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Articles Tagged with: Credit Analytics
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Mounting Pressure in Non-QM Credit: What March 2025 Data Signals for Risk Managers

This is a monthly update on non-QM delinquency rate and roll rate trends based on the March 2025 remittance data. Similar to last month’s post, I use the CoreLogic Non-Agency loan data to split out the Non-QM population by loan type. I compare the relative delinquency performance of mortgages backed by Investor properties vs. loans with full documentation vs. other Non-QM loan types (this last bucket comprises mainly Bank Statement loans). I use a slightly revised and more inclusive definition of Non-QM this month so the overall balance figures are higher and delinquency rates and roll rates are slightly lower than those reported in last month’s post.

The first chart shows that the non-performing delinquency rate (60+ dpd loans as a percentage of the overall population) has risen from a post-COVID low of 0.85% in July 2022 to 3.09% as for the most recent remit month. This increase has been driven by deterioration in the credit performance across all Non-QM loan types. Notably, the delinquency rate for Investor loans increased to 3.56% as of March, up more than three-fold from post-COVID lows of 1.1% in October 2022 and up 91bp year over year. Full Doc Non-QM loans continue to outperform other segments significantly, but their delinquency rates still rose to 0.85%, a new post COVID recovery high.

The other driver of the increase in delinquency rates for the Aggregate Non-QM loan population is a gradual shift in their mix away from the Full Doc loans, which have a better credit profile. As shown in the graph below, Full Doc loans as a percentage of the overall NQM mix have fallen from over 50% of NQM population as of the end of 2018 to only just under 31% in March. Meanwhile, Investor loans have increased from only 3% of the Non-QM population as of the end of 2018 to 10% just before COVID to over 24% as of March.

The last graph considers the gateway transition of mortgages to non-performing status: the current to 30 roll rates, or the percentage of current loans that roll to 30 days delinquent in any given month. These trends are broadly in line with what we see for the overall delinquency rates: roll rates have increased significantly since their post COVID lows.

In the March remittance data, overall Non-QM C->30 roll rates increased to 1.18%, their highest level since December 2020. All 3 non-QM segments broken out in this graph also hit new post COVID highs, with Investor-backed loans experiencing a 1.43% C->30 roll rate, 2.9x the 0.50% roll rate experienced by Full Doc Non-QM loans.

As non-QM mortgages show signs of growing distress amid broader economic uncertainty, we recommend heightened vigilance for investors and risk managers with Non-QM exposure in their portfolios. RiskSpan’s credit models forecast 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.

Contact me to discuss.

RiskSpan’s April 2025 Models & Market Call: Credit Model v7, Prepay Volatility, and Credit Trends to Watch

Register here for our next monthly model update call: Thursday, May 15th at 1:00 ET.

Note: This post contains highlights from our April 2025 monthly modeling call, which delivered insights into the current economic climate, mortgage model enhancements, and borrower behavior trends. You can register here to watch a recording of the full 28-minute call.

Here’s what you missed:

Market Overview: A Climate of Volatility

With mortgage rates rebounding to 7%, the panel began by acknowledging the choppy waters ahead, flagging 2025 as a year likely to see persistent rate volatility. As recession risks grow and consumer stress indicators rise, modeling accuracy becomes more important than ever.

Notably, consumers are already strained:

  • Rising consumer debt burdens
  • Increased use of personal loans and second liens for debt consolidation
  • Spikes in HEL/HELOC originations and securitizations
  • Climbing Non-QM delinquencies, particularly among 2022–2023 vintages

Model Update: Credit Model v. 7.0

RiskSpan’s newly released Credit Model v7 marks a significant upgrade in loan performance modeling:

  • Delinquency Transition Matrix core structure
  • The model projects:
    • Monthly CDR, CPR, and delinquency balances (0 through REO)
    • Loss severities, liquidated balances, and P&I flows
  • Modular components include:
    • State Transition Model
    • Severity and Liquidation Timeline Modules
  • The model is fully integrated within RiskSpan’s platform, enabling custom inputs for whole loans and securities

This model empowers users with granular delinquency and cash flow forecasting, critical for managing portfolios amid market uncertainty.

