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Webinar: Geocoding Mortgage Data for ESG and Climate Risk Analysis

February 16th | 1:00 p.m. ET

Geocoding remains a particularly vexing challenge for the mortgage industry. Lenders, servicers, and loan/MSR investors know the addresses of the properties securing their mortgage assets. But most data pertaining to climate and other ESG considerations is available only by matching to a census tract or latitude/longitude.

And if you have ever tried mapping addresses, you know this exercise can be a lot harder than it looks. Fortunately, a growing body of geocoding tools and techniques is emerging to make the process more manageable than ever, even with less than perfect address data.

Join us on for a free RiskSpan webinar presenting a how-to guide on geocoding logic and its specific application to the mortgage space. You will learn a useful waterfall approach for linking census-tract-level, geo-specific data for climate risk and ESG to the property addresses in your portfolio.


Featured Speakers

Suhrud Dagli

Chief Innovation Officer, RiskSpan

Jason Huang

Manager, RiskSpan

Jason Lee

Software Engineer, RiskSpan

Institutionally Focused Broker-Dealer: Product Service

As a new MBS operation, this institutional broker-dealer needed trade capture and analytics functionality, particularly for risk management purposes. The broker-dealer also required an application to track MBS pass-through positions in real-time, given the active trading style of its pass-through desk (an average of 3 trades per minute).

The Solution

The client adopted the Edge Platform and RiskSpan provided custom development services that included:

  • A real-time  pass-through matrix  Start-of-Day/ Intra-day firm-wide position upload (taking a feed from a proprietary books-and-records system)
  • Real-time trade capture from Bloomberg and internal sources

The pass-though desk actively used the pass-through matrix for several years. When the client developed its own internal solution, it continued using the Edge Platform to run daily risk scenarios on the firm’s positions.

Total development time for all these projects was about 6 weeks.

National Property and Casualty Insurance Carrier : Claims Platform Migration

A national property and casualty insurance carrier was struggling with an antiquated claims platform. Built on the IBM AS400 Mainframe system, the existing platform was unable to scale up to the growing needs of the organization and was based on legacy code plagued with significant “technical debt.

The Solution

RiskSpan partnered with the client to identify and vet out a cloud-based SaaS solution provider to function as the system of record for all claims processed within the organization. This partnership ran from the discovery phase all the way through the production roll-out and post roll-out business-as-usual phase.  

RiskSpan also assisted with the data migration ETL project necessary to transfer existing open and recent claims to the new platform. All existing interfaces with internal systems and third parties were reconfigured to be functional with the new claims platform. 

Client Benefit

Cloud adoption enabled the client to improve its technology capability score from AM Best Ratings, a key metric for evaluating the health of insurance carriers. 

Project deliverables included: 

  • Documentation of the current state of all internal and external interfaces  
  • Design and Solution Architecture for impacted interfaces 
  • API Design and Data Normalization across the Claims enterprise 
  • API Interfaces using Reactive Programming principles 
  • Implementation of the required security compliance for all data transmissions to and from the public cloud 
  • Integration with Azure identity systems for SSO Integration 
  • Automated Integration testing 
  • Data Lake on SQL Server to facilitate data migration 
  • Financial Reporting from Data Lake using Tableau Server 
  • Agile Project Delivery with 4-week sprints using SAFe Release Trains. 
  • Technology Stack – Java, Spring Cloud, Spring Boot, Spring Security, AS400 DataQueues, Azure SSO, JIRA, Jenkins, Gradle, OAuth 2, Tomcat, GIT, Gerrit, JSON, XML, SQL, Stored Procedures. 

National Property and Casualty Insurance Carrier: Customer Self Service Portal

A national property and casualty insurance carrier needed to modernize its customer selfservice portal. The complexity of the existing portal made it difficult for customers to find what they were looking for, resulting in declining customer engagement. The existing system was also inflexible and failed to align with the company’s new products and brand identity. 

The Solution

RiskSpan led the development of a state-of-the-art, web-based application enabling customers to resolve a full range of self-service needs without resorting to customer service callThe solution was developed using the Design Thinking approach, putting users first and offering a simplified and seamless experience when servicing policies.  

Client Benefits

The technical architecture laid the foundation for all future web-based applications to be developed within the organization. 

The new portal was sufficiently flexible to support rapid deployment of new features relating to the changing product landscape owing to increases in recent catastrophic events. 

Other client deliverables included: 

  • Technical Architecture for the proposed JavaScript front-end platform which would be supported by a micro-services based backend system 
  • Proof of Concept delivery with Rapid Prototyping 
  • Implemented a React based Front end application with reusable component libraries 
  • Component libraries have been implemented in the organization’s new branding aesthetic and will be made available through an internal component repository that can be leveraged by all applications. 
  • Optimized solution to meet future performance demands and scaling to ensure a consistent user experience 
  • A/B Testing capabilities to ensure continual user engagement 
  • Agile Project Delivery with 2-week sprints using SAFe Release Trains 
  • Kanban Development adopted post production roll-out with interrupt capacity planning to rapidly address issues that might come up in production. 
  • Unified code-base to support application delivery over mobile apps as well 
  • Technology Stack – Typescript, React.js, React Native, Material UI, Redux, Java, Spring Boot, Spring Security, JIRA, Jenkins, Gradle, OAuth 2, Node.js, Tomcat, GIT, Gerrit, JSON. 

Will a Rising VQI Materially Impact Servicing Costs and MSR Valuations?

RiskSpan’s Vintage Quality Index computes and aggregates the percentage of Agency originations each month with one or more “risk factors” (low-FICO, high DTI, high LTV, cash-out refi, investment properties, etc.). Months with relatively few originations characterized by these risk factors are associated with lower VQI ratings. As the historical chart above shows, the index maxed out (i.e., had an unusually high number of loans with risk factors) leading up to the 2008 crisis.

RiskSpan uses the index principally to fine-tune its in-house credit and prepayment models by accounting for shifts in loan composition by monthly cohort.

Will a rising VQI translate into higher servicing costs?

The Vintage Quality Index continued to climb during the third quarter of 2021, reaching a value of 85.10, compared to 83.40 in the second quarter. The higher index value means that a higher percentage of loans were originated with one or more defined risk factors.

The rise in the index during Q3 was less dramatic than Q2’s increase but nevertheless continues a trend going back to the start of the pandemic. The increase continues to be driven by a subset of risk factors, notably the share of cash-out refinances and investor properties (both up significantly) and high-DTI loans (up modestly). On balance, fewer loans were characterized by the remaining risk metrics.

What might this mean for servicing costs?

Servicing costs are highly sensitive to loan performance. Performing Agency loans are comparatively inexpensive to service, while non-performing loans can cost thousands of dollars per year more — usually several times the amount a servicer can expect to earn in servicing fees and other ancillary servicing revenue.

For this reason, understanding the “vintage quality” of newly originated mortgage pools is an element to consider when forecasting servicing cash flows (and, by extension, MSR pricing).

Each of the risk layers that compose the VQI contributes to marginally higher default risk (and, therefore, a theoretically lower servicing valuation). But not all risk layers affect expected cash flows equally. It is also important to consider the VQI in relationship to its history. While the index has been rising since the pandemic, it remains relatively low by historical standards — still below a local high in early 2018 and certainly nowhere near the heights reached leading up to the 2008 financial crisis.

A look at the individual risk metrics driving the increase would also seem to reduce any cause for alarm. While the ever-increasing number of loans with high debt-to-income ratios could be a matter of some concern, the other two principal contributors to the overall VQI rise — loans on investment properties and cash-out refinances — do not appear to jeopardize servicing cash flows to the same degree as low credit scores and high DTI ratios do.

Consequently, while the gradual increase in loans with one or more risk factors bears watching, it likely should not have a significant bearing (for now) on how investors price Agency MSR assets.

Population assumptions:

  • Monthly data for Fannie Mae and Freddie Mac.
  • Loans originated more than three months prior to issuance are excluded because the index is meant to reflect current market conditions.
  • Loans likely to have been originated through the HARP program, as identified by LTV, MI coverage percentage, and loan purpose, are also excluded. These loans do not represent credit availability in the market as they likely would not have been originated today but for the existence of HARP.

Data assumptions:

  • Freddie Mac data goes back to 12/2005. Fannie Mae only back to 12/2014.
  • Certain fields for Freddie Mac data were missing prior to 6/2008.

GSE historical loan performance data release in support of GSE Risk Transfer activities was used to help back-fill data where it was missing.

An outline of our approach to data imputation can be found in our VQI Blog Post from October 28, 2015.

RiskSpan Wins Risk as a Service Category for Second Consecutive Year, Leaps 12 Spots in RiskTech100® 2022 Ranking

RiskSpan’s Edge Platform, a leading provider of risk analytics, data, and behavioral modeling to the structured finance industry, is the “Risk as a Service” category winner for the second consecutive year in Chartis Research’s prestigious RiskTech100® ranking of the world’s 100 top risk technology firms. 

The win accompanies a 12-point improvement in RiskSpan’s overall ranking, placing the firm among the year’s most significant movers.  

“RiskSpan’s continued growth and ongoing partnership strategy have made it one of the big risers in the rankings this year,” said Phil Mackenzie, Research Principal at Chartis Research. “Its strength in securitization and analytics as a service is reflected in its 12-point jump.” 

Licensed by some of the largest asset managers, broker/dealers, hedge funds, mortgage REITs and insurance companies in the U.S., Edge is a one-stop shop for research, analytics, pricing, risk metrics, and reporting. Edge’s cloud-native infrastructure scales as individual client needs change and is supported by RiskSpan’s unparalleled team of mortgage and structured finance experts.  

“This year’s award reflects a year marked by an unprecedented wave of enhancements to our risk platform, noted Bernadette Kogler, RiskSpan’s co-founder and CEO. “Our loan-level analytics has been a hit, while our fully managed risk option continues to tailor scalable offerings to individual client needs. Our best-in-class portfolio analytics for structured products are fast becoming the talk of the industry.”

About RiskSpan 

RiskSpan offers end-to-end solutions for data management, risk analytics, and visualization on a highly secure, fast, and fully scalable, cloud-native platform that has earned the trust of the mortgage and structured finance industry’s largest firms. Combining the strength of subject matter experts, quantitative analysts, and technologists, RiskSpan’s Edge Platform integrates a range of datasets – structured and unstructured – and off-the-shelf analytical tools providing users with powerful insights and a competitive advantage. Learn more at

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. 

Non-Linear Paths to Leadership: RiskSpan to Join Structured Finance Association WiS NextGen Panel

On Tuesday, November 16th RiskSpan CEO Bernadette Kogler joined fellow Women in Securitization NextGen panelists Beth O’Brien, Adama Kah, and Libby Cantrill, CFA to discuss Seizing Opportunites at Every Stage of Your Career, moderated by Structured Finance Association President Kristi Leo.

Watch here:

Topics included:

  • Why it’s essential to take risks in your career
  • How to seize opportunities and take on challenges
  • Leveraging an entrepreneurial spirit when exploring possibilities that don’t align with a preset career path – and taking that leap

RiskSpan, Arete Risk Advisors Announce Strategic Consulting Partnership

RiskSpan, a leading provider of data and analytics solutions to the mortgage industry, has announced a partnership with Arete Risk Advisors, LLC, to complement RiskSpan’s existing team of data science, modeling, and financial engineering consultants.  A woman-owned firm boasting a deep bench of experienced housing finance professionals, Arete delivers unparalleled expertise in applying operations, information technology, governance, risk management, and internal controls best practices to every aspect of home lending.  Arete is led by managing partner Patricia Black, an industry-leading executive in home lending. Prior to founding Arete, Patricia served as Fannie Mae’s Chief Audit Executive, Chief of Staff at Caliber Home Loans, the Head of Sales and Operations at SoFi, and a Senior Manager at KPMG Consulting/BearingPoint.    “I’m very excited to be involved with a growing woman-owned business while simultaneously expanding our own advisory offering,” said Bernadette Kogler, Co-Founder and CEO of RiskSpan. “Arete’s emphasis on delivering top-qualify mortgage compliance, controls, governance, and operations services creates a natural synergy with RiskSpan’s data and modeling capabilities. This partnership promises to benefit clients of both firms.”  Patricia Black added, “the opportunity to grow with Bernadette and the RiskSpan team to expand women-owned businesses in the home lending space is inspiring and I am excited about contributing to the continued success of Bernadette and her team.”  Learn more about Arete’s range of services at Questions about the firm may be directed to  About RiskSpan  RiskSpan offers end-to-end solutions for data management, risk analytics, and visualization on a highly secure, fast, and fully scalable, cloud-native platform that has earned the trust of the mortgage and structured finance industry’s largest firms. Combining the strength of subject matter experts, quantitative analysts, and technologists, RiskSpan’s Edge Platform integrates a range of datasets – structured and unstructured – and off-the-shelf analytical tools providing users with powerful insights and a competitive advantage. Learn more at 

Senior Housing Wealth Exceeds Record $9.57 Trillion

Homeowners 62 and older saw their housing wealth grow by 3.7 percent in the second quarter to a record $9.57 trillion, according to the latest quarterly release of the NRMLA/RiskSpan Reverse Mortgage Market Index.

For a comprehensive commentary, please see NRMLA’s press release.

How RiskSpan Computes the RMMI

To calculate the RMMI, RiskSpan developed an econometric tool to estimate senior housing value, mortgage balances, and equity using data gathered from various public resources. These resources include the American Community Survey (ACS), Federal Reserve Flow of Funds (Z.1), and FHFA housing price indexes (HPI). The RMMI represents the senior equity level at time of measure relative to that of the base quarter in 2000.[1] 

A limitation of the RMMI relates to Non-consecutive data, such as census population. We use a smoothing approach to estimate data in between the observable periods and continue to look for ways to improve our methodology and find more robust data to improve the precision of the results. Until then, the RMMI and its relative metrics (values, mortgages, home equities) are best analyzed at a trending macro level, rather than at more granular levels, such as MSA.

[1] There was a change in RMMI methodology in Q3 2015 mainly to calibrate senior homeowner population and senior housing values observed in 2013 American Community Survey (ACS).

Top 10 National Mortgage Servicer: MSR Pricing Model Review, Analysis and Enhancements

One of the nation’s leading mortgage lenders had recently acquired several large MSR portfolios and required assistance reviewing, documenting and recommending enhancements to the underlying assumptions of the model used to price the MSR portfolios at acquisition.

Requiring review and documentation included collateral assumptions, cost and revenue assumptions, and prepayment (CDR/CRR/CPR) assumptions.

The Solution

RiskSpan comprehensively analyzed the cash flow impact of each major assumption (e.g., CDR/CRR/CPR, servicing advances, fees, cost) — the collateral assumptions in the model as well as documented forecast vs. actual outcomes.

RiskSpan worked in concert with the servicer’s finance and pricing teams to collect and analyze roll rates and to forecast actual loan-level data around losses, servicing advances, servicing fees, ancillary fees, PIF, and scheduled principal payments.  


A comprehensive pricing model validation report that included the following:

  • Consolidated CDR-, CRR-, CPR-related pricing model data, including balance, delinquency status, recapture, scheduled payments, default, etc. for all acquired portfolios. The resulting dataset could be used both for deal tracking and pricing model validation 
  • Documentation of the calculation and location of pricing model fields.
  • Reconciliation of the different methods for calculating CDR, CRR, and CPR.
  • Deep dives into model predictions of short sales and foreclosure turn-times
  • Loan-state transition model forecasts and comparison of the model variables between two version of the forecast, including shift analyses.
  • Drivers of forecast variance. 
  • Identification of dials responsible for short sale and foreclosure turn forecast shifting.
  • SAS-based streamlined process for comparing model variables for sub-segment and sub-models in loan state
  • Transition Model:  Incorporation of actual and forecast into pricing models to compare with original pricing model cash flow results for acquired portfolios
  • Creation and standardization of the pricing model validation report output.
  • Automation of reporting.  
  • Improvement of the process by creating a calculation template that could be easily replicated for other portfolios. 
  • Documentation of the validation process and comprehensive review of the validation results with the servicer’s risk team, finance team and pricing team management.

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