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Introducing: RS Edge for Loans and Structured Products

RiskSpan Introduces RS Edge for Loans and Structured Products

RiskSpan, the leading mortgage data and analytics provider, is excited to announce the release of RS Edge for Loans and Structured Products.

RS Edge is the next generation of RiskSpan’s data, modeling, and analytics platform that manages portfolio risk and delivers powerful analysis for loans and structured products.  Users can derive insights from historical trends and powerful predictive forecasts under a range of economic scenarios on our cloud-native solution. RS Edge streamlines analysis by bringing together key industry data and integrations with leading 3rd party vendors.

An on-demand team of data scientists, quants, and technologists with fixed-income portfolio expertise support the integration, calibration, and operation across all RS Edge modules.

RMBS Analytics in Action

RiskSpan has developed a holistic approach to RMBS analysis that combines loan collateral, historical, and scenario analysis with deal comparison tools to more accurately predict future performance. Asset managers can define an acceptable level of risk and ground pricing decisions with data-driven analysis. This approach illuminates risk from shifting collateral and provides investors with confidence in their positions.

Loan Analytics in Action

Whole loan asset managers and investors use RiskSpan’s Loan Analytics to enhance and automate partnerships with Non-Qualified Mortgage originators and servicers. The product enhances the on-boarding, pricing analytics, forecasting, and storage of loan data for historical trend analytics. RS Edge forecasting analytics support rate–sheet validation and loan pricing.

About RiskSpan

RiskSpan provides innovative technology and services to the financial services industry. Our mission is to eliminate inefficiencies in loans and structured finance markets to improve investors’ bottom line through incremental cost savings, improved return on investment, and mitigated risk.

RiskSpan is holding a webinar on November 6 to show how RS Edge pulls together past, present, and future for insights into new RMBS deals. Click below to register.

EDGE: Revisiting WALA-ramps on FNMA Majors

In the past few months, recent-vintage FNMA Major pools have shown significant acceleration in prepay speeds, significantly impacting TBA prices and dollar rolls. In our August report, we showed a progression of ever faster WALA ramps on FNMA Major pools¹. In this installment, we update that behavior using data from Edge, the online prepayment graphing tool.

We start with a population of recent FNMA Majors and generate WALA ramps at loan level, to capture the precise behavior of the WALA ramp. In the first chart, we show loans from Majors that are 75-125bp in the money, approximately TBA 4s, over three different time periods:

August 2018 to July 2019 (“baseline”)
August-September 2019
October 2019

In October, aggregate speeds on Majors hit a new high of 60 CPR for loans in the 9-10 WALA range. More troubling: the tail of the WALA ramp moved higher by roughly 5 CPR. This acceleration impacts carry in the 12mo+ seasoning range and is a potential negative for valuations in the TBA sector.

Graph: Speeds on loans from FN Major pools, holding refi incentive 75-125bp over three different periods.

In the next graph, we use Edge to isolate loans in Major pools that are 25-75bp in the money (approximately 3.5s). Similar to 4s, the progression in the aging curve shows the same story: a faster tail for loans 10+ months seasoned.

Graph: Speeds on loans from FN Major pools, holding refinancing incentive 25-75bp over three different periods.

We next look at the change in prepayment speeds from the Aug-Sep period to October and attribute that change to the origination channel. On average, FNMA Major pools are 50:50 Retail origination versus TPO, and we break down the speed contribution into these two groups. In the analysis below, we look at the speed change in each WALA bucket.

For Major 3.5s, the TPO loans accelerated more than the Retail origination loans. But in Major 4.0s, the speeds increased almost equally across each bucket.

In summary, the WALA ramp for TPO is more sensitive than Retail loans when refinancing incentive is small. But when loans are far enough in the money the increase in the WALA ramps are evenly distributed across origination channel.

We continue to monitor the ever-accelerating speeds on FNMA Majors and Freddie Giants, but the trend is clear – the fastest, cheapest to deliver TBA continues to be faster for longer. This makes the ongoing analysis of prepays, whether specified pools or non-spec deliverables, more important that it has been in previous rate cycles.

If you interested in seeing variations on this theme, contact us. Using Edge, we can examine any loan characteristic and generate a S-curve, WALA ramp, or time series.

