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Articles Tagged with: Agency MBS

Bumpy Road Ahead for GNMA MBS?

In a recent webinar, RiskSpan’s Fowad Sheikh engaged in a robust discussion with two of his fellow industry experts, Mahesh Swaminathan of Hilltop Securities and Mike Ortiz of DoubleLine Group, to address the likely road ahead for Ginnie Mae securities performance.


The panel sought to address the following questions:

  • How will the forthcoming, more stringent originator/servicer financial eligibility requirements affect origination volumes, buyouts, and performance?
  • Who will fill the vacuum left by Wells Fargo’s exiting the market?
  • What role will falling prices play in delinquency and buyout rates?
  • What will be the impact of potential Fed MBS sales.

This post summarizes some the group’s key conclusions. A recording of the webinar in its entirety is available here.

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Wells Fargo’s Departure

To understand the the likely impact of Wells Fargo’s exit, it is first instructive to understand the declining market share of banks overall in the Ginnie Mae universe. As the following chart illustrates, banks as a whole account for just 11 percent of Ginnie Mae originations, down from 39 percent as recently as 2015.

Drilling down further, the chart below plots Wells Fargo’s Ginnie Mae share (the green line) relative to the rest of the market. As the chart shows, Wells Fargo accounts for just 3 percent of Ginnie Mae originations today, compared to 15 percent in 2015. This trend of Wells Fargo’s declining market share extends all the way back to 2010, when it accounted for some 30 percent of Ginnie originations.

As the second chart below indicates, Wells Fargo’s market share, even among banks has also been on a steady decline.

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Three percent of the overall market is meaningful but not likely to be a game changer either in terms of origination trends or impact on spreads. Wells Fargo, however, continues to have an outsize influence in the spec pool market. The panel hypothesized that Wells’s departure from this market could open the door to other entities claiming that market share. This could potentially affect prepayment speeds – especially if Wells is replaced by non-bank servicers, which the panel felt was likely given the current non-bank dominance of the top 20 (see below) – since Wells prepays have traditionally been slightly better than the broader market.

The panel raised the question of whether the continuing bank retreat from Ginnie Mae originations would adversely affect loan quality. As basis for this concern, they cited the generally lower FICO scores and higher LTVs that characterize non-bank-originated Ginnie Mae mortgages (see below). 

These data notwithstanding, the panel asserted that any changes to credit quality would be restricted to the margins. Non-bank servicers originate a higher percentage of lower-credit-quality loans (relative to banks) not because non-banks are actively seeking those borrowers out and eschewing higher-credit-quality borrowers. Rather, banks tend to restrict themselves to borrowers with higher credit profiles. Non-banks will be more than happy to lend to these borrowers as banks continue to exit the market.

Effect of New Eligibility Requirements

The new capital requirements, which take effect a year from now, are likely to be less punitive than they appear at first glance. With the exception of certain monoline entities – say, those with almost all of their assets concentrated in MSRs – the overwhelming majority of Ginnie Mae issuers (banks and non-banks alike) are going to be able meet them with little if any difficulty.

Ginnie Mae has stated that, even if the new requirements went into effect tomorrow, 95 percent of its non-bank issuers would qualify. Consequently, the one-year compliance period should open the door for a fairly smooth transition.

To the extent Ginnie Mae issuers are unable to meet the requirements, a consolidation of non-bank entities is likely in the offing. Given that these institutions will likely be significant MSR investors, the potential increase in MSR sales could impact MSR multiples and potentially disrupt the MSR market, at least marginally.

Potential Impacts of Negative HPA

Ginnie Mae borrowers tend to be more highly leveraged than conventional borrowers. FHA borrowers can start with LTVs as high as 97.5 percent. VA borrowers, once the VA guarantee fee is rolled in, often have LTVs in excess of 100 percent. Similar characteristics apply to USDA loans. Consequently, borrowers who originated in the past two years are more likely to default as they watch their properties go underwater. This is potentially good news for investors in discount coupons (i.e., investors who benefit from faster prepay speeds) because these delinquent loans will be bought out quite early in their expected lives.

