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Articles Tagged with: Agency MBS

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


EDGE: Cash-Out Refi Speeds 

Mortgage rates have risen nearly 200bp from the final quarter of 2021, squelching the most recent refinancing wave and leaving the majority of mortgage holders with rates below the prevailing rate of roughly 5% (see chart below). For most homeowners, it no longer makes sense to refinance an existing 30yr mortgage into another 30yr mortgage.

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But, as we noted back in February, the rapid rise in home prices has left nearly all households with significant, untapped gains in their household balance sheets. For homeowners with consumer debt at significantly higher rates than today’s mortgage rates, it can make economic sense to consolidate debt using a cash-out refi loan against their primary residence. As we saw during 2002-2003, cash-out refinancing can drive speeds on discount mortgages significantly higher than turnover alone. Homeowners can also become “serial cash-out refinancers,” tapping additional equity multiple times.  

In this analysis, we review prepayment speeds on cash-out refis, focusing on discount MBS, i.e., mortgages whose note rates are equal to or below today’s prevailing rates. 

The volume of cash-out refis has grown steadily but modestly since the start of the pandemic, whereas rate/term refis surged and fell dramatically in response to changing interest rates. Despite rising rates, the substantial run-up in home prices and increased staffing at originators from the recent refi boom has left the market ripe for stronger cash-out activity. 

The pivot to cash-out issuance is evidenced by the chart below, illustrating how the issuance of cash-out refi loans (the black line below) in the first quarter of this year was comparable with issuance in the summer of 2021, when rates near historic lows, while rate/term refis (blue line) have plunged over the same period. 

With cash-out activity set to account for a larger share of the mortgage market, we thought it worthwhile to compare some recent cash-out activity trends. For this analysis, the graphs consist of truncated S-curves, showing only the left-hand (out-of-the-money) side of the curve to focus on discount mortgage behavior in a rising rate environment where activity is more likely to be influenced by serial cash-out activity. 

This first chart compares recent performance of out-of-the money mortgages by loan purpose, comparing speeds for purchase loans (black) with both cash-out refis (blue) and rate/term refis (green). Notably, cash-out refis offer 1-2 CPR upside over rate/term refis, only converging to no cash out refis when 100bp out of the money.[1] 

Next, we compare cash-out speeds by servicer type, grouping mortgages that are serviced by banks (blue) versus mortgages serviced by non-bank servicers (green). Non-bank servicers produce significantly faster prepay speeds, an advantage over bank-serviced loans for MBS priced at a discount. 

Finally, we drill deeper into the faster non-bank-serviced discount speeds for cash-out refis. This chart isolates Quicken (red) from other non-bank servicers (green). While Quicken’s speeds converge with those of other non-banks at the money, Quicken-serviced cash-out refis are substantially faster when out of the money than both their non-bank counterparts and the cash-out universe as a whole.[2]

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We suspect the faster out-of-the-money speeds are being driven by serial cash-out behavior, with one servicer in particular (Quicken) encouraging current mortgage holders to tap home equity as housing prices continue to rise. 

This analysis illustrates how pools with the highest concentration of Quicken-serviced cash-out loans may produce substantially higher out-of-the-money speeds relative to the universe of non-spec pools. To find such pools, users can enter a list of pools into the Edge platform and simultaneously filter for both Quicken and cash-out refi. The resultant query will show each pool’s UPB for this combination of characteristics. 

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EDGE: Recent Performance of GNMA RG Pools

In early 2021, GNMA began issuing a new class of custom pools with prefix “RG.” These pools are re-securitizations of previously delinquent loans which were repurchased from pools during the pandemic.[1] Loans in these pools are unmodified, keeping the original rate and term of the mortgage note. In the analysis below, we review the recent performance of these pools at loan-level detail. The first RG pools were issued in February 2021, growing steadily to an average rate of $2B per month from Q2 onward, with a total outstanding of $21 billion. 

 
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The majority of RG issuance has included loans that are two to seven years seasoned and represent a consistent 2-3% of the total GNMA market for those vintages, dashed line below.

