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Is Your Prepay Analysis Ready for the Rate Cut?

The forthcoming Federal Reserve interest rate cuts loom large in minds of mortgage traders and originators. The only remaining question is by how much rates will be cut. As the economy cools and unemployment rises, recent remarks by the Fed Chair have made the expectation of rate cuts essentially universal, with the market quickly repricing to a 50bp ease in September. This anticipated move by the Fed is already influencing mortgage rates, which have already experienced a notable decline.

Understanding the Lock-in Effect

One of the key factors influencing prepayments in the current environment is the lock-in effect, where borrowers are deterred from selling their current home due to the large difference between their current mortgage rate and prevailing market rates (which they would incur when purchasing their next home). As rates decrease, the gap narrows, reducing the lock-in effect and freeing more borrowers to sell and move.

As Chart 1 illustrates, a significant share of borrowers continues to hold mortgages between 2 and 3 percent. These borrowers clearly still have no incentive to refinance. But historical data suggests that the sizeable lock-in effect, which is currently depressing turnover, diminishes as the magnitude of their out-of-the-moneyness comes down. In other words, even a 100-basis point reduction can significantly increase housing turnover, as borrowers who were previously 300 basis points out of the money move to 200 basis points, making selling their old home and buying a new one, despite the higher interest rate, more palatable.

CHART 1: Distribution of Note Rates for 30-Year Conventional Mortgages: July 2024

Current Market Dynamics

Recent data from Mortgage News Daily indicates that mortgage rates have dropped over the past four weeks from around 6.8% to nearly 6.4%. This decrease is expected to continue, potentially bringing rates below 6% by the end of the year. This will likely have a profound impact on mortgage prepayments, particularly in the Agency MBS market.

Most outstanding mortgages, particularly those in Fannie and Freddie securities, currently have low prepayment speeds, with many loans sitting at 2% to 3% coupons. While a drop in mortgage rates to 6% (or lower) will still leave most of these mortgages out of the money for traditional rate-and-term refinances, it may bring a growing number of them into play for cash-out refinances, given significant home price appreciation and equity buildup over last 4 years. It will also loosen the grip of the lock-in effect for a growing number of homeowners currently paying below-market interest rates.

Implications for Prepayment Speeds

Factoring in the potential increase in turnover and cash-out refis, the impact of rate cuts on prepayment speeds could be substantial. For instance, with a 100-bp drop in rates, loans that are deeply out of the money could see their prepayment speeds increase by 1 to 2 CPR based on the turnover effect alone. Loans that are just at the money or slightly out of the money will see a more pronounced effect, with prepayment speeds potentially doubling. Chart 2, below, illustrates both the huge volume of loans deep out of the money to refinance as well as the small (but significant) uptick in CPR that a 100-bp shift in interest rates can have on CPR even for loans as much as 300 bps out of the money.

CHART 2: CPR by Refinance Incentive (dotted line reflects UPB of each bucket)

Historical data suggests that if mortgage rates move to 6.4%, the volume of loans moving into the money to refinance could increase up to eightfold — from $39 billion to $247 billion (see chart 3, below.) This surge in refinance activity will significantly influence prepays — impacting both turnover and refi volumes.

CHART 3: Volume and CPR by Coupon (dotted line reflects UPB of each bucket)

The Broader Housing Market

Beyond prepayments, the broader housing market may also feel the effects of rate cuts, but perhaps in a nuanced way. A reduction in rates generally improves affordability, potentially sustaining or even increasing home prices despite the increased supply from unlocked homes. However, this dynamic is complex. While lower rates make homes more affordable, the release of previously locked-in homes could counterintuitively depress home prices due to increased supply. With housing affordability at multi-decade lows, an uptick in housing supply could swamp any effect of somewhat lower rates.

While a modest rate cut may primarily boost turnover, a more significant cut could trigger a wave of refinancing. Additionally, cash-out refinances may become more attractive, offering a cheaper alternative to HELOCs and other more expensive options.