More seasoned borrowers, in contrast, have experienced considerable positive HPA in recent years. The coming forecasted decline should not materially impact these borrowers’ performance. Similarly, if HPD in 2023 proves to be mild, then a sharp uptick in delinquencies is unlikely, regardless of loan vintage or LTV. Most homeowners make mortgage payments because they wish to continue living in their house and do not seriously consider strategic defaults. During the financial crisis, most borrowers continued making good on their mortgage obligations even as their LTVs went as high as the 150s.

Further, the HPD we are likely to encounter next year likely will not have the same devastating effect as the HPD wave that accompanied the financial crisis. Loans on the books today are markedly different from loans then. Ginnie Mae loans that went bad during the crisis disproportionately included seller-financed, down-payment-assistance loans and other programs lacking in robust checks and balances. Ginnie Mae has instituted more stringent guidelines in the years since to minimize the impact of bad actors in these sorts of programs.

This all assumes, however, that the job market remains robust. Should the looming recession lead to widespread unemployment, that would have a far more profound impact on delinquencies and buyouts than would HPD.

Fed Sales

The Fed’s holdings (as of 9/21, see chart below) are concentrated around 2 percent and 2.5 percent coupons. This raises the question of what the Fed’s strategy is likely to be for unwinding its Ginnie Mae position.

Word on the street is that Fed sales are highly unlikely to happen in 2022. Any sales in 2023, if they happen at all, are not likely before the second half of the year. The panel opined that the composition of these sales is likely to resemble the composition of the Fed’s existing book – i.e., mostly 2s, 2.5s, and some 3s. They have the capacity to take a more sophisticated approach than a simple pro-rata unwinding. Whether they choose to pursue that is an open question.

The Fed was a largely non-economic buyer of mortgage securities. There is every reason to believe that it will be a non-economic seller, as well, when the time comes. The Fed’s trading desk will likely reach out to the Street, ask for inquiry, and seek to pursue an approach that is least disruptive to the mortgage market.

Conclusion

On closer consideration, many of these macro conditions (Wells’s exit, HPD, enhanced eligibility requirements, and pending Fed sales) that would seem to portend an uncertain and bumpy road for Ginnie Mae investors, may turn out to be more benign than feared.

Conditions remain unsettled, however, and these and other factors certainly bear watching as Ginnie Mae market participants seek to plot a prudent course forward.


New Refinance Lag Functionality Affords RiskSpan Users Flexibility in Higher Rate Environments 

ARLINGTON, Va., September 29, 2022 — RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced that users of its award-winning Edge Platform can now fine-tune the assumed time lag between a rate-incentivized borrower’s decision to refinance and ultimate payoff. Getting this time lag right unveils a more accurate understanding of the rate incentive that borrowers responded to and thus better predictions of coming prepayments. 

The recent run-up in interest rates has caused the number of rate-incentivized mortgage refinancings to fall precipitously. Newfound operational capacity at many lenders, created by this drop in volume, means that new mortgages can now be closed in fewer days than were necessary at the height of the refi boom. This “lag time” between when a mortgage borrower becomes in-the-money to refinance and when the loan actually closes is an important consideration for MBS traders and analysts seeking to model and predict prepayment performance. 

Rather than confining MBS traders to a single, pre-set lag time assumption of 42 days, users of the Edge Platform’s Historical Performance module can now adjust the lag assumption when building their S-curves to better reflect their view of current market conditions. Using the module’s new Input section for Agency datasets, traders and analysts can further refine their approach to computing refi incentive by selecting the prevailing mortgage rate measure for any given sector (e.g., FH 30Y PMMS, MBA FH 30Y, FH 15Y PMMS and FH 5/1 PMMS) and adjusting the lag time to anywhere from zero to 99 days.   

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

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

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About RiskSpan, Inc. 

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

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

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

Media contact: Timothy Willis

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Webinar Recording: Bumpy Road Ahead for GNMA MBS?

Recorded: Thursday, September 29th | 3:30 p.m. EDT

The panel discusses the likely impact of recent, and potential future, market events on GNMA MBS. Topics for discussion will include:

  • How will the forthcoming, more stringent originator/servicer financial eligibility requirements affect origination volumes, buyouts, and performance?
  • Who will fill the vacuum left by Wells Fargo?
  • What role will falling prices play in delinquency and buyout rates?
  • What will be the impact of potential Fed MBS sales.

