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Modeling Credit and the Impact of COVID-19

Notwithstanding action taken at every level of government (including emergency measures taken by the Federal Reserve) to attempt to limit the economic fallout of the COVID-19 pandemic, markets remain highly volatileHow loans and structured credit are modeled needs to be modified (and quickly) to reflect the emerging expectations, moral hazard, and risks from the current crisis.  In the consumer finance market, existing models and data for the primary, secondary, and tertiary markets are strong, but these have a high probability of performing poorly as they’re based on historic data that doesn’t reflect the current crisis. To address this issue, RiskSpan has created a topdown framework that incorporates data from select historical events as well as a user-defined view of macro-economic forecasts. 

A Framework for Modeling Mortgage Credit in COVID-19

Compile data from past catastrophes 

The basis of our approach continues to be data-driven as historic events can serve as data points to inform analysis for the current crisis. Relevant catastrophes to look upon include: 

  • Natural disasters, including Hurricane Katrina and the impact on regional economies 
  • The Great Recession and its impact on certain borrowers 
  • The federal government’s response to the Great Recession 
  • The Great Depression  
  • Federal Reserve Board stress tests  

These events can inform some part of the modeling framework for key performance drivers, including: 1) unemployment and short-term delinquencies, 2) government relief programs, 3) default and foreclosure, and 4) home price changes and losses to investors. 

Unemployment and Mortgage Delinquencies  

The impact of unemployment on mortgage delinquencies will be severe. As the graph below showsthe relationship between unemployment and delinquencies is highly correlatednearly 1:1 relationship.  

We further expect that unemployment and subsequent delinquencies will correlate to regional, state, or business sector unemployment. Certain industries are more susceptible to COVID-19 related disruption stemming from the decline of consumer demand, state and federal orders, and international government actions that affect tourism. Further, statelevel executive orders and COVID-19 responses have been inconsistentsome state orders are more severe than others. This will lead to a corresponding impact at the state versus federal level and highlightthe importance of taking geo-specific macroeconomic factors into account 

Economic forecasts of unemployment related to the current crisis vary widely so we look to past levels to inform possible boundaries. During the Great Recessionnearly 9 million people lost their jobs within one year leading to an unemployment rate of 10%, according to the BLS. In contrastnew unemployment claims spiked on March 26, 2020, to 3.28 million according to the Labor Department, and then to an astounding 6.6 million today. These figures far exceed the previous high of 665,000 claims during the Great Recession. The Great Depression can also serve as a benchmark when unemployment peaked at 24.9% in 1933. This can be particularly relevant for certain geographies or industries. The Federal Reserve 2020 Severely Adverse Scenario, with unemployment peaking at 10% in 2021, suddenly looks more akin to a base or optimistic scenario.  

Government Relief and Delinquencies  

The national scope of COVID-19 is forcing governments and mortgage guarantors into nationwide mortgage forbearance, foreclosure moratoriums, and government relief programs. Regardless of type or reason, mortgage non-payment will result in a peak in delinquencies which may remain elevated through a forbearance period. 

We can look to natural disasters, like New Orleans and Puerto Rico hurricanes and the Houston floods, to find dramatic and immediate spikes in delinquencies. However, these natural disasters did not result in a corresponding spike in serious mortgages delinquencies or defaults. In these examples, forbearance and moratorium programs provided relief to borrowers until insurance companies paid claims

Seriously Delinquent and Default  

As the table below shows, the Great Recession produced multiyear elevated delinquency and default rates with delinquencies spiking at near 10% in 2010. Peak default rates for some private investor programs exceeded 40%. 

However, mortgage defaults from the Great Recession included the impact of aggressively expanded underwriting with rampant and unfettered fraud in the form of subprime and NINA (No Income No Assets) mortgage programs. The historical trend of RiskSpan’s Vintage Quality Index reflects the degree to which underwriting guidelines have generally tightened and steadied over the past decade.  