Conclusion

The forthcoming Fed interest rate cuts are poised to have a significant impact on mortgage prepayments. As rates decline, the lock-in effect will ease, encouraging more refinancing and increasing prepayment speeds. The broader housing market will also feel the effects, with potential implications for home prices and overall market dynamics. Monitoring these trends closely will be crucial for market participants, particularly those in the agency MBS market, as they navigate the changing landscape.

Contact us to staying informed and prepared and learn more about how RiskSpan can help you make strategic decisions that align with evolving market conditions.

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.

AI Prompt Structuring — Does it Even Matter?

At the mesh point of human ingenuity and artificial intelligence, the importance of appropriately structured prompts is frequently underestimated. Within this dynamic (and, at times, delicate) ecosystem, the meticulous craftmanship of prompts serves as the linchpin, orchestrating a seamless collaboration between human cognition and machine learning algorithms. Not unlike to a conductor directing an ensemble, judicious prompt structuring lays the foundation for AI systems to synchronize with human intent, thereby facilitating the realization of innovative endeavors. Given the large number of interactions with Large Language Models (LLMs) based on 1:1 digital chats, it is important to carefully prompt gen AI models to generate accurate and tailored outputs.

Gartner predicts that more than 80% of enterprises will have used generative artificial Intelligence (gen AI) or deployed gen AI-enabled applications in production environments by 2026, up from less than 5% in 2023.[1] As gen AI adoption continues to accelerate, understanding proper prompt engineering structures and techniques is becoming more and more important.

With this in mind, we are going to discuss the criticality of the structure of AI prompting to the accuracy of AI outputs. Specifically, we discuss how defining objectives, assigning roles, providing context, specifying the output format, and reviews each play a role in crafting effective prompts.  

@Indian_Bronson. “salmon swimming in a river.” 15 Mar. 2023. X(Twitter), https://twitter.com/Indian_Bronson/status/1636213844140851203/photo/2. Accessed 3 Apr. 2024

Interacting with LLMs through a chat bot function may result in frustrations as users are faced with outputs that are not on par with their expectations. However, the more detail and clarity given to the model, the more resources it will have to understand and execute the task properly. In this context, “detail and clarity” means:

    1. Defining the objective

    1. Assigning Roles and Providing context

    1. Specifying the output format

    1. Reviewing & Refining

1. Define the Objective
Some good questions to ask oneself before providing a prompt to the gen AI include: What needs to be done? What tone does it have to be in? What format do we need? A 2023 Standford University study found that models are better at using relevant information that occurs at the very beginning or the end of the request.[2] Therefore, it is important to generate prompts that are context rich, and concise. 

2. Assign Roles and Provide Context
Arguably the most important part of prompting, providing context is critical because gen AI machines cannot infer meanings beyond the given prompts. Machines also lack the years of experience necessary to grasp the sense of what is needed and what is not without some explicit direction. The following principles are important to bear in mind:

Precision and Personalization: Providing detailed context and a clear role enables the AI system to deliver responses that are both accurate and tailored to individual user needs, preferences, and the specificity of the situation.

Delimiters like XML tags: & angle brackets: <> are a great way to separate instructions, data, and examples from one another. Think of XML tags as hash tagging on social media.

For example:

 

I want to learn about Mortgage Finance and its history

What are some key institutions in the industry?

 

Efficiency and Clarity in Communication: By understanding its expected role, whether as a consultant, educator, or support assistant, an AI application can adjust its communication style, level of detail, and prioritization accordingly. This alignment not only streamlines interactions but also ensures that the dialogue is efficiently directed towards achieving the user’s goals, minimizing misunderstandings and maximizing productivity.

Appropriateness and Ethical Engagement: Knowledge of the context in which it operates, and the nuance of its role allows an AI to navigate sensitive situations with caution, ensuring that responses are both appropriate and considerate. Moreover, this awareness aids in upholding ethical standards in an AI’s responses — crucial for maintaining user trust and ensuring a responsible use of technology.

