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Agency Social Indices & Prepay Speeds

Do borrowers in “socially rich” pools respond to refinance incentives differently than other borrowers? 

The decision by Fannie and Freddie to release social index disclosure data in November 2022 makes it possible for investors to direct their capital in support of first-time homebuyers, historically underserved borrowers, and people who purchase homes in traditionally underserved areas. Because socially conscious investors likely also have interest in understanding how these social pools are likely to perform, we were curious to examine and learn whether mortgage pools with higher social ratings behaved differently than pools with lower social ratings (and if a difference existed, how significant it was). To the extent that pools rich in social factors perform better (i.e., prepay more slowly) than pools generally, we expect investors to put an even higher premium on them. This in turn should result in lower rates for the borrowers whose loans contribute to pools with higher social scores. 

The data is new and we are still learning things, but we are beginning to discern some differences in prepay speeds.

Definitions 

First, a quick refresher on Fannie’s and Freddie’s social index terminology: 

  • Social Criteria Share (SCS): The percentage of loans in a given pool that meet at least one of the “social” criteria. The criteria are low-income, minority, and first-time homebuyers; homes in low-income areas, minority tracts, high-needs rural areas; homes in designated disaster areas and manufactured housing. As of December 2022, 42.12 percent of loans in the average pool satisfy at least one of these criteria. 
  • Social Density Score (SDS): A measure of how many criteria the average loan in a given pool satisfies. For simplicity, the index consolidates the criteria into three categories – those pertaining to income, those pertaining to the borrower, and those pertaining to the property. A pool’s SDS can be zero, 1, 2, or 3 depending on the number of categories within which the loan satisfies at least one criterion. The average SDS as of December 2022 is 0.62 (out of 3). 

Do social index scores impact prepay speeds? 

While it remains too early to answer this question with a great deal of certainty, historical performance data appears to show that pools with below-average social index scores prepay faster than more “social” bonds. 

We first looked at a high-level, simplistic relationship between prepayments and Social Density Score. In Figure 1, below, pools with below-average Social Density Scores (blue line) prepay faster than both pools with above-average SDS (black line) and pools with the very highest SDS (green line) when they are incentivized by interest rates to do so. (Note that very little difference exists among the curves when borrowers are out of the money to refi.)  


Fig. 1: Speeds by Prepay Incentive and Social Density Score 

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We note a similar trend when it comes to Social Criteria Share (see Fig. 2, below).  


Fig. 2: Speeds by Prepay Incentive and Social Criteria Share 

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Social Pool Performance Relative to Spec Pools 

Investors pay up for mortgage pools with specified characteristics. We thought it worthwhile to compare how certain types of spec pools perform relative to socially rich pools with no other specified characteristics. 

Figure 3, below, compares the performance of non-spec pools with above-average Social Criteria Share (orange line) vs. spec pools for low-FICO (blue line), high-LTV (black line) and max $250k (green line) loans. 

Note that, notwithstanding a lack of any other specific characteristics that investors pay up for, the high-SCS pools exhibit a somewhat better convexity profile than the max-700 FICO and min-95 LTV pools and slightly worse convexity (in most refi incentive buckets) than max-250k pools. 


Fig. 3: Speeds by Prepay Incentive and Social Criteria Share: Socially Rich (Non-Spec) Pools vs. Selected Spec Pools

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We observe a similar effect when we compare non-spec pools with an above-average Social Density Score to the same spec pools (Fig. 4, below).   


Fig. 4: Speeds by Prepay Incentive and Social Density Score: Socially Rich (Non-Spec) Pools vs. Selected Spec Pools 

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See how social index scores affect speeds relative to other spec pools.

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Edge Platform Adds Fannie and Freddie Social Index Data

ARLINGTON, Va., January 18, 2023 — RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced the incorporation of Fannie Mae’s and Freddie Mac’s Single-Family Social Index data into its award-winning Edge Platform.

Fannie and Freddie rolled out their social index disclosures in November 2022. Consisting of two measures, the Social Criteria Score and the Social Density Score, the social index discloses the share of loans in a given pool that are made to low-income, minority, and first-time homebuyers, as well as mortgages on homes in low-income areas, minority tracts, high-needs rural areas, and designated disaster areas. Manufactured housing loans also contribute to the score.

Rather than classifying each individual bond as “social” or “not social,” the new Agency data available on the Edge Platform assigns every pool two fully transparent scores – one indicating the percentage of loans in a pool that satisfy any of the defined social criteria, the other reflecting how many criteria a pool’s average loan satisfies.

Taken together, these enable Agency traders and investors to view and understand each pool along a full continuum of the social index, as opposed to simply assigning a binary social designation. Because borrowers behave differently at various places along this continuum, traders and investors fine-tune their analytics in ways never before possible to isolate pools with potentially slower prepayment speeds in a way that transcends what has traditionally been available using so-called “spec. pool” stories alone.

Comprehensive details of this and other new capabilities are available by requesting a no-obligation live demo at riskspan.com.

This new functionality is the latest in a series of enhancements that further the Edge Platform’s objective of providing frictionless insight to Agency MBS traders and investors, knocking down barriers to efficient, clear and data-driven valuation and risk assessment.

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About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments.

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.

Get a Demo

About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics. 

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. 

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com. 

Media contact: Timothy Willis 


Webinar: New Mobility Trends: The Impacts of Covid & Climate

As the Covid-19 pandemic began taking hold three years ago, very few people foresaw the dramatic impact it would have on household mobility.

Wednesday, January 25th | 2:00 p.m. EST

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5 foundational steps for investors to move towards loan-level analyses

Are you curious about how your organization can uplevel the accuracy of your MSR cost forecasting? The answer lies in leveraging the full spectrum of your data and running analyses at the loan level rather than cohorting. But what does it take to make the switch to loan-level analytics? Our team has put together a short set of recommendations and considerations for how to tackle an otherwise daunting project…

It begins with having the data. Most investors have access to loan-level data, but it’s not always clean. This is especially true of origination data. If you’re acquiring a pool – be it a seasoned pool or a pool right after origination – you don’t have the best origination data to drive your model. You also need a data store, like Snowflake, that can generate loan-loan level output to drive your analytics and models.  

The second factor is having models that work at the loan level – models that have been calibrated using loan-level performance and that are capable of generating loan-level output. One of the constraints of several existing modeling frameworks developed by vendors is they were created to run at a rep line level and don’t necessarily work very well for loan-level projections.

The third requirement is a compute farm. It is virtually impossible to run loan-level analytics if you’re not on the cloud because you need to distribute the computational load. And your computational distribution requirements will change from portfolio to portfolio based on the type of analytics that you are running, based on the types of scenarios that you are running, and based on the models you are using. The cloud is needed not just for CPU power but also for storage. This is because once you go to the loan level, every loan’s data must be made available to every processor that’s performing the calculation. This is where having the kind of shared databases, which are native to a cloud infrastructure, becomes vital. You simply can’t replicate it using an on-premise setup of computers in your office or in your own data center. Adding to this, it’s imperative for mortgage investors to remember the significance of integration and fluidity. When dealing with loan-level analytics, your systems—the data, the models, the compute power—should be interlinked to ensure seamless data flow. This will minimize errors, improve efficiency, and enable faster decision-making.

Fourth—and an often-underestimated component—is having intuitive user interfaces and visualization tools. Analyzing loan-level data is complex, and being able to visualize this data in a comprehensible manner can make all the difference. Dashboards that present loan performance, risk metrics, and other key indicators in an easily digestible format are invaluable. These tools help in quickly identifying patterns, making predictions, and determining the next strategic steps.

Fifth and finally, constant monitoring and optimization are crucial. The mortgage market, like any other financial market, evolves continually. Borrower behaviors change, regulatory environments shift, and economic factors fluctuate. It’s essential to keep your models and analytics tools updated and in sync with these changes. Regular back-testing of your models using historical data will ensure that they remain accurate and predictive. Only by staying ahead of these variables can you ensure that your loan-level analysis remains robust and actionable in the ever-changing landscape of mortgage investment.


Webinar Recording: New Mobility Trends: The Impacts of Covid & Climate

Recorded: Wednesday, January 25th | 2:00 p.m. EST

As the Covid-19 pandemic began taking hold three years ago, very few people foresaw the dramatic impact it would have on household mobility. And yet within a year, millions of people had resettled – some temporarily, some permanently – to locations untethered to where their jobs were. Notwithstanding a gradual return to some offices, a tight labor market has enabled the increased mobility initially brought about by Covid to persist.

Will these mobility trends persist as other pandemic-era practices continue to recede? What role will climate change play in mobility as an increasing number of areas grapple with questions of insurability and other challenges tied to climate risk.

Housing economist Amy Crews Cutts, Freddie Mac chief economist and head of housing research Sam Khater, and RiskSpan head of modeling Divas Sanwal and head of climate analytics Janet Jozwik explore how these otherwise unrelated macro factors — Covid and climate – are combining to impact household mobility in the coming years.


Presenters

Amy Cutts

Amy Crews Cutts

President, AC Cutts and Associates and Chief Economist, NACM

Sam Khater FM Picture (3)

Sam Khater

VP, Chief Economist, and Head of Freddie Mac’s Economic Housing and Research Division

Janet Jozwik

Senior Managing Director and Head of Climate Analytics, RiskSpan  

Divas Sanwal Photo (3)

Divas Sanwal

Managing Director and Head of Modeling, RiskSpan


Case Study: Using Snowflake to Create Single Family Credit Risk Grids for a Federal Agency

The Client

Government Sponsored Enterprise (GSE)

The Problem

The client sought to transition its ERCF spot capital reporting process from legacy systems and processes to a new, fully integrated system with automated processes. 

This required the re-creation and automation in Snowflake of a legacy report for FHFA consisting of 30 credit risk and risk factor grids rolled up from the loan level.

The Solution

RiskSpan led a cross-functional effort including the data and reporting teams to implement a fully automated report using data and SQL in Snowflake.

The Deliverables

  • Loan attributes re-mapped from legacy data to Snowflake data
  • Reverse-engineered logic mapping attribute values to grid cohorts​
  • Complex and efficient SQL developed in Snowflake to transform loan-level spot capital data into cohorts for credit risk grids​
  • Conversion of 13 million loan records into more than 2,200 grid cells in less than 3 minutes​
  • Design and execution UAT​ in cooperation with the business team
  • Fully automated FHFA credit risk report populated by calling SQL

Case Study: Hadoop to Snowflake Migration

The Client

Government Sponsored Enterprise (GSE)

The Problem

The client sought to improve the performance and forecasting capabilities of its loan valuation and forecast engine. As part of this strategic initiative, the client planned to migrate the underlying platform from Hadoop to the Snowflake Data Cloud to achieve an increase in data loading and querying speeds and an overall optimization of system performance.​

RiskSpan identified a need for project management and implementation planning, as well as data pipeline and ETL migration analysis to ensure a successful integration of the Snowflake data cloud into the loan valuation and forecast engine.​​

The Solution

RiskSpan led the data migration effort for the loan valuation engine and integrated its pipelines from multiple data sources. The RiskSpan team also executed planning, testing, and overall project management of the implementation effort to ensure a high quality, on-schedule delivery.

