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Rates, Prepays and Consumer Stress: What the Data is Telling Us at the Start of 2026

 Register here for next month’s call: Thursday, February 19th, 2026, 1 p.m. ET. 

In the January Models & Markets call, our quantitative modeling team hosts their first monthly deep dive of the year into prepayment model performance, an updated analysis of second liens and HELOCs using Equifax data, and the evolving macroeconomic backdrop shaping mortgage markets. 

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

(Click here for the entire 20-minute recording or continue reading for a summary.)  

Revised HELOC and HEL Results Using Equifax ADS Data

  We performed a comprehensive analysis of second liens and HELOCs using Equifax’s Analytic Data Set (ADS), which represents a 10% anonymized sample of U.S. consumer credit data at the tradeline level. 

Following the resolution of data quality issues identified in an earlier analysis, the revised results now align much more closely with economic intuition. Prepayment speeds behave consistently across vintages, credit score bands, and refinancing regimes. 

One key takeaway holds that higher credit score borrowers tend to prepay faster, particularly during refinancing waves, while lower credit score segments remain slower. This pattern is especially evident in post-COVID vintages. Overall credit quality for HELOCs and second liens remains strong, with performance clustering closer to the highest credit score bands. 

Another notable observation is the role of seasonality in newer HELOC vintages. In a high-rate environment with limited refinancing activity, turnover-driven prepayments become more prominent. Baseline prepayment speeds for HELOCs are running around 15 CPR, higher than what is typically observed in first-lien portfolios under similar conditions. These dynamics provide useful signals for understanding how first-lien behavior may differ when second liens or HELOCs are present on the same property. 

  We plan to expand this analysis further, including deeper investigation into correlations between first- and second-lien prepayment behavior. 

Mortgage Rates Remain Likely to Stay Higher for Longer 

The broader economic outlook remains one of persistence rather than relief. Federal Reserve projections point to unemployment stabilizing around the low-4% range and real GDP growth near 2% over the medium term. Meanwhile, expectations for the fed funds rate suggest limited room for significant cuts beyond 2026. 

Longer-term rates tell a similar story. Consensus forecasts indicate the 10-year Treasury is unlikely to fall meaningfully below 4% over the next two to three years, implying mortgage rates are likely to remain near (and potentially above) the 6% level for much of the period ahead. Temporary dips tied to policy announcements or market events have proven short-lived, with rates quickly reverting back toward recent levels. 

Consumer Stress Continues to Build 

While headline spending remained strong during the most recent holiday season, the composition of that spending tells a more cautious story. Consumers increasingly favored lower-cost retailers, suggesting budget sensitivity and selective spending behavior. 

Survey data reinforces this theme. Year-over-year consumer sentiment and expectations have declined meaningfully, and perceptions of job insecurity (particularly among college-educated workers) have become more negative. These dynamics could have important implications for credit performance and housing activity as economic uncertainty persists. 

Prepayment Model Performance: v. 3.7 Continuing to Track Market Performance Well 

RiskSpan’s prepayment models continue to perform well across Agency collateral. 

RiskSpan’s Prepayment Model v3.7 continues to demonstrate strong performance across collateral types. Recent back-testing shows that model projections remain closely aligned with realized speeds, even as seasonal effects and calendar nuances influence month-to-month results. 

For conventional 30-year loans with lower coupons, December’s modest uptick in observed CPRs was largely attributable to four additional collection days relative to November. After adjusting for day count effects, actual prepayment speeds continue to trend lower, consistent with expectations in a higher-rate environment. 

Premium cohorts also remained largely stable. Despite a brief decline in mortgage rates late last year, the move was insufficient to trigger a meaningful new refinance wave. Most refinance-eligible borrowers have already acted, and the refinancing “pull-forward” effect appears largely exhausted. This dynamic is also visible in the S-curve, which has flattened back toward historical averages after October’s temporary acceleration. 

Agency collateral shows similar patterns. Ginnie Mae discount cohorts tracked model expectations closely, while premium cohorts remained flat. One area of ongoing refinement is deep in-the-money, very high-coupon Ginnie Mae loans, where actual speeds have run slightly slower than model projections as refinance incentives flatten out earlier than in prior cycles. 

Looking Ahead 

In summary: 

  • RiskSpan’s Prepayment Model v3.7 continues to perform well across most collateral segments 
  • HELOC and second-lien analysis using Equifax data now shows economically intuitive and stable results 
  • Mortgage rates are likely to remain near 6% in the absence of a major macro shock 
  • Consumer behavior is showing increasing signs of stress and caution 
  • RiskSpan plans to release additional analytics later this year, including a new non-QM credit model in the first half of the year and a next-generation prepayment model in the second half. 