Key findings here included:

  • Daily prepay data showing extreme volatility, but offering early trend visibility
  • Trend lines derived from daily data offering good proxies for future behavior
  • Notable discrepancies within MBS-level data, especially among higher-coupon pools

RiskSpan’s continued focus on benchmarking these data sources helps refine both near-term and long-term modeling strategies.

Prepayment Behavior of Top-Tier Borrowers

The panel spotlighted borrowers with FICO scores over 800, revealing some counterintuitive dynamics:

  • Initial refinance activity is higher in the 800+ cohort—”fastest out of the gate”
  • But post-seasoning, refinance rates fall below those of the 700–750 FICO group
  • This “crossover pattern” reflects a phenomenon the team called “Accelerated Burnout”
  • Assumed strategic behavior, like exploiting lender credits, may amplify early refinance intensity

These insights underscore the nonlinear and evolving nature of borrower behavior, especially under fluctuating rate environments.

Model Performance: Staying on Track

RiskSpan’s Prepayment Model continues to track closely with actuals, validating its calibration even in today’s turbulent landscape. Combined with Credit Model v7, clients now have powerful tools for capturing credit and prepayment risk with more accuracy than ever.

Be sure to register for next month’s model update call on Thursday, May 15th at 1:00 ET.

Want a deeper dive into the new Credit Model or Prepay insights? Contact me to schedule a session with our modeling experts.

Get a Demo or Free Trial of RiskSpan’s Platform

From AI Hype to Helpful Assistant: AI Agents are coming soon to the RiskSpan Platform!

When agentic AI first hit the scene, we were intrigued—but skeptical. Was this just another over-hyped trend or something that could drive real value?

Fast forward a few months, and we’ve got our answer.

At RiskSpan, we’ve quietly integrated AI agents into our internal workflows through a dedicated Agent Desktop. These agents are now core to how we manage our business—monitoring client health, tracking system usage and perhaps most impressively, performing deep research across the massive datasets we store. What began as an experiment has become indispensable.

The real breakthrough is manifest, however, when Agents proactively uncover insights, flag anomalies, and automate routine analyses. Our Dev and Client teams are saving hours and making faster, more informed decisions because relevant information finds them.

Coming soon, our clients will be able to use Agents in the RiskSpan Platform to query their own data, analyze GSE performance data and run on-demand analysis instantly—all without waiting on a queue or building custom reports.

Designed for portfolio risk, surveillance, analyzing loan-level data, or exploring market trends, the AI agents will help you go from question to answer in seconds. Check out a sample below and reach out to learn more!

Non-QM Credit Stress by the Numbers: Investor and Full Doc Loan Performance Diverge

This is a follow-up to Bernadette Kogler’s short piece last month on stress in the Non-QM mortgage market. In this post, I use the CoreLogic Non-Agency loan data to split out the Non-QM population by loan type and look at the relative delinquency performance of mortgages backed by Investor properties vs. loans with full documentation vs. other Non-QM loan types (this last bucket comprises mainly Bank Statement loans).

As the following chart illustrates, the non-performing delinquency rate (60+ dpd loans as a percentage of the overall population) has risen from a post-COVID low of 1.01% to 3.59% as of March 2025. This increase has been driven by deterioration in the credit performance across all Non-QM loan types. Notably, the delinquency rate for Investor loans increased to 3.82% as of March, up more than three-fold from post-COVID lows of 1.1% in October 2022. While they remain the best performing loan type, even the Full Doc loans have seen a doubling of delinquency rate, to 1.11%.

The other driver of the sharp uptick in delinquency rates for the Aggregate Non-QM loan population is a shift in their mix away from the strongly performing Full Doc loans. As shown in the graph below, Full Doc loans as a percentage of the overall NQM mix have fallen from over 50% of NQM population as of the end of 2018 to only 22% in March. Meanwhile, Investor loans have increased from only 3% of the Non-QM population as of the end of 2018 to 10% just before COVID to 28% as of March.

Finally, we look at the gateway transition of mortgages to non-performing status: the current to 30 roll rates, or the percentage of current loans that roll to 30 days delinquent in any given month due to a missed payment. Not surprisingly, these trends are broadly in line with what we see for the overall delinquency rates: roll rates have increased significantly since their late 2022 lows.