3. Specify the output format
In crafting a prompt for AI text generation, specifying the output format is crucial to ensuring that the generated output is not only relevant, but also suitable for the intended purpose and audience or stakeholders. To this end:

  • Provide clear instructions that include details of the text’s purpose, the audience it’s intended for, and any specific points or information that should be included. Clear instructions help prevent ambiguity and ensure that the AI produces relevant and coherent output.
  • Set the desired tone, language, and topics so that the output is properly tailored to a business need or setting, whether it is an informative email or a summary of a technical report. Outlining specific topics in combination with language and tone setting aids in generating output that resonates with the stakeholders at the appropriate level of formality and delegates the correct purpose of such output to the end user.
  • Define constraints (length, count, tools, terminology) to help guide the AI’s text generation process within predetermined boundaries. These constraints ensure that the generated output meets the task’s requirements and is consistent with existing systems or workflows. It also minimizes review time and reduces the possibility of submitting additional prompts.

    • Supply output examples. This is a great way to encompass all the above tricks for specifying the output format. Examples serve as reference points for style, structure, and content, helping the AI understand the desired outcome more effectively. By providing a tangible example to the gen AI, a user increases the likelihood of achieving a satisfactory result that aligns with expectations.

4. Review & Refine
Last, but nevertheless important, is to review the prompt before submitting it to the gen AI. Check for consistency of terminology and technical terms usage throughout the prompt and formatting, such as tags and bullet points, to avoid confusion in the responses. Make sure the prompt follows logical flow, avoids repetition and unnecessary information to maintain the desired level of specificity and to avoid skewing the response onto the undesired path.

As users navigate the complexities of AI integration, remembering these prompting structures ensures maximization of AI’s potential while mitigating risks associated with misinformation.

Contact us to learn more about how we are helping our clients harness AI’s capabilities, informed by a strategic and mindful approach.

[1] “Gartner Says More than 80% of Enterprises Will Have Used Generative AI Apis or Deployed Generative AI-Enabled Applications by 2026.” Gartner, 11 Oct. 2023, www.gartner.com/en/newsroom/press-releases/2023-10-11-gartner-says-more-than-80-percent-of-enterprises-will-have-used-generative-ai-apis-or-deployed-generative-ai-enabled-applications-by-2026.

[2] Liu, Nelson F., et al. Lost in the Middle: How Language Models Use Long …, July 2023, cs.stanford.edu/~nfliu/papers/lost-in-the-middle.arxiv2023.pdf.

MSR Tape to Bid in 6 Easy Steps

Creating an MSR bid using RiskSpan’s Edge Platform is designed to be easy.

How easy?

So easy that we challenged a user to create a storylane illustrating how to get from uploading a tape to generating a price in the fewest steps possible.

She was able to get to a bid in just six easy steps!

  1. Upload the CSV file
  2. Click once to map the necessary fields using the Platform’s AI-powered Smart Mapper
  3. Click again to view the transformed and fully mapped loan-level data
  4. Select a segmentation level (loan-level, aggregate, or somewhere in-between)
  5. Select the appropriate anchor, prepay, credit, loan model and MSR inputs
  6. Click run and get your bid. (If you don’t mind more than six steps, you can iterate your inputs and model assumptions through the Platform’s easy-to-use Scenario Library module.)

How is this possible? Ultimately, it boils down to using a platform that was purpose-built to facilitate the process. RiskSpan’s platform boasts:

  1. User-Friendly Interface: The Edge Platform features an intuitive interface that allows users to navigate through different modules and functions with ease. The design focuses on minimizing the learning curve for new users.
  2. Data Integration: The platform integrates seamlessly with various data sources, allowing users to import the necessary data quickly. This integration supports the efficient preparation and analysis of MSR bids.
  3. Automated Processes: Edge offers automation for several steps in the bid creation process. This includes automated data validation, pricing models, and risk assessment tools, which help streamline the workflow.
  4. Advanced Analytics: The platform provides powerful analytics and modeling tools to assess the value and risk of MSRs accurately. Users can leverage these tools to generate insights and make informed decisions.
  5. Collaboration Tools: Edge facilitates collaboration among team members, enabling multiple users to work on a bid simultaneously. This collaborative approach enhances efficiency and ensures all relevant expertise is applied to the bid.
  6. Support and Resources: RiskSpan offers comprehensive support and resources, including tutorials, documentation, and customer service, to help users navigate the platform and utilize its features effectively.
  7. Customization Options: Users can customize the platform to fit their specific needs, including setting up custom workflows, reports, and analytics. This flexibility ensures that the platform can adapt to different bidding strategies and requirements.
  8. Security and Compliance: The Edge Platform is built with robust security measures to protect sensitive data and ensure compliance with industry standards and regulations.

Contact us to try it yourself and see how easy it is to go from a CSV file of loans to a preliminary MSR bid in just minutes.

Get Free Access to RiskSpan’s Non-Agency Performance Trends Dashboard

The latest addition to RiskSpan’s free RS Insights dashboard enables users to delve into insightful non-Agency delinquency trends. Slice and dice CoreLogic loan-level data by LTV, FICO, balance, vintage, doc type and program.

This post identifies just a few of the trends in non-QM loan performance that users can explore more deeply (and for free) by registering at https://riskspan.com/request-access/.

Accelerating Delinquency Rates Among Low-FICO Cohorts

Plotting delinquency rates by FICO bands makes it easy to visualize why the market punishes low-FICO loan cohorts the way it does.

In this first visualization, we look at non-QM loans irrespective of document type. We see that following the recovery from the Covid shock in early 2020, serious delinquency rates (60+ days) have begun to creep higher again across all FICO bands, but more dramatically among loans with sub-740 FICO scores, and particularly among loans with sub-680 FICO scores.

Drilling deeper, we can visualize the impact documentation type has on this difference. While the most pronounced difference between the lowest-FICO borrowers and other borrowers can be seen in full-doc loans, the steepness of the upward slope of the delinquency curve for these borrowers is more pronounced among bank statements loans and DSCR Investor Cash Flow loans, as well:

The relationship among 60+ default rates by documentation type (regardless of FICO) score is depicted in the visualization below:

Not surprisingly, the data reveals a sharp increase in delinquency rates for sub-680 FICO borrowers, regardless of doc type:

Accelerating Delinquency Rates Among High-LTV Cohorts

Analogously to low-FICO loans, markets punish loans with high loan-to-value ratios. The reason why is clear in the data.

As has been observed with FICO scores, the performance differences between the LTV bands are more pronounced among full-doc loans than among bank statement and DSCR loans.

Conclusion

Non-Agency Loan Performance Trends is only the latest addition to the suite of free dashboards available to subscribers and non-subscribers alike of RiskSpan’s award-winning Edge Platform. The Platform’s free dashboards include:

  • Daily GSE prepayment data
  • Whole loan trading market color
  • TBA Pricing Reports
  • Interactive prepayment model back-testing reports

The Non-Agency Loan Performance Trends dashboards enables users to create their own fully customized overview of the current state of non-QM performance by evaluating the collective and individual impacts of vintage, documentation type, loan size, and purpose on delinquency performance.

The dashboard provides a roadmap for analysts seeking to closely monitor delinquency trends in a dynamic economic environment, navigate non-QM credit and adopt strategies to mitigate risks and support borrowers.