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. 

The Data Model That Powers Private ABS: Why Purpose-Built Architecture Changes Everything  

Private asset-backed securities don’t follow the same rules as public securitizations. The structures are more diverse. The triggers are more nuanced. The collateral is more diverse. And yet, most market participants still try to manage these instruments with tools designed for a different world entirely—or worse, with disconnected spreadsheets that multiply risk with every manual handoff. 

RiskSpan’s Private ABF platform was built specifically to solve this problem. Not as an adaptation of existing tools, but as a purpose-built data architecture designed from the ground up for the unique demands of private structured finance. The difference isn’t incremental—it’s foundational. 

Built for the Full Lifecycle 

RiskSpan’s Private ABF platform isn’t just a database—it’s a relational architecture where every entity maintains its identity and relationships across the entire deal lifecycle. The platform connects deals to their tranches, tranches to their collateral, collateral to individual loans, and all of these to the triggers, waterfalls, fees, and reserves that govern cash flow distribution. 

This matters because private ABS transactions aren’t static. Collateral performs. Triggers trip. Waterfalls redirect. Reserves build and release. A platform that can’t maintain these relationships in real time isn’t managing deals—it’s creating snapshots that are stale before they’re finished. 

Loan-Level Depth That Powers Real Analysis 

At the heart of any ABS transaction is the underlying collateral. RiskSpan’s Private ABF platform maintains comprehensive loan-level data with over 200 attributes per asset, tracking everything from origination characteristics through current performance status. This includes credit metrics like FICO scores and debt-to-income ratios, property and collateral details, payment history and delinquency tracking, modification and loss mitigation status, and ARM reset schedules and rate mechanics. 

The platform currently manages nearly half a billion loan records across active transactions. Each loan maintains its full history—not just current state, but the complete trajectory that informs forward-looking projections. When you run a cash flow model, you’re not working from aggregated pool statistics. You’re working from the actual loans, with their actual characteristics, generating projections that reflect real collateral behavior. 

Depth for Esoteric Structures 

RiskSpan’s Private ABF platform currently supports over several collateral types and structures. Each asset class has its own performance characteristics, prepayment behaviors, and loss dynamics. The platform’s architecture accommodates this diversity without forcing artificial standardization. 

But diversity in collateral is only part of the challenge. Private ABS triggers represent some of the most complex conditional logic in structured finance. Unlike standardized agency delinquency tests, private deal triggers can involve multi-step calculations with lookback periods, cure provisions that allow temporary breaches, step-rate adjustments that phase in over time, and early amortization events with distinct severity levels. 

The platform models triggers as executable logic, not static documentation. When a trigger breaches, the system knows what happens next—which waterfall priorities shift, which reserve requirements change, which reporting obligations activate. This is the deal’s immune system, and it needs to function in real time. 

Time-Series Without the Chaos 

Every entity in RiskSpan’s Private ABF platform exists in time. The platform maintains separate performance histories for tranches, collateral pools, fees, and reserves—each keyed by reporting date to enable point-in-time reconstruction of any deal state. 

This architecture solves one of the most persistent problems in private ABF operations: answering the question “what did we know, and when did we know it?” Whether for regulatory compliance, investor reporting, or internal risk management, the ability to reconstruct historical deal states isn’t a luxury—it’s a requirement that spreadsheet-based approaches simply cannot meet reliably. 

The Waterfall as Working Code 

Cash flow waterfalls in private ABF can run to dozens of steps with conditional branches, pro-rata splits, and priority reversions. RiskSpan’s Private ABF platform models these waterfalls as executable payment sequences—not flowcharts, but actual logic that routes cash from sources to destinations based on current deal state. 

Each waterfall step defines its priority in the payment sequence, its source of funds and destination, its allocation basis (pro-rata, sequential, or targeted), and the conditions under which it activates or suspends. 

When combined with the platform’s cash flow engine, these waterfall definitions become working models. You can project payments under any scenario, stress collateral performance, and see exactly how cash moves through the structure period by period. 

An Architecture Built for AI 

The same architectural principles that make RiskSpan’s Private ABF platform effective for traditional analytics make it exceptionally well-suited for artificial intelligence and machine learning applications. This isn’t a coincidence—it’s a consequence of building a data model that prioritizes structure, relationships, and semantic clarity. 

AI systems thrive on clean, well-organized data with explicit relationships. RiskSpan’s Private ABF platform delivers exactly this: normalized entities with consistent identifiers, clear hierarchies from deals down to individual loans, and temporal versioning that distinguishes current state from historical snapshots. When an AI model needs to understand a transaction, it doesn’t have to infer structure from unstructured sources—the relationships are already defined and traversable. 

