, accelerates operational maturity, and allows firms to stand up institutional-grade workflows much faster. 

Some firms may selectively build around a unique competitive edge. But even these tech-savvy firms often choose to buy the foundational plumbing (data ingestion, monitoring, reporting, analytics) and then build their specialized layers on top. 

In other words: 

Buy the infrastructure. 
Build the differentiation. 

This is the model we believe will dominate the next decade of private ABF technology evolution. 

The decision to build or buy isn’t binary—and it’s not permanent. The firms scaling quickly while maintaining institutional-grade operations are those that make deliberate choices about where to invest their technical resources, based on their competitive positioning and strategic priorities. If you’re evaluating your technology infrastructure options, we are happy to share more about how we’ve helped firms navigate this decision.

Register here for next month’s call: Thursday, December 18th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call reviewed recent prepayment performance, presented new research on identifying cash-out refinance activity in GSE data, and walked through key macroeconomic and consumer-debt indicators shaping mortgage behavior going into 2026. 

Here’s a quick recap in case you missed it. 

(Click here for the entire 24-minute recording or continue reading for a summary.)  

New Research: Estimating Cash-Out Refinance Activity Using GSE Data 

Cash-out refinance is a component of prepayment modeling that has traditionally been difficult to observe directly. Shane Lee explained how we have been getting at it using publicly available GSE performance data.

Originations vs. Prepayments: Understanding the Gaps 

Voluntary prepayments consist of turnover, rate-refinance, and cash-out refinance components. While originations include a loan-purpose indicator (“purchase,” “refinance,” “cash-out”), payoff data does not. 

Nationally, the gap between prepaid loan counts and contemporaneous originations is significant, especially in earlier years. This is driven in part by new construction, properties without existing liens, and cross-region relocations. 

To improve attribution, our team has been evaluating data at the ZIP3 level, where prepay and origination volumes show much tighter alignment. Shane presented examples, including ZIPs near Ventura, Tucson, St. Louis, Boulder, and Austin, demonstrating that refinances and cash-outs can be reasonably inferred when prepaid loan totals track closely with origination totals in the same geography. 

Where origination and prepay counts align well, origination loan-purpose shares can serve as a proxy for prepay-purpose shares, enabling estimation of the cash-out fraction among prepaid loans. 

Prepayment Model Performance: Stable Overall, With Pockets of Divergence

Guanlin Chen presented a review of our v3.7 model back-testing results. In summary: 

Low-Coupon (≤5.5%) Conventional and Ginnie Cohorts 

Actual October CPRs tracked the model closely for low-coupon pools across Fannie, Freddie, and Ginnie. October’s slight upward movement in discount speeds (which the model had projected to decline) was explained by a calendar effect: one additional collection day offset typical seasonal slowdown. 

When adjusting for day-count, both actual and projected CPRs show similar downward trends. The alignment reinforced Guanlin’s point that lock-in remains firmly intact. Despite lower rates during parts of October, borrowers with sub-4% or low-4% mortgages still show little inclination to refinance, consistent with recent months. 

High-Coupon (≥6%) Cohorts: Speeds Running Hotter Than Expected 

The premium sector told a different story. Borrowers holding 6%–7% coupons responded more aggressively to rate movements than historical incentive-matched periods would suggest. The S-curve steepened further in October, with realized CPRs meaningfully exceeding v3.7 model predictions. 

To address this, RiskSpan’s v3.8 prepayment model introduces a configurable “in-the-money multiplier” that allows users to steepen the S-curve to better capture this more responsive behavior. 

Outliers and Ongoing Calibrations 

While most premium segments prepaid faster than expected, deep-in-the-money Ginnies (WAC >7%) actually prepaid slower than v3.7 projected. We are actively evaluating updated calibration approaches for these cohorts. 

Market Indicators: Rates, Labor Markets, Home Prices, and the Fed 

Mortgage News Daily data showed a recent ~25bp increase in the 30-year fixed rate. The prevailing question on clients’ minds—“Where do rates go from here?”—was addressed via futures and FedWatch probability data: 

Labor Market Softening 

The latest (delayed) September unemployment rate rose to 4.4%. Rising unemployment, paired with persistent inflation pressures, creates a challenging backdrop for housing demand. 

Home Price Growth Slowing Nationally 

Case-Shiller data, nationally and across metros, showed: 

While inventory is rising toward a buyer-leaning market, transaction volumes remain soft. 

Consumer Debt: Elevated, Shifting & Stress-Inducing 

Debt rose $200B quarter-over-quarter, with long-term increases far outpacing inflation and population growth in several categories: 

Inflation (+71% cumulative since 2003) and adult population growth (~6%) alone cannot explain these increases. 