Presenters

Mahesh Swaminahtan, CFA

Managing Director, MBS/ABS Strategist, Hilltop Securities

Fowad Sheikh

Senior Managing Director, RiskSpan

Mike Ortiz

Agency MBS Analyst, DoubleLine Group LP

 


It’s time to move to DaaS — Why it matters for REITs, loan and MSR investors

Data as a service, or DaaS, for REITs, loans and MSR investors is fast becoming the difference between profitable trades and near misses.

Granularity of data is creating differentiation among investors. To win at investing in loans and mortgage servicing rights requires effectively managing a veritable ocean of loan-level data. Buried within every detailed tape of borrower, property, loan and performance characteristics lies the key to identifying hidden exposures and camouflaged investment opportunities. Understanding these exposures and opportunities is essential to proper bidding during the acquisition process and effective risk management once the portfolio is onboarded.

Investors know this. But knowing that loan data conceals important answers is not enough. Even knowing which specific fields and relationships are most important is not enough. Investors also must be able to get at that data. And because mortgage data is inherently messy, investors often run into trouble extracting the answers they need from it.

For investors, it boils down to two options. They can compel analysts to spend 75 percent of their time wrangling unwieldy data – plugging holes, fixing outliers, making sure everything is mapped right. Or they can just let somebody else worry about all that so they can focus on more analytical matters.

RiskSpan’s DaaS is the “just let somebody else worry about all that” solution.

Don’t get left behind — DaaS for REITs, loan and MSR investors

It should go without saying that the “let somebody else worry about all that” approach only works if “somebody else” possesses the requisite expertise with mortgage data. Self-proclaimed data experts abound. But handing the process over to an outside data team lacking the right domain experience risks creating more problems than it solves.

Ideally, DaaS for loan and MSR investors consists of a data owner handing off these responsibilities to a third party that can deliver value in ways that go beyond simply maintaining, aggregating, storing and quality controlling loan data. All these functions are critically important. But a truly comprehensive DaaS provider is one whose data expertise is complemented by an ability to help loan and MSR investors understand whether portfolios are well conceived. A comprehensive DaaS provider helps investors ensure that they are not taking on hidden risks (for which they are not being adequately compensated in pricing or servicing fee structure).

True DaaS frees up loan and MSR investors to spend more time on higher-level tasks consistent with their expertise. The more “blocking and tackling” aspects of data management that every institution that owns these assets needs to deal with can be handled in a more scalable and organized way. Cloud-native DaaS platforms like RiskSpan’s are what make this scalability possible.

Scalability — stop reinventing the wheel with each new servicer

One of the most challenging aspects of managing a portfolio of loans or MSRs is the need to manage different types of investor reporting data pipelines from different servicers. What if, instead of having to “reinvent the wheel” to figure out data intake every time a new servicer comes on board, “somebody else” could take care of that for you?

An effective DaaS provider is one not only that is well versed in building and maintain loan data pipes from servicers to investors but also has already established a library of existing servicer linkages. An ideal provider is one already set-up to onboard servicer data directly onto its own DaaS platform. Investors achieve enormous economies of scale by having to integrate with a single platform as opposed to a dozen or more individual servicer integrations. Ultimately, as more investors adopt DaaS, the number of centralized servicer integrations will increase, and greater economies will be realized across the industry.

Connectivity is only half the benefit. The DaaS provider not only intakes, translates, maps, and hosts the loan-level static and dynamic data coming over from servicers. The DaaS provider also takes care of QC, cleaning, and managing it. DaaS providers see more loan data than any one investor or servicer. Consequently, the AI tools an experienced DaaS provider uses to map and clean incoming loan data have had more opportunities to learn. Loan data that has been run through a DaaS provider’s algorithms will almost always be more analytically valuable than the same loan data processed by the investor alone.  

Investors seeking to increase their footprint in the loan and MSR space obviously do not wish to see their data management costs rise in proportion to the size of their portfolios. Outsourcing to a DaaS provider that specializes in mortgages, like RiskSpan, helps investors build their book while keeping data costs contained.

Save time and money – Make better bids

For all these reasons, DaaS is unquestionably the future (and, increasingly, the present) of loan and MSR data management. Investors are finding that a decision to delay DaaS migration comes with very real costs, particularly as data science labor becomes increasingly (and often prohibitively) expensive.