The Deliverables

  • An integrated project plan with transition from current state to target state and production parallel
  • A system and data flow comparing existing state to target state
  • SQL code to efficiently compare 13 million records and more than 100 attributes loaded to Snowflake with legacy data in just 2 minutes.
  • Review of target state database ETL patterns
  • Review of loan valuation engine output using data in Snowflake
  • Comprehensive report presented to Senior Management

HECM Loan Data, Smart Assumptions, and Cross-Sector Trade Impact Headline New Edge Platform Functionality

ARLINGTON, Va., December 8, 2022RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced a flurry of new functionality on its award-winning Edge Platform.

GNMA HECM Datasets and Involuntary Prepayment Breakdown: The GNMA HECM dataset is now available to subscribers in Edge’s Historical Performance module, allowing market participants to find performance differentials within FHA reverse mortgage data. As with conventional datasets available on Edge, users slice and dice by any loan attribute to create S-curves, aging curves, time series and other decision-useful analytics.

Edge users also can now parse GNMA buyout metrics by reason, based on whether individual loans were in delinquency, loss mitigation, or foreclosure when they were removed from the security.

Smart Assumptions: Rather than relying on static assumptions to back-fill missing credit scores, DTIs, LTVs and other data on loan acquisition tapes, the Edge Platform has begun employing a smart, dynamic approach to creating more educated estimates of missing assumptions based on other loan characteristics. Users have the option of accepting these assumptions or substituting their own.

Cross-Sector Trade Impact: As a provider of loan and securities analytics, RiskSpan is making it easier to forecast the combined performance of loan and securities portfolios together in a single view. This allows traders and analysts tools to evaluate the risk and return impact of not only different loan selections or bond selections but also cross-sector reallocation.

These new enhancements all further the Edge Platform’s purpose of providing frictionless insight, knocking down barriers to efficient, clear and data-driven valuation and risk assessment.

Comprehensive details of this and other new capabilities are available by requesting a no-obligation live demo at riskspan.com.

This new functionality is the latest in a series of enhancements that is making the Edge Platform increasingly indispensable for Agency MBS traders and investors.

Get a Demo

About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics. 

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. 

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com. 

Media contact: Timothy Willis 


RiskSpan Wins Risk as a Service Category for Third Consecutive Year, Rises 6 Places in RiskTech100® 2023 Ranking

ARLINGTON, Va., December 6, 2022RiskSpan’s Edge Platform, the only single solution to include data management, models, and analytics on fully scalable, cloud-native architecture, wins “Risk as a Service” category for a third consecutive year in Chartis Research’s vaunted RiskTech100® ranking of the world’s 100 top risk technology companies.

RiskSpan was also called out as a most significant mover, climbing 6 places in the overall ranking and improving its position for the fourth year in a row.

Chartis_RiskTech100 “RiskSpan’s strong innovation in data management helped drive its six-place rise in the rankings this year,’ said Sid Dash, Research Director at Chartis. ‘The company has won the RaaS award for three consecutive years, reflecting its tech-centric and pragmatic approach in a key area of the risk management space.” 

Licensed by some of the largest asset managers, broker/dealers, hedge funds, mortgage REITs and insurance companies in the U.S., the Edge Platform is a fully managed risk solution across all asset classes with specialization in residential mortgage and structured products.  

 This year’s award reflects the Edge Platform’s unique ability to help users find alpha, execute transactions with ease, and effectively manage portfolio risks,” noted Bernadette Kogler, RiskSpan’s co-founder and CEO. It is satisfying to be recognized for our continued efforts to help clients transform their business with modern workflows and operations to optimize productivity, cost, and resilience.” 

CONTACT US

About RiskSpan, Inc.  

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics. 

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. 

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com. 

 About Chartis Research:  

Chartis Research is the leading provider of research and analysis on the global market for risk technology. It is part of Infopro Digital, which owns market-leading brands such as Risk and WatersTechnology. Chartis’ goal is to support enterprises as they drive business performance through improved risk management, corporate governance and compliance, and to help clients make informed technology and business decisions by providing in-depth analysis and actionable advice on virtually all aspects of risk technology.  