Aging Households Carrying More Debt Than Ever 

A striking trend: borrowers 60+ years old have experienced 300–500% increases in total debt held. 

In 2003, the 70+ population held only 4% of total U.S. household debt. 
In 2025, that share stands at 10%. This is an extraordinary shift.

This appears to be evidence of structural strain: As people age, they are unable to pay down their debts. Also, wage growth has not kept up with inflation.

Younger households, meanwhile, face increasing difficulty obtaining new credit.

We continue to add additional analytics reports on the Platform. Please visit www.riskspan.com/request-access to request free access. 

As always, please feel free to contact us to discuss or learn more. 

Recent history is showing a persistent (and widening) gap between VA and FHA loan prepayment speeds in Ginnie Mae securities.  

Over the past 33 months, VA 30-year loans are prepaying 40 percent faster than FHA 30-year loans (9.4% CPR for VA vs. Just 6.6% for FHA. VA speeds over this period are ranging from 1.15x to 1.77x FHA speeds. 

This divergence is not incidental. With a median spread between the two of around 230 bps, the difference compounds significantly in modeling cash flow expectations and MBS pricing. 

Why this divergence? 

At least three structural factors contribute to faster VA speeds relative to FHA: 

  1. Borrower Profiles: VA borrowers tend to have higher credit scores (727 average FICO as of Sept 2025) than FHA borrowers (678 average FICO). This makes VA borrowers more likely to refinance quickly when market conditions shift. 
  1. Program Rules: VA’s streamlined refinancing programs are generally more accessible, lowering the cost of refinancing compared to FHA. 
  1. Servicing Practices: The VA loan servicing ecosystem has historically been more efficient, which can accelerate churn relative to FHA pools. 
  1. Larger Loan Size: The average VA loan size is typically larger than the average FHA loan size, making refinancing more impactful for VA borrowers. 

What does this mean for Ginnie II TBA & Custom pools? 

Ginnie II TBAs typically combine both VA and FHA collateral. Most of the loans are FHA, but VA loans still account for a significant share. Because VA loans prepay substantially faster, TBA investors are effectively buying into faster prepayment risk than they would see in a purely FHA pool.  

This risk manifests itself both in the form of shorter duration and more negative convexity exposure. Investors in Ginnie II TBAs may see faster principal return than modeled if VA share is high, especially if the model fails to differentiate between VA and FHA loans, and the additional negative convexity in VA loans will adversely impact OASes, ceteris paribus. 

For investors seeking more tailored exposure, custom Ginnie pools provide a way to isolate or avoid VA prepayment risk. For instance: 

Given current market spreads, the differential between VA and FHA speeds is unlikely to narrow materially in the near term. As refinancing incentives fluctuate, VA borrowers will continue to exhibit faster churn than FHA counterparts. 

For Agency portfolio managers and traders, this reality underscores the importance of collateral composition within Ginnie II TBAs. It also highlights the importance of prepayment models capable of recognizing the differences between FHA and VA loans and taking those differences into account when making forecasts. 

Book a demo for RiskSpan’s Edge platform for Agency MBS Traders and Analysts. 

Register here for next month’s call: Thursday, November 20th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call focused on the impact of the Fed rate cut, key macro indicators and a spotlight on the surging second-lien market. 

Here’s a quick recap in case you missed it. 

(Click here for the entire 23-minute recording or continue reading for a summary.)  

Rates Ease, but Headwinds Persist 

October has brought a modest reprieve in mortgage rates, with the 30-year fixed rate having fallen to approximately 6.2%, the lowest level in nearly a year. 

Affordability remains constrained, however, and long-term headwinds appear far from resolved. Specifically: 

Unemployment remains near 4.2%, and core PCE inflation continues to hover around 2.8%. While steady, this remains above the Fed’s comfort zone. 

Home price growth is slowing nationally, with several major metros posting month-over-month declines. 

Fed Funds futures suggest rates will stay elevated into 2026, with year-end 2025 expectations still in the 3.5–3.75% range. 

Together, these indicators suggest a “higher for longer” policy regime even as the market eyes rate cuts later this year. 

HELOC and Second-Lien Insights: Delinquencies on the Rise 

Leveraging the Equifax Analytic Dataset, a 10% sample of active U.S. credit borrowers with anonymized tradeline-level detail, enables us to dive deep into Home Equity Loans (HELs) and Home Equity Lines of Credit (HELOCs). These asset classes are gaining renewed investor attention as homeowners tap existing equity rather than selling into a high-rate market.

