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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.

SPEAK to An EXPERT

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. 

GET STARTED WITH A RISKSPAN EXPERT TODAY!

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.

 

GET A DEMO

 

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

Webinar: Geocoding Mortgage Data for ESG and Climate Risk Analysis

Recorded: February 16th | 1:00 p.m. ET

Geocoding remains a particularly vexing challenge for the mortgage industry. Lenders, servicers, and loan/MSR investors know the addresses of the properties securing their mortgage assets. But most data pertaining to climate and other ESG considerations is available only by matching to a census tract or latitude/longitude.

And if you have ever tried mapping addresses, you know this exercise can be a lot harder than it looks. Fortunately, a growing body of geocoding tools and techniques is emerging to make the process more manageable than ever, even with less than perfect address data.

Our panel presents a how-to guide on geocoding logic and its specific application to the mortgage space. You will learn a useful waterfall approach for linking census-tract-level, geo-specific data for climate risk and ESG to the property addresses in your portfolio.

 

Featured Speakers

Suhrud Dagli

Chief Innovation Officer, RiskSpan

Jason Huang

Manager, RiskSpan

Jason Lee

Software Engineer, RiskSpan

Improving MSR Pricing Using Cloud-Based Loan-Level Analytics — Part II: Addressing Climate Risk

Modeling Climate Risk and Property Valuation Stability

Part I of this white paper seriesKey Takeaways introduced the case for why loan-level (as opposed to rep-line level) analytics are increasingly indispensable when it comes to effectively pricing an MSR portfolio. Rep-lines are an effective means for classifying loans across many important categories. But certain loan, borrower, and property characteristics simply cannot be “rolled up” to the rep-line level as easily as UPB, loan age, interest rate, LTV, credit score, and other factors. This is especially true when it comes to modeling based on available information about a mortgage’s subject property.

Assume for the sake of simplicity that human and automated appraisers do a perfect job of assigning property values for the purpose of computing origination and updated LTVs (they do not, of course, but let’s assume they do). Prudent MSR investors should be less interested in a property’s current value than in what is likely to happen to that value over the expected life of their investment. In other words, how stable is the valuation? How likely are property values within a given zip code, or neighborhood, or street to hold?

The stability of any given property’s value is tied to the macroeconomic prospects of its surrounding community. Historical and forecast trends of the local unemployment rate can be used as a rough proxy for this and are already built into existing credit and prepayment models. But increasingly, a second category of factors is emerging as an important predictor of home price stability, the property’s exposure to climate risk and natural hazard events.

Climate exposure is becoming increasingly difficult to ignore when it comes to property valuation. And accounting for it is more complicated than simply applying a premium to coastal properties. Climate risk is not just about hurricanes and storm surges anymore. A growing number of inland properties are being identified as at risk not just to wind and water hazards, but to wildfire and other perils as well. The diversity of climate risks means that the problem of quantifying and understanding them will not be solved simply by fixing out-of-date flood plain maps.

MSR investors are exposed to climate risk in ways that whole loan or securities investors are not. When climate events force borrowers into forbearance or other repayment plans, MSR investors not only forego the cash flows associated with missed interest payments that will never be made, but also incur the additional costs of administering the loss mitigation programs and making necessary P&I and escrow advances.

Overlaying climate scenario analysis on top of traditional credit modeling is unquestionably the future of quantifying mortgage asset exposure. And in many respects, the future is already here. Regulatory guidance is forthcoming requiring public companies to quantify their exposure to climate risk across three categories: acute physical risk, chronic physical risk, and economic transition risk.

Acute Risk

Acute climate risk describes a property’s exposure to individual catastrophic events. As a result of climate change, these events are expected to increase in frequency and severity. The property insurance space already has analytical tools in place to quantify property damage to hazard risks such as:

  • Hurricane, including wind, storm surge, and precipitation-induced flooding
  • Flooding, including “fluvial” and “pluvial” – on- and off-plan flooding
  • Wildfire
  • Severe thunderstorm, including exposure to tornadoes, hail, and straight-line wind, and
  • Earthquake – though not tied to climate change, earthquakes remain a massively underinsured risk that can impact MSR holders

Acute risks are of particular concern for MSR holders as disaster events have proven to increase both mortgage delinquency and prepayment. The chart below illustrates these impacts after hurricane Katrina.

