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Articles Tagged with: MSRs

Live Demo of RiskSpan’s Award-Winning Edge Platform–3

Wednesday, August 24th | 1:00 p.m. EDT

Live Demo of RiskSpan’s award-winning Edge Platform. Learn more and ask questions at our bi-weekly, 45-minute demo.

Historical Performance Tool: Slice and dice historical loan performance in the Agency and PLRMBS universe to find outperforming cohorts.

Predictive Loan-Level Pricing and Risk Analytics: Produce loan-level pricing and risk on loans, MSRs, and structured products in minutes – with behavioral models applied at the loan-level, and other assumptions applied conveniently to inform bids and hedging.

Loan Data Management: Let RiskSpan’s data scientists consolidate and enhance your data across origination and servicing platforms, make it analytics-ready, and maintain if for ongoing trend analysis.


About RiskSpan:

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.

Presenters

Joe Makepeace

Director, RiskSpan

Jordan Parker

Sales Executive, RiskSpan


RiskSpan Introduces Multi-Scenario Yield Table 

ARLINGTON, Va., August 4, 2022

RiskSpan, a leading provider of residential mortgage and structured product data and analytics, has announced a new Multi-Scenario Yield Table feature within its award-winning Edge Platform.  

REITs and other mortgage loan and MSR investors leverage the Multi-Scenario Yield Table to instantaneously run and compare multiple scenario analyses on any individual asset in their portfolio. 

An interactive, self-guided demo of this new functionality can be viewed here. 

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

Request a No-Obligation Live Demo

With a single click from the portfolio screen, Edge users can now simultaneously view the impact of as many as 20 different scenarios on outputs including price, yield, WAL, dv01, OAS, discount margin, modified duration, weighted average CRR and CDR, severity and projected losses. The ability to view these and other model outputs across multiple scenarios in a single table eliminates the tedious and time-consuming process of running scenarios individually and having to manually juxtapose the resulting analytics.  

Entering scenarios is easy. Users can make changes to scenarios right on the screen to facilitate quick, ad hoc analyses. Once these scenarios are loaded and assumptions are set, the impacts of each scenario on price and other risk metrics are lined up in a single, easily analyzed data table. 

Analysts who determine that one of the scenarios is producing more reasonable results than the defined base case can overwrite and replace the base case with the preferred scenario in just two clicks.   

The Multi-Scenario Yield Table is the latest in a series of enhancements that is making the Edge Platform increasingly indispensable for mortgage loan and MSR portfolio managers. 


 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 

twiilis@riskspan.com


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.

REQUEST A DEMO

Live Demo of RiskSpan’s Award-Winning Edge Platform

Wednesday, July 27th | 1:00 p.m. EDT

Register for the next Live Demo of RiskSpan’s award-winning Edge Platform. Learn more and ask questions at our bi-weekly, 45-minute demo.

Historical Performance Tool: Slice and dice historical loan performance in the Agency and PLRMBS universe to find outperforming cohorts.

Predictive Loan-Level Pricing and Risk Analytics: Produce loan-level pricing and risk on loans, MSRs, and structured products in minutes – with behavioral models applied at the loan-level, and other assumptions applied conveniently to inform bids and hedging.

Loan Data Management: Let RiskSpan’s data scientists consolidate and enhance your data across origination and servicing platforms, make it analytics-ready, and maintain if for ongoing trend analysis.


About RiskSpan:

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.

Presenters

Joe Makepeace

Director, RiskSpan

Jordan Parker

Sales Executive, RiskSpan


RiskSpan Introduces Media Effect Measure for Prepayment Analysis, Predictive Analytics for Managed Data 

ARLINGTON, Va., July 14, 2022

RiskSpan, a leading provider of residential mortgage  and structured product data and analytics, has announced a series of new enhancements in the latest release of its award-winning Edge Platform.

Comprehensive details of these new capabilities are available byrequesting a no-obligation demo at riskspan.com.

Speak to An Expert

Media Effect – It has long been accepted that prepayment speeds see an extra boost as media coverage alerts borrowers to refinancing opportunities. Now, Edge lets traders and modelers measure the media effect present in any active pool of Agency loans—highlighting borrowers most prone to refinance in response to news coverage—and plot the empirical impact on any cohort of loans. Developed in collaboration with practitioners, it measures rate novelty by comparing rate environment at a given time to rates over the trailing five years. Mortgage portfolio managers and traders who subscribe to Edge have always been able to easily stratify mortgage portfolios by refinance incentive. With the new Media Effect filter/bucket, market participants fine tune expectations by analyzing cohorts with like media effects.

Predictive Analytics for Managed Data – Edge subscribers who leverage RiskSpan’s Data Management service to aggregate and prep monthly loan and MSR data can now kick off predictive analytics for any filtered snapshot of that data. Leveraging RiskSpan’s universe of forward-looking analytics, subscribers can generate valuations, market risk metrics to inform hedging, credit loss accounting estimates and credit stress test outputs, and more. Sharing portfolio snapshots and analytics results across teams has never been easier.

These capabilities and other recently released Edge Platform functionality will be on display at next week’s SFVegas 2022 conference, where RiskSpan is a sponsor. RiskSpan will be featured at Booth 38 in the main exhibition hall. RiskSpan professionals will also be available to respond to questions on July 19th following their panels, “Market Beat: Mortgage Servicing Rights” and “Technology Trends in Securitization.”


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.


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.  


Webinar Recording: How Much Will That MSR Portfolio Really Cost You?

Recorded: June 8th | 1:00 p.m. ET

Accurately valuing a mortgage servicing rights portfolio requires accurately projecting MSR cash flows. And accurately projecting MSR cash flows requires a reliable forecast of servicing costs. Trouble is, servicing costs vary extensively from loan to loan. While the marginal cost of servicing a loan that always pays on time is next to nothing, seriously delinquent loans can easily cost hundreds, if not thousands, of dollars per year.

The best way to account for this is to forecast and assign servicing costs at the loan level – a once infeasible concept that cloud-native technology has now brought within reach. Our panelists present a novel, granular approach to servicing cost analytics and how to get to a truly loan-by-loan MSR valuation (without resorting to rep lines).

 

Featured Speakers

Venkat Mullur

SVP, Capital Markets, Ocwen

Paul Gross

Senior Quantitative Analyst, New Residential Investment Corp.

Dan Fleishman

Managing Director, RiskSpan

Joe Makepeace

Director, RiskSpan


Webinar: Tailoring Stress Scenarios to Changing Risk Environments

July 13th | 1:00 p.m. ET

Designing market risk stress scenarios is challenging because of the disparate ways in which various risk factors impact different asset classes. No two events are exactly alike, and the Covid-19 pandemic and the Russian invasion of Ukraine each provide a case study for risk managers seeking to incorporate events without precise precedents into existing risk frameworks.
 
Join RiskSpan’s Suhrud Dagli and Martin Kindler on Wednesday, June 15th at 1 p.m. ET as they illustrate an approach for correlating rates, spreads, commodity prices and other risk factors to analogous historical geopoltical disruptions and other major market events. Market risk managers will receive an easily digestable tutorial on the math behind how to create probability distributions and reliably model how such events are most likely to impact a portfolio.

 

Featured Speakers

Suhrud Dagli

Co-Founder and CIO, RiskSpan

Photo of Martin Kindler

Martin Kindler

Managing Director, RiskSpan


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. 

 

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.


Recent Edge Platform Updates

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.


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.


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.


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