Efforts to reduce default rates during the 2008 financial crisis were further hampered by initially slow government responses and uncoordinated efforts between investors and federal and state agencies. In the current crisis, we can expect government responses to COVID-19 to be immediate, aggressive, and coordinated. The U.S. federal government has already enacted relief legislation, recognizing that forbearance, loan modifications, and moratoriums are all proven tools to reduce mortgage delinquencies and severities. Unlike in 2008, however, the mortgages impacted by the current crisis are primarily federally insured and likely skewed towards lowincome and lowFICO borrowersBecause federally insured mortgages tend to find their way into Ginnie Mae MBS, emerging issues relating to advances on those securities may require new and unproven programs. 

  

Home Prices

The residential real estate market was strong prior to the pandemic. Home sales in February rose 6.5% to 5.77 million, according to the National Association of Realtors, and median home prices rose 8.0% year-over-year. As COVID-19 spreads, new sales activity is already coming to a halt, yet the impact on HPA is uncertain. A review of natural disasters, such as the Houston and Louisiana hurricanes, shows little negative longterm impact on HPA after the events. By comparison, the Great Recession produced nationwide declines that did not begin to rebound until 2012 (see below) 

Supporting the argument of a short-term impact on HPA are the recent strength of the U.S. economy and continued discipline in credit lending standards. Further, there is also strong generational demand for housing during a nationwide housing shortageArguments for a less optimistic view are based on the potential for a longer-thanexpected national economic shutdown and structural impacts to the economy, employment, and industries even after the pandemic ends.  

 

Summary 

The aphorism “things work until they don’t” is commonly used to explain financial markets and behaviorsThe COVID-19 crisis is simply the latest manifestation of this realityRisk managers and finance executives have to decide whether to rely on current models built on historical events and data – the models that got you here – or to start rethinking and retesting hypotheses and assumptions to manage and quantify new risks.   Email us at info@riskspan.com to talk about how RiskSpan can help and click here to see RS Edge in action.


RiskSpan VQI: Current Underwriting Standards – February 2020

riskspan-VQI-report

The RiskSpan Vintage Quality Index (“VQI”) edged higher for mortgages originated during February despite remaining low (90.41) by historical, pre-crisis standards. Low-FICO and high-LTV loans continued to trend downward, while high-DTI loans, investment properties, and cash-out refinances continued to rebound after declining through much of 2019.

As the historical trend of risk layering (see below) shows, mortgages with one borrower—now accounting for more than 50 percent of originations—remain a consistent and important driver of the index. High-DTI loans today drive the index more than they did during the years immediately after the 2008 crisis but not nearly so much as they did during the years leading up to it. High-LTV loans continue to be originated in abundance, while adjustable-rate mortgages and loans with subordinate financing, in contrast, have practically vanished.

riskspan-VQI-report

RiskSpan introduced the VQI in 2015 as a way of quantifying the underwriting environment of a particular vintage of mortgage originations. The idea is to provide credit modelers a way of controlling for a particular vintage’s underwriting standards, which tend to shift over time. The VQI is a function of the average number of risk layers associated with a loan originated during a given month. It is computed using:

  1. The loan-level historical data released by the GSEs in support of Credit Risk Transfer initiatives (CRT data) for months prior to December 2005, and
  2. Loan-level disclosure data supporting MBS issuances through today.

The value is then normalized to assign January 1, 2003 an index value of 100. The peak of the index, a value of 139 in December 2007, indicates that loans issued in that month had an average risk layer factor 39% greater (i.e., loans issued that month were 39% riskier) than loans originated during 2003. In other words, lower VQI values indicate tighter underwriting standards (and vice-versa).