Delinquency rates are trending upward for both HELs and HELOCs, particularly among lower-credit-score borrowers. Aggregated five-year views on page 11 highlight the steady climb, with 600-score cohorts showing the sharpest deterioration. 

These findings echo broader signals of consumer strain visible across other loan products. 

Consumer Balance Sheets Under Pressure

The New York Fed’s Q2 2025 Household Debt and Credit Report underscored the strain many consumers face. Total household debt continues to climb, driven by non-housing credit categories—auto loans, student debt, and revolving balances in particular. 

Credit card and auto loan delinquencies have risen sharply, while mortgage and HELOC performance, though still comparatively solid, are trending downward. Even with stable macro indicators, consumers remain financially stretched. This dynamic is likely to influence credit performance and securitization trends into 2026.

Prepayment Model Updates 

Our prepayment models continue to align well with observed speeds across both Conventional and Ginnie collateral. Lower-coupon collateral (WAC ≤ 5.5%) experienced some deceleration versus forecasts—a function of seasonality and slower housing turnover.  

Higher-coupon cohorts (WAC ≥ 6.0%) reflected more volatility, consistent with recent refinance activity at the margins. 

We continue to add additional analytics reports on the platform. Please visit www.riskspan.com/request-access to request free access. 

Also, please feel free to contact us to discuss or learn more.

The August 7, 2025, Executive Order on “Democratizing Access to Alternative Assets for 401(k) Investors” marks one of the most consequential shifts in U.S. retirement policy in decades. If implemented, it could permit alternative assets including private equity, real estate, digital assets, and private asset-backed finance (ABF) within 401(k) investments. With 70+ million participants and ~$10 trillion in plan assets, even modest policy changes could reshape both the retirement landscape and the ABF market.

Balancing Innovation and Integrity

For plan sponsors, the appeal of alternative investments is clear: greater diversification and the potential for enhanced returns. The challenges are equally clear — illiquidity, valuation opacity, higher fees, fee complexity, and fiduciary exposure. Historically, sponsors have avoided alternative investments not simply because of cost, but because of legal and operational risk. Under ERISA, fiduciaries are held to a “prudent expert” standard — and can be liable if investments are deemed imprudent, insufficiently transparent, or overpriced relative to their value.

Without daily valuations, clear benchmarks, or transparent pricing data, it becomes far more difficult to demonstrate prudence or defend against claims of excessive fees — a new regulatory framework won’t erase these risks. It will instead demand a higher standard of disclosure, governance, and prudence. Transparency must become the organizing principle. Clarity in valuation methodologies and procedures, cost structures, and risk metrics will be essential to any sustainable integration of alternative investments into 401K plans.

The Transparency Imperative

Unlike public securities, many alternative and private ABF investments rely on subjective, lagged, or model-based valuations. Within the ABF market, inconsistent reporting furthers the complexity and challenges — particularly across private securitized structures. Institutional investors often struggle to obtain consistent and reliable data on underlying asset performance. For alternative investments to work responsibly within 401(k) plans, private issuers, fiduciaries, and regulators must align on a framework that enforces transparent reporting and valuations, with greater frequency. Transparency is not a compliance exercise — it’s the foundation of investor trust (let’s not forget the great financial crisis and its lingering effect for decades).

Much of the current discussion centers on establishing fiduciary safe harbors — clear rules that provide plan sponsors protection when offering alternative assets. Leading law firms have all emphasized that safe harbors must: define prudent due diligence and monitoring standards; clarify valuation, fee, and liquidity protocols and establish documentation frameworks that demonstrate fiduciary prudence.

Technology as an Enabler of Fiduciary Transparency

As fiduciaries navigate this evolving landscape, it’s clear that data transparency, independent valuation, and performance reporting will be critical. This is precisely where technology platforms like RiskSpan play a pivotal role. For more than two decades, RiskSpan has been a leader in driving transparency and data standardization across the private and structured credit markets — helping investors, regulators, and plan sponsors understand and manage complex risks with clarity. Our analytics and data infrastructure are purpose-built to deliver loan-level transparency, consistent valuation, and performance reporting across complex, illiquid and structured credit markets. By standardizing data and surfacing risks clearly, we help plan sponsors, managers, and fiduciaries meet the heightened expectations for accuracy, accountability, and auditability that this new environment demands.

The Path Forward

The success of including alternative assets in 401(k) plans will depend less on regulatory permission and more on industry discipline — our collective ability to balance innovation with responsibility. If the ABF market can meet this moment with rigor, transparency, and integrity, it can play a transformative role in the next chapter of U.S. retirement investing. The conversation is just beginning — and collaboration will be key.