But these roll rates give us a more real-time perspective on how different loan types are performing relative to each other than the delinquency rate levels, which represent the cumulative effect of historical performance. In the most recent remittance data, Investor-backed loans experienced a 1.42% C->30 roll rate, which was 2.5x the 0.58% roll rate experienced by Full Doc Non-QM loans. By contrast, that multiple was only 1.8x in October 2022 when NQM loans were experiencing their lowest post-COVID roll rate performance.

Given the deteriorating performance of Non-QM mortgages and backdrop of 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.

Contact me to discuss.

Mortgage Prepayment and Credit Trends to Watch

Register here for our next monthly model update call: Thursday, April 17th at 1:00 ET.

Note: This post contains highlights from our March 2025 monthly modeling call. You can register here to watch a recording of the full 28-minute call.

Mortgage and credit markets remain dynamic in early 2025, with macroeconomic conditions driving both volatility and opportunity. In yesterday’s monthly model call, my team and I shared key insights into current market trends, model performance, and what to expect in the coming months.

Market Snapshot: A Mixed Bag

After trending downward in February, mortgage rates ticked up slightly in early March. Despite the fluctuation, expectations are for rates to remain relatively stable until at least summer 2025. Most mortgage-backed securities (MBS) are still deeply out of the money, making housing turnover—not rate refinancing—the dominant prepayment driver.

Macroeconomic signals remain mixed. While unemployment is still low and wage growth continues, inflation shows signs of persistence. The Fed is expected to hold the Fed Funds Rate steady through mid-year, with a potential first cut projected for June. Credit usage is creeping higher—especially in second liens and credit cards—hinting at growing consumer debt stress.

Model Performance and Updates

Prepayment Model

RiskSpan’s prepayment model continues to track closely with actuals across Fannie Mae, Freddie Mac, and Ginnie Mae collateral. The model shows:

  • Prepayments rising slightly, particularly among 2023 vintage loans in response to rate moves.
  • Delinquent loan behavior providing rich insights: For “out of the money” (OTM) collateral, delinquent loans are showing higher turnover speeds than performing ones, as borrowers try to avoid foreclosure.
  • Turnover sensitivity to borrower FICO scores is especially pronounced for delinquent loans—highlighting the need for granular credit analytics.

These behavioral insights are informing the next version of our prepayment model, which will incorporate GSE data research to enhance forecast accuracy.

Credit Model v7: A Leap Forward

RiskSpan’s new Credit Model v7—now available—is a significant upgrade, built on a delinquency transition matrix framework. This state-transition approach enables monthly projections of:

  • Conditional Default Rates (CDR)
  • Conditional Prepayment Rates (CPR)
  • Loss severity and liquidated balances
  • Scheduled and total principal & interest (P&I)

The model’s core components include:

  • A vector-based severity model
  • A robust liquidation timeline module
  • Loan-level outputs by delinquency state (including foreclosure and REO)

By modeling the lifecycle of loans and MSRs more explicitly, Credit Model v7 delivers deeper insight into portfolio credit performance, even in volatile markets.

Emerging Risks and Opportunities

Consumer credit balances—especially HELs and HELOCs—have grown significantly, fueled in part by debt consolidation. Credit card utilization has jumped from 22% in 2020 to nearly 30% as of late 2024, indicating growing financial strain.

Meanwhile, delinquencies in the Non-QM space (2022-2023 vintages) are rising—suggesting that investors need enhanced tools to monitor and manage these risks. RiskSpan’s tools, including the enhanced credit model and daily prepay monitoring, help investors keep pace with these shifting dynamics.

Looking Ahead

RiskSpan’s modeling team remains focused on:

  • Continuing to improve prepayment modeling with newly available GSE data
  • Rolling out and enhancing Credit Model v7 for broader use cases
  • Providing clients with forward-looking analytics to anticipate credit stress and capitalize on market dislocations

Be sure to register for next month’s model update call on Thursday, April 17th at 1:00 ET.

Want a deeper dive into the new Credit Model or Prepay insights? Contact me to schedule a session with our modeling experts.