REGISTER FOR FREE DASHBOARD ACCESS

The newest, fastest and easiest way to access and analyze Agency MBS data

TL;DR Summary of Benefits

  • Data normalization and enhancement: RiskSpan’s MBS data on Snowflake normalizes Fannie, Freddie, and Ginnie loan-level data, consolidating everything into one set of field names. It also offers enhanced loan level-data fields, including current coupon, spec pool category, and mark-to-market LTV, which are not available in the raw data from the agencies. The data also includes pool-level factors like pool prefix and pool age, as well as full loan histories not available from the GSEs directly.
  • Data access and querying: Users access the data in Snowflake using SQL or Python connectors. Snowflake functions essentially as a cloud SQL server that allows for instantaneous data sharing across entities. In just a few clicks, users can start analyzing MBS data using their preferred coding language—no data, ETL, or IT Teams required.
  • Data merging and analytics: Users can merge the data in Snowflake with other available loan level or macroeconomic data, including interest rates, home prices, and unemployment, for advanced analytics. Users can also project performance, monitor portfolios, and create spec pools, among other features.
CHECK OUT RISKSPAN’S OFFERING ON SNOWFLAKE MARKETPLACE

The Problem

Even though Fannie, Freddie and Ginnie have been making MBS performance data publicly available for years, working with the raw data can be challenging for traders and back-office analysts.

Traders and analysts already have many of the tools they need to write powerful queries that can reveal hidden patterns and insights across different markets – patterns that can reveal lucrative trading opportunities based on prepayment analysis. But one big obstacle often stands in the way of getting the most out of these tools: the data from the agencies is large and unwieldy and is not formatted in a consistent way, making it hard to compare and combine.

What’s more, the Agencies do not maintain full history of published data on the websites for download. Only recent history is available.

The Solution: RiskSpan’s new MBS loan-level historical offering on Snowflake Marketplace

Using RiskSpan’s new MBS Loan-Level Historical Data Offering, MBS traders and analysts can now leverage the power of Snowflake, the leading cloud data platform, to perform complex queries and merge data from multiple sources like never before.

This comprehensive data offering provides a fully normalized view of the entire history of loan-level performance data across Agencies – allowing users to interact with the full $9T Agency MBS market in unprecedented ways.

A list of normalized Fannie and Freddie fields can be found at the end of this post.

In addition to being able to easily compare different segments of the market using a single set of standardized data fields, MBS traders and analysts also benefit from derived and enhanced data, such as current coupon, refinance incentive, current loan-to-value ratio, original specified pool designation, and normalized seller and servicer names.

The use cases are practically limitless.

MBS traders and analystscan track historical prepayment speeds, find trading opportunities that offer relative value, and build, improve, or calibrate prepayment models. They can see how prepayment rates vary by loan size, credit score, geographic location, or other factors. They can also identify pools that have faster or slower prepayments than expected and exploit the differences in price.

Loan originators can see how their loans perform compared to similar loans issued by other originators, servicers, or agencies, allowing them to showcase their ability to originate high-quality loans that command premium pricing.

Enhanced fields provide users with more comprehensive insights and analysis capabilities. They include a range of derived and enhanced data attributes beyond the standard dataset: derived fields useful for calculations, additional macroeconomic data, and normalized field names and enumerations. These fields give users the flexibility to customize their analyses by incorporating additional data elements tailored to their specific needs or research objectives.

Enhanced loan-level fields include:

  • Refi Incentive: The extent to which a borrower’s interest rate exceeds current prevailing market rates
  • Spread at Origination (SATO): a representation of the total opportunities for refinancing within a mortgage servicing portfolio. SATO encompasses all potential refinance candidates based on prevailing market conditions, borrower eligibility, and loan characteristics
  • Servicer Normalization: A standardization of servicer names to ensure consistency and accuracy in reporting and analysis
  • Scheduled Balance: A helper field necessary to easily calculate CPR and other performance metrics
  • Spec Pool Type: A designation of the type of spec story on the loan’s pool at origination
  • Current LTV: a walked forward LTV based on FHFA’s HPI and the current balance of the loan

Not available in the raw data from the agencies, these fields allow MBS traders and analysts to seamlessly project loan and pool performance, monitor portfolios, create and evaluate spec pools, and more.

Access the Data on Your Terms

Traders and analysts can access the data in Snowflake using SQL or Python connectors. Alternatively, they can also access the data through the Edge UI, our well-established product for ad hoc querying and visualization. RiskSpan’s Snowflake listing provides sample queries and a data dictionary for reference. Data can be merged with macroeconomic data from other sources – rates, HPI data, unemployment – for deeper insights and analytics.

