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Articles Tagged with: non-Agency

Monitoring Non-QM Mortgage Delinquencies in a Shifting Market

This post provides an update on delinquency rate trends observed in the Non-Agency mortgage market with a deep dive on different segments of the fast growing Non-QM mortgage market. All of the figures in this post are based on queries of historical CoreLogic Non-Agency data via our proprietary RiskSpan Edge Historical module.

After reaching post-Covid highs in May 2025, delinquency rates have stabilized at slightly lower levels in August 2025, the most recent factor date available from CoreLogic: 

  • As shown in figures 1 and 2, the 60+ delinquency rate for Private Label Securities 2.0 (loans originated after 2010) is 2.21%, while the DQ rate for Legacy products (originated prior to 2010) continues to fall below the 10% threshold, hitting a post-COVID low of 9.61% 
  • Prime Jumbo mortgages continue to demonstrate the strongest performance from a credit perspective, with delinquency rates at 0.57%. 
  • 2nd Lien loans, comprising HELOCs and closed end mortgages, had a delinquency rate of 1.01%. 
  • Non-QM loans saw delinquency rates remain stable at 3.05%, slightly below the post-COVID peak of 3.17% in May.  

Figure 1. 

Figure 2.

Figure 3 shows the relative delinquency performance of mortgages across 4 segments of the Non-QM population, which represents 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 (see Figure 4). In 2025, the combined volume of originations in the DSCR/Investor and Bank statement segments was about four times the volume of loans originated with full documentation. 

  • Fully documented loans have the lowest 60+ delinquency rate at 0.89%, though as this segment seasons, the DQ rate continues to creep up from the post-COVID lows of 0.39% seen in October 2022. 
  • Delinquency rates for DSCR/Investor and Bank Statement loans stabilized in August at 3.34% and 4.41% respectively, slightly lower than their post-COVID peaks seen in May 2025  

Figure 3. 

Figure 4. 

Figures 5 and 6 show the relative delinquency performance of Non-QM mortgages by year of origination. For these charts, we exclude vintages prior to 2021 to avoid the distorting impact of the COVID delinquency shock. 

Figure 5 shows the 60+ delinquency rate for each vintage by factor date. 

  • The delinquency rate for the 2023 vintage hit 5.25% in August, surpassing 2022 as the vintage with the highest delinquency rate. 
  • In spite of being the most seasoned, the 2021 vintage’s 2.04% DQ rate was significantly lower than the subsequent 2022 and 2023 vintage. This is largely due to the disproportionately high share of full documentation loans in this first post-COVID cohort of Non-QM rates, which can be seen in Figure 4. By contrast, the 2022 and 2023 vintages  are composed primarily of the higher risk DSCR and Bank Statement originations. 

Figure 6 shows the 60+ delinquency rate for each vintage by loan age.

  • Consistent with the trends observed in Figure 5, we see the 2023 vintage DQ rates ramp up faster than any of the other vintages. 
  • The 2024 vintage is tracking between the 2022 and 2023 vintages. 
  • While there are only a few months of observations available the 2025 vintage, its delinquency ramp-up is tracking with the other post-2021 vintages 

Figure 5. 

Figure 6.

Given the elevated delinquency rates of Non-QM mortgages relative to Agency and Prime Jumbo mortgages and the backdrop of housing and macroeconomic uncertainty, 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.


RiskSpan Introduces Enhanced Non-QM Prepayment Model Leveraging Loan-Level Data

Arlington, VA – February 18, 2025 – RiskSpan, a leading provider of innovative trading, risk management and data analytics for loans, securities and private credit, has announced the release of its latest Non-QM Prepayment Model (Version 3.11), incorporating CoreLogic’s loan-level non-QM performance data. This update significantly enhances prepayment forecasting accuracy for non-QM loans and mortgage-backed securities by leveraging a robust, segmented modeling approach.

RiskSpan’s new non-QM prepayment model introduces a two-component framework that improves the precision of prepayment predictions:

  • The first component is a Unified Turnover Model, designed to capture base prepayment trends.
  • The second component, a Refinance Model Categorized by Documentation Type, is capable of distinguishing among and modeling behavioral characteristics specific to bank statement, debt service coverage ratio/investor, full documentation, and other documentation types

The model is built on loan performance data spanning October 2019 to March 2024 and intelligently incorporates long-term prepayment behavior with conventional loans, addressing the challenge of limited non-QM data history. Key enhancements include:

  • Sensitivity to SATO (Spread at Origination) and Burnout Effects, refining prepayment behavior projections.
  • DSCR-Specific Adjustments, incorporating prepayment penalty terms and amounts to refine refinance calculations.

By integrating granular loan-level insights from CoreLogic, this release enhances market participants’ ability to accurately assess non-QM prepayment risk, optimize portfolio strategies, and improve secondary market pricing.

“Our latest model delivers a more precise view of non-QM borrower behavior, equipping market participants with the insights needed to manage risk effectively,” said Divas Sanwal, Senior Managing Director and RiskSpan’s Head of Modeling. “By leveraging CoreLogic’s expansive dataset and an expansive GSE dataset, we’re enabling investors to better anticipate prepayment trends and make more informed decisions.” The new model is now available for integration into RiskSpan’s Platform.

The new model is now available for integration into RiskSpan’s Platform.


About RiskSpan

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


Private-Label RMBS

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Trading and risk management analysis and analytics powered by RiskSpan’s proprietary tools and loan-level models

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View a bond’s credit support and collateral composition and performance alongside user-selected benchmarks.

  • Compare bond credit support and thickness

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