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Nearly $8 Trillion in Senior Home Equity Pushes Reverse Mortgage Market Index Upward

The NRMLA/RiskSpan Reverse Mortgage Market Index (RMMI) rose to 280.99 during the third quarter of 2020, an all-time high. This reflects a 1.6% increase in senior home equity, which now stands at an estimated $7.82 trillion. Growth in senior homeowner’s wealth was largely attributable to an estimated 1.6% (or $149 billion) increase in senior housing value, offset by 1.6% (or $28 billion) increase of senior-held mortgage debt.

The National Reverse Mortgage Lenders Association (NRMLA) and RiskSpan have published the Reverse Mortgage Market Index (RMMI) since the beginning of 2000. The RMMI provides a trending measure of home equity among U.S. homeowners age 62 and older.

The RMMI defines senior home equity as the difference between the aggregate value of homes owned and occupied by seniors and the aggregate mortgage balance secured by those homes. This measure enables NRMLA to help gauge the potential market size of those who may be qualified for a reverse mortgage product. The chart above illustrates the steady increase in this index since the end of the 2008 recession.

Increasing house prices drive the index’s upward trend, mitigated to some extent by a corresponding modest increase in mortgage debt held by seniors. The most recent RMMI report (reflecting data as of the end of Q3 20202) was published last week on NRMLA’s website.

Note on the Limitations of RMMI

To calculate the RMMI, an econometric tool is developed to estimate senior housing value, senior mortgage level, and senior equity using data gathered from various public resources such as American Community Survey (ACS), Federal Reserve Flow of Funds (Z.1), and FHFA housing price indexes (HPI). The RMMI is simply the senior equity level at time of measure relative to that of the base quarter in 2000.[1]  The main limitation of RMMI is 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 Sponsoring IMN’s Non-QM Virtual Forum, January 21, 2021

RiskSpan is thrilled to be sponsoring IMN’s Non-QM Virtual Forum on Thursday, January 21, 2021. RiskSpan Senior Managing Director Bill Moretti will be moderating a panel at 2:50-3:35 PM.  

Click HERE to view the agenda along with details on Bill’s panel: “Underwriting Credit Standards, Assessing Ability To Pay & Evaluating Default Risk: Are You Protecting Yourself Against A Second Wave Of Coronavirus Or Going All Out?” 

Bill will be joined at the forum by a team of RiskSpan executives and other leaders, including CEO Bernadette Kogler, CBO Maulik Doshi, and managing directors, Pat GreeneFowad Sheikh, and David Andrukonis. They will be available for virtual meetings throughout the day. 


January 13 Workshop: Pattern Recognition in Time Series Data

Recorded: January 13, 2021 | 1:00 p.m. ET

Traders and investors rely on time series patterns generated by asset performance to inform and guide their trading and asset allocation decisions. Economists take advantage of analogous patterns in macroeconomic and market data to forecast recessions and other market events.

But you need to be able to spot these patterns in order to use them.

Catch the latest in RiskSpan’s series of machine learning and data workshops as Chirag Soni and Jing Liu, two of RiskSpan’s experts working at the intersection of data science and capital markets, demonstrate how advanced machine learning techniques such as Dynamic Time Warping and KShape can be applied to automate time series analysis and effectively detect patterns hiding in your data.

Chirag and Jing will discuss specific applications, explain popular algorithms, and walk through code examples.

Join us on Wednesday, January 13th! 



Top Hedge Fund Administrator: Risk Metrics & Performance Reports via Tableau and the Cloud​

A leading hedge fund administrator sought a better way to provide compliance reporting and overnight risk and portfolio reporting for its clients.

Reporting at this scale requires extraordinarily flexibility in computational bandwidth.

The Solution

RiskSpan delivered computation and distribution via the cloud of all required analytics and risk metrics to all relevant parties using the flexibility and attractive visualization of a seamless Tableau integration.

  • Ingestion, validation, and integration of disparate data sources (rates, implied volatility data and terms and conditions from six data vendor sources)
  • Reporting, distribution and publishing of the client’s full range of risk metrics, including VaR, custom aggregation, scenario analyses, interest rate shocks and other stress testing — all readily viewable to every client stakeholder via the cloud using Tableau.

The Edge We Provided

A fully hosted, outsourced solution. The administrator’s highly dynamic reports are delivered by way of a secure, hosted environment to a large number of diverse, institutional clients.


December 2 Workshop: Structured Data Extraction from Image with Google Document AI

Recorded: Dec. 2nd | 1:00 p.m. EDT

RiskSpan Director Steven Sun shares a procedural approach to tackling the difficulties of efficiently extracting structured data from images, scanned documents, and handwritten documents using Google’s latest Document AI Solution. This approach greatly improves:

  • Effectiveness and accuracy of extracting data which will be otherwise difficult or impossible, and 
  • Automating and streamlining the process of feeding extracted data into a data analytic framework

Steven Sun

Director, RiskSpan


Chart of the Month: Fed Impact on Credit ETF Performance

On March 23rd, The Fed announced that its Secondary Market Corporate Credit Facility (SMCCF) would begin purchasing investment-grade corporate bonds in the secondary market, first through ETFs and directly in a later phase. 

In June, we charted the impact of this announcement on the credit spreads of various corporate bonds. This month we are charting its impact on ETF performance.

This month’s chart plots the price of ETFs relative to their price as of March 23rd 2020 (i.e., all ETF prices are set to 1.00 as of that date). Data runs from Feb 24th to Nov 16th 2020.


RiskSpan’s EDGE Platform Named Risk-as-a-Service Category Winner by Chartis Research

ARLINGTON, Va., November 19, 2020 – RiskSpan’s EDGE Platform has been named the “Risk-as-a-Service” Category Winner for 2021 in Chartis Research’s prestigious RiskTech100 Rankings. In winning the RaaS category, RiskSpan edged out strong competing offerings from many other long-established risk service providers.

Chartis_RiskTech100-2021The award caps a successful year for RiskSpan and the EDGE Platform, which underwent a number of key enhancements and, notwithstanding the pandemic, has experienced a 29 percent increase in its subscriber base since the start of 2020.

 

EDGE is an end-to-end, cloud-native platform leveraging mortgage and other structured finance data, modeling, and application layers to allow information to seamlessly flow across products while being securely accessible anytime and anywhere. The Risk Service’s data, insights, and reports are available via the website, integrated via an API, or ingested and distributed as part of a fully managed service.

“The Risk-as-a-Service award was especially competitive this year with many strong offerings,” observed Mark Feeley, Chartis Global Brand Director. “The RiskSpan EDGE solution’s ability to scale and deliver via the cloud is reflected in its category win.”

“We are honored to receive this recognition,” noted Bernadette Kogler, RiskSpan’s co-founder and CEO. “I am proud of our design and development teams who have worked tirelessly through challenging circumstances to continuously enhance EDGE’s Risk component for our expanding community of client users.”

About RiskSpan

RiskSpan offers end-to-end solutions for data management, 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–including both 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.

Media Contact

Timothy Willis
Email: twillis@riskspan.com


Executive Interview: Inside the OCC

Watch RiskSpan CEO Bernadette Kogler’s interview with Acting Comptroller of the Currency Brian Brooks.

They discuss many topics include the OCC’s Project REACh, machine learning models to expand the credit box, blockchain’s role in housing finance, and the expanding definition of a chartered institution.

WATCH THE INTERVIEWRead More


Workshop: Measuring and Visualizing Feature Impact & Machine Learning Model Materiality

Recorded: Oct. 28th | 1:00 p.m. EDT

RiskSpan CIO Suhrud Dagli, who discussed how ML is being incorporated into model risk management during our Sep. 30 webinar: Machine Learning in Model Validation, demonstrates in greater detail how machine learning can be used:

  • In input data validations,
  • To measure feature impact, and
  • To visualize how multiple features interact with each other

Suhrud Dagli


Co-Founder & Fintech Lead, RiskSpan


Why Model Validators Need to Care About the LIBOR Transition

The transition to the Secured Overnight Financing Rate (SOFR) as a LIBOR replacement after 2021 creates layers of risk for banks. Many of these risks are readily apparent, others less so. But the factors banks must consider while choosing replacement rates and correctly implementing contractual fallback language makes a seamless transition a daunting proposition. Though sometimes overlooked, model risk managers have an important role in ensuring this happens correctly and in a way that does not jeopardize the reliability of model outputs.   

LIBOR, SOFR and the need for transition

A quick refresher: The London Interbank Offered Rate (LIBOR) currently serves as the benchmark at which major global banks lend to one another on a short-term basis in the international interbank market. LIBOR is calculated by the Intercontinental Exchange (ICE) and is published daily. LIBOR is based on a combination of five currencies and seven maturities. The most common of these is the three-month U.S. Dollar rate.

Accusations of manipulation by major banks going back as early as 2008, however, raised concerns about the sustainability of LIBOR. A committee convened by the Federal Reserve Board and the Federal Reserve Bank of New York in 2017—the Alternative Reference Rates Committee (ARRC)—identified a broad Treasury repurchase agreement (repo) financing rate as its preferred alternative reference rate to replace LIBOR after 2021. This repo rate (now known as SOFR) was chosen for its ability to provide liquidity to underlying markets and because the volumes underlying SOFR are far larger than any other U.S. money market. This combination of size and liquidity contributes to SOFR’s transparency and protects market participants from attempts at manipulation.

What Does This Mean for MRM?

Because the transition has potential bearing on so many layers of risk—market risk, operational risk, strategic risk, reputation risk, compliance risk, not to mention the myriad risks associated with mispricing assets—any model in a bank’s existing inventory that is tasked with gauging or remediating these risks is liable to be impacted. Understanding how and the extent to which models are considering how LIBOR transition may affect pricing and other core processes are (or should be) of principal concern to model validators.

Ongoing Monitoring and Benchmarking

Regulatory guidance and model validation best practices require testing model inputs and benchmarking how the model performs with the selected inputs relative to alternatives. For this reason, the validation any model whose outputs are sensitive to variable interest rates should include an assessment of how a replacement index (such as SOFR) and adjustment methodology were selected.

Model validators should be able to ascertain whether the model developer has documented enough evidence relating to:

  • Available reference rates and the appropriateness of each to the bank’s specific products
  • System capabilities for using these replacement rates with the bank’s products.
  • Control risks associated with unavailable alternative rates


Fallback Language considerations:

Fallback language—contractual provisions that govern the process for selecting a replacement rate in the event of LIBOR termination—should also factor into a validator’s assessment of model inputs. While many existing fallback provisions can be frustratingly vague when it comes to dealing with a permanent cessation of LIBOR, validators of models that rely on reference rates as inputs have an obligation to determining compliance with fallback language containing clear and executable terms. These include:

  • Specific triggers to enact the replacement rate
  • Clarity regarding the replacement rate and spread adjustments
  • Permissible options under fallback language – and whether other options might be more appropriate than the one ultimately selected based on the potential for valuation changes, liquidity impact, hedging implications, system changes needed, and customer impact

In November 2019, the ARRC published the finalized fallback language for residential adjustable rate mortgages, bilateral business loans, floating rate notes, securitizations, and syndicated loans. It has also actively engaged with the International Swap Derivatives Association (ISDA) to finalize the fallback parameters for derivatives.

The ARRC also recommended benchmark replacement rates adjusted for spread that would replace the current benchmark due to circumstances that trigger the replacement. The recommendation included the following benchmark replacement waterfalls. Validators of models relying on these replacements may choose, as part of their best practices review, to determine the extent to which existing fallback provisions align with the recommendations.

Replacement Description
Term SOFR + spread adjustment Forward-looking term SOFR for the applicable corresponding tenor. Note: Loan recommendations allow use of the next longest tenor term SOFR rate if the corresponding tenor is unavailable  
Compounded SOFR + spread Adjustment Compounded average of daily SOFRs over the relevant period depending on the tenor of USD LIBOR being replaced
Relevant selected rate + spread adjustment   Rate selected by the Relevant Governmental Body, lender, or borrower & administrative agent
Relevant ISDA replacement rate + spread adjustment The applicable replacement rate (without spread adjustment) that is embedded in ISDA’s standard definitions  
Issuer, designated transaction representative or noteholder replacement + spread adjustment An identified party will select a replacement rate, in some cases considering any industry-accepted rate in the related market. Note: in certain circumstances this step could be omitted


Model risk managers can sometimes be lulled into believing that the validation of interest rate inputs consists solely of verifying their source and confirming that they have been faithfully brought into the model. Ultimately, however, model validators are responsible for verifying not only the provenance of model inputs but also their appropriateness. Consequently, ensuring a smooth transition to the most appropriate available reference rate replacement is of paramount importance to risk management efforts related to the models these rates feed.


RESOURCES:

https://www.insideafricalaw.com/blog/benchmark-reform-the-impact-of-libor-transition-on-the-african-project-finance-market

https://www.occ.treas.gov/news-issuances/bulletins/2020/bulletin-2020-68.html

https://www.isda.org/a/n6tME/Supplemental-Consultation-on-USD-LIBOR-CDOR-HIBOR-and-SOR.pdf

https://www.investopedia.com/terms/l/libor.asp

https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2020/ARRC-factsheet.pdf

https://www.newyorkfed.org/arrc/sofr-transition

https://www.newyorkfed.org/medialibrary/Microsites/arrc/files/2019/LIBOR_Fallback_Language_Summary

https://www.isda.org/a/n6tME/Supplemental-Consultation-on-USD-LIBOR-CDOR-HIBOR-and-SOR.pdf

http://assets.isda.org/media/50b3fed0/47be9435-pdf/


The information within this section has been taken directly from the https://www.occ.treas.gov/news-issuances/bulletins/2020/bulletin-2020-68.html [AR1]


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