The sooner an investor opts to outsource these functions to a DaaS provider like RiskSpan, the sooner that investor will begin to reap the benefits of an optimally cost-effective portfolio structure. One RiskSpan DaaS client reported a 50 percent reduction in data management costs alone.

Investors continuing to make do with in-house data management solutions will quickly find themselves at a distinct bidding disadvantage. DaaS-aided bidders have the advantage of being able to bid more competitively based on their more profitable cost structure. Not only that, but they are able to confidently hone and refine their bids based on having a better, cleaner view of the portfolio itself.

Rethink your mortgage data. Contact RiskSpan to talk about how DaaS can simultaneously boost your profitability and make your life easier.

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Senior Home Equity Rises Again to $11.12 Trillion

Senior home equity rises again. Homeowners 62 and older saw their housing wealth grow by an estimated 4.9 percent ($520 billion) during the first quarter of 2022 to a record $11.1 trillion according to the latest quarterly release of the NRMLA/RiskSpan Reverse Mortgage Market Index.

The NRMLA/RiskSpan Reverse Mortgage Market Index (RMMI) rose to 388.83, another all-time high since the index was first published in 2000. The increase in older homeowners’ wealth was mainly driven by an estimated $563 billion (4.4 percent) increase in home values, offset by a $43 billion (2.1 percent) increase in senior-held mortgage debt.

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).


Automated Legal Disclosure Generator for Mortgage and Asset-Backed Securities

Issuing a security requires a lot of paperwork. Much of this paperwork consists of legal disclosures. These disclosures inform potential investors about the collateral backing the bonds they are buying. Generating, reviewing, and approving these detailed disclosures is hard and takes a lot of time – hours and sometimes days. RiskSpan has developed an easy-to-use legal disclosure generator application that makes it easier, reducing the process to minutes.

RiskSpan’s Automated Legal Disclosure Generator for Mortgage and Asset-Backed Securities automates the generation of prospectus-supplements, pooling and servicing agreements, and other legal disclosure documents. These documents contain a combination of static and dynamic legal language, data, tables, and images.  

The Disclosure Generator draws from a collection of data files. These files contain collateral-, bond-, and deal-specific information. The Disclosure Generator dynamically converts the contents of these files into legal disclosure language based on predefined rules and templates. In addition to generating interim and final versions of the legal disclosure documents, the application provides a quick and easy way of making and tracking manual edits to the documents. In short, the Disclosure Generator is an all-inclusive, seamless, end-to-end system for creating, editing and tracking changes to legal documents for mortgage and asset-backed securities.   

The Legal Disclosure Generator’s user interface supports:  

  1. Simultaneous uploading of multiple data files.
  2. Instantaneous production of the first (and subsequent) drafts of legal documents, adhering to the associated template(s).
  3. A user-friendly editor allowing manual, user-level language and data changes. Users apply these edits either directly to a specific document or to the underlying data template itself. Template updates carry forward to the language of all subsequently generated disclosures. 
  4. A version control feature that tracks and retains changes from one document version to the next.
  5. An archiving feature allowing access to previously generated documents without the need for the original data files.
  6. Editing access controls based on pre-defined user level privileges.
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Overview

RiskSpan’s Automated Legal Disclosure Generator for Mortgage and Asset-Backed Securities enables issuers of securitized assets to create legal disclosures efficiently and quickly from raw data files.

The Legal Disclosure Generator is easy and intuitive to use. After setting up a deal in the system, the user selects the underlying collateral- and bond-level data files to create the disclosure document. In addition to the raw data related to the collateral and bonds, these data files also contain relevant waterfall payment rules. The data files can be in any format — Excel, CSV, text, or even custom file extensions. Once the files are uploaded, the first draft of the disclosures can be easily generated in just a few seconds. The system takes the underlying data files and creates a draft of the disclosure document seamlessly and on the fly.  In addition, the Legal Disclosure Generator reads custom scripts related to waterfall models and converts them into waterfall payment rules.

Here is a sample of a disclosure document created from the system.