 Media contact:  Timothy Willis 


How Rithm Capital leverages RiskSpan’s expertise and Edge Platform to enhance data management and achieve economies of scale

 

BACKGROUND

 

One of the nation’s largest mortgage loan and MSR investors was hampered by a complex data ingestion process as well as slow and cumbersome on-prem software for pricing and market risk.

A complicated data wrangling process was taking up significant time and led to delays in data processing. Further, month-end risk and financial reporting processes were manual and time-pressured. The data and risk teams were consumed with maintaining the day-to-day with little time available to address longer-term data strategies and enhance risk and modeling processes.

 

OBJECTIVES

  1. Modernize Rithm’s mortgage loan and MSR data intake from servicers — improve overall quality of data through automated processes and development of a data QC framework that would bring more confidence in the data and associated use cases, such as for calculating historical performance.

  2. Streamline portfolio valuation and risk analytics while enhancing granularity and flexibility through loan-level valuation/risk.

  3. Ensure data availability for accounting, finance and other downstream processes.

  4. Bring scalability and internal consistency to all of the processes above.

THE SOLUTION



THE EDGE WE PROVIDED

By adopting RiskSpan’s cloud-native data management, managed risk, and SaaS solutions, Rithm Capital saved time and money by streamlining its processes

Adopting Edge has enabled Rithm to access enhanced and timely data for better performance tracking and risk management by:

  • Managing data on 5.5 million loans, including source information and monthly updates from loan servicers (with ability in the future to move to daily updates)
  • Ingesting, validating and normalizing all data for consistency across servicers and assets
  • Implementing automated data QC processes
  • Performing granular, loan-level analysis​

 


With more than 5 million mortgage loans spread across nine servicers, Rithm needed a way to consume data from different sources whose file formats varied from one another and also often lacked internal consistency. Data mapping and QC rules constantly had to be modified to keep up with evolving file formats. 

Once the data was onboarded Rithm required an extraordinary amount of compute power to run stochastic paths of Monte Carlo rate simulations on all 4 million of those loans individually and then discount the resulting cash flows based on option adjusted yield across multiple scenarios.

To help minimize the computing workload, Rithm had been running all these daily analytics at a rep-line level—stratifying and condensing everything down to between 70,000 and 75,000 rep lines. This alleviated the computing burden but at the cost of decreased accuracy and limited reporting flexibility because results were not at the loan-level.

Enter RiskSpan’s Edge Platform.

Combining the strength of RiskSpan’s subject matter experts, quantitative analysts, and technologists together with the power of the Edge platform, RiskSpan has helped Rithm achieve its objectives across the following areas: 

Data management and performance reporting

  • Data intake and quality control for 9 servicers across loan and MSR portfolios
  • Servicer data enrichment
  • Automated data loads leading to reduced processing time for rolling tapes
  • Ongoing data management support and resolution
  • Historical performance review and analysis (portfolio and universe)

Valuation and risk

  • Daily reporting of MSR, mortgage loan and security valuation and risk analytics based on customized Tableau reports
  • MSR and whole loan valuation/risk calculated based at the loan-level leveraging the scalability of the cloud-native infrastructure
  • Additional scenario analysis and other requirements needed for official accounting and valuation purposes

Interactive tools for portfolio management

  • Fast and accurate tape cracking for purchase/sale decision support
  • Ad-hoc scenario analyses based on customized dials and user-settings

The implementation of these enhanced data and analytics processes and increased ability to scale these processes has allowed Rithm to spend less time on day-to-day data wrangling and focus more on higher-level data analysis and portfolio management. The quality of data has also improved, which has led to more confidence in the data that is used across many parts of the organization.


LET US BUILD YOUR SOLUTION

Models + Data management = End-to-end Managed Process

The economies of scale we have achieved by being able to consolidate all of our portfolio risk, interactive analytics, and data warehousing onto a single platform are substantial. RiskSpan’s experience with servicer data and MSR analytics have been particularly valuable to us.

          — Head of Analytics


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