The platform’s semantic richness enables natural language interfaces that actually work. Because every field has meaning within a consistent schema, AI can translate questions like “show me deals where the senior OC trigger is within 50 basis points of breach” into precise queries without ambiguity. The loan-level depth means AI models can identify patterns across hundreds of millions of records—finding correlations between origination characteristics and performance outcomes that would be invisible to traditional analysis. 

Time-series architecture is particularly critical for AI applications. Machine learning models for credit risk, prepayment prediction, and loss forecasting require historical sequences, not point-in-time snapshots. RiskSpan’s Private ABF platform maintains this temporal context natively, enabling training datasets that capture how loans and deals evolve over their lifecycles. 

RiskSpan is already deploying AI capabilities on this foundation: automated anomaly detection that flags unusual performance patterns, intelligent document extraction that populates deal records from offering documents, natural language querying that makes complex analytics accessible to non-technical users, and predictive models that leverage the full depth of loan-level history. The platform doesn’t just store data—it organizes knowledge in a form that AI can reason about. 

Designed for the Enterprise 

RiskSpan’s Private ABF platform operates as a multi-tenant platform with granular access controls. Every record carries client and user identifiers that enable sophisticated permission model—issuers see their deals, investors see their positions, servicers see their portfolios, and rating agencies see what they’re authorized to review. 

This isn’t just about security (though it is that). It’s about enabling collaboration across the structured finance ecosystem without compromising confidentiality. A single platform can serve all participants in a transaction, each with their appropriate view of the data. 

Built for What Comes Next 

Private ABS is an evolving market. New asset classes emerge. New structures get tested. New regulatory requirements arrive. RiskSpan’s Private ABF platform accommodates this evolution through flexible schema design that allows custom attributes without requiring database modifications. 

When a client brings a novel structure—say, a securitization of an asset class we haven’t seen before—the platform can ingest and model it without waiting for a software release. This extensibility is what allows a platform to stay current with market innovation rather than constantly playing catch-up. 

What This Means in Practice 

The architectural decisions in RiskSpan’s Private ABF platform translate directly to operational capabilities. For deal structuring, you can model waterfall variations and see their impact on tranche economics before going to market. For pricing, you can run scenarios against actual loan-level collateral, not simplified pool assumptions. For risk management, you can monitor trigger proximity and project breach timing under stress. For surveillance, you can track every metric that matters, with full audit trails and historical reconstruction. 

Private ABF deserves purpose-built infrastructure. RiskSpan’s Private ABF platform delivers it. 

A Day of Rest? Explaining November’s Spike in Non-QM Delinquencies

The just-released non-agency performance data (from November 2025) grabbed more than a few headlines.  

Non-QM loans saw a notable jump in early-stage delinquencies, raising understandable questions around the office (ours and others) about whether this move reflects emerging credit stress or something more benign – like, say, bad data. 

We ultimately concluded that the increase, while real, is likely temporary. The most plausible explanation for November’s spike in Non-QM delinquencies points to a calendar effect tied to the month ending on a Sunday. 

Benign, indeed. 

So what happened in November? 

In the latest data release from Cotality (formerly CoreLogic), delinquency rates rose meaningfully across the non-agency universe, driven almost entirely by a surge in the 30–59 days past due bucket. 

For the servicing month ending November: 

  • Total Non-Agency 30-day DQs increased 48 basis points, from 3.07% to 3.55% 
  • Non-QM 30-day DQs increased 41 basis points, from 1.66% to 2.07% 

For Non-QM loans, this one-month increase represents the largest jump in early-stage delinquencies since the COVID-related shock in April 2020, when these rates surged from 2.99% to 12.51%. For the broader non-agency universe, the increase was the largest since June 2024. 

These figures appear alarming. But a closer examination reveals that, in this case, the calendar may be doing most of the work. 

The Sunday Payment effect 

November ended on a Sunday (not just that, but on a Sunday that was, for many folks, the end of a four-day holiday weekend). When the final day of the month falls on a weekend, payments made on that day typically do not post until the following business day (in this instance, Monday, Dec. 1). As a result, loans that were paid “on time” (or less than 30 days late at least) can be temporarily classified as 30+ days delinquent for November reporting purposes, even though the borrower ultimately made the scheduled payment. 

This “Sunday month-end effect” is well documented and understood. And both internal discussions and external market commentary point to this being the primary driver of November’s delinquency spike. Among external commentators, ICE’s Andy Walden may have summarized it most succinctly: “While the topline delinquency numbers show a sharp increase, we’ve seen comparable spikes in prior years when November ended on a Sunday and scheduled payments didn’t post until early December.” 