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Blackline Version(s)

In addition to creating draft disclosure documents, the Legal Disclosure Generator enables users to make edits and changes to the disclosures on the fly through an embedded editor. The Disclosure Generator saves these edits and applies them to the next version. The tool creates blackline versions with a single integrated view for managing multiple drafts.

The following screenshot of a sample blackline version illustrates how users can view changes from one version to the next.

Tracking of Drafts

The Legal Disclosure Generator keeps track of a disclosure’s entire version history. The system enables email of draft versions directly to the working parties, and additionally retains timestamps of these emails for future reference.

The screenshot below shows the entire lifecycle of a document, from original creation to print, with all interim drafts along the way. 


Automated QC System

The Legal Disclosure Generator’s automated QC system creates a report that compares the underlying data file(s) to the data that is contained in the legal disclosure. The automated QC process ensures that data is accurate and reconciled.

Downstream Consumption

The Legal Disclosure Generator creates a JSON data file. This consolidated file consists of collateral and bond data, including waterfall payment rules. The data files are made available for downstream consumption and can also be sent to Intex, Bloomberg, and other data vendors. One such vendor noted that this JSON data file has enabled them to model deals in one-third the time it took previously.

Self-Serve System

The Legal Disclosure Generator was designed with the end-user in mind. Users can set up the disclosure language by themselves and edit as needed, with little or no outside help.

The ‘System’ Advantage

  • Remove unnecessary, manual, and redundant processes
  • Huge Time Efficiency – 24 Hours vs 2 Mins (Actual time savings for a current client of the system)
  • Better Managed Processes and Systems
  • Better Resource Management – Cost Effective Solutions
  • Greater Flexibility
  • Better Data Management – Inbuilt QCs


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Why Accurate MSR Cost Forecasting Requires Loan-by-Loan Analytics

When it comes to forecasting MSR cash flows, the practice of creating “rep lines,” or cohorts, of loans with similar characteristics for analytical purposes has its roots in the Agency MBS market. One of the most attractive and efficient features of Agencies is the TBA market. This market allows originators and issuers to sell large pools of mortgages that have not even been originated yet. This is possible because all parties understand what these future loans will look like. All these loans will all have enough in common as to be effectively interchangeable with one another.  

Institutions that perform the servicing on such loans may reasonably feel they can extend the TBA logic to their own analytics. Instead of analyzing a hundred similar loans individually, why not just lump them into one giant meta-loan? Sum the balances, weight-average the rates, terms, and other features, and you’re good to go. 

Why the industry still resorts to loan cohorting when forecasting MSR cash flows

The simplest explanation for cohort-level analytics lies in its simplicity. Rep lines amount to giant simplifying assumptions. They generate fewer technological constraints than a loan-by-loan approach does. Condensing an entire loan portfolio down to a manageable number of rows requires less computational capacity. This takes on added importance when dealing with on-premise software and servers. It also facilitates the process of assigning performance and cost assumptions. 

What is more, as OAS modeling has evolved to dominate the loans and MSR landscape, the stratification approach necessary to run Monte Carlo and other simulations lends itself to cohorting. Lumping loans into like groups also greatly simplifies the process of computing hedging requirements. 

Advantages of loan-level over cohorting when forecasting MSR cash flows

Treating loan and MSR portfolios like TBA pools, however, has become increasingly problematic as these portfolios have grown more heterogeneous. Every individual loan has a story. Even loans that resemble each other in terms of rate, credit score, LTV, DTI, and documentation level have unique characteristics. Some of these characteristics – climate risk, for example – are not easy to bucket. Lumping similar loans into cohorts also runs the risk of underestimating tail risk. Extraordinarily high servicing/claims costs on just one or two outlier loans on a bid tape can be enough to adversely affect the yield of an entire deal. 

Conversely, looking at each loan individually facilitates the analysis of portfolios with expanded credit boxes. Non-banks, which do not usually have the benefit of “knowing” their servicing customers through depository or other transactional relationships, are particularly reliant on loan-level data to understand individual borrower risks, particularly credit risks. Knowing the rate, LTV, and credit score of a bundled group of loans may be sufficient for estimating prepayment risk. But only a more granular, loan-level analysis can produce the credit analytics necessary to forecast reliably and granularly what a servicing portfolio is really going to cost in terms of collections, loss mitigation, and claims expenses.  