¹See RiskSpan for a similar analysis on newer WALA multi-lender Giants

Navigating the Impact of ASU 2016-13 on AFS Securities

In Collaboration With Our Partners at Grant Thornton

Navigating the impact of ASU 2016-13 on the impairment of AFS debt securities

When the Financial Accounting Standards Board (FASB) issued Accounting Standards Update (ASU) 2016-13, Financial Instruments – Credit Losses, in June of 2016, most of the headlines regarding the ASU focused on its introduction of Subtopic 326-20, commonly referred to as the Current Expected Credit Losses (or, “CECL”) framework. The CECL framework requires entities to measure lifetime expected credit losses on all financial instruments measured at amortized cost – financial assets like loans receivable and held-to-maturity debt securities. The focus on the CECL framework was understandable – it represents a sea change in the accounting for a significant class of assets for many entities, particularly lending institutions.

However, ASU 2016-13 affected the accounting for credit losses on other financial instruments as well, such as debt securities held as available-for-sale (or “AFS”). Below, we will discuss how ASU 2016-13 changed the accounting for credit losses on AFS debt securities.

AFS Framework prior to adopting ASU 2016-13: OTTI

Prior to an entity’s adoption of ASU 2016-13, the guidance concerning impairment of AFS debt securities is found in Subtopic 320-10, particularly in paragraphs 320-10-35-18 through 35-34, and is known as the Other-Than-Temporary Impairment (or “OTTI”) framework.

Generally, AFS debt securities are carried on the balance sheet at fair value, and changes in the fair value of AFS debt securities are recognized outside of earnings as a component of Other Comprehensive Income (OCI). However, if an AFS debt security’s fair value is less than its amortized cost – that is, the AFS debt security is impaired – the entity must evaluate whether the impairment is an OTTI.

An entity should recognize an OTTI on an impaired security when one of three conditions exists:

The entity intends to sell the security
It is more likely than not the entity will be required to sell the security prior to recovery of the amortized cost basis of the security
The entity does not expect to recover the amortized cost basis of the security

If condition (1) or (2) exists, then the entity will reduce the amortized cost basis of the AFS debt security to its current fair value. Any subsequent increases in the fair value of the AFS debt security would be recognized outside of earnings as a component of OCI until the gains are realized via cash collection or sale.

If neither condition (1) nor (2) exists, then the entity must evaluate whether it does not expect to recover the amortized cost basis of the security. The entity may perform a qualitative analysis, considering factors such as the magnitude of the impairment, the duration of the impairment, factors relevant to the issuer of the security, factors relevant to the industry in which the issuer of the security operates, and any other relevant information. Alternatively, an entity may perform a quantitative analysis by comparing the net present value (NPV) of expected cash flows of the AFS debt security to its amortized cost basis, as described below.

If the entity does not expect to recover the amortized cost basis of the security, an OTTI exists and the security should be written down to its fair value. The entity must then separate the total impairment (the amount by which the AFS debt security’s amortized cost exceeds its fair value) between the amount of impairment related to (a) credit losses and (b) all other factors. To make this distinction, the entity compares the NPV of the expected future cash flows on the debt security, discounted at the security’s effective interest rate (or “EIR”), to the amortized cost basis of the security. The amount by which the amortized cost of the AFS debt security exceeds its NPV is recognized in earnings as a credit loss, while any remaining impairment is recognized outside of earnings as a component of OCI.

AFS Framework upon adopting ASU 2016-13

ASU 2016-13 largely keeps the OTTI framework from Subtopic 320-10 intact. If either (1) an entity intends to sell, or (2) it is more likely than not that it will be required to sell an AFS debt security whose amortized cost exceeds its fair value, the entity shall write that AFS debt security’s amortized cost basis down to its fair value through earnings. For AFS debt securities that are impaired, but for which neither (1) the entity intends to sell, nor (2) it is more likely than not that it will be required to sell an AFS debt security whose amortized cost exceeds its fair value, the entity will still need to assess whether it expects to recover the amortized cost basis of the impaired AFS debt security either via a qualitative analysis or via the same quantitative framework in Subtopic 320-10 today (as described above).

However, ASU 2016-13 makes a few important changes. The most significant changes include:

Entities may no longer consider the duration of an impairment when qualitatively assessing whether the entity does not expect to recover the amortized cost basis of an impaired AFS debt security.
If an entity recognizes a credit loss on an AFS debt security, the entity will establish an allowance for credit loss (or “ACL”) rather than perform a direct write-down of the amortized cost basis of the AFS debt security. Accordingly, subsequent reductions in the estimated ACL will be recognized in earnings as they occur.
The amount of credit losses to be recognized is limited by a “fair value floor” – that is, total credit losses cannot exceed the total amount by which the amortized cost of the AFS debt security exceeds its fair value.