Article content

Breaking Down VA vs FHA Prepayment Speeds 

This use case demonstrates how agentic interaction with the MBS Data Tool helps investors move from a general observation (“VA speeds are fast”) to a data-backed, multidimensional explanation tied directly to credit, LTV, and program structure. 

Business Question 

Why are VA loans — particularly those with low FICO and high LTV profiles — prepaying so much faster than historically, and faster than FHA loans with comparable characteristics? 

For the full output of our AI MBS Agent, read on or watch our two-minute demo video:

Workflow with Agentic AI 

1. Baseline Comparison 

Based on the data, I can now provide a comprehensive analysis of VA vs FHA loan prepayment trends since 2020:

Key Findings:

1. Current Prepayment Speeds (as of September 2025):

– VA loans: 11.74% CPR

– FHA loans: 8.68% CPR

– VA loans are currently prepaying about 3 percentage points faster than FHA loans

2. Historical Trends:

2020-2021 (Refinance Wave):

– Both loan types experienced very high prepayment speeds

– Peak CPRs:

* FHA: 38.48% (April 2021)

* VA: ~30-31% (Late 2021)

– VA loans consistently showed higher prepayment sensitivity during this period

2022-2023 (Rising Rate Environment):

– Both loan types saw significant slowdown in prepayment speeds

– Lowest CPRs:

* FHA: 4.42% (February 2023)

* VA: 5.07% (February 2023)

– The spread between VA and FHA prepayment speeds narrowed but remained positive

2024-2025 (Current Environment):

– Moderate prepayment speeds for both loan types

– VA loans maintain faster prepayment speeds

– Recent range:

* FHA: 5-10% CPR

* VA: 7-14% CPR

3. Key Observations:

– VA loans consistently prepay faster than FHA loans across different rate environments

– The spread between VA and FHA prepayment speeds tends to widen in more favorable refinancing environments

– Both loan types show similar seasonal patterns but with different magnitudes

– VA loans show greater prepayment sensitivity to market conditions

4. Portfolio Sizes:

– FHA: Currently ~7.45 million loans with $1.47 trillion in balance

– VA: Currently ~3.77 million loans with $1.03 trillion in balance

This analysis suggests that investors should expect consistently faster prepayment speeds from VA loans compared to FHA loans, particularly during periods of favorable refinancing opportunities. The higher prepayment sensitivity of VA loans likely reflects the generally better credit quality of VA borrowers and their greater ability to refinance when rates are favorable.

2. High-Credit Borrowers 

Based on the analysis of high-FICO (740+) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-FICO VA: 9.37% CPR

– High-FICO FHA: 5.96% CPR

– Spread: 3.41% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-FICO VA Loans:

– Average FICO: 779

– Average WAC: 3.85%

– Average CLTV: 58.1%

– Balance: $443.4B (43% of VA portfolio)

– Loan Count: 1.47M loans

High-FICO FHA Loans:

– Average FICO: 769

– Average WAC: 4.55%

– Average CLTV: 58.4%

– Balance: $188.0B (13.8% of FHA portfolio)

– Loan Count: 879K loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-FICO VA: 4.26% to 14.76% CPR

– High-FICO FHA: 3.56% to 8.68% CPR

Seasonal Patterns:

– Both show seasonal increases in spring/summer months

– VA loans show more pronounced seasonal effects

– Peak prepayment typically occurs in October/November

4. Notable Observations:

– The prepayment speed differential between VA and FHA remains significant even at high FICO levels

– High-FICO VA loans have consistently lower WACs (about 70bps lower)

– Similar CLTV profiles between high-FICO VA and FHA

– VA has a much larger proportion of high-FICO borrowers (43% vs 13.8% for FHA)

– High-FICO VA loans show greater prepayment sensitivity to market conditions

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– The spread between VA and FHA prepayment speeds has remained relatively stable

– High-FICO VA loans maintain faster prepayment speeds even in rising rate environments

This analysis suggests that even when controlling for credit quality (FICO ≥ 740), VA loans consistently prepay faster than FHA loans. This is likely due to:

1. Lower average note rates (WAC)

2. VA’s no-down-payment benefit making it easier to refinance

3. Generally better borrower demographics and income profiles

4. More streamlined refinancing options for VA loans

Investors should expect continued faster prepayment speeds from high-FICO VA loans compared to high-FICO FHA loans, particularly during favorable rate environments or seasonal peaks.