Coupons of RG pools are primarily concentrated between 3.0s through 4.5s, with the top-10 Issuers of RG pools account for nearly 90% of the issuance.

Below, we compare speeds on GNMA RG pools under various conditions. First, we compare speeds on loans in RG pools (black) versus same-age multi-lender pools (red) over the last twelve months. When out of the money, RG pools are 4-5 CPR slower than comparably aged multi-lender pools but provide a significantly flatter S-curve when in-the-money.

Next, we plot the S-curve for all GNMA RG loans with overlays for loans that are serviced by banks (green) and non-banks (blue). Bank-serviced RG loans prepay significantly slower than non-banks by an average of 9 CPR weighted across all incentives. Further, this difference is caused by voluntary prepays, with buyouts averaging a steady 4% CBR, plus or minus 1 CBR, for both banks and non-banks with no discernable difference between the two (second graph).

Finally, we analyzed the loan-level transition matrix by following each RG loan through its various delinquency states over the past year. We note that the transition rate from Current to 30-day delinquent for RG loans is 1.6%, only marginally worse than that of the entire universe of GNMA loans at 1.1%. RG loans transitioned back from 30->Current at similar rates to the wider Ginnie universe (32.3%) and the 30->60 transition rate for RG loans was marginally worse than the Ginnie universe, 30.8% versus  24.0%.[2]

Monthly Transition Rates for Loans in GNMA RG Pools: In summary, loans in RG pools have shown a substantial level of voluntary prepayments and comparatively low buyouts, somewhat unexpected especially in light of their recent delinquency. Further, their overall transition rates to higher delinquency states, while greater than the GNMA universe, is markedly better than that of reperforming loans just prior to the outbreak of COVID.

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EDGE: Extension Protection in a Rising Rate Environment

With the Fed starting their tightening cycle and reducing balance sheet, mortgage rates have begun rising. Since late summer, 30-year conforming rates have risen more than 100bp, with 75bp of that occurring since the end of December. The recent flight-to-quality rally has temporarily eased that, but the overall trend remains in place for higher mortgage rates.

With this pivot, mortgage investors have switched from focusing on prepayment protection to mitigating extension risk. In this post, we offer analysis on extension risk and turnover speeds for various out-of-the-money Fannie and Freddie cohorts.[1]

In the chart below, we first focus on out-of-the-money prepays on lower loan balance loans. For this analysis, we analyzed speeds on loans that were 24 to 48 months seasoned. We further grouped the loan balance stories into meta-groups, as the traditional groupings of “85k-Max”, etc, showed little difference in out-of-the-money speeds. When compared to loans with balances above 250k, speeds on lower loan balance loans were a scant 1-2 CPR faster than borrowers with larger loan balances, when prevailing rates were 25bp to 100bp higher than the borrower’s note rate.

We next compare borrowers in low FICO pools, high LTV pools, and 100% investor pools. Speeds on low-FICO pools (blue) offer some extension protection due to higher involuntary speeds. At the other end, loans in 100% investor pools were dramatically slower than non-spec pools when out-of-the money.

Finally, we look at the behavior of borrowers in non-spec pools segregated by loan purpose, again controlling for loan age. Borrowers with refi loans pay significantly faster than purchase loans when only slightly out-of-the money. As rates continue to rise, refi speeds converge to purchase loans at 75bp out of the money and pay slower when 75-100bp out of the money, presumably due to a stronger lock-in effect.

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We also separated these non-spec borrowers by originators, grouping the largest banks and non-bank originators together. Out-of-the-money speeds on refi loans were significantly faster for loans originated by non-bank originators (blue and green) versus those originated by banks (red and orange). Speeds on purchase loans were only 1-2 CPR faster for non-banks versus banks and were omitted from this graph for readability.

In the current geopolitical climate, rates may continue to drop over the short term. But given the Fed’s tightening bias, it’s prudent to consider extension risk when looking at MBS pools, in both specified and non-specified pools.

[1] For investors interested in GNMA analysis, please contact RiskSpan


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