Build-Up of VQI

The following chart illustrates how each of the following risk layers contributes to the overall VQI:

  • Loans with low credit scores (FICO scores below 660)
  • Loans with high loan-to-value ratios (over 80 percent)
  • Loans with subordinate liens
  • Loans with only one borrower
  • Cash-out refinance loans
  • Loans secured by multi-unit properties
  • Loans secured by investment properties
  • Loans with high debt-to-income ratios (over 45%)
  • Loans underwritten based on reduced documentation
  • Adjustable rate loans
riskspan-VQI-report
riskspan-VQI-report
riskspan-VQI-report

Industry Leaders Ted Janulis and Ron Weibye Join RiskSpan Advisory Board

ARLINGTON, VA, February 21, 2020 – Ted Janulis and Ron Weibye have joined the Advisory Board of RiskSpan, a leading provider of fixed-income data and analytics. They bring a wealth of leadership experience in structured products, mortgages, and credit in the financial services industry.Read More


FHFA Prepayment Monitoring Reports are Powered by RS Edge

[vc_row][vc_column][vc_column_text]To help enforce prepayment alignment across Fannie’s and Freddie’s Uniform MBS, the Federal Housing Finance Agency publishes a quarterly monitoring report comparing the prepayment speeds of UMBS issued by the two Agencies. This report helps ensure that prepayment performance remains consistent—so that market expectations of a Fannie-issued UMBS are fundamentally indistinguishable from those of a Freddie-issued UMBS and the two Agencies UMBS are both deliverable into passthrough “TBA” trades.

Last week, the FHFA released the most recent version of this report containing performance data from the fourth quarter of 2019. The charts in the FHFA’s publication, which are generated using RiskSpan’s Edge Platform, compare Fannie and Freddie UMBS prepayment rates (1-month and 3-month CPRs) across a variety of coupons and vintages.

RiskSpan's RS Edge Graphs on FHFA Report

Relying on RiskSpan’s Edge Platform for this sort of analysis is fitting in that it is precisely the type of comparative analysis for which Edge was developed.

Edge allows traders, portfolio managers, and analysts to compare performance across a virtually unlimited number of loan subgroups. Users can cohort on multiple loan characteristics, including servicer, vintage, loan size, geography, LTV, FICO, channel, or any other borrower characteristic.

Edge’s easy-to-navigate user interface makes it accessible to traders and PMs who want to be able to set up queries and tweak constraints on the fly without having to write SQL code. Edge also offers an API for users that want programmatic access to the data, useful for generating customized reporting and systematic analysis of loan sectors.

Comparing Fannie’s and Freddie’s prepay speeds only scratches the surface of Edge’s analytical capabilities. Schedule a demo to see what this tool can really do.[/vc_column_text][/vc_column][/vc_row]


RiskSpan Named Category Leader in Two Chartis RiskTech Quadrants, Best of Breed in a Third

Chartis Research has classified RiskSpan among “Category leaders” in two RiskTech Quadrants® and “Best of breed” in a third Quadrant in two research reports released at the end of 2019. 

RiskSpan figures into two independent reports released by Chartis in December of last year: Fixed-Income Technology Solutions, 2019: Market and Vendor Landscape and Technology Solutions for Credit Risk 2.0: Vendor Landscape, 2019. These reports summarize Chartis’s research on leading technology offerings in these two areas (fixed-income securities and credit risk) and assigns vendors into one of four sections of its RiskTech Quadrant® based on the combined strength of the completeness of each vendor’s offering and each offering’s market potential. Offerings that rate highly in both completeness and market potential are classified as “Category leaders” while offerings with strong market potential that cover a portion of the fixed-income landscape are classified as “Best in breed.” 

The classification of RiskSpan’s Edge platform as “Best in breed” reflects its high degree of specialization in the ABS and MBS components of the U.S. fixed-income market. (This market also includes government, corporate, and municipal bond. RS Edge has some coverage of these instruments, but itcore competency is built around ABS in general and mortgages in particular.)  

Chartis’s 2019 edition of Fixed Income Technology Solutions differed from past years’ reports in that it included a separate RiskTech Quadrant® for fixed-income securitization technology solutions. RiskSpan’s classification among “Category leaders” in this sub-category affirms Edge’s preeminence in the MBS realmChartis’s addition of this classification spotlights the growth potential of the securitization market as well as the potential for change. 