3. Low-Credit Borrowers 

Based on the analysis of low-FICO (<640) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– Low-FICO VA: 21.04% CPR

– Low-FICO FHA: 12.07% CPR

– Spread: 8.97% faster for VA

2. Portfolio Characteristics (as of September 2025):

Low-FICO VA Loans:

– Average FICO: 611 – Average WAC: 4.77%

– Average CLTV: 66.2%

– Average Age: 52.3 months

– Balance: $77.5B (8.3% of VA portfolio)

– Loan Count: 316K loans

Low-FICO FHA Loans:

– Average FICO: 609

– Average WAC: 4.83%

– Average CLTV: 60.4%

– Average Age: 62.8 months

– Balance: $290.7B (21.3% of FHA portfolio)

– Loan Count: 1.49M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– Low-FICO VA: 9.08% to 27.54% CPR

– Low-FICO FHA: 6.31% to 12.73% CPR

Notable Patterns:

– Much higher volatility in VA prepayment speeds

– Larger seasonal effects in VA loans

– Consistently wider spread between VA and FHA vs high-FICO segment

4. Key Differences:

Portfolio Composition:

– FHA has a much larger concentration in low-FICO loans (21.3% vs 8.3% for VA)

– FHA’s low-FICO portfolio is nearly 4x larger than VA’s by balance

Performance Characteristics:

– VA loans show significantly higher prepayment speeds despite similar credit profiles

– VA loans exhibit greater prepayment volatility

– VA loans have slightly better average FICO scores within the low-FICO segment

– VA loans have higher CLTVs but lower WACs

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds are consistently higher and more volatile

– Seasonal patterns are more pronounced in VA loans

– The spread between VA and FHA prepayment speeds has remained wide

6. Notable Observations:

– The prepayment speed differential between VA and FHA is actually larger in the low-FICO segment than in the high-FICO segment

– Low-FICO VA loans show surprisingly high prepayment speeds despite lower credit scores

– FHA loans show more consistent, less volatile prepayment patterns

– VA loans maintain faster speeds despite higher CLTVs

This analysis reveals that the prepayment behavior difference between VA and FHA loans is even more pronounced in the low-FICO segment than in the high-FICO segment. This could be due to:

1. VA’s stronger refinancing program benefits for lower-credit borrowers

2. More flexible VA streamline refinance requirements

3. Better servicing support for VA borrowers

4. Lower average note rates in the VA program

5. Potentially different borrower demographics within the same FICO band

Investors should expect:

– Higher and more volatile prepayment speeds from low-FICO VA loans

– More stable, lower prepayment speeds from low-FICO FHA loans

– Larger seasonal effects in VA loans

– Greater market sensitivity in VA loans

4. High-LTV Loans 

Based on the analysis of high-LTV (≥95%) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-LTV VA: 12.37% CPR

– High-LTV FHA: 7.77% CPR

– Spread: 4.60% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-LTV VA Loans:

– Average FICO: 714

– Average WAC: 4.33%

– Average CLTV: 70.9%

– Average Original LTV: 100.3%

– Average Age: 52.9 months

– Balance: $607.2B (61.1% of VA portfolio)

– Loan Count: 2.11M loans

High-LTV FHA Loans:

– Average FICO: 679

– Average WAC: 4.61%

– Average CLTV: 62.9%

– Average Original LTV: 97.6%

– Average Age: 71.7 months

– Balance: $1.04T (72.0% of FHA portfolio)

– Loan Count: 5.08M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-LTV VA: 5.60% to 19.73% CPR

– High-LTV FHA: 4.50% to 8.89% CPR

Notable Patterns:

– Higher volatility in VA prepayment speeds

– Strong seasonal patterns in VA prepayments

– More stable FHA prepayment patterns

– Consistent spread between VA and FHA

4. Key Differences:

Portfolio Composition:

– Both programs have significant high-LTV concentration

– FHA has larger absolute exposure but similar relative exposure

– VA allows higher original LTV (100%+) compared to FHA

Performance Characteristics:

– VA loans consistently prepay faster despite higher LTVs

– VA borrowers have notably higher FICO scores (+35 points)

– VA loans have lower WACs (-28bps)

– VA loans are significantly younger (-19 months)

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds remain more volatile and responsive to seasonality

– Peak prepayment periods occur in spring/summer months

– The spread between VA and FHA has remained relatively stable

6. Notable Observations:

– High-LTV concentration is a defining characteristic of both programs

– VA’s higher prepayment speeds persist despite higher LTVs

– Better credit quality of VA borrowers likely contributes to faster speeds

– Younger average loan age in VA portfolio may influence prepayment behavior

– Lower WACs in VA portfolio suggest more efficient refinancing history

Implications for Investors:

1. Portfolio Management:

– Expect faster and more volatile prepayments from high-LTV VA loans

– Consider seasonal patterns in VA prepayments for timing decisions

– Factor in the significant WAC differential when evaluating prepayment risk

2. Risk Considerations:

– Higher prepayment risk in VA despite similar LTV profiles

– Greater market sensitivity in VA portfolio

– More stable, predictable prepayment patterns in FHA

3. Valuation Impact:

– Need to apply larger prepayment speed differentials for VA vs FHA

– Consider stronger seasonal effects in VA valuations

– Account for credit quality differences within high-LTV segment

The analysis suggests that even in the high-LTV segment, VA loans maintain significantly faster prepayment speeds than FHA loans, driven by:

1. Better borrower credit quality

2. Lower note rates

3. More flexible refinancing options

4. Younger loan age

5. More efficient VA refinancing program

These factors appear to overcome the theoretical constraints of high LTV ratios on refinancing ability.

5. Program Features as Drivers

The data strongly supports that program features drive faster VA prepayment speeds. Here’s the detailed analysis:

1. Early Prepayment Behavior (0-24 months):

– VA loans age 13-24 months: 25.29% CPR

– FHA loans age 13-24 months: 17.04% CPR

– The 8.25% difference reflects VA’s more flexible refinancing rules

– No seasoning requirement for VA vs 210 days for FHA

2. Refinance Incentive Response:

– High incentive (>100bp) VA: 50.43% CPR

– High incentive (>100bp) FHA: 26.55% CPR

– The dramatic difference (23.88%) shows VA borrowers can more easily act on rate incentives

– Even medium incentive (0-100bp) shows large gap:

* VA: 29.59% CPR

* FHA: 18.37% CPR

3. Credit Quality Impact:

– VA average FICO: 715-724 across age buckets

– FHA average FICO: 672-681 across age buckets

– Better VA credit quality + easier program rules = faster speeds

4. Cost Structure Effects:

VA Advantages:

– Lower funding fee (0.5%)

– No ongoing MIP

– Can include closing costs

– Results in lower all-in refinancing costs FHA Constraints:

– Higher upfront MIP (1.75%)

– Ongoing annual MIP

– More restricted closing cost inclusion

– Higher total refinancing costs

5. LTV Considerations:

– VA has no maximum LTV for IRRRL

– FHA generally limited to 97.75% LTV

– Current data shows:

* VA high-LTV loans (>95%): 12.37% CPR

* FHA high-LTV loans (>95%): 7.77% CPR

6. Program Flexibility Evidence:

Response to Rate Environment:

– VA loans show stronger reaction to refi incentives

– Higher CPRs across all incentive buckets

– Faster response to market opportunities

Seasoning Effects:

– VA shows earlier prepayment spikes

– More pronounced early-age prepayment behavior

– Less constrained by seasoning rules

7. Servicer Impact:

– VA servicers may be more proactive due to program ease

– Lower documentation requirements reduce operational friction

– Simpler approval process enables faster execution

The data demonstrates that VA’s program features create a more efficient refinancing mechanism:

1. Lower Costs:

– Reduced fees

– No ongoing MIP

– More included costs

2. Fewer Restrictions:

– No LTV limits

– No seasoning requirement

– Limited credit checks

3. Simpler Process:

– Less documentation

– Faster approval

– Lower operational burden

These advantages manifest in:

– Consistently faster prepayment speeds

– Stronger response to rate incentives

– Earlier refinancing activity

– Higher refinancing success rates

For investors, this means:

1. Expect faster VA prepayments across market cycles

2. Anticipate stronger response to refinancing opportunities

3. Factor in program differences when valuing VA vs FHA MBS

4. Consider seasoning rules when evaluating young loans

The combination of program features and data evidence clearly shows that VA’s more flexible refinancing rules are a primary driver of faster prepayment speeds compared to FHA.

6. Scenario Stress Test 

Based on the current portfolio characteristics and historical refinancing behavior, here’s how a 100bps rate drop would likely impact VA vs FHA prepayment speeds:

1. Current Portfolio Rate Distribution:

VA Portfolio:

– 7.5%+ WAC: $4.6B (0.4%)

– 7.0-7.49% WAC: $18.5B (1.8%)

– 6.5-6.99% WAC: $71.1B (6.9%)

– 6.0-6.49% WAC: $105.3B (10.2%)

– 5.5-5.99% WAC: $118.7B (11.5%)

– <5.5% WAC: $713.0B (69.2%)

FHA Portfolio:

– 7.5%+ WAC: $23.6B (1.6%)

– 7.0-7.49% WAC: $68.5B (4.7%)