Loan-level analysis also eliminates the reliance on stratification limitations. It facilitates portfolio composition analysis. Slicing and dicing techniques are much more simply applied to loans individually than to cohorts. Looking at individual loans also reduces the risk of overrides and lost visibility into convexity pockets. 

RiskSpan’s cloud-native Edge Platform projects prepayment, default, severity and MSR cash flows (income and costs) at the loan level

Loan-Level MSR Analytics

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Potential challenges and other considerations 

So why hasn’t everyone jumped onto the loan-level bandwagon when forecasting MSR cash flows? In short, it’s harder. Resistance to any new process can be expected when existing aggregation regimes appear to be working fine. Loan-level data management requires more diligence in automated processes. It also requires the data related to each individual loan to be subjected to QC and monitoring. Daily hedging and scenario runs tend to focus more on speed than on accuracy at the macro level. Some may question whether the benefits of such a granular, case-by-case analysis that identifying the most significant loan-level pickups requires actually justifies the cost of such a regime. 

Rethink. Why now? 

Notwithstanding these challenges, there has never been a better time for loan and MSR investors to abandon cohorting and fully embrace loan-level analytics when forecasting MSR cash flows. The emergence of cloud-native technology and enhanced database and warehouse infrastructure along with the ability to outsource the hosting and computational requirements out to third parties creates practically limitless scalability. 

The barriers between MSR experts and IT professionals have never been lower. This, combined with the emergence of a big data culture in an increasing number of organizations, has brought the granular daily analysis promised by loan-level analytics tantalizingly within reach.  


FHFA Prepayment Monitoring Reports (Q1 2022) Powered by RiskSpan’s Edge Platform

To help enforce alignment of Agency prepayments across Fannie’s and Freddie’s Uniform MBS, the Federal Housing Finance Agency publishes a quarterly monitoring report. This report compares prepayment speeds of UMBS issued by the two Agencies. The objective is to help ensure that prepayment performance remains consistent. This consistency ensures that market expectations of a Fannie-issued UMBS are fundamentally indistinguishable from those of a Freddie-issued UMBS. The two Agencies’ UMBS should be interchangeably deliverable into passthrough “TBA” trades.

This week, the FHFA released the Q1 2022 version of this report. The charts in the FHFA’s publication, which it generates 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.

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 seeking 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. This is 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 more of what the platform can do.

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Senior Home Equity Rises Again to $10.6 Trillion

Homeowners 62 and older saw their housing wealth grow by some $405 billion (3.8 percent) during the fourth quarter of 2021 to a record $10.6 trillion according to the latest quarterly release of the NRMLA/RiskSpan Reverse Mortgage Market Index.

The NRMLA/RiskSpan Reverse Mortgage Market Index (RMMI) rose to 370.56, another all-time high since the index was first published in 2000. The increase in older homeowners’ wealth was mainly driven by an estimated $452 billion (3.7 percent) increase in home values, offset by a $44 billion (2.3 percent) increase in senior-held mortgage debt.

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).


Surge in Cash-Out Refis Pushes VQI Sharply Higher

A sharp uptick in cash-out refinancing pushed RiskSpan’s Vintage Quality Index (VQI) to its highest level since the first quarter of 2019.

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.

Rising Rates Mean More Cash-Out Refis (and more risk)

As the following charts plotting the individual VQI components illustrate, a spike in cash-out refinance activity (as a percentage of all originations) accounted for more of the rise in overall VQI than did any other risk factor.

This comes as little surprise given the rising rate environment that has come to define the first quarter of 2022, a trend that is likely to persist for the foreseeable future.

As we demonstrated in this recent post, the quickly vanishing number of borrowers who are in the money for a rate-and-term refinance means that the action will increasingly turn to so-called “serial cash-out refinancers” who repeatedly tap into their home equity even when doing so means refinancing into a mortgage with a higher rate. The VQI can be expected to push ever higher to the extent this trend continues.

An increase in the percentage of loans with high debt-to-income ratios (over 45) and low credit scores (under 660) also contributed to the rising VQI, as did continued upticks in loans on investment and multi-unit properties as well as mortgages with only one borrower.

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.

Data Source: Fannie Mae PoolTalk®-Loan Level Disclosure


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