The listing is available for a 15-day free trial and can be purchased on a monthly or annual basis. Users don’t need to have a Snowflake account to try it out. Learn more and get started at the Snowflake Marketplace or contact us to schedule a demo or discussion.

CHECK OUT RISKSPAN’S OFFERING ON SNOWFLAKE MARKETPLACE

Fannie/Freddie Normalized Fields

NAMETYPEDESCRIPTION
AGENumberLoan Age in Months
AGENCYVarcharFN [Fannie Mae], FH [Freddie Mac]
ALTDQRESOLUTIONVarcharPayment deferral type: CovidPaymentDeferral,DisasterPaymentDeferral,PaymentDeferral,Other/NA
BORROWERASSISTPLANVarcharType of Assistance: Forbearance, Repayment, TrialPeriod, OtherWorkOut, NoWorkOut, NotApplicable, NotAvailable
BUSINESSDAYSNumberBusiness Day in Factor Period
COMBINEDLTVFloatOriginal Combined LTV
CONTRIBUTIONFloatContribution of Loan to the Pool, to be used to correctly attribution Freddie Mirror Pools
COUPONFloatNet Coupon or NWAC in %
CURRBALANCEFloatCurrent Balance Amount
CURRENTCOUPONFloatPrimary rate in the market (PMMS)
CURRENTLTVFloatCurrent Loan to Value Ratio based on rolled-forward home value calculated by RiskSpan based on FHFA All-Transaction data
CURTAILAMOUNTFloatDollar amount curtailed in the period
DEFERRALAMOUNTFloatDollar amount deferred
DQSTRINGVarcharDelinquency History String, left most field in the current period
DTIFloatDebt to Income Ratio %
FACTORDATEDatePerformance Period
FICONumberBorrower FICO Score [300,850]
FIRSTTIMEBUYERVarcharFirst time home buyer flag Y,N,NA
ISSUEDATEDateLoan Origination Date
LOANPURPOSEVarcharLoan Purpose: REFI,PURCHASE,NA
LTVFloatOriginal Loan to Value Ratio in %
MATURITYDATEDateLoan Maturity Date
MICOVERAGEFloatMortgage Insurance Coverage %
MOSDELINQVarcharDelinquency Status: Current, DQ_30_Day, DQ_60_Day, DQ_90_Day, DQ_120_Day, DQ_150_Day, DQ_180_Day, DQ_210_Day, DQ_240_Day, DQ_270_Day, DQ_300_Day, DQ_330_Day, DQ_360_Day, DQ_390_Day, DQ_420_Day, DQ_450_Day, DQ_480_Day, DQ_510_Day, DQ_540_Day, DQ_570_Day, DQ_600_Day, DQ_630_Day, DQ_660_Day, DQ_690_Day, DQ_720pls_Day
MSAVarcharMetropolitian Statistical Area
NUMBEROFBORROWERSNumberNumber of Borrowers
NUMBEROFUNITSVarcharNumber of Units
OCCUPANCYTYPEVarcharOccupancy Type: NA,INVESTOR,OWNER,SECOND
ORIGBALANCEFloatOriginal Loan Balance
ORIGSPECPOOLTYPEVarcharSpec Story of the pool that the loan is a part of. Please see Spec Pool Logic in our linked documentation
PERCENTDEFERRALFloatPercentage of the loan balance that is deferred
PIWVarcharProperty Inspection Waiver Type: Appraisal,Waiver,OnsiteDataCollection, GSETargetedRefi, Other,NotAvailable
POOLAGENumberAge of the Pool
POOLIDVarcharPool ID

Snowflake and the Future of Data Sharing Across Financial Institutions

The digitization of the financial services industry has opened countless doors to streamlining operations, building customer bases, and more accurately modeling risk. Capitalizing on these opportunities, however, requires financial institutions to address the immense data storage and sharing requirements that digitization requires.  