The effect appears to be amplified with Sunday-ending Novembers in particular (perhaps because of the four-day weekend effect). As noted in the ICE piece, this has most recently happened in 2014, 2008, and 2003, when delinquency rates spiked by 61 bp, 112 bp, and 57 bp, respectively. All of those increases exceeded this year’s roughly 50 bp shift. 

Approach 1: A History Lesson

To test whether November’s increase fits a broader historical pattern, we examined the relationship between month-over-month delinquency changes and the day on which the month ended. 

Since 2006, there have been 33 months that ended on a Sunday. Over that nearly 20-year period, overall non-agency delinquency levels are broadly unchanged. And yet, those Sunday-ending months consistently exhibit upward pressure on reported 30-day DQ rates. 

Key observations: 

  • Across those 33 Sunday-ending months, non-agency 30-day DQ rates increased by an average of 37 bp 
  • 30-day DQ rates declined in only 4 of those months 
  • In the remaining 29 months, delinquency rates increased 
  • Importantly, 27 of those 29 increases were at least partially reversed in the subsequent month 

In other words, when months end on a Sunday, reported delinquencies tend to rise mechanically, only to then fall back once payments post and reporting normalizes. 

Chart 1: Month-over-Month Change in Non-Agency 30-day DQ rates (Sunday Month-Ends Highlighted, with green indicating a decline, and red indicating an increase) 

Approach 2: Agency Data as a Leading Indicator 

Non-agency delinquency data are reported with a one-month lag relative to Agency MBS. As a result, we can use Agency performance as a sort of real-time proxy for how non-Agency data may evolve in the following release. 

For the December factor date (corresponding to payments due November 30): 

  • Fannie/Freddie D30 jumped 19 bp, from 0.73% to 0.92% 
  • GNMA D30 jumped 41 bp, from 3.84% to 4.25% 

Crucially, both measures recovered sharply in December, declining back toward their October levels: 

  • Fannie/Freddie D30 fell to 0.78% 
  • GNMA D30 fell to 3.89% 

That represents a recovery of 74% and 88%, respectively, of the November spike. 

If Non-Agency and Non-QM delinquencies follow a similar pattern, a comparable recovery would imply: 

  • Non-QM D30 falling back to roughly 1.77% 
  • Total Non-Agency D30 falling back to roughly 3.20% 

These levels would be broadly consistent with pre-November trends and inconsistent with a narrative of accelerating credit stress. 

Chart 2: Agency vs. Non-Agency 30-day DQ Rate Changes and Subsequent Recovery (the dashed green and blue lines for December 2025 represent extrapolated D30 rates if Non-agency mortgages see similar recoveries to those experienced by Fannie/Freddie mortgages) 

Conclusion 

November’s spike in Non-QM delinquencies looks dramatic, but the weight of evidence points to a calendar artifact, not a structural shift in credit performance. Similar spikes usually occur when any month ends on a Sunday and are particularly pronounced when November does. History suggests 2025’s anomaly will be largely reversed in December. 

Investors should continue to monitor delinquency trends closely, and we will revisit this analysis when the next Cotality data are released in early February. For now, the data argue for caution, not alarm.

What a Year of Building AI in Structured Finance Actually Taught Us 

The lessons nobody puts in the demo. 

In 2025, our team built production AI systems that process billions of performance records for tens of millions of mortgages, develop cash flow models for complex private ABF structures directly from documents, and connect large language models directly to bond analytics APIs. 

We built dashboards, connectors, and credit analytics. Some of them worked. Some of them taught us more by failing. 

This is what we learned—not the polished conference talk version, but the notes we’d share with a peer team starting the same journey. 

The Value Shift Nobody Prepares You For 

A portfolio delinquency analysis that used to take three hours now takes twenty minutes. 

That sounds like a win. It is a win. But it also raises a question that’s harder to answer than any technical problem we solved this year: 

If AI handles in minutes what took us hours, what are we contributing? 

When we started pulling this thread, we realized that a significant portion of what felt like skilled analytical work was actually mechanical labor—data extraction, formatting, applying the same methodology we’d applied dozens of times before. The expertise was real, but it was wrapped in hours of execution that masked how much of the work was routine. 

Here’s where we landed: 

AI handles the “how.” Humans own the “why” and “so what.” 

The value now lives in knowing which questions matter. Understanding what the client really needs versus what they say they need. Recognizing when output is wrong because we understand the domain deeply enough to see the error. 

That’s an entirely different skill set. It requires judgment, contextual awareness, and domain intuition that deepens over years—the kind of expertise AI can’t simply replicate, unlike procedural analytical work.

Not everyone will make this transition comfortably. The analysts who built their identity around being fast and thorough at execution face a harder adjustment than those who always saw execution as a means to an end. 