– 6.5-6.99% WAC: $163.8B (11.2%)

– 6.0-6.49% WAC: $158.4B (10.8%)

– 5.5-5.99% WAC: $149.0B (10.2%)

– <5.5% WAC: $903.5B (61.5%)

2. Projected Prepayment Speeds by Refi Incentive:

Based on current behavior with similar incentives:

High Incentive (>100bps):

– VA: 50-55% CPR

– FHA: 25-27% CPR

– Differential: ~25-28% faster for VA

Medium Incentive (50-100bps):

– VA: 35-40% CPR

– FHA: 20-25% CPR

– Differential: ~15% faster for VA

Low Incentive (0-50bps):

– VA: 25-30% CPR – FHA: 15-18% CPR

– Differential: ~10-12% faster for VA

3. Portfolio Impact of 100bps Rate Drop:

VA Portfolio:

– ~$200B would move to >100bps incentive

– ~$119B would move to 50-100bps incentive

– Weighted average CPR projection: 32-37% CPR

FHA Portfolio:

– ~$256B would move to >100bps incentive

– ~$149B would move to 50-100bps incentive

– Weighted average CPR projection: 18-22% CPR

4. Key Factors Driving Divergence:

Program Features:

– VA IRRRL has no LTV restrictions

– VA has lower funding fees

– VA has simpler documentation requirements

– VA has no seasoning requirement

Borrower Characteristics:

– VA has higher average FICOs (714 vs 675)

– VA has lower average WACs

– VA has younger average loan age

5. Projected Timeline of Response:

Months 1-3:

– VA speeds increase rapidly to 30-35% CPR

– FHA speeds lag, reaching 15-18% CPR

– Differential: 15-17% CPR

Months 4-6:

– VA peaks at 35-40% CPR – FHA reaches 20-22% CPR

– Differential: 15-18% CPR

Months 7-12:

– VA moderates to 30-35% CPR

– FHA stabilizes at 18-20% CPR

– Differential: 12-15% CPR

6. Risk Factors to Consider:

Upside Risks (faster speeds):

– Pent-up refinance demand

– Improved technology/processing

– Competitive lender environment

Downside Risks (slower speeds):

– Housing market uncertainty

– Credit tightening

– Operational capacity constraints

7. Investment Implications:

1. Premium Pools:

– Expect significantly faster VA prepayments

– Greater price impact on VA premium pools

– Wider VA-FHA OAS spreads

2. Current Coupon Pools:

– Moderate prepayment differential

– Less price impact

– Smaller OAS spread widening

3. Discount Pools:

– Minimal impact on speeds

– Limited price effect

– Stable OAS spreads

Recommendations:

1. Portfolio Positioning:

– Reduce exposure to premium VA pools

– Consider FHA alternatives in premium segment

– Maintain VA exposure in current coupon/discount segments

2. Risk Management:

– Increase prepayment protection in VA holdings

– Consider specified pool stories

– Monitor servicer behavior closely

3. Relative Value:

– Look for opportunities in FHA premium pools

– Consider VA-FHA switches in premium segment

– Evaluate specified pool pay-ups

The analysis suggests a significant divergence in VA vs FHA prepayment speeds following a 100bps rate drop, with VA speeds likely to be 12-25% CPR faster depending on the segment and timing. This differential is driven by both program features and borrower characteristics, with the impact most pronounced in premium pools.

Questions or Need Support? 

Our team is standing by to help you make the most of these new features. Contact us at techsupport@riskspan.com or reach out to your dedicated account manager. 

Interested in a Complimentary Trial or Demo?

Contact us here.

Register here for next month’s call: Thursday, October 16th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call focused on the impact of the Fed rate cut, key macro indicators and a spotlight on the surging second-lien market. 

Here’s a quick recap in case you missed it. 

(Click here to listen to the entire 27-minute recording or continue reading for a summary.)  

Market Backdrop: September 2025

Mortgage rates have hit their lowest levels in nearly a year, averaging close to six percent. The Federal Reserve delivered its first rate cut of the current cycle in September 2025, reducing the target range from 400–425 basis points to a projected 350–375 basis points by year-end. Despite this easing, markets continue to anticipate relatively high rates into 2026. 

Inflation and unemployment are holding stable, but long-term headwinds persist, including sluggish real wage growth and affordability constraints in the housing market. Longer-dated Treasury yields are the key driver of mortgage rates, making them essential for investors to keep tabs on. As bond yields set the tone for borrowing costs across the economy, their movement will be critical in shaping both origination volumes and prepayment activity in the coming quarters. 

Spotlight on Second Liens 

The second lien mortgage market continues its emergence as one of the most active areas in structured finance. Issuance and securitization of second lien products have been increasing rapidly, with no signs of slowing. This expansion is driven in part by rising homeowner demand for tapping into the accumulated equity and lenders’ interest in capturing additional credit exposure in a higher-rate environment. 

Prepayment behavior in second lien mortgages, however, differs significantly from that of first liens. This divergence makes specialized model calibration critical. RiskSpan’s Prepayment Models, calibrated against actual second lien performance, indicate that the models are capturing observed dynamics effectively. With issuance expected to continue climbing, accurate modeling of second lien prepayment risk will remain an essential tool for market participants seeking to price and manage these assets. 

Prepayment Model Updates 

Back-testing continues to show that RiskSpan’s prepayment models are tracking well against observed performance across a variety of collateral types. Recent analysis of agency MBS vintages from 2021 and 2022 revealed that higher-coupon pools, particularly those in the 6.5% range, are slowing more than originally anticipated. 

FN/FH 2021-2022 6.5s

By contrast, lower- and mid-coupon pools—those ranging from 1.5% through 5.5%—have remained steady and closely aligned with model expectations. This outcome reinforces the robustness of the models across different coupon bands and provides confidence in their ability to capture nuanced prepayment behavior. 

FN/FH 2021-2022 1.5s – 3.5s

FN/FH 2021-2022 4s – 5.5s

As we introduced during our August call, consumer credit remains a major focus of RiskSpan’s modeling enhancements. Using the Equifax Analytic Dataset, the team has constructed prepayment aging curves for both auto loans and personal loans. These analyses confirm that borrower credit score bands, measured using VantageScore 4.0, influence prepayment behavior in a manner similar to mortgage loans. For auto loans, the score sensitivity is particularly evident across borrower segments. Personal loan data show similar trends, with one notable difference: the effect of loan term is more pronounced after the first year of loan seasoning. This suggests that term structure plays a more significant role in personal loan prepayment decisions compared to auto loans. 

Auto Loan Prepayment Aging Curves

Personal Loan Prepayment Aging Curves

We are in the process of finalizing these consumer loan prepayment models and will release them shortly on the RiskSpan Platform. This will give clients the ability to incorporate a new level of borrower insight into their own portfolio analytics. 

Looking Ahead 

The integration of Equifax ADS into the construction of prepayment aging curves is just the beginning. We continue to expand our modeling capabilities and data integration in order to provide clients with deeper and more actionable insights. Credit card and student loan models are already in the pipeline, and their release will extend RiskSpan’s modeling coverage across the full spectrum of consumer credit products. 

In addition, the team is adding new analytics reports to the Platform, giving clients free access to timely updates and market intelligence. These ongoing enhancements underscore our commitment to equipping the investment management community with the tools and data needed to navigate complex and evolving credit markets. 

Contact us to discuss or learn more.

Register here for next month’s call: Thursday, September 18th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call was a wide-ranging update on new model developments, consumer credit insights, and macroeconomic trends shaping structured finance. 

Here’s a quick recap in case you missed it. 

(Click here to listen to the entire 30-minute recording or continue reading for a summary.)  

Market Outlook: August 2025

Stable employment and inflation notwithstanding, the macro backdrop remains dominated by persistent headwinds: 

New Equifax Data Integration 

We introduced our latest research leveraging the Equifax Analytic Dataset (ADS), a borrower-level anonymized sample representing 10% of the U.S. active credit population. Using tradeline-level detail (credit scores, balances, payments, etc.), we have constructed aging curves for auto loans and personal loans segmented by credit score bands. 

Some key takeaways: 

The final versions of these datasets will be accessible directly within the RiskSpan platform, allowing clients to benchmark their portfolios against robust national trends. 

Model Updates 

Prepayment Models (Versions 3.2 & 3.7) 

Our prepayment models continue to perform strongly against observed market behavior. The latest back-testing of agency cohorts (Fannie Mae and Freddie Mac 2021/2022 vintages across 1.5%–6.5% coupons) shows that speeds remain broadly consistent with expectations. However, higher coupon pools have recently exhibited slower-than-expected speeds, reflecting both tighter refinancing conditions and borrower credit constraints. 

1.5 to 3.5 Coupons 

6.5 Coupons 

Credit Model 7.0 

Our much-anticipated Credit Model v7 is now available in production on the RiskSpan Platform. Key features include: 

These enhancements equip investors and risk managers with deeper tools for analyzing loss dynamics across mortgage, GSE, FHA, and VA loan cohorts. 

Contact us to learn more.