Recognizing this need, Snowflake has emerged as an industry-leading provider of cloud-computing services for the financial industry. According to estimates, some 57 percent of financial service companies in the Fortune 500 have partnered with Snowflake to address their data needs.1 In this article, we highlight some of Snowflake’s revolutionary data sharing capabilities that have contributed to this trend and RiskSpan’s decision to become a Snowflake partner.     

Financial institutions contemplating migration to the cloud are beset by some common concerns. Chief among these are data sharing capabilities and storage costs. Fortunately, Snowflake is well equipped to address both. 

Data Sharing Between Snowflake Customers

Ordinarily, sharing information across institutions inflates storage costs and imposes security and data integrity concerns.  

Snowflake’s Secure Data Sharing eliminates these concerns because no physical data transfer occurs between accounts. When one Snowflake customer desires to share data with another Snowflake customer, a services layer and metadata store facilitate all sharing activities. As a result, shared data does not occupy any storage in the institution consuming the data, nor does it impact that institution’s monthly data storage expenses. Data consumers are only charged for the compute resources, such as virtual warehouses, they use to query the shared data.  

The setup for Secure Data Sharing is streamlined and straightforward for data providers, while consuming institutions can access shared data almost instantaneously.   

Organizations can easily: 

  • Establish a share from a database within their account, granting access to specified objects within that database.  
  • Share data across multiple databases, provided all databases are under the same account.  
  • Add, remove, and edit access for all users. 

Data Sharing with Non-Snowflake Customers

For institutions desiring to share data with non-Snowflake customers, Snowflake offers an alternative secure data sharing method, known as a “reader account.” Reader accounts offer an efficient and cost-effective solution for data sharing without requiring consumers to register for Snowflake. They are associated exclusively with the provider’s account that established them. Data providers share databases with reader accounts, but each reader account can only access data from its originating provider account. Individuals using a reader account can perform queries on shared data but are restricted from carrying out DML operations, such as data loading, insertions, updates, and other data manipulations. These accounts serve as cost-effective solutions for organizations seeking to limit the number of more expensive user profiles. 

Secure Sharing with Data Clean Rooms

Clean room managed accounts are another way for Snowflake customers to share data with non-Snowflake customers. Data clean rooms are created by data providers to avoid privacy concerns when sharing their data. This is accomplished by allowing data consumers to compile aggregated results and analysis without permitting access to query the original raw data. Data providers can granularly control how their data is accessed and the types of analysis that can be run using their data. The data is encrypted and uses differential privacy techniques for further protection.   

How Can RiskSpan Help?

Knowing that you want to be on Snowflake isn’t always enough. Getting there can be the hardest part, and many organizations face challenges migrating from legacy systems and lack the expertise to fully utilize new technology after implementation. RiskSpan has partnered with numerous companies to help guide them towards a sustainable framework that holistically addresses all their data needs. No matter where the organization is within their data journey, RiskSpan has the expertise to help overcome the challenges associated with the new technology.    

RiskSpan is equipped to help institutions with the following as they embark on their Snowflake migration journey: 

  • End-to-end migration services, including architecture design, setting up the Snowflake environment, and properly validating the new platform.   
  • Adaptive project management. 
  • Data governance including the creation of a data catalog, tracing data lineage, and compliance and security requirements. 
  • Establishing data warehouses and data pipelines to facilitate collaboration and analysis. 
  • Creating security protocols including role-based access controls, disaster recovery solutions, and ensuring the utmost protection of personally identifiable information.   
  • Optimizing extract, transform and load solutions   

Snowflake’s data sharing capabilities offer an innovative solution for businesses looking to leverage real-time data without the hassle of traditional data transfer methods. These features not only enhance operational efficiency but also provide the scalability and security necessary for handling extensive datasets in a cloud environment.

Contact us with any questions or to discuss how Snowflake can be tailored to your specific needs.