Chronic Risk

Chronic risk characterizes a property’s exposure to adverse conditions brought on by longer-term concerns. These include frequent flooding, sea level rise, drought hazards, heat stress, and water shortages. These effects could erode home values or put entire communities at risk over a longer period. Models currently in use forecast these risks over 20- and 25-year periods.

Transition Risk

Transition risk describes exposure to changing policies, practices or technologies that arise from a broader societal move to reduce its carbon footprint. These include increases in the direct cost of homeownership (e.g., taxes, insurance, code compliance, etc.), increased energy and other utility costs, and localized employment shocks as businesses and industry leave high-risk areas. Changing property insurance requirements (by the GSEs, for example) could further impact property valuations in affected neighborhoods.

———–

Converting acute, chronic and transition risks into mortgage modeling scenarios can only be done effectively at the loan level. Rep-lines cannot adequately capture them. As with most prepayment and credit modeling, accounting for climate risk is an exercise in scenario analysis. Building realistic scenarios involves taking several factors into account.

Scenario Analysis

Quantifying physical risks (whether acute or chronic) entails identifying:

  • Which physical hazard types the property is exposed to
  • How each hazard type threatens the property[1]
  • The materiality of each hazard; and
  • The most likely timeframes over which these hazards could manifest

Factoring climate risk into MSR pricing requires translating the answers to the questions above into mortgage modeling scenarios that function as credit and prepayment model inputs. The following table is an example of how RiskSpan overlays the impact of an acute event – specifically a category 5 hurricane in South Florida — on home price, delinquency, turnover and macroeconomic conditions.

 

Chart

 

Chart

Applying this framework to an MSR portfolio requires integration with an MSR cash flow engine. MSR cash flows and the resulting valuation are driven by the manner in which the underlying delinquency and prepayment models are affected. However, at least two other factors affect servicing cash flows beyond simply the probability of the asset remaining on the books. Both of these are likely impacted by climate risk.

  • Servicing Costs: Rising delinquency rates are always accompanied by corresponding increases in the cost of servicing. An example of the extent to which delinquencies can affect servicing costs was presented in our previous paper. MSR pricing models take this into account by applying a different cost of servicing to delinquent loans. Some believe, however, that servicing loans that enter delinquency in response to a natural disaster can be even more expensive (all else equal) than servicing a loan that enters delinquency for other reasons. Reasons for this range from the inherent difficulty of reaching displaced persons to the layering impact of multiple hardships such events tend to bring upon households at once.[2]
  • Recapture Rate: The data show that prepayment rates consistently spike in the wake of natural disasters. What is less clear is whether there is a meaningful difference in the recapture rate for these prepayments. Anecdotally, recapture appears lower in the case of natural disaster, but we do not have concrete data on which to base assumptions. This is clearly only relevant to MSR investors that also have an origination arm with which to capture loans that refinance.

Climate risk encompasses a wide range of perils, each of which affects MSR values in a unique way. Hurricanes, wildfires, and droughts differ not only in their geography but in the specific type of risk they pose to individual properties. Even if there were a way of assigning every property in an MSR portfolio a one-size-fits-all quantitative score, computing a “weighted average climate risk” value and applying it to a rep-line would be problematic. Such an average would be denuded of any nuance specific to individual perils. Peril-specific data is critical to being able to make the LTV, delinquency, turnover and macroeconomic assumption adjustments outlined above.

And there is no way around it. Doing all this requires a loan-by-loan analysis. RiskSpan’s Edge Platform was purpose built to analyze mortgage portfolios at the loan level and is becoming the industry’s go-to solution for measuring and managing exposures to market, credit and climate events.

Contact us to learn more.

[1] Insurability of hazards varies widely, even before insurance requirements are considered.

[2] In addition, because servicers normally staff for business-as-usual levels of delinquencies, a large acute event will create a significant spike in the demand for servicer personnel. If a servicer’s book is heavily concentrated in the Southeast, for example, a devastating storm could result in having to triple the number of people actively servicing the portfolio.

Improving MSR Pricing Using Cloud-Native Loan-Level Analytics (Part II)

Improving MSR Pricing Using Cloud-Native Loan-Level Analytics (Part II)

  1. MSR investors are more exposed to acute climate risk than whole loan or securities investors are. MSR investors are not in a favorable position to recoup cash flows lost to climate disruptions.
  2. Climate risk can be acute, chronic, or transitional. Each affects MSR values in a different way.
  3. Integrating climate scenario analysis into traditional credit and prepayment modeling – both of which are critical to modeling MSR cash flows and pricing — requires a loan-by-loan approach.
  4. Climate risk cannot be adequately expressed or modeled using a traditional rep-line approach.

Improving the Precision of MSR Pricing Using Cloud-Native Loan-Level Analytics (Part I)

Traditional MSR valuationTake Away approaches based on rep lines and loan characteristics important primarily to prepayment models fail to adequately account for the significant impact of credit performance on servicing cash flows – even on Agency loans. Incorporating both credit and prepayment modeling into an MSR valuation regime requires a loan-by-loan approach—rep lines are simply insufficient to capture the necessary level of granularity. Performing such an analysis while evaluating an MSR portfolio containing hundreds of thousands of loans for potential purchase has historically been viewed as impractical. But thanks to today’s cloud-native technology, loan-level MSR portfolio pricing is not just practical but cost-effective. Introduction Mortgage Servicing Rights (MSRs) entitle the asset owner to receive a monthly fee in return for providing billing, collection, collateral management and recovery services with respect to a pool of mortgages on behalf of the beneficial owner(s) of those mortgages. This servicing fee consists primarily of two components based on the current balance of each loan:  a base servicing fee (commonly 25bps of the loan balance) and an excess servicing fee.  The latter is simply the difference between each loan rate and the sum of the pass-through rate of interest and the base servicing. The value of a portfolio of MSRs is determined by modeling the projected net cash flows to the owner and discounting them to the present using one of two methodologies:

  1. Static or Single-Path Pricing: A single series of net servicing cash flows are generated using current interest and mortgage rates which are discounted to a present value using a discount rate reflecting current market conditions.
  2. Stochastic or Option-Adjusted Spread (OAS) Pricing: Recognizing that interest rates will vary over time, a statistical simulation of interest rates is used to generate many time series (typically 250 to 1,000) of net servicing cash flows.  Each time series of cash flows is discounted at a specified spread over a simulated base curve (generally the LIBOR or Treasury curve) and the resulting present value is averaged across all of the paths.

While these two pricing methodologies have different characteristics and are based on very different conceptual frameworks, they both strongly depend on the analyst’s ability to generate reliable forecasts of net servicing cashflows.  As the focus of this white paper is to discuss the key factors that determine the net cashflows, we are indifferent here as to the ultimate methodology used to convert those cashflows into a present value and for simplicity will look to project a single path of net cash flows.  RiskSpan’s Edge platform supports both static and OAS pricing and RiskSpan’s clients use each and sometimes both to value their mortgage instruments.

Modeling Mortgage Cash Flows Residential mortgages are complex financial instruments. While they are, at their heart, a fixed income instrument with a face amount and a fixed or a floating rate of interest, the ability of borrowers to voluntarily prepay at any time adds significant complexity.  This prepayment option can be triggered by an economic incentive to refinance into a lower interest rate, by a decision to sell the underlying property or by a change in life circumstances leading the borrower to pay off the mortgage but retain the property. The borrower also has a non-performance option. Though not usually exercised voluntarily, forbearance options made available to borrowers in response to Covid permitted widespread voluntary exercise of this option without meaningful negative consequences to borrowers. This non-performance option ranges from something as simple as a single late payment up to cessation of payments entirely and forfeiture of the underlying property. Forbearance (a payment deferral on a mortgage loan permitted by the servicer or by regulation, such as the COVID-19 CARES Act) became a major factor in understanding the behavior of mortgage cash flows in 2020. Should a loan default, ultimate recovery depends on a variety of factors, including the loan-to-value ratio, external credit support such as primary mortgage insurance as well as costs and servicer advances paid from liquidation proceeds. Both the prepayment and credit performance of mortgage loans are estimated with the use of statistical models which draw their structure and parameters from an extremely large dataset of historical performance.  As these are estimated with reference to backward-looking experience, analysts often adjust the models to reflect their experience adjusted for future expectations. Investors in GSE-guaranteed mortgage pass-through certificates are exposed to voluntary and, to a far less extent, involuntary (default) prepayments of the underlying mortgages.  If the certificates were purchased at a premium and prepayments exceed expectations, the investor’s yield will be reduced.  Conversely, if the certificates were purchased at a discount and prepayments accelerated, the investor’s yield will increase.  Guaranteed pass-through certificate investors are not exposed to the credit performance of the underlying loans except to the extent that delinquencies may suppress voluntary prepayments. Involuntary prepayments and early buyouts of delinquent loans from MBS pools are analogous to prepayments from a cash flow perspective when it comes to guaranteed Agency securities. Investors in non-Agency securities and whole loans are exposed to the same prepayment risk as guaranteed pass-through investors are, but they are also exposed to the credit performance of each loan. And MSR investors are exposed to credit risk irrespective of whether the loans they service are guaranteed. Here is why. The mortgage servicing fee can be simplistically represented by an interest-only (IO) strip carved off of the interest payments on a mortgage. Net MSR cash flows are obtained by subtracting a fixed servicing cost. Securitized IOs are exposed to the same factors as pass-through certificates, but their sensitivity to those factors is magnitudes greater because a prepayment constitutes the termination of all further cash flows – no principal is received.  Consequently, returns on IO strips are very volatile and sensitive to interest rates via the borrower’s prepayment incentive. While subtracting fixed costs from the servicing fee is still a common method of generating net MSR cash flows, it is a very imprecise methodology, subject to significant error. The largest component of this error arises from the fact that servicing cost is highly sensitive to the credit state of a mortgage loan. Is the loan current, requiring no intervention on the part of the servicer to obtain payment, or is the loan delinquent, triggering additional, and potentially costly, servicer processes that attempt to restore the loan to current? Is it seriously delinquent, requiring a still higher level of intervention, or in default, necessitating a foreclosure and liquidation effort? According to the Mortgage Bankers Association, the cost of servicing a non-performing loan ranged from eight to twenty times the cost of servicing a performing loan during the ten-year period from 2009 to 1H2019 (Source: Servicing Operations Study and Forum; PGR 1H2019). Using 2014 as both the mid-point of this ratio and of the time period under consideration, the direct cost of servicing a performing loan was $156, compared to $2,000 for a non-performing loan. Averaged across both performing and non-performing loans, direct servicing costs were $171 per loan, with an additional cost of $31 per loan arising from unreimbursed expenditures related to foreclosure, REO and other costs, plus an estimated $58 per loan of corporate administration expense, totaling $261 per loan. The average loan balance of FHLMC and FNMA loans in 2014 was approximately $176,000, translating to an annual base servicing fee of $440. The margins illustrated by these figures demonstrate the extreme sensitivity of net servicing cash flows to the credit performance of the MSR portfolio. After prepayments, credit performance is the most important factor determining the economic return from investing in MSRs.  A 1% increase in non-performing loans from the 10yr average of 3.8% results in a $20 per loan net cash flow decline across the entire portfolio.  Consequently, for servicers who purchase MSR portfolios, careful integration of credit forecasting models into the MSR valuation process, particularly for portfolio acquisitions, is critical. RiskSpan’s MSR engine integrates both prepayment and credit models, permitting the precise estimation of net cash flows to MSR owners. The primary process affecting the cash inflow to the servicer is prepayment; when a loan prepays, the servicing fee is terminated. The cash outflow side of the equation depends on a number of factors:

  1. First and foremost, direct servicing cost is extremely sensitive to loan performance. The direct cost of servicing rises rapidly as delinquency status becomes increasingly severe. Direct servicing cost of a 30-day delinquent loan varies by servicer but can be as high as 350% of a performing loan. These costs rise to 600% of a performing loan’s cost at 60 days delinquent.
  2. Increasing delinquency causes other costs to escalate, including the cost of principal and interest as well as tax and escrow advances, non-reimbursable collateral protection, foreclosure and liquidation expenses. Float decreases, reducing interest earnings on cash balances.

    Improving-MSR-Pricing-GraphSource: Average servicing cost by delinquency state as supplied by several leading servicers of Agency and non-Agency mortgages.

RiskSpan’s MSR platform incorporates the full range of input parameters necessary to fully characterize the positive and negative cash flows arising from servicing. Positive cash flows include the servicing and other fees collected directly from borrowers as well as various types of ancillary and float income. Major contributors to negative cash flows include direct labor costs associated with performing servicing activities as well as unreimbursed foreclosure and liquidation costs, compensating interest and costs associated with financing principal, interest and escrow advances on delinquent loans. The net cash flows determined at the loan level are aggregated across the entire MSR portfolio and the client’s preferred pricing methodology is applied to calculate a portfolio value.

Improving-MSR-Pricing-Graph

Aggregation of MSR Portfolio Cash Flows – Loan-by-Loan vs “Rep Lines”

Historically, servicer net cash flows were determined using a simple methodology in which the base servicing fee was reduced by the servicing cost, and forecast prepayments were projected using a prepayment model. The impact of credit performance on net cash flows was explicitly considered by only a minority of practitioners.

Because servicing portfolios can contain hundreds of thousands or millions of loans, the computational challenge of generating net servicing cash flows was quite high. As the industry moved increasingly towards using OAS pricing and risk methodologies to evaluate MSRs, this challenge was multiplied by 250 to 1,000, depending on the number of paths used in the stochastic simulation.

In order to make the computational challenge more tractable, loans in large portfolios have historically been allocated to buckets according to the values of the characteristics of each loan that most explained its performance. In a framework that considered prepayment risk to be the major factor affecting MSR value, the superset of characteristics that mattered were those that were inputs to the prepayment model. This superset was then winnowed down to a handful of characteristics that were considered most explanatory. Each bucket would be converted to a “rep line” that represented the average of the values for each loan that were input into the prepayment models.

Improving-MSR-Pricing-Graph

Medium-sized servicers historically might have created 500 to 1,500 rep lines to represent their portfolio. Large servicers today may use tens of thousands.

The core premise supporting the distillation of a large servicing portfolio into a manageable number of rep lines is that each bucket represents a homogenous group of loans that will perform similarly, so that the aggregated net cash flows derived from the rep lines will approximate the performance of the sum of all the individual loans to a desired degree of precision.

The degree of precision obtained from using rep lines was acceptable for valuing going-concern portfolios, particularly if variations in the credit of individual loans and the impact of credit on net cash flows were not explicitly considered.  Over time, movement in MSR portfolio values would be driven mostly by prepayments, which themselves were driven by interest rate volatility. If the modeled value diverged sufficiently from “fair value” or a mark provided by an external provider, a valuation adjustment might be made and reported, but this was almost always a result of actual prepayments deviating from forecast.

Once an analyst looks to incorporate credit performance into MSR valuation, the number of meaningful explanatory loan characteristics grows sharply.  Not only must one consider all the variables that are used to project a mortgage’s cash flows according to its terms (including prepayments), but it also becomes necessary to incorporate all the factors that help one project exercise of the “default option.” Suddenly, the number of loans that could be bucketed together and be considered homogenous with respect to prepayment and credit performance would drop sharply; the number of required buckets would increase dramatically –to the point where the number of rep lines begins to rival the number of loans. The sheer computational power needed for such complex processing has only recently become available to most practitioners and requires a scalable, cloud-native solution to be cost effective.

Two significant developments have forced mortgage servicers to more precisely project net mortgage cash flows:

  1. As the accumulation of MSRs by large market participants through outright purchase, rather than through loan origination, has been growing dramatically, imprecision in valuation became less tolerable as it could result in the servicer bidding too low or too high for a servicing package.
  2. FASB Accounting Standard 2016-13 obligated entities holding “financial assets and net investment in leases that are not accounted for at fair value through net income” to estimate “incurred losses,” or estimated futures losses over the life of the asset. While the Standard does not necessarily apply to MSRs because most MSR investors account for the asset at fair value and flow fair value mark-to-market through income, it did lead to a statement from the major regulators:

“If a financial asset does not share risk characteristics with other financial assets, the new accounting standard requires expected credit losses to be measured on an individual asset basis.” 

(Source: Board of Governors of the Federal Reserve System, Federal Deposit Insurance Corporation, National Credit Union Administration, and Office of the Comptroller of the Currency. “Joint Statement on the New Accounting Standard on Financial Instruments – Credit Losses.” June 17, 2016.).

The result of these developments is that a number of large servicers are revisiting their bucketing methodologies and considering using loan-level analyses to better incorporate the impact of credit on MSR value, particularly when purchasing new packages of MSRs. By enabling MSR investors to re-combine and re-aggregate cash flow results on the fly, loan-level projections open the door to a host of additional, scenario-based analytics. RiskSpan’s cloud-native Edge Platform is uniquely positioned to support these emerging methodologies because it was envisioned and built from the ground up as a loan-level analytical engine. The flexibility afforded by its 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.

In Part II of this series, we will delve into property-level risk characteristics—factors that are not easily rolled up into portfolio rep lines and must be evaluated at the loan level—impact credit risk and servicing cash flows. We will also quantify the impact of a loan-level analysis incorporating these factors on an MSR valuation.

Contact us to learn more.

Top 10 National Mortgage Servicer: MSR Pricing Model Review, Analysis and Enhancements

One of the nation’s leading mortgage lenders had recently acquired several large MSR portfolios and required assistance reviewing, documenting and recommending enhancements to the underlying assumptions of the model used to price the MSR portfolios at acquisition.

Requiring review and documentation included collateral assumptions, cost and revenue assumptions, and prepayment (CDR/CRR/CPR) assumptions.

The Solution

RiskSpan comprehensively analyzed the cash flow impact of each major assumption (e.g., CDR/CRR/CPR, servicing advances, fees, cost) — the collateral assumptions in the model as well as documented forecast vs. actual outcomes.

RiskSpan worked in concert with the servicer’s finance and pricing teams to collect and analyze roll rates and to forecast actual loan-level data around losses, servicing advances, servicing fees, ancillary fees, PIF, and scheduled principal payments.  

Deliverables 

A comprehensive pricing model validation report that included the following:

  • Consolidated CDR-, CRR-, CPR-related pricing model data, including balance, delinquency status, recapture, scheduled payments, default, etc. for all acquired portfolios. The resulting dataset could be used both for deal tracking and pricing model validation 
  • Documentation of the calculation and location of pricing model fields.
  • Reconciliation of the different methods for calculating CDR, CRR, and CPR.
  • Deep dives into model predictions of short sales and foreclosure turn-times
  • Loan-state transition model forecasts and comparison of the model variables between two version of the forecast, including shift analyses.
  • Drivers of forecast variance. 
  • Identification of dials responsible for short sale and foreclosure turn forecast shifting.
  • SAS-based streamlined process for comparing model variables for sub-segment and sub-models in loan state
  • Transition Model:  Incorporation of actual and forecast into pricing models to compare with original pricing model cash flow results for acquired portfolios
  • Creation and standardization of the pricing model validation report output.
  • Automation of reporting.  
  • Improvement of the process by creating a calculation template that could be easily replicated for other portfolios. 
  • Documentation of the validation process and comprehensive review of the validation results with the servicer’s risk team, finance team and pricing team management.

Mortgage Servicing Rights

MSR Buyers & Sellers

Eliminate Missed Opportunities and Costly Pricing Errors

  • Get complete coverage, including Agency, FHA/VