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Why Climate Risk Matters for Mortgage Loan & MSR Investors 

The time has come for mortgage investors to start paying attention to climate risk.

Until recently, mortgage loan and MSR investors felt that they were largely insulated from climate risk. Notwithstanding the inherent risk natural hazard events pose to housing and the anticipated increased frequency of these events due to climate change, it seemed safe to assume that property insurers and other parties in higher loss position were bearing those risks. 

In reality, these risks are often underinsured. And even in cases where property insurance is adequate, the fallout has the potential to hit investor cash flows in a variety of ways. Acute climate events like hurricanes create short-term delinquency and prepayment spikes in affected areas. Chronic risks such as sea level rise and increased wildfire risk can depress housing values in areas most susceptible to these events. Potential impacts to property insurance costs, utility costs (water and electricity in areas prone to excessive heat and drought, for example) and property taxes used to fund climate-mitigating infrastructure projects all contribute to uncertainty in loan and MSR modeling. 

Moreover, dismissing climate risk “because we are in fourth loss position” should be antithetical to any investor claiming to espouse ESG principles. After all, consider who is almost always in the first loan position – the borrower. Any mortgage investment strategy purporting to be ESG friendly must necessarily take borrower welfare into account. Dismissing climate risk because borrowers will bear most of the impact is hardly a socially responsible mindset. This is particularly true when a disproportionate number of borrowers prone to natural hazard risk are disadvantaged to begin with. 

Hazard and flood insurers typically occupy the loss positions between borrowers and investors. Few tears are shed when insurers absorb losses. But society at large ultimately pays the price when losses invariably lead to higher premiums for everybody.    

Evaluating Climate Exposure

For these and other reasons, natural hazards pose a systemic risk to the entire housing system. For mortgage loan and MSR investors, it raises a host of questions. Among them: 

  1. What percentage of the loans in my portfolio are susceptible to flood risk but uninsured because flood maps are out of date? 
  2. How geographically concentrated is my portfolio? What percentage of my portfolio is at risk of being adversely impacted by just one or two extreme events? 
  3. What would the true valuation of my servicing portfolio be if climate risk were factored into the modeling?  
  4. What will the regulatory landscape look like in coming years? To what extent will I be required to disclose the extent to which my portfolio is exposed to climate risk? Will I even know how to compute it, and if so, what will it mean for my balance sheet? 

 

Incorporating Climate Data into Investment Decision Making

Forward-thinking mortgage servicers are at the forefront of efforts to get their arms around the necessary data and analytics. Once servicers have acquired a portfolio, they assess and triage their loans to identify which properties are at greatest risk. Servicers also contemplate how to work with borrowers to mitigate their risk.  

For investors seeking to purchase MSR portfolios, climate assessment is making its way into the due diligence process. This helps would-be investors ensure that they are not falling victim to adverse selection. As investors increasingly do this, climate assessment will eventually make its way further upstream, into appraisal and underwriting processes. 

Reliably modeling climate risk first requires getting a handle on how frequently natural hazard events are likely to occur and how severe they are likely to be. 

In a recent virtual industrial roundtable co-hosted by RiskSpan and Housing Finance Strategies, representatives of Freddie Mac, Mr. Cooper, and Verisk Analytics (a leading data and analytics firm that models a wide range of natural and man-made perils) gathered to discuss why understanding climate risk should be top of mind for mortgage investors and introduced a framework for approaching it. 

WATCH THE ENTIRE ROUNDTABLE

Building the Framework

The framework begins by identifying the specific hazards relevant to individual properties, building simulated catalogs of thousands of years worth of simulated events, computing likely events simulating damage based on property construction and calculating likely losses. These forecasted property losses are then factored into mortgage performance scenarios and used to model default risk, prepayment speeds and home price impacts. 

Connecting to Mortgage Performance Analysis

 

Responsibility to Borrowers

One member of the panel, Kurt Johnson, CRO of mega-servicer Mr. Cooper, spoke specifically of the operational complexities presented by climate risk. He cited as one example the need to speak daily with borrowers as catastrophic events are increasingly impacting borrowers in ways for which they were not adequately prepared. He also referred to the increasing number of borrowers incurring flood damage in areas that do not require flood insurance and spoke to how critical it is for servicers to know how many of their borrowers are in a similar position.

Johnson likened the concept of credit risk layering to climate risk exposure. The risk of one event happening on the heels of another event can cause the second event to be more devastating than it would have been had it occurred in a vacuum. As an example, he mentioned how the spike in delinquencies at the beginning of the covid pandemic was twice as large among borrowers who had just recovered from Hurricane Harvey 15 months earlier than it was among borrowers who had not been affected by the storm. He spoke of the responsibility he feels as a servicer to educate borrowers about what they can do to protect their properties in adverse scenarios.


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.

30-year CPR Comparison All Coupons 1-month CPR

30-year CPR Comparison All Coupons 1-month CPR

30-year CPR Comparison All Coupons 1-month CPR

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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Recent Edge Platform Updates

Riskspan

Edge Platform Updates


MSR Engine

The Platform’s extensive library of available MSR analytic outputs has been expanded to include Effective Recapture Rate and other Income and Expense fields.

Base servicing cost inputs for MSR assumptions have also been enhanced.

MSR Engine


LOANS

The ETL tool for loan onboarding has been further enhanced with machine learning capabilities.

New fields for querying options and enhanced segmentation have been added. And SOFRWalSpread and SOFRSpotSpread are now captured in static analysis output.

Loans


HISTORICAL PERFORMANCE

Special Eligibility Program fields have been added to Fannie and Freddie pool data outputs along with a complementing SpecialProgram100 filter

Fannie and Freddie datasets now include CBR and CPR metrics (previously only available for Ginnies).

New support has been added for saving CoreLogic LLD queries with complement filters.

Enhanced historical date-based queries in Edge Perspective (e.g., option to run and save queries with relative factor dates rather than specifically coded date.

Historical Performance


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Daniel Fleishman Joins RiskSpan’s MSR Team

ARLINGTON, Va., May 3, 2022 — RiskSpan, a leading provider of residential mortgage and structured product data and analytics, has appointed Daniel Fleishman as Managing Director within its recently announced Mortgage Servicing Rights unit.

Fleishman’s career includes 17 years at BlackRock where he worked extensively with banks, mortgage companies and REITs to support MSR valuation, risk measurement and hedging practices. In that role, Fleishman gained deep expertise in MSR cash flow and mortgage modeling as well as experience managing diverse client needs ranging from model validation to MSR acquisition analysis. Earlier in his career, he also spent more than a decade at the Federal Reserve Bank of New York.

“Dan’s extensive expertise with mortgage and MSR analytics is a wonderful complement to our Edge Platform,” said Bernadette Kogler, CEO of RiskSpan. “With the MSR application starting to gain real traction, Dan is just the person to help ensure our clients are getting all they can out of the capability.”

“I am delighted about this opportunity to be a part of such a dynamic company in this new role,” said Fleishman. “I look forward to helping Edge users manage multiple loan-level datasets with ease and visualize servicing cash flows and analytics rapidly and with granularity.”

As announced last week, RiskSpan’s cloud-native MSR application is a new component of its award-winning Edge Platform. It enables investors to price MSRs and run cash flows on the fly at the loan level, opening the door to a virtually limitless array of scenario-based analytics. The flexibility afforded by RiskSpan’s parallel computing framework allows for complex net cash flow calculations on hundreds of thousands of individual mortgage loans simultaneously. The speed and scalability this affords makes the Edge Platform ideally suited for pricing even the largest portfolios of MSR assets and making timely trading decisions with confidence.


About RiskSpan, Inc.
RiskSpan offers end-to-end solutions for data management, trading risk management analytics, and visualization on a highly secure, fast, and fully scalable platform that has earned the trust of the industry’s largest firms. Combining the strength of subject matter experts, quantitative analysts, and technologists, RiskSpan’s Edge platform integrates a range of datasets – structured and unstructured – and off-the-shelf analytical tools to provide you with powerful insights and a competitive advantage. Learn more at www.riskspan.com.

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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.

Historical Changes in Aggregate Senior Home Values Q! 2000 - Q4 2021

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).


RiskSpan Announces Cloud-Native Mortgage Servicing Rights Application

ARLINGTON, Va., Mortgage fintech leader RiskSpan announced today that it has added a Mortgage Servicing Rights (MSR) application to its award-winning on-demand analytics Edge Platform.

The application expands RiskSpan’s unparalleled loan-level mortgage analytics to MSRs, an asset class whose cash flows have previously been challenging to forecast at the loan level. Unlike conventional MSR tools that assume large numbers of loans bucketed into “rep lines” will perform identically, the Edge Platform’s granular approach makes it possible to forecast MSR portfolio net cash flows and run valuation and scenario analyses with unprecedented precision.   

RiskSpan’s MSR platform integrates RiskSpan’s proprietary prepayment and credit models to calculate option-adjusted risk metrics while also incorporating the full range of client-configurable input parameters (costs and recapture assumptions, for example) necessary to fully characterize the cash flows arising from servicing. Further, its integrated data warehouse solution enables easy access to time-series loan and collateral performance. 

“Our cloud-native platform has enabled us to achieve something that has long eluded our industry – on-demand, loan-level cash flow forecasting,” observed RiskSpan CEO Bernadette Kogler. “This has been an absolute game changer for our clients.”

Loan-level projections enable MSR investors to re-combine and re-aggregate loan-level cash flow results on the fly, opening the door to a host of additional, scenario-based analytics – including climate risk and responsible ESG analysis. The flexibility afforded by RiskSpan’s parallel computing framework allows for complex net cash flow calculations on hundreds of thousands of individual mortgage loans simultaneously. The speed and scalability this affords makes the Edge Platform ideally suited for pricing even the largest portfolios of MSR assets and making timely trading decisions with confidence.

About RiskSpan 
RiskSpan offers end-to-end solutions for data management, trading risk management analytics, and visualization on a highly secure, fast, and fully scalable platform that has earned the trust of the industry’s largest firms. Combining the strength of subject matter experts, quantitative analysts, and technologists, RiskSpan’s Edge platform integrates a range of data-sets – structured and unstructured – and off-the-shelf analytical tools to provide you with powerful insights and a competitive advantage. Learn more at www.riskspan.com. 

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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


Industry Virtual Roundtable: The Intersection of Climate Risk Management with Mortgage Loan & MSR Investing

April 14th | 2:00-3:15 p.m. ET

With both the public and private sectors increasingly making climate risk management a priority, attention in our industry is turning to what it means for mortgage loan and MSR investors.

Industry experts join RiskSpan and Housing Finance Strategies for a roundtable event where they engage in a discussion on the latest approaches and technology for mitigating climate risk management in mortgage portfolios.

The loan-level cash flows discussed in this webinar were generated using RiskSpan’s Edge Platform.

 

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Agenda (all times Eastern)

2:00-2:05 pm | WELCOME AND PROGRAM OVERVIEW 

Faith Schwartz, Founder & CEO, Housing Finance Strategies

2:05-2:20 pm | CLIMATE RISK’S IMPACT ON MORTGAGE FINANCE AND TOOLS TO MANAGE RISK

Janet Jozwik, Senior Managing Director and Head of Climate Risk, RiskSpan
Dan Raizman, Global Resilience Manager, Verisk Analytics

2:20-3:00 pm | PANEL DISCUSSION: CLIMATE RISK IN HOUSING FINANCE—RISK MANAGEMENT AND REGULATORY PERSPECTIVES

Faith Schwartz, Moderator
Mark Hanson, SVP, Freddie Mac
Kurt Johnson, CRO, Mr. Cooper
Sean Becketti, former Freddie Mac
Bernadette Kogler, CEO, RiskSpan

3:00-3:15 pm | QUESTIONS AND DISCUSSION OF POLLING RESULTS


Asset Managers Improving Yields With Resi Whole Loans

An unmistakable transformation is underway among asset managers and insurance companies with respect to whole loan investments. Whereas residential mortgage loan investing has historically been the exclusive province of commercial banks, a growing number of other institutional investors – notably life insurance companies and third-party asset managers – have shifted their attention toward this often-overlooked asset class.

Life companies and other asset managers with primarily long-term, risk-sensitive objectives are no strangers to residential mortgages. Their exposure, however, has traditionally been in the form of mortgage-backed securities, generally taking refuge in the highest-rated bonds. Investors accustomed to the AAA and AA tranches may understandably be leery of whole-loan credit exposure. Infrastructure investments necessary for managing a loan portfolio and the related credit-focused surveillance can also seem burdensome. But a new generation of tech is alleviating more of the burden than ever before and making this less familiar and sometimes misunderstood asset class increasingly accessible to a growing cadre of investors.

Maximizing Yield

Following a period of low interest rates, life companies and other investment managers are increasingly embracing residential whole-loan mortgages as they seek assets with higher returns relative to traditional fixed-income investments (see chart below). As highlighted in the chart below, residential mortgage portfolios, on a loss-adjusted basis, consistently outperform other investments, such as corporate bonds, and look increasingly attractive relative to private-label residential mortgage-backed securities as well.

Nearly one-third of the $12 trillion in U.S. residential mortgage debt outstanding is currently held in the form of loans.

And while most whole loans continue to be held in commercial bank portfolios, a growing number of third-party asset managers have entered the fray as well, often on behalf of their life insurance company clients.

Investing in loans introduces a dimension of credit risk that investors do need to understand and manage through thoughtful surveillance practices. As the chart below (generated using RiskSpan’s Edge Platform) highlights, when evaluating yields on a loss-adjusted basis, resi whole loans routinely generate yield.

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In addition to higher yields, whole loans investments offer investors other key advantages over securities. Notably:

Data Transparency

Although transparency into private label RMBS has improved dramatically since the 2008 crisis, nothing compares to the degree of loan-level detail afforded whole-loan investors. Loan investors typically have access to complete loan files and therefore complete loan-level datasets. This allows for running analytics based on virtually any borrower, property, or loan characteristic and contributes to a better risk management environment overall. The deeper analysis enabled by loan-level and property-specific information also permits investors to delve into ESG matters and better assess climate risk.

Daily Servicer Updates

Advancements in investor reporting are increasingly granting whole loan investors access to daily updates on their portfolio performance. Daily updating provides investors near real-time updates on prepayments and curtailments as well as details regarding problem loans that are seriously delinquent or in foreclosure and loss mitigation strategies. Eliminating the various “middlemen” between primary servicers and investors (many of the additional costs of securitization outlined below—master servicers, trustees, various deal and data “agents,” etc.—have the added negative effect of adding layers between security investors and the underlying loans) is one of the things that makes daily updates possible.

Lower Transaction Costs

Driven largely by a lack of trust in the system and lack of transparency into the underlying loan collateral, private-label securities investments incur a series of yield-eroding transactions costs that whole-loan investors can largely avoid. Consider the following transaction costs in a typical securitization:

  • Loan Data Agent costs: The concept of a loan data agent is unique to securitization. Data agents function essentially as middlemen responsible for validating the performance of other vendors (such as the Trustee). The fee for this service is avoided entirely by whole loan investors, which generally do not require an intermediary to get regularly updated loan-level data from servicers.
  • Securities Administrator/Custodian/Trustee costs: These roles present yet another layer of intermediary costs between the borrower/servicer and securities investors that are not incurred in whole loan investing.
  • Deal Agent costs: Deal agents are third party vendors typically charged with enhancing transparency in a mortgage security and ensuring that all parties’ interests are protected. The deal agent typically performs a surveillance role and charges investors ongoing annual fees plus additional fees for individual loan file reviews. These costs are not borne by whole loan investors.
  • Due diligence costs: While due diligence costs factor into loan and security investments alike, the additional layers of review required for agency ratings tends to drive these costs higher for securities. While individual file reviews are also required for both types of investments, purchasing loans only from trusted originators allows investors to get comfortable with reviewing a smaller sample of new loans. This can push due diligence costs on loan portfolios to much lower levels when compared to securities.
  • Servicing costs: Mortgage servicing costs are largely unavoidable regardless of how the asset is held. Loan investors, however, tend to have more options at their disposal. Servicing fees for securities vary from transaction to transaction with little negotiating power by the security investors. Further, securities investors incur master servicing fees which is generally not a required function for managing whole loan investments.

Emerging technology is streamlining the process of data cleansing, normalization and aggregation, greatly reducing the operational burden of these processes, particularly for whole loan investors, who can cut out many of these intermediary parties entirely.

Overcoming Operational Hurdles

Much of investor reluctance to delve into loans has historically stemmed from the operational challenges (real and perceived) associated with having to manage and make sense of the underlying mountain of loan, borrower, and property data tied to each individual loan. But forward-thinking asset managers are increasingly finding it possible to offload and outsource much of this burden to cloud-native solutions purpose built to store, manage, and provide analytics on loan-level mortgage data, such as RiskSpan’s Edge Platform supporting loan data management and analytics. RiskSpan solutions make it easy to mine available loan portfolios for profitable sub-cohorts, spot risky loans for exclusion, apply a host of credit and prepay scenario analyses, and parse static and performance data in any way imaginable.

At an increasing number of institutions, demonstrating the power of analytical tools and the feasibility of applying them to the operational and risk management challenges at hand will solve many if not most of the hurdles standing in the way of obtaining asset class approval for mortgage loans. The barriers to access are coming down, and the future is brighter than ever for this fascinating, dynamic and profitable asset class.


RiskSpan a Winner of 2022 HousingWire’s Tech100 Mortgage Award

RiskSpan named to HousingWire’s Tech100 for a fourth consecutive year — recognition of the firm’s continuous commitment to advancing mortagage, technology, data and analytics.

Our cloud-native data and predictive modeling analytical platform uncovers insights and mitigates risks for loans and structured products.

HWrech100

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