The following flow chart illustrates the how an entity would evaluate an AFS debt security for impairment upon adoption of ASU 2016-13:

Example

Background

Entity A has an investment in an AFS debt security issued by Company X with an amortized cost of $100
At 12/31/X1, the fair value of the AFS debt security is $90
The of the AFS debt security is 10% (as determined in accordance with ASC 310-20)

Entity A does not intend to sell the AFS debt security, nor is it more likely than not that Entity A will be required to sell the AFS debt security prior to recovery of the amortized cost basis. Entity A elects to perform a qualitative analysis to determine whether the AFS debt security has experienced a credit loss. In performing that qualitative assessment, Entity A consider the following:

Extent of impairment: 10%
Adverse conditions: Company X is in an industry that is in decline
Company X’s credit rating was recently downgraded

Accordingly, Entity A determines that a credit loss has occurred. Next, Entity A makes its best estimate of expected future cash flows, and discounts those cash flows to their NPV at the AFS debt security’s EIR of 10% as follows:

In this case, the NPV is $85, which would indicate a $15 ACL. However, the fair value of the AFS debt security is $90, so the ACL is limited to $10 due to the “fair value floor”. Accordingly, Entity A would recognize a credit loss expense of $10 and create an ACL, also for $10.

In subsequent periods, Entity A would continue to determine the NPV of future expected cash flows and adjust the ACL up or down as those changes occur, subject to the fair value floor.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space][startapp_block_title animation=”” title=”About the Author”][/vc_column][/vc_row][vc_row][vc_column width=”1/6″][vc_single_image image=”2439″][/vc_column][vc_column width=”5/6″][vc_column_text]Graham Dyer, CPA Grant Thornton

Graham is a partner in Grant Thornton, LLP’s national office where he provides technical accounting guidance to clients across the globe. Graham has a particular focus on financial institutions, including matters such as the ALLL, consolidations, Purchased Credit Impaired loan income recognition, complex financial instruments, business combinations, and SOX/FDICIA matters.

Graham also serves on a number of industry technical committees, including the IASB’s IFRS 9 Impairment Transition Group and the FASB’s CECL Transition Resource Group. Graham was previously a professional accounting fellow at the OCC.

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”.

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.

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.

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.

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

RiskSpan Edge & CRT Data

For participants in the credit risk transfer (CRT) market, managing the massive quantity of data to produce clear insights into deal performance can be difficult and demanding on legacy systems. Complete analysis of the deals involves bringing together historical data, predictive models, and deal cash flow logic, often leading to a complex workflow in multiple systems. RiskSpan’s Edge platform (RS Edge) solves these challenges, bringing together all aspects of CRT analysis. RiskSpan is the only vendor to bring together everything a CRT analyst needs:

Normalized, clean, enhanced data across programs (STACR/CAS/ACIS/CIRT),
Historical Fannie/Freddie performance data normalized to a single standard,
Ability to load loan-level files related to private risk transfer deals,
An Agency-specific, loan-level, credit model,
Seamless Intex integration for deal and portfolio analysis,
Scalable scenario analysis at the deal or portfolio level, and
Vendor and client model integration capabilities.
Ability to load loan-level files related to private risk transfer deals.

All of these features are built into RS Edge, a cloud-native, data and analytics platform for loans and securities. The RS Edge user interface is accessible via any web browser, and the processing engine is accessible via an application programming interface (API). Accessing RS Edge via the API allows access to the full functionality of the platform, with direct integration into existing workflows in legacy systems such as Excel, Python, and R. To tailor RS Edge to the specific needs of a CRT investor, RiskSpan is rolling out a series of Excel tools, built using our APIs, which allow for powerful loan-level analysis from the tool everyone knows and loves. Accessing RS Edge via our new Excel templates, users can:

Track deal performance,
Compare deal profiles,
Research historical performance of the full GSE population,
Project deal and portfolio performance with our Agency-specific credit model or with user-defined CPR/CDR/severity vectors, and
Analyze various macro scenarios across deals or a full portfolio

The web-based user interface allows for on-demand analytics, giving users specific insights on deals as the needs arise. The Excel template built with our API allows for a targeted view tailored to the specific needs of a CRT investor.