Get a Demo or Free Trial of RiskSpan’s Platform

February 2025 Model Update: Mortgage Prepayment and Credit Trends to Watch

Note: This post contains highlights from our February 2025 monthly modeling call. You can register here to watch a recording of the full call (approx. 25 mins).

As we move further into 2025, key trends are emerging in the mortgage and credit markets, shaping risk management strategies for lenders, investors, and policymakers alike. RiskSpan’s latest model update highlights critical developments in mortgage prepayments, credit performance, and consumer debt trends—offering valuable insights for investors, traders, and portfolio/risk managers in these spaces.

Prepayment speeds have continued to decline in Q1 2025, largely due to a lack of housing turnover and persistently high mortgage rates. While a drop in rates during Q3 2024 temporarily mitigated lock-in effects for borrowers with very low rates, MBS speeds remain low across most cohorts.

Key drivers of observed prepayment behavior include:

  • Mortgage rates are expected to stay high (~6.5%+) throughout 2025, keeping refinancing activity muted.
  • Turnover remains the primary driver of prepayments, with most MBS pools significantly out of the money.
  • RiskSpan’s Prepayment Model v3.7 effectively captures these dynamics, particularly the impact of deep out-of-the-money (OTM) speeds based on moneyness.

Growth in Non-QM and Second Lien Originations

The private credit market continues to expand, with increasing Non-QM and second lien originations. However, a concerning delinquency trend has emerged, with delinquencies among 2022-2023 Non-QM vintages now rising faster than among older vintages.

Consumer Debt Pressures Mounting

Consumer debt continues to rise rapidly, raising concerns about long-term credit performance:

  • Credit card balances have increased significantly, with utilization climbing from 22% in 2020 to 30% by late 2024.
  • More consumers are turning to personal loans for debt consolidation, a sign of financial strain.
  • Second liens (HEL/HELOCs) are being used to pay off high-interest debt, fueled by strong home equity growth since 2020.

Model Enhancements

To address these evolving market conditions, RiskSpan has rolled out key enhancements to its mortgage and credit models:

  • Prepayment Model v3.7 – Captures deep out-of-the-money lock-in effects with improved accuracy across Fannie, Freddie, and Ginnie collateral.
  • Credit Model v7 – Introduces a Delinquency Transition Matrix, providing more granular forecasting for loans and MSR valuation.
  • Non-QM Prepayment Model – Developed using CoreLogic data, offering improved prepayment insights for Non-QM loans.

Looking Ahead

  • Rates are likely to remain high, with no reductions expected before summer.
  • Home equity growth remains strong, driving continued second lien origination.
  • Debt servicing costs are beginning to strain consumers, as high interest rates persist.
  • Delinquency rates show strong correlation to credit quality, signaling potential risks ahead.

The evolving mortgage and credit landscape underscores the importance of robust modeling and risk assessment. With prepayments slowing, debt burdens rising, and consumer credit trends shifting, lenders and investors must adapt their strategies accordingly.

Leveraging Pool-Specific Performance and Recapture Analysis: A Game Changer for MSR Investors

Successfully forecasting MSR cash flows demands a level of precision and granularity in data analysis that few other asset classes require. This is especially true for investors seeking to estimate how much prepayment runoff they can reasonably expect to recapture, which is key to the performance of the asset. And often investors need to measure that performance by the specific pools of MSRs they purchase — as each pool may have its own unique recapture arrangements.

RiskSpan’s Edge Platform has incorporated a robust framework for managing MSR investment performance by enabling investors to track pool-specific performance and recapture analyses, thus obtaining a more nuanced understanding of their portfolios. In this post, we delve into some of the specific challenges MSRs pose, the benefits of transaction-specific segmentation, and the unique capabilities of RiskSpan’s Edge Platform.

Understanding Pool-Specific Performance

Owning MSRs requires investors to track the performance of various loan pools over time. For example, an investor may purchase an MSR pool and rely on a sub-servicer to service the loans as well as make efforts to recapture borrowers that are looking to refinance. It is important for the investor to understand and track the returns on that pool which may be largely driven by recapture efficiency.  