 RiskTech Quadrant for fixed-income securitization technology solutions, 2019 

fixed-income-risk-quadrant

The RiskTech Quadrant® evaluates risk vendors on the depth of functionality of their product offerings, including their sophistication, innovativeness, practical relevance of features, and user-friendliness, among other factors 

Chartis also considered each solution’s breadth of functionality. RiskSpan’s Edge Platform differentiated itself from the other “Category leaders” in fixed-income securitization technology solutions by being the only solution rated “Advanced” across the entire spectrum (nine categories in all) of data analytical capabilities.   

 Vendor capabilities for fixed-income securitization technology solutions, 2019 

risktech-top-marks-in-all-categories

This classification reflects RiskSpan’s long-standing and continued commitment to data at the heart of its technologyFor well over a decade, RiskSpan’s articulated vision has been (and remains) to bring efficiencies to the lending and structured product markets. This vision necessitates accurate and reliable data. To this end, RiskSpan continues to develop technologies that leverage machine learning to improve data accuracy and facilitate data managementRS Edge employs a host of techniques, which go far beyond the rules-based methods of the past, for cleaning, validating and normalizing data from all manner of sources 

RiskSpan’s commitment to data and analytics was further reflected in the firm’s classification as a Category leader in Chartis’s RiskTech Quadrant® for credit risk solutions (banking book) at the end of 2019. 

RiskTech Quadrant for credit risk solutions (banking book), 2019 

  Credit-risk-solutions-quadrant

According to the Chartis report, ranking highly in “Completeness of Offering” reflects RS Edge’s competence the areas of analytics, credit portfolio management, data management, risk data aggregation and allocation, enterprise stress testing and scenario management, and reporting and visualization. Consistent with RiskSpan’s defined emphasis, Edge received its highest marks in analytics and data management. 

Ranking highly in “Market Potential” reflects Chartis’s favorable assessment of RiskSpan’s customer satisfaction, market penetration, growth strategy, financials, and business model. 

While we are always seeking to improve, we are pleased with Chartis’s assessment of our capabilities and feel it is indicative of our commitment to continually enhancing these data and analytical offerings, thereby ensuring that RiskSpan continues to deliver uncompromising quality and value to our clients. This, of course, remains our first priority. 

About RiskSpan 

RiskSpan offers powerful pre-trade analytics, modeling, and risk across both loans and structured products on one cloud-native, scalable platform: RS Edge. Traders, portfolio managers, and risk managers can easily use complex models to analyze whole loans, mortgage-backed securities, asset-backed securities, Agency MBS and credit risk transfer securities.  


Bill Moretti, Industry Leader in Structured Finance and Fintech Joins RiskSpan to Lead Innovation Lab

ARLINGTON, VA, January 10, 2020 

Bill Moretti, an industry leader at the intersection of structured finance, financial technology, and portfolio management, has joined RiskSpan as a Senior Managing Director and head of its SmartLink innovation lab.



Over the course of his two-decade tenure as a senior investment executive with MetLife, Bill became recognized as an innovative and energetic leader, strategic thinker, change agent, and savvy risk manager. As MetLife’s head of Global Structured Finance, Bill created proprietary analytical systems, which he paired with traditional fundamental credit analysis to maximize portfolio income and returns through market rallies while preserving investment capital during crises.

“Bill is exactly who we were looking for, and we are delighted to have his unique blend of expertise,” said RiskSpan CEO Bernadette Kogler. “Bill’s track record as a successful implementer of disruptive solutions in capital markets—an industry with a history of stubbornness when it comes to technology innovation—makes him a perfect complement to RiskSpan’s talent portfolio.”

Bill co-chairs the Structured Finance Association’s Technology Innovation Committee and is a past chairman and current member of the American Council of Life Insurers’ Advisory Committee.