For teams that prefer to focus their time on outcomes rather than the build, RiskSpan’s data team can build custom templates around specific customer processes. RiskSpan offers support from premiere data scientists who work with clients to understand their unique concerns and objectives to integrate our analytics with their legacy system of choice. The images are examples of a RiskSpan template for CRT deal comparison: profile comparison, loan credit score distribution, and delinquency performance for five Agency credit risk transfer deals, pulled via the RiskSpan Data API and rendered in Excel. ______________________________________________________________________________________________

Fannie Mae’s New CAS REMIC: Why REITs Are Suddenly Interested in CRT Deals

Fannie Mae has been issuing credit-risk-transfer (CRT) deals under its Connecticut Avenue Securities (CAS) program since 2013. The investor base for these securities has traditionally been a diverse group of asset managers, hedge funds, private equity firms, and insurance companies. The deals had been largely ignored by Real Estate Investment Trusts (REITs), however. The following pie charts illustrate the investor breakdown of Fannie Mae’s CAS 2018-C06 deal, issued in October 2018. Note that REITs accounted for only 11 percent of the investor base of the Group 1 and Group 2 M-2 tranches (see note below for information on how credit risk is distributed across tranches), and just 4 percent of the Group 1 B-1 tranche. Things began to change in November 2018, however, when Fannie Mae began to structure CAS offering as notes issued by trusts that qualify as Real Estate Mortgage Investment Conduits (REMICs). The first such REMIC offering, CAS 2018-R07, brought about a substantial shift in the investor distribution, with REITs now accounting for a significantly higher share. As the pie charts below illustrate, REITs now account for some 22 percent of the M-2 tranche investor base and nearly 20 percent of the B-1 tranche.

What Could Be Driving This Trend?
It seems reasonable to assume that REITs are flocking to more favorable tax treatment of REMIC-based structures. These will now be more simplified and aligned with other mortgage-related securities, as Fannie Mae points out. Additionally, the new CAS REMIC notes meet all the REIT income and asset tests for tax purposes, and there is a removal on tax withholding restrictions for non-U.S. investors in all tranches. The REMIC structure offers additional benefits to REITs and other investors. Unlike previous CAS issues, the CAS REMIC—a bankruptcy-remote trust—issues the securities and receives the cash proceeds from investors. Fannie Mae pays monthly payments to the trust in exchange for credit protection, and the trust is responsible for paying interest to the investors and repaying principal less any credit losses. Since it is this new third-party trustee issuing the CAS REMIC securities, investors will be shielded from exposure to any future counterparty risk with Fannie Mae. The introduction of the REMIC structure represents an exciting development for the CAS program and for CRT securities overall. It makes them more attractive to REITs and offers these and other traditional mortgage investors a new avenue into credit risk previously available only in the private-label market.

End Note: How Are CAS Notes Structured?
Notes issued prior to 2016 as part of the CAS program are aligned to a structure of six classes of reference tranches, as illustrated below:

Two mezzanine tranches of debt are offered for sale to investors. The structure also consists of 4 hypothetical reference tranches, retained by Fannie Mae and used for allocation of cash flows. When credit events occur, write-downs are first applied to the Fannie Mae retained first loss position. Only after the entire first loss position is written down are losses passed on to investors in mezzanine tranche debt – first M2, then M1. Loan prepayment is allocated along an opposite trajectory. As loans prepay, principal is first returned to the investors in M1 notes. Only after the full principal balance of M1 notes have been repaid do M2 note holders receive principal payments. Beginning with the February 2016 CAS issuance (2016-C01), notes follow a new structure of seven classes of reference tranches, as illustrated below:

In addition to the two mezzanine tranches, a portion of the bottom layer is also sold to investors. This allows Fannie Mae to transfer a portion of the initial expected loss. When credit events occur, both Fannie Mae and investors incur losses. Additionally, beginning with this issuance, the size of the B tranche was increased to 100 bps, effectively increasing the credit support offered to mezzanine tranches. Beginning with the January 2017 CAS issuance (2017-C01), notes follow a structure of eight classes of reference tranches, as illustrated below:

Fannie Mae split the B tranche horizontally into two equal tranches, with Fannie Mae retaining the first loss position. The size of the B1 tranche is 50 bps, and Fannie Mae retains a vertical slice of the B1 tranche.

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