We don’t have this all figured out yet. But we’ve stopped pretending the shift isn’t happening. 

Stop Asking AI to Write Code—Start Asking It to Think With You 

For years, we used Claude as a coding assistant. “Write a function that does X.” “Convert this data from format A to format B.” “Generate a script that calculates Y.” 

That works. But it captures maybe 20% of the value. 

The shift that changed our results was: treating Claude not as a tool to instruct, but as an analyst to think alongside. 

The difference looks like this: 

Before (instruction mode): 

“Write a Python script to calculate delinquency rates from this loan data.” 

After (thinking partner mode): 

“We need to identify hidden credit risk in this CLO portfolio—issuers that resemble recent defaults but haven’t shown price distress yet. What factors should we consider? What data would we need? Let’s build a scoring model together.” 

That second conversation led to identifying hidden exposure across issuers. Claude suggested factors we hadn’t considered—CLO concentration patterns, industry clustering effects, the relationship between coupon levels and distress signals. We debated the weighting. We refined the methodology. The output was genuinely collaborative. 

The code that emerged from the second approach was better, but that’s almost beside the point. The thinking was better. The model was better. The insight was better. 

This requires a different posture than most of us learned. You have to think out loud. Admit what you don’t know. Explain your reasoning and invite critique. Treat the AI as a colleague who happens to have read every document and doesn’t get tired—not as a sophisticated autocomplete. 

The developers and analysts on our team who made this shift produce substantively different work than those who are still in instruction mode. And the gap is widening. 

The First Version Will Be Wrong—Plan for It 

We built a benchmark analysis comparing a client’s NonQM loan portfolio against the broader market. The analysis looked solid: the portfolio showed a 1.37% delinquency rate advantage versus the universe. Strong results. Ready to present. 

Then someone asked about DSCR loans. 

In NonQM lending, DSCR (debt service coverage ratio) loans are a category unto themselves—with measurably better performance than other NonQM products. When we segmented the data, we discovered the universe was comprised of 43% DSCR loans while the client’s portfolio had only 30% DSCR loans. 

This changed everything. 

The client’s portfolio had less exposure to DSCR loans (the better-performing segment) yet still outperformed the benchmark. That alone was impressive, but our initial analysis understated the true picture. Once we compared performance within segments (DSCR vs. DSCR, non-DSCR vs. non-DSCR), the client’s edge was even larger than we’d initially observed. 

If we had presented the first version, we would have undersold our client’s own performance. The insight that mattered most—superior underwriting across both loan categories—would have been invisible. 

Lesson: “Wrong” doesn’t mean broken. It means the output doesn’t fully reflect reality. Have a domain expert review the work before drawing conclusions. 

Deploying AI Agents for End Users Is a Security Project 

Building an AI agent that works in a demo is straightforward. Deploying that agent in a production UI where real users interact with real data took us months. 

We built an agent that lets users query our bond analytics platform conversationally. The AI worked. Making it production-ready required solving problems: 

Prompt injection: When users can type anything into a text box processed by an LLM, you inherit a new attack surface. We implemented input validation, output filtering, tightly scoped permissions, and logging that captures every agent action for audit. 

Rate limiting: A single conversational turn might trigger 50 API calls. We built tiered limits—per-user, per-session, per-token—plus circuit breakers for runaway queries. 

Session management: Agent sessions need conversational context across multiple turns, isolated per user, with graceful expiration handling and automatic cleanup. 

Audit trails: Regulated industries need to know what the AI did. Every query, tool invocation, and response needs to be logged immutably. 

The agent itself was 20% of the effort. Authentication, authorization, input validation, rate limiting, session management, and security review were the other 80%. 

Lesson: In production, the agent is the easy part. The security wrapper is the product. 

Post script: AgentCore from AWS and Agent Framework from Microsoft are solving the deployment and security headaches.  

AI Is Good at Finding Information But Sometimes Overstates What It Means 

While building the credit risk analysis, we asked Claude to research distressed issuers—companies that had defaulted or were showing signs of stress. We wanted to understand patterns we could use to identify similar risks in the portfolio. 

Claude surfaced real-time signals we wouldn’t have found efficiently on our own: FTC antitrust actions, rating agency downgrades, refinancing walls, fraud allegations. Information that wouldn’t appear in pricing data for months was available in news coverage and regulatory filings. The research phase that would have taken days was completed in hours. 

But we also caught Claude drawing confident conclusions from weak sources. In one case, it attributed claims to “industry reports” that didn’t exist when we followed the links. The search results were real. The sources were ‘real’. But the synthesis drew conclusions the sources didn’t support. 

The lesson: use AI-powered search aggressively. It’s the difference between stale knowledge and current intelligence, especially in fast-moving situations. But verify specific claims. Click the links. Read the actual sources. 