On January 30th, Bill will join industry veterans Bernadette Kogler and Suhrud Dagli for a free webinar discussing the need for better data and analytics in in a changing fixed-income market. Register now for 2020: Entering The Decade in Data & Smart Analytics.

About RiskSpan

RiskSpan simplifies the management of complex data and models in the capital markets, commercial banking, and insurance industries. We transform seemingly unmanageable loan data and securities data into productive business analytics.

Media Contact

Timothy Willis
Email: info@riskspan.com
Phone: (703) 956-5200


RiskSpan Joins AICPA for CECL Task Force Auditing Subgroup Meeting

RiskSpan joined a dozen other vendors and auditors from the top-ten accounting firms for the AICPA’s CECL Task Force Auditing Subgroup meeting at Ernst & Young’s offices in New York on April 29th. The AICPA just released the “Key takeaways” from the meeting.

Among those key takeaways are:

  • Overarching Themes:
    • CECL is a “fresh start” from the incurred loss model.
      • CECL model estimates will be evaluated against ASC 326, not anchored to incurred loss model estimates.
      • Management may find it useful in validating their CECL model to understand what drove changes from ALLL levels today. However, management should be aware of potential anchoring, confirmation, availability biases that might occur when implementing the new standard.
  • Qualitative Adjustment Factors:
    • Conceptually, qualitative adjustments compensate for known limitations of the model. A less sophisticated model will likely require more qualitative adjustments and those adjustments may be greater in magnitude. Conversely, a more sophisticated model will likely require fewer qualitative adjustments and those adjustments may be less in magnitude
    • Due to fundamental changes in the model, nature and magnitude of the qualitative adjustments in the CECL model should be independently generated and not anchored to, or grounded in, the qualitative adjustments used in the current incurred loss model.
    • Management should not pre-determine the magnitude of the adjustment and then produce documentation to support it – the amount should be determined by a rigorous, repeatable, well documented process with appropriate internal controls around that process.
    • Adjustments to historical information and forecasts could be negative, positive, or no change. Regardless, it is important for management to understand, document, and support their rationale in all three scenarios.
  • Forecasting/Reversion
    • Forecasting
      • Reasonable and supportable forecasts should be objectively supported, analyzed and appropriately updated in a timely manner.
        • Adjustments should be determined through a concrete sequential thought process (rather than calculated and backed into).
        • Transition from reasonable and supportable forecasts to reversion techniques should be specific to the circumstances (i.e. reversion period and method may change, depending on economic conditions).
      • Should be developed by parties with relevant expertise
      • Should have internal controls in place over the selection of forecasted data and the source
      • Forecasted economic data utilized should be relevant to the portfolio (i.e. data specific to lending market may be more relevant than general, country-wide data).
      • Multiple scenarios
        • No requirement to consider multiple scenarios but may be helpful
        • Need robust support for the weighting used, which may be challenging
  • Data
    • Data used in models should be subject to controls that are designed to ensure completeness, accuracy and relevance to the portfolio (i.e., similar economic conditions, loan structure and underwriting). Data will also need to be available to external auditors for substantive testing.
    • Data should be evaluated for consistency – is the data consistent period over period (i.e., definition of default)?
    • Data aggregated by vendors may not have previously been subject to traceable, internal controls. Vendors, management, auditors and other interested parties must consider how to address such industry limitations prior to standard implementation.
    • If management is not able to validate the data (relevance, reliability and consistency), that data may be difficult to use in the financial reporting process.

RiskSpan joined the AICPA’s CECL Task Force Auditing Subgroup for a second meeting on June 27th. We will publish the “Key Takeaways” from that meeting when they are released.

Institutions are invited to reach out to us with any questions.


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 JozwikRiskSpan’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 industryTdirectly gain from her expertise, check out her publications in the Journal of Structured Finance: Building a Credit Model Using GSE Loan-Level DataCredit 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! 


CRT Deal Monitor: April 2019 Update

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. 

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

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