While performance needs to be monitored on a pool-level, the modeling of the underlying loans is dependent on the distinct characteristics of the loans within a pool and will be more accurate if the models are run at the loan-level (or at granular rep lines determined by smart rep line logic).  The ability to capture and analyze these pool-specific cash flows based on granular loan-level modeling is crucial for several reasons:

  1. Valuation Accuracy: Each loan can be valued more accurately by considering its unique attributes, such as the original loan terms, interest rates, and borrower profiles (e.g., FICO, LTV); at the same time, pools can be valued based on pool-specific assumptions such as recapture rates and prepayment penalties.
  2. Risk Management: Understanding the performance of individual pools helps in identifying which pools are more prone to prepayments or defaults, enabling more focused efforts on recapture and other risk management activities.
  3. Performance Tracking: Investors can track historical returns, CPRs, CDRs, Recapture and other historical performance metrics for each pool, facilitating more informed decision-making.

Supporting this functionality is RiskSpan’s ability to share and integrate data on Snowflake’s data cloud. RiskSpan’s Snowflake integration enhances the data management and analytics capabilities available to clients. Investors can easily share transaction-specific data through Snowflake, which is then seamlessly integrated into the Edge platform. The platform can then handle the large datasets (tens of millions of loans in some instances), providing real-time analytics and insights.

Recapture Analysis: Enhancing Portfolio Performance

Recapture analysis is a critical component for MSR portfolio risk management. When borrowers refinance or otherwise pay off their loans, the servicer’s cash flows usually vanish entirely. However, if, in the case of refinance, the investor retains the rights to service the loan replacing the refinanced loan, then the new loan can be considered as a recapture. RiskSpan’s Edge platform excels in tracking these recaptures, offering several advantages:

  1. Detailed Tracking: The platform allows for the separation and detailed tracking of original loans and their recaptures, maintaining the distinction between the two. Recaptures should have better performance (i.e., lower CPRs) than original loans.
  2. Performance Comparison: By comparing the performance of original loans and recaptures, investors can gauge the effectiveness of their recapture strategies.
  3. Granular Assumptions: Edge supports highly granular recapture rate assumptions used for projecting cash flows, which can be tailored to specific pools or deals, enhancing the precision of valuation.

A Case Study: Supporting a Large Mortage REIT’s MSR Portfolio Management Regime

A practical example of these capabilities involves a mortgage REIT, which relies on RiskSpan’s platform to manage a large MSR portfolio. Specifically, the Edge platform has enabled the REIT investor to accomplish the following:

  • Capture Transaction-Specific Data: the investor can track and analyze data at the transaction level, maintaining detailed records of each pool’s performance and its recaptures. This allows, for example, investors to review performance with sub-servicers and evaluate whether certain changes can be made to enhance performance either on the existing pool or on future pools.
  • Custom Assumption Setting: The platform allows for custom segmentation and assumption setting for valuation purposes, such as different recapture rates based on prepayment projections or loan age. This provides an ability to more accurately measure future projected cash flows and factor that into valuation of owned MSRs as well as potential purchases.

RiskSpan’s Edge platform offers MSR investors a robust toolset for managing their portfolios with precision not available anywhere else. By enabling pool-specific performance and detailed recapture analysis, Edge helps investors optimize their strategies and enhance portfolio performance. The ability to capture and analyze nuanced data points sets RiskSpan apart, making it a valuable ally in the complex landscape of MSR investments.

MSR investors, contact us to discover how tailored analytics and granular data management can transform your investment strategy and give you a competitive edge.

Loans & MSRs: Managing model assumptions and tuners the easy way

One of the things that makes modeling loan and MSR cash flows hard is appropriately applying assumptions to individual loans. Creating appropriate assumptions for each loan or MSR segment is crucial to estimating realistic performance scenarios, stress testing, hedging, and valuation. However, manually creating and maintaining such assumptions can be time-consuming, error-prone, and inconsistent across different segments and portfolios.

Fortunately, hidden among some of the Edge Platform’s better-known features is a powerful and flexible way of running loan-level analytics on a portfolio using the Platform’s segment builder and loan model assumptions features.

These sometimes-overlooked features allow users to create and apply granular and customized modeling assumptions to a particular loan portfolio, based on its various, unique loan characteristics. Assumptions can be saved and reused for future analysis on different loans tapes.  This feature allows clients to effectively build and manage a complex system of models adjustment and tuners for granular sub-segments.

Applying the segment builder and loan model assumptions features, loan investors can:

    • Decouple how they run and aggregate results from how they assign modeling assumptions, and seamlessly assign different assumptions to various segments of the portfolio, based on user-defined criteria and preferences. For example, investors can assign different prepayment, default, and severity assumptions to loans based on their state, LTV, UPB, occupancy, purpose, delinquency status, loan type, collateral features, or virtually any other loan characteristic.

 

    • Choose from a variety of models and inputs, including RiskSpan models and vector inputs for things like CPR and CDR. Investors can define their own vector inputs as an aging curves by loan age or based on the forecast month, and apply them to different segments of the portfolio. For example, they can define their own CDR and CPR curves for consumer or C&I loans, based on the age of the loans.

    • Set up and save modeling assumptions one time, and then reference them over and over again whenever new loan tapes are uploaded. This saves time and effort and ensures consistency and accuracy in the analysis.

This hidden feature enables investors to customize their analysis and projections for different asset classes and scenarios, and to leverage the Edge Platform’s embedded cash flow, prepayment and credit models without compromising the granularity and accuracy of the results. Users can create and save multiple sets of loan model assumptions that include either static inputs, aging curves, or RiskSpan models, and apply them to any loan tape they upload and run in the forecasting UI.

Contact us and request a free demo or trial to learn more about how to use these and other exciting hidden (and non-hidden) features and how they can enhance your loan analytics.

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How an MSR Analytical Solution Can Boost Your Mortgage Banking Business

And why it’s probably less expensive than you think

Mortgage servicing rights (MSRs) are complex and volatile assets that require careful management and analysis. Inherent in MSR risk management is the need to monitor portfolio performance, assess risks and opportunities, evaluate and implement risk-reducing strategies such as recapture and interest rate hedging, and effectively communicate all this to investors and regulators. Handling all this has traditionally required an enormous budget for data, software, and consultants. Many mortgage banks are left with either using outdated and inflexible internal systems or outsourcing their analytics to third parties that lack full transparency and bill clients for every request. 

Not anymore.

The answer is a cloud-native MSR analytical solution that includes slice-and-dice-able Agency loan performance data as well as the models necessary to produce valuations, risk analytics and cash flows across both MSRs and associated derivative hedges, where applicable.

By integrating data, models, and reports, this combined solution enables mortgage banks to:

  • Generate internal metrics to compare with those received from third party brokers and consultants
  • Measure the fair value and cash flows of their MSRs under different scenarios and assumptions including a variety of recapture assumptions
  • Analyze the sensitivity of their MSRs (and associated hedges) to changes in interest rates, prepayment speeds, defaults, home prices and other factors
  • Compare their portfolio’s performance and characteristics with the market and industry peers
  • Generate customized reports and dashboards to share with investors, auditors, and regulators

More specifically, RiskSpan’s comprehensive data and analytics solution enables you to do the following:

1. Check assumptions used by outside analysts to run credit and prepayment analytics

Even in cases where the analytics are provided by a third party, mortgage banks frequently benefit from having their own analytical solution. Few things are more frustrating than analytics generated by a black box with no/limited visibility into assumptions or methodology. RiskSpan’s MSR tool provides mortgage banks with an affordable means of checking the assumptions and methodologies used by outside analysts to run credit and prepayment analytics on their portfolio.

Different analysts use different assumptions and models to run credit and prepayment analytics, often leading to inconsistent results that are difficult to explain. Some analysts use historical data while others rely on forward-looking projections. Some analysts simple models while others turn to complex one. Some analysts are content with industry averages while others dig into portfolio-specific data.

Having access to a fully transparent MSR analytical solution of their own allows mortgage banks to check the assumptions and models used by outside analysts for reasonableness and consistency. In addition to helping with results validation and identification of discrepancies or errors, it also facilitates communication of the rationale and logic behind assumptions and models to investors and regulators.  Lastly, the ability for a mortgage bank to internally generate MSR valuations and cash flows allows for a greater understanding of the economic value (vs. accounting value) of the asset they hold.

2. Understand how your portfolio’s prepayment performance stacks up against the market

Prepayment risk is one of the main drivers of MSR value and volatility. Mortgage banks need to know how their portfolio’s prepayment performance compares with the market and their peers. Knowing this helps mortgage banks field questions from investors, who may be concerned about the impact of prepayments on profitability and liquidity. It also helps identify areas of improvement and opportunity for the portfolio.

RiskSpan’s MSR analytical solution helps track and benchmark portfolio prepayment performance using various metrics, including CPR and SMM. It also helps analysts understand the drivers and trends of prepayments, such as interest rates, loan age, loan type, credit score, and geographic distribution. RiskSpan’s MSR analytical solution combined with its historical performance data provides a deeper understanding of how a portfolio’s prepayment performance stacks up against the market and what factors affect it.

And it’s less expensive than you might think

You may think that deploying an MSR analytical solution is too costly and complex, as it requires a lot of data, software, and expertise. However, this is not necessarily true.

Bundling RiskSpan’s MSR analytical solution with RiskSpan’s Agency historical performance tool actually winds up saving clients money by helping them optimize their portfolios and avoid costly mistakes. The solution:

  • Reduces the need for external data, software, and consultants because all the information and tools needed are in one platform
  • Maximizes portfolio performance and profitability by helping to identify and capture opportunities and mitigate risks, including through recapture analysis and active hedging
  • Enhances reputation and credibility by improving transparency to investors and regulators

RiskSpan’s solution is affordable and easy to use, with flexible pricing and deployment options, as well as user-friendly features and support, including intuitive interfaces, interactive dashboards, and comprehensive training and guidance. Its cloud-native, usage-based pricing structure means users pay only for the compute they need (in addition to a nominal licensing fee).

Contact us to learn more about how RiskSpan’s Edge Platform can help you understand how your MSR portfolio’s performance stacks up against the market, check assumptions used by outside analysts to run credit and prepayment analytics, and, most important, save money and time.

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Enriching Pre-Issue Intex CDI Files with [Actual, Good] Loan-Level Data

The way RMBS dealers communicate loan-level details to prospective investors today leaves a lot to be desired.

Any investor who has ever had to work with pre-issue Intex CDI files can attest to the problematic nature of the loan data they contain. Some are better than others, but virtually all of them lack information about any number of important loan features.

Investors can typically glean enough basic information about balances and average note rates from preliminary CDI files to run simple, static CPR/CDR scenarios. But information needed to run complex models — FICO scores, property characteristics and geography, and LTV ratios to name a few — is typically lacking. MBS investors who want to run to run more sophisticated prepayment and credit models – models that rely on more comprehensive loan-level datasets to run deeper analytics and scenarios – can be left holding the bag when these details are missing from the CDI file.

The loan-level detail exists – it’s just not in the CDI file. Loan-level detail often accompanies the CDI file in a separate spreadsheet (still quaintly referred to in the 21st Century as a “loan tape”). Having this data separate from the CDI file requires investors to run the loan tape through their various credit and prepayment models and then manually feed those results back into the Intex CDI file to fully visualize the deal structure and expected cash flows.

This convoluted, multi-step workaround adds both time and the potential for error to the pre-trade analytics process.

A Better Way

Investors using RiskSpan’s Edge Platform can streamline the process of evaluating a deal’s structure alongside the expected performance of its underlying mortgage loans into a single step.

EDGEPLATFORM

Here is how it works.

As illustrated above, when investors set up their analytical runs on Edge, RiskSpan’s proprietary credit and prepayment models automatically extract all the required loan-level data from the tape and then connect the modeling results to the appropriate corresponding deal tranche in the CDI file. This seamlessness reduces all the elements of the pre-trade analytics process down to a matter of just a few clicks.

Making all this possible is the Edge Platform’s Smart Mapper ETL solution, which allows it to read and process loan tapes in virtually any format. Using AI, the Platform recognizes every data element it needs to run the underlying analytics regardless of the order in which the data elements are arranged and irrespective of how (or even whether) column headers are used.

Contact us to learn more about how RMBS investors are reaping the benefits of consolidating all of their data analytics on a single cloud-native platform.

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