AI is excellent at finding relevant information across large volumes of text. It is sometimes too confident about what that information means when synthesized. The combination of broad retrieval and skeptical verification is more powerful than either alone. 

Your Org Chart Isn’t Ready for This 

Our AI strategy deck included projections: reduction in onboarding costs, increased client capacity and margin expansion. 

The numbers were defensible. The business case was clear. 

What the projections didn’t address: the organizational implications of realizing the promised efficiencies. 

If analysts can serve five times more clients, do you need fewer analysts—or do you pursue five times more clients? If the answer is “more clients,” do you have the sales capacity? The support infrastructure? The management bandwidth? 

If developers now own adoption metrics for the features they build, then what happens to the product managers who previously owned that? Are product managers freed up for more strategic work, or are they defending territory? 

If AI drafts client communications, who reviews them? What error rate are we willing to accept? Who’s accountable when the AI gets something wrong? 

These aren’t hypothetical questions. We’re navigating them now, and the answers aren’t obvious. 

AI doesn’t just improve workflows. It reshapes roles. And most organizations—including ours—are making it up as they go. 

The companies that figure out the organizational design will outperform those that simply purchase better software. The differentiation in 2026 won’t come from adopting AI. It will come from redesigning teams, incentives, and accountability structures around what AI makes newly possible. 

What We’re Taking Into Next Year 

A year of building AI systems in structured finance clarified a few things: 

AI is more powerful than the hype suggests—once you integrate it into real workflows rather than treating it as a research toy. 

AI is more frustrating than the demos show—the gap between “works in claude.ai” and “works in production” is where most of the time goes. 

AI is more dependent on domain expertise than the automation narrative implies—it generates analyses quickly, but distinguishing plausible from accurate requires human judgement that compounds over years. The “why” and “so what” remain stubbornly human problems.  

AI changes more than technology—it changes job descriptions, team structures, and how people understand their own value. The skill isn’t operating the tool; it’s knowing when the output reflects reality. 

We don’t have all the answers. We’re still learning what this means for how we build software, how we serve clients, and how we organize ourselves. 

But we’re no longer wondering whether AI will change our industry. We’re focused on making sure we’re the ones defining how. 

Higher for Longer: What RiskSpan’s December Models & Markets Call Signals for 2026 

Register here for this month’s call: Thursday, January 22nd, 2026, 1 p.m. ET. 

Just before the holidays, RiskSpan’s quantitative modeling team hosted its December Models & Markets call, offering its monthly, detailed look at prepayment model performance, evolving macroeconomic conditions, and what to expect in 2026. Led by Shane Lee and Divas Sanwal, the discussion highlighted a housing and credit market navigating elevated rates, slowing growth, and increasing consumer stress. 

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

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

Why Rate Cuts Aren’t Lowering Mortgage Rates 

Although the Federal Reserve delivered multiple rate cuts toward the end of 2025, the Fed Funds rate remains in the 350–375 basis point range, with futures markets expecting only gradual additional cuts in 2026. As the following charts and tables illustrate, even a move toward 300–325 bps next year leaves policy rates well above pre-pandemic norms. 

More importantly for housing, longer-term rates continue to dominate mortgage pricing. Market consensus forecasts presented on the slides show the 10-year Treasury remaining above 4% for the next two to three years, a view that has remained remarkably stable across forecasting sources. As a result, mortgage rates have been largely unchanged over recent months despite easing monetary policy. 

The implication is clear: refinance and cash-out activity remain extremely constrained and are likely to stay that way well into 2026. Any incremental increase in prepayment activity will come principally from turnover, not rate-driven refinancing. 

Home Prices: Growth Slows, Regional Divergence Emerges 

We used unadjusted Case-Shiller and FHFA data to highlight that month-over-month home prices declined across many large metro areas, even where seasonally adjusted figures appear more stable. Seasonal patterns have shifted materially in recent years, making unadjusted trends especially informative. 

The FHFA four-quarter appreciation map illustrated this growing regional dispersion. Parts of the Sun Belt, including California, Texas, and Florida, have experienced notable price declines, with the Fort Myers area standing out as a recent weak spot. At the same time, select Northeast markets continue to see positive appreciation, with areas near New York showing some of the strongest gains. 

Overall, while a broad-based housing downturn has not materialized, slowing appreciation reduces borrowers’ financial flexibility and reinforces the current lock-in environment. 

Consumers Under Pressure 

As has been a recurring theme in several of our recent monthly calls, the consumer credit environment is showing increasing signs of strain. 

Unemployment has edged higher, reaching 4.6% in November, with younger workers (ages 16–25) experiencing disproportionately higher joblessness. Inflation, while easing slightly, remains stubbornly above target, with recent CPI readings still near 2.7% year over year. 