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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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What Do 2024 Origination Trends Mean for MSRs?

While mortgage rates remain stubbornly high by recent historical standards, accurately forecasting MSR performance and valuations requires a thoughtful evaluation of loan characteristics that go beyond the standard “refi incentive” measure.

As we pointed out in 2023, these characteristics are particularly important when it comes to predicting involuntary prepayments.

This post updates our mortgage origination trends for the first quarter of 2024 and takes a look at what they could be telling us.

Average credit scores, which were markedly higher than normal during the pandemic years, have returned and stayed near the averages observed during the latter half of the 2010s.

The most credible explanation for this most recent reversion to the mean is the fact that the Covid years were accompanied by an historically strong refinance market. Refis traditionally have higher FICO scores than purchase mortgages, and this is apparent in the recent trend.

Purchase markets are also associated with higher average LTV ratios than are refi markets, which accounts for their sharp rise during the same period.

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Consequently, in 2023 and 2024, with high home prices persisting despite extremely high interest rates, new first-time homebuyers with good credit continue to be approved for loans, but with higher LTV and DTI ratios.

Between rates and home prices, ​​borrowers simply need to borrow more now than they would have just a few years ago to buy a comparable house. This is reflected not just in the average DTI and LTV, but also the average loan size (below) which, unsurprisingly, continues to trend higher as well.

Recent large increases to the conforming loan limit are clearly also contributing to the higher average loan size.

What, then, do these origination trends mean for the MSR market?

The very high rates associated with newer originations clearly translate to higher risk of prepayments. We have seen significant spikes in actual speeds when rates have taken a leg down — even though the loans are still very new. FICO/LTV/DTI trends also potentially portend higher delinquencies down the line, which would negatively impact MSR valuations.

Nevertheless, today’s MSR trading market remains healthy, and demand is starting to catch up with the high supply as more money is being raised and put to work by investors in this space. Supply remains high due to the need for mortgage originators to monetize the value of MSR to balance out the impact from declining originations.

However, the nature of the MSR trade has evolved from the investor’s perspective. When rates were at historic lows for an extended period, the MSR trade was relatively straightforward as there was a broader secular rate play in motion. Now, however, bidders are scrutinizing available deals more closely — evaluating how speeds may differ from historical trends or from what the models would typically forecast.

These more granular reviews are necessarily beginning to focus on how much lower today’s already very low turnover speeds can actually go and the extent of lock-in effects for out-of-the-money loans at differing levels of negative refi incentive. Investors’ differing views on prepays across various pools in the market will often be the determining factor on who wins the bid.

Investor preference may also be driven by the diversity of an investor’s other holdings. Some investors are looking for steady yield on low-WAC MSRs that have very small prepayment risk while other investors are seeking the higher negative convexity risk of higher-WAC MSRs — for example, if their broader portfolio has very limited negative convexity risk.

In sum, investors have remained patient and selective — seeking opportunities that best fit their needs and preferences.

So what else do MSR holders need to focus on that may may impact MSR valuations going forward? 

The impact from changes in HPI is one key area of focus.

While year-over-year HPI remains positive nationally, servicers and other investors really need to look at housing values region by region. The real risk comes in the tails of local home price moves that are often divorced from national trends. 

For example, HPIs in Phoenix, Austin, and Boise (to name three particularly volatile MSAs) behaved quite differently from the nation as a whole as HPIs in these three areas in particular first got a boost from mass in-migration during the pandemic and have since come down to earth.

Geographic concentrations within MSR books will be a key driver of credit events. To that end, we are seeing clients beginning to examine their portfolio concentration as granularly as zipcode level. 

Declining home values will impact most MSR valuation models in two offsetting ways: slower refi speeds will result in higher MSR values, while the increase in defaults will push MSRs back downward. Of these two factors, the slower speeds typically take precedence. In today’s environment of slow speeds driven primarily by turnover, however, lower home prices are going to blunt the impact of speeds, leaving MSR values more exposed to the impact of higher defaults.

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