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RiskSpan CEO Bernadette Kogler Featured at MBA of Florida’s Annual Eastern Secondary Market Conference 

On Wednesday, June 19th, RiskSpan Co-founder and CEO Bernadette Kogler will join a lineup of top-notch experts speaking at the Mortgage Bankers Association of Florida’s Annual Eastern Secondary Market Conference. She will speak about the role of blockchain and other innovative technologies in the field. Kogler will also join a panel on the second day of the event: Modernizing the Housing Finance Marketplace, Leveraging Blockchain, and Bringing Mortgage into the 21st Century. The conference will last from June 18th to 20th and will feature a variety of topics surrounding the current and future states of secondary markets. As a co-founder of SmartLink Lab, RiskSpan’s fintech affiliate, Kogler brings an innovative and expert perspective on improving market efficiencies through machine learning and distributed ledger technologies for structured finance.

RiskSpan’s Janet Jozwik Receives WHF’s 40 Under 40 Award

Janet Jozwik, RiskSpan’s Managing Director and Head of Data Analytics and Credit Modeling, is making waves in the housing and finance industry. Jozwik’s continued outreach and dedication has recently won her two notable recognitions in the community. This year, she has been recognized as a 2019 Rising Star by Housing Wire. She has also been honored by Women in Finance & Housing, Inc. (WHF) as one of 40 professionals under 40 years of age who have achieved great success and influence in the housing and finance industry. WHF will formally recognize her and the other award recipients at their 40th Anniversary Celebration on Tuesday, June 11.  

Jozwik plays a critical role in both the consulting and platform divisions of RiskSpan. Her deep industry knowledge and technical creativity allow her to serve as one of the firm’s leading subject matter experts on mortgage credit risk. An influential leader and thoughtful collaborator, Jozwik is regarded highly not only by the RiskSpan team, but by clients and researchers throughout the housing and finance industry. To directly gain from her expertise, check out her publications in the Journal of Structured Finance: Building a Credit Model Using GSE Loan-Level Data, Credit Risk Transfers: Investor and GSE Perspectives, or in this video clip on our website. 

We are proud to honor Janet for all that she has accomplished here at RiskSpan and in the industry at large. Congratulations, Janet! 

Fannie Mae and Freddie Mac Launch New Uniform Mortgage-Backed Security (UMBS)

Today, Fannie Mae and Freddie Mac begin issuing the long-awaited Uniform Mortgage-Backed Security (UMBS). The Federal Housing Finance Administration (FHFA) conceived of this new standard in its 2012 “A Strategic Plan for Enterprise Conservatorships,” which marked the start of the Single Security Initiative (the history of which is laid out in the graphic below). 

RiskSpan produces FHFA’s quarterly performance reports, most recently published Wednesday, May 29, which will support the agency’s oversight of the UMBS. The FHFA uses this report to monitor prepayment performance of passthroughs issued by Fannie and Freddie. These reports provide market participants with additional transparency on prepayment behavior alignment. They also allow the FHFA to monitor and address differences in conditional prepayments rates (CPR) between the two issuers and to align programs, policies, and practices that affect the cash flows of “To-Be-Announced” (TBA)-eligible Mortgage-Backed Securities (MBS). 

 The importance of RiskSpan’s contributions to the FHFA’s efforts are highlighted in Bloomberg’s May 30 article, “A $4 Trillion Plan Could Make or Break Dreams of U.S. Homebuyers”.

FHFA 2Q2019 Prepayment Monitoring Report

FHFA’s 2014 Strategic Plan for the Conservatorships of Fannie Mae and Freddie Mac includes the goal of improving the overall liquidity of Fannie Mae’s and Freddie Mac’s (the Enterprises) securities through the development of a common mortgage-backed security. This report provides insight into how FHFA monitors the consistency of prepayment rates across cohorts of the Enterprises’ TBA-eligible MBS.

Download Report

RiskSpan Credit Risk Transfer Solution

RiskSpan Managing Director, Janet Jozwik, explains how the RS Edge Platform serves as an end-to-end Credit Risk Transfer (CRT) solution designed to help investors in each stage of CRT deal analysis. The RS Edge Platform hosts historical GSE data (STACR/CAS/CIRT/ACIS) and gives users the ability to conduct historical and surveillance analysis as well as predictive and scenario analysis. Additionally, RiskSpan gives users full access to our proprietary agency-specific prepayment and credit models and is integrated with Intex for deal cash flow analysis.