We are also continuing to see historically high levels of consumer debt and a notable slowdown in spending growth. Unlike typical holiday-season patterns, consumer spending has not accelerated meaningfully, suggesting households are becoming more selective and cautious. 

One particularly telling trend is the rapid growth of buy now, pay later (BNPL) usage. Increasing reliance on BNPL for essential purchases points to tighter household budgets and reduced financial resilience. 

Taken together, these indicators support expectations—also shown in the Fed’s December Summary of Economic Projections—that GDP growth is likely to remain near or below 2% over the next several years, while credit performance warrants close monitoring. 

Prepayment Model Performance: Holding Up Across Collateral Types 

RiskSpan’s prepayment models continue to perform well across Agency collateral. 

For Fannie Mae and Freddie Mac pools with WACs of 5.5% and below, observed turnover speeds declined modestly month over month. As highlighted below, this softness largely reflects seasonal effects and a shorter reporting month. While the model projected slightly higher speeds, overall alignment with observed behavior remained strong. 

For higher-coupon GSE collateral (6.0% and above), December marked a normalization following unusually aggressive prepayment speeds observed in the prior month. As shown in the charts, observed speeds moderated, allowing the model to close the gap and better track realized behavior. 

A similar pattern emerged in the Ginnie Mae collateral, with both discounted and premium coupon cohorts showing improved alignment between modeled and observed speeds. In particular, the moderation in higher-coupon Ginnie Mae prepayments mirrored trends seen in the GSE universe, underscoring the consistency of borrower behavior across agency channels. 

During Q&A, the team also addressed VA loan performance. Internal loan-level analysis suggests VA loans tend to prepay faster than baseline model projections, an area RiskSpan continues to evaluate closely.  

Looking Ahead: 2025 in Review and What’s Coming in 2026 

In 2025, RiskSpan delivered several major Platform enhancements: 

  • Prepayment Model v3.7, introducing an out-of-the-money (OTM) slope to better capture turnover lock-in effects 
  • Prepayment Model v3.8, adding a new ARM sub-model and additional tuning controls 
  • Prepayment Model v3.11, a fully redeveloped framework for non-QM collateral 
  • Credit Model v7.0, featuring a full delinquency transition matrix for GSE and Ginnie Mae loans 

Looking ahead, we outlined an ambitious 2026 release schedule, including: 

  • A Non-QM Credit Model v7.1 with full delinquency transitions, expected in the first half of the year 
  • A broader non-agency credit model later in 2026 
  • A completely new prepayment framework—currently referred to as Prepayment Model 4.0—built from the ground up 

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. 

Update on Delinquency Trends in the Non-Agency Mortgage Market

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:

  • As shown in Figures 1 and 2, the 60+ delinquency rate for Private Label Securities 2.0 (loans originated after 2010) is 1.98% as of December, 2025, down from 2.21% in August. The DQ rate for Legacy products (originated prior to 2010) dropped to 9.32%.
  • Prime Jumbo mortgages continue to demonstrate the strongest performance from a credit perspective, with delinquency rates at 0.53%.
  • 2nd Lien loans, comprising HELOCs and closed end mortgages, had a delinquency rate of 0.91% in Decemeber, down from 1.0% in August
  • Non-QM loans delinquency rates declined to 2.68% in December, down from 3.0% in August

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.

  • Fully documented loans have the lowest 60+ delinquency rate at 0.76%, though this DQ rate is higher than the post-COVID lows of 0.39% seen in October 2022.
  • Delinquency rates for DSCR/Investor and Bank Statement loans fell in December to 2.92% and 3.99% respectively.
  • Non-QM delinquency rates vary significantly by vintages
    • DQ rates are lowest for the 2021 Vintage at 1.94%, driven in part by the much higher proportion of Full Doc loans in this vintage (54%, compared to 29% for the Non-QM population as a whole)
    • DQ rates are highest for the 2023 Vintage at 6.02%. This is partially explained by the low proportion of Full Doc loans in this vintage (only 14%). But even when controlling for documentation type, the DQ rates are higher for the 2023 vintage, as shown in Figure 5. This could in part be explained by adverse selection through refinancing, where the borrowers with stronger credit have refinanced into rates that are lower than the 2023 peaks.

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:

  • As shown in Figure 6, the 640-680 FICO bucket for the full Non-QM universe has a 60+ delinquency rate that is 10x the rate for the 760+ FICO bucket (8.35% vs, 0.80%). On a relative basis, the delinquency rate is even more differentiated for the Full Doc population, where the 640-680 FICO bucket has a 6.37% delinquency rate compared to a 0.19% delinquency rate for the 760+ cohort.
  • As observed in Figure 1, the Full Doc Non-QM loans have a significantly higher FICO score than the DSCR and Bank Statement Non-QM loans (763 vs. 744 and 737 respectively). However, this higher FICO score does not fully explain the lower delinquency rates for the Full Doc loans. Figure 7 shows that delinquency rates for Fully Documented loans are significantly lower than those for the DSCR and Bank Statement loans even within the same FICO bucket.

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 shows the 60+ delinquency rate for each vintage by factor date.
    • After eclipsing the delinquency rate of the 2022 vintage in July, the delinquency rate for the 2023 vintage continued to increase, hitting 6.02% in December
    • The 2021 vintage’s 1.94% DQ rate is significantly lower than subsequent vintages in spite of being the most seasoned. This is in part due to the disproportionately high share of full documentation loans in this first post-COVID cohort of Non-QM loans.
  • Figure 9 shows the 60+ delinquency rate for each vintage by loan age
    • Consistent with the trends observed in Figure 8, the 2023 vintage DQ rates ramp up faster than any of the other vintages.
    • The delinquency rates for the 2024 and 2025 vintages are tracking with the 2022 vintages.

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.

Modernizing the Advance: Using Data to Innovate Collateral-Backed Lending  

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: 

  • Current performance metrics: Track delinquency rates, payment patterns, and modification activity as they evolve. 
  • Mark-to-market property valuations: Geo-specific house price trends drive updated valuations reflecting current market conditions 
  • Updated loan-to-value ratios: See how LTVs migrate as property values and loan balances change. 
  • Geographic concentration analysis: Understand where collateral is concentrated and how markets are correlating. 

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: 

  • Delinquency probability models identify loans likely to become troubled before they miss payments 
  • Geographic risk assessments flag markets experiencing deteriorating economic conditions 
  • Portfolio stress testing models how collateral would perform under various adverse scenarios 
  • Early warning indicators surface concerning trends while multiple mitigation options remain available 

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: 

  • Start with high-level portfolio metrics and drill down into specific concentrations. 
  • Compare collateral quality across members. 
  • Identify specific loans or segments driving portfolio-level trends. 
  • Generate detailed reports for management, regulators, and members. 

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: 

  • Differentiated pricing tiers recognize superior collateral quality, incentivizing members to pledge higher-quality collateral and enabling FHLBanks to confidently extend advances across a broader range of risk profiles. 
  • Dynamic advance terms respond to changing collateral conditions, with transparent triggers tied to observable metrics. 
  • Forward-looking eligibility standards incorporate predictive analytics, adjusting concentration limits and eligibility based on real-time market conditions and stress-test performance. 

Enhanced Member Value 

Modern analytics deliver more value to members: 

  • More efficient collateral usage allows haircuts to be precisely calibrated to actual risk, potentially increasing borrowing capacity. 
  • Faster advance processing results from continuous monitoring and accelerated data processing. 
  • Valuable portfolio insights strengthen member relationships, positioning FHLBanks as strategic partners. 

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. 

Build vs. Buy: A Strategic Framework for Private ABF Technology Decisions

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: 

  • The private credit universe now spans ABF, corporate credit, IG, HY, specialty finance, and more. 
  • Scaling successfully requires clean data infrastructure, automated workflows, real-time portfolio and risk monitoring, and transparent reporting – capabilities that take significant time and expertise to build from scratch. 
  • LPs are scrutinizing managers based on their data maturity and operational systems, not just portfolio performance. Technology infrastructure is becoming a competitive differentiator in fundraising. 
  • Growth and diversification create enormous operational complexity. Inefficiencies can compound quickly if tech is not architected for scale. 

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.

Are Lock-In Effects Really Easing? Insights from November’s Models & Markets Call

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: 

  • Fed Funds futures suggest the policy rate will likely remain unchanged in December, despite fresh unemployment data. 
  • Projections show the 10-year Treasury hovering around 4% for the next several years, implying mortgage rates likely remain above 6% through 2026. 

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: 

  • A 0.3% month-over-month national decline in the latest reading. 
  • Major metros increasingly showing broad-based price deterioration, with formerly resilient cities like Los Angeles slipping negative. 

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: 

  • Student loans: +600% since 2003 
  • Mortgage balances: +165% 
  • Auto loans: similarly elevated 

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. 

Are You Overpaying for VA Prepay Risk in Ginnie II Pools?

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: 

  • FHA-only pools offer slower, more stable prepayment behavior, attractive for investors prioritizing duration stability. 
  • VA-dominant pools may appeal to investors willing to take on higher turnover in exchange for price discounts or optionality in certain market environments. 

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. 

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