Low MI No Problem: Analyzing the Historical Performance of Home Affordable Loans

Introduction In our last CRT Deal Monitor post, we touched on a trend we have noticed- that the number of loans being originated with less-than-standard MI coverage has been increasing. This is a trend we will be covering in a series of blog posts. The following analysis provides a historical view of the performance of loans with less than standard MI coverage, like those being originated through the Fannie Mae HomeReady and Freddie Mac HomePossible programs. Fannie Mae CAS Deals contain a steadily growing percent of UPB in the HomeReady program. While Freddie Mac does not currently include a HomePossible indicator we suspect the same trend is occurring. In the coming months Freddie Mac will add this disclosure enhancement and we will investigate. Historical data indicates that these HomeReady loans perform just as well, if not better, than similar loans not in an affordability program (see appendix for the cohort definitions). However, this trend appears to be shifting as newer vintages with standard MI have experienced less (albeit slightly) losses than their HomeReady counterparts, though there is significantly less performance history available. The table below shows the cumulative default rate for each vintage segmented by LTV cutoffs for the HomeReady Program. Analysis The plots below present a profile of Fannie Mae HomeReady and Standard MI cohorts via the distributions of UPB, LTV, FICO, and DTI dating back to 1999. The cohorts are similar, though the Standard MI cohort does present a slightly better credit profile. The Standard MI cohort contains more loans with <= 95% LTV, slightly higher FICOs, slightly lower DTIs, and higher average loan sizes. All plots in this post are interactive:

  • Click and drag in any of the plots to zoom on a region.
  • Isolate groups by double clicking on the legend entries, and single click to add groups back in.

Cohort Characteristics Plots: To compare performance through time each cohort has been grouped by Vintage. The plot below shows the cumulative default rate based on months from origination for each Vintage MI cohort. Based on the data, the older HomeReady population has experienced a lower overall default rate vs. the same vintage with Standard MI. This effect is exaggerated for vintages originated immediately preceding the crisis and is observed consistently through 2011. Unsurprisingly, since the Low MI cohorts experienced a lower overall default rate, they also experienced a lower cumulative net loss which is displayed for each vintage on hover. Select a single vintage from the dropdown menu or isolate vintage(s) by clicking the lines or legend. Cumulative Default Rate Plot: Since the HomeReady population is characterized by having less than standard MI, we should expect this population to have a higher loss severity. This relationship is seen in the data and is most prominent from the 2005 vintage onward. With the exception of the 2011 vintage, the gap between severity for Low and Standard MI has grown stronger through time. Cumulative Severity Plot: In the next installment of this series we will cover specific loss characteristics for the HomeReady and Standard MI populations, and discuss the impact of Borrower Area Median Income, which is an eligibility requirement for the HomeReady population. Appendix: Cohort Selection Criteria: For this analysis, the historical performance of two cohorts ‘Low MI’ and ‘Standard MI’ were pulled from RiskSpan’s Edge Platform from the Fannie Mae Loan Performance Dataset. The cohorts contain approximately 800,000 and 2,1M loans respectively. The cohorts were established based on the current MI coverage requirements set by Fannie Mae, and were limited to loans with LTV > 90.1%. The matrix below shows MI coverage requirements for the HomeReady (Low MI) cohort and Standard MI cohort. Cohort 1 – Low MI Coverage: Cohort 2 – Standard MI Coverage:

Automate Your Data Normalization and Validation Processes

Robotic Process Automation (RPA) is the solution for automating mundane, business-rule based processes so that organizations high value business users can be deployed to more valuable work. 

McKinsey defines RPA as “software that performs redundant tasks on a timed basis and ensures that they are completed quickly, efficiently, and without error.” RPA has enormous savings potential. In RiskSpan’s experience, RPA reduces staff time spent on the target-state process by an average of 95 percent. On recent projects, RiskSpan RPA clients on average saved more than 500 staff hours per year through simple automation. That calculation does not include the potential additional savings gained from the improved accuracy of source data and downstream data-driven processes, which greatly reduces the need for rework. 

The tedious, error-ridden, and time-consuming process of data normalization is familiar to almost all organizations. Complex data systems and downstream analytics are ubiquitous in today’s workplace. Staff that are tasked with data onboarding must verify that source data is complete and mappable to the target system. For example, they might ensure that original balance is expressed as dollar currency figures or that interest rates are expressed as percentages with three decimal places. 

Effective data visualizations sometimes require additional steps, such as adding calculated columns or resorting data according to custom criteria. Staff must match the data formatting requirements with the requirements of the analytics engine and verify that the normalization allows the engine to interact with the dataset. When completed manually, all of these steps are susceptible to human error or oversight. This often results in a need for rework downstream and even more staff hours. 

Recently, a client with a proprietary datastore approached RiskSpan with the challenge of normalizing and integrating irregular datasets to comply with their data engine. The non-standard original format and the size of the data made normalization difficult and time consuming. 

After ensuring that the normalization process was optimized for automation, RiskSpan set to work automating data normalization and validation. Expert data consultants automated the process of restructuring data in the required format so that it could be easily ingested by the proprietary engine.  

Our consultants built an automated process that normalized and merged disparate datasets, compared internal and external datasets, and added calculated columns to the data. The processed dataset was more than 100 million loans, and more than 4 billion records. To optimize for speed, our team programmed a highly resilient validation process that included automated validation checks, error logging (for client staff review) and data correction routines for post-processing and post-validation. 

This custom solution reduced time spent onboarding data from one month of staff work down to two days of staff work. The end result is a fully–functional, normalized dataset that can be trusted for use with downstream applications. 

RiskSpan’s experience automating routine business processes reduced redundancies, eliminated errors, and saved staff time. This solution reduced resources wasted on rework and its associated operational risk and key-person dependencies. Routine tasks were automated with customized validations. This customization effectively eliminated the need for staff intervention until certain error thresholds were breached. The client determined and set these thresholds during the design process. 

RiskSpan data and analytics consultants are experienced in helping clients develop robotic process automation solutions for normalizing and aggregating data, creating routine, reliable data outputs, executing business rules, and automating quality control testing. Automating these processes addresses a wide range of business challenges and is particularly useful in routine reporting and analysis. 

Talk to RiskSpan today about how custom solutions in robotic process automation can save time and money in your organization. 

CRT Deal Monitor: April 2019 Update

Loans with Less than Standard MI Coverage

CRT Deal Monitor: Understanding When Credit Becomes Risky 

This analysis tracks several metrics related to deal performance and credit profile, putting them into a historical context by comparing the same metrics for recent-vintage deals against those of ‘similar’ cohorts in the time leading up to the 2008 housing crisis.  

Some of the charts in this post have interactive features, so click around! We’ll be tweaking the analysis and adding new metrics in subsequent months. Please shoot us an email if you have an idea for other metrics you’d like us to track. 

Monthly Highlights: 

The seasonal nature of recoveries is an easy-to-spot trend in our delinquency outcome charts (loan performance 6 months after being 60 days-past-due). Viewed from a very high level, both Fannie Mae and Freddie Mac display this trend, with visible oscillations in the split between loans that end up current and those that become more delinquent (move to 90+ days past due (DPD)). This trend is also consistent both before and after the crisis – the shares of loans that stay 60 DPD and move to 30 DPD are relatively stable. You can explore the full history of the FNMA and FHLMC Historical Performance Datasets by clicking the 6-month roll links below, and then clicking the “Autoscale” button in the top-right of the graph. Loans with Less-than-Standard MI Coverage

This trend is salient in April of 2019, as both Fannie Mae Connecticut Avenue Securities (CAS) and Freddie Mac Structured Agency Credit Risk (STACR) have seen 6 months of steady decreases in loans curing, and a steady increase in loans moving to 90+ DPD. While both CAS and STACR hit lows for recovery to current – similar to lows at the beginning of 2018 – it is notable that both CAS and STACR saw multi-year highs for recovery to current in October of 2018 (see Delinquency Outcome Monitoring links below). While continued US economic strength is likely responsible for the improved performance in October, it is not exactly clear why the oscillation would move the recoveries to current back to the same lows experienced in early 2018.  

Current Performance and Credit Metrics

Delinquency Trends:

The simplest metric we track is the share of loans across all deals that is 60+ days past due (DPD). The charts below compare STACR (Freddie) vs. CAS (Fannie), with separate charts for high-LTV deals (G2 for CAS and HQA for STACR) vs. low-LTV deals (G1 for CAS and DNA for STACR).

For comparative purposes, we include a historical time series of the share of loans 60+ DPD for each LTV group. These charts are derived from the Fannie Mae and Freddie Mac loan-level performance datasets. Comparatively, today’s deal performance is much better than even the pre-2006 era.

Low LTV Deals 60 DPD

High LTV Deals 60 DPD

Delinquency Outcome Monitoring:

The tables below track the status of loans that were 60+ DPD. Each bar in the chart represents the population of loans that were 60+ DPD exactly 6 months prior to the x-axis date.  

The choppiness and high default rates in the first few observations of the data are related to the very low counts of delinquent loans as the CRT program ramped up.  

STACR 6 Month Roll

CAS 6 Month Roll

The table below repeats the 60-DPD delinquency analysis for the Freddie Mac Loan Level Performance dataset leading up to and following the housing crisis. (The Fannie Mae loan level performance set yields a nearly identical chart.) Note how many more loans in these cohorts remained delinquent (rather than curing or defaulting) relative to the more recent CRT loans.

Fannie Performance 6 Month Roll

Freddie Performance 6 Month Roll

Deal Profile Comparison:

The tables below compare the credit profiles of recently issued deals. We focus on the key drivers of credit risk, highlighting the comparatively riskier features of a deal. Each table separates the high–LTV (80%+) deals from the low–LTV deals (60%-80%). We add two additional columns for comparison purposes. The first is the ‘Coming Cohort,’ which is meant to give an indication of what upcoming deal profiles will look like. The data in this column is derived from the most recent three months of MBS issuance loan–level data, controlling for the LTV group. These are newly originated and acquired by the GSEs—considering that CRT deals are generally issued with an average loan age between 6 and 15 months, these are the loans that will most likely wind up in future CRT transactions. The second comparison cohort consists of 2006 originations in the historical performance datasets (Fannie and Freddie combined), controlling for the LTV group. We supply this comparison as context for the level of risk that was associated with one of the worst–performing cohorts. 

Credit Profile LLTV – Click to see all deals

Credit Profile HLTV – Click to see all deals

Deal Tracking Reports:

Please note that defaults are reported on a delay for both GSEs, and so while we have CPR numbers available for the most recent month, CDR numbers are not provided because they are not fully populated yet. Fannie Mae CAS default data is delayed an additional month relative to STACR. We’ve left loss and severity metrics blank for fixed-loss deals.

STACR Performance – Click to see all deals

CAS Performance – Click to see all deals

Robotic Process Automation – Warehouse Line Reporting

Robotic Process Automation (RPA) is the solution for automating mundane, business-rule based processes so that your high value business users can be deployed to more valuable work.

McKinsey defines RPA as “software that performs redundant tasks on a timed basis and ensures that they are completed quickly, efficiently, and without error.” RPA has enormous savings potential. In RiskSpan’s experience, RPA reduces staff time spent on the target-state process by an average of 95 percent. On recent projects, RiskSpan RPA clients on average saved more than 500 staff hours per year through simple automation. That calculation does not include the potential additional savings gained from the improved accuracy of source data and downstream data-driven processes, which greatly reduces the need for rework.

Managing warehouse lines of credit pose a unique set of challenges to both lending and borrowing institutions. These lines revolve based on frequent, periodic transactions. The loan-level data underlying these transactions, while similar from one transaction to the next, are sufficiently nuanced to require individual review. These reviews are painstaking and can take an inordinate amount of time.

Recently, a consumer financing provider approached RiskSpan with the challenge of tracking its requests to a warehouse lender, so that it could better manage its warehouse loan portfolio. This client had a series of manual reporting processes that it ran upon each request to the warehouse lender to inform oversight of its portfolio. It needed assistance improving the accuracy and resource burden required to produce the reports.

RiskSpan responded to the challenge by completing a rapid RPA readiness assessment and by implementing automation to solve for the data challenges it uncovered. In the readiness assessment, RiskSpan deployed a consultant to ensure that the existing reports were enough to meet the needs of the organization; that source data was enough for the desired reporting; and that data transformation processes (people and systems) were maintaining data quality from input to output.

Once these processes were analyzed and a target-state was confirmed, RiskSpan consultants quickly got to work. We automated ingestion of data for two of the existing reports, automated high-value parts of the data normalization processes and created automated quality control tests for each report.

This custom solution reduced the cycle time from one hour of staff work to 5 minutes of staff work at each warehouse lender request. This saved more than two full weeks of staff time over the course of the year and dramatically increased the scalability of this valuable process.

RiskSpan’s experience automating routine business processes reduced redundancies, eliminated errors, and saved staff time. Our solution reduced resources wasted on rework and its associated operational risk and key-person dependencies. Routine tasks were automated with customized validations. This customization effectively eliminated the need for staff intervention until certain error thresholds were breached. The client determined and set these thresholds during the design process.

RiskSpan data and analytics consultants are experienced in helping clients develop robotic process automation solutions for normalizing and aggregating data, creating routine, reliable data outputs, executing business rules, and automating quality control testing. Automating these processes addresses a wide range of business challenges and is particularly useful in routine reporting and analysis.

Talk to RiskSpan today about how custom solutions in robotic process automation can save time and money in your organization.

FHFA 1Q2019 Prepayment Monitoring Report

FHFA’s 2014 Strategic Plan for the Conservatorships of Fannie Mae and Freddie Mac includes the goal of improving the overall liquidity of Fannie Mae’s and Freddie Mac’s (the Enterprises) securities through the development of a common mortgage-backed security.

This report provides insight into how FHFA monitors the consistency of prepayment rates across cohorts of the Enterprises’ TBA-eligible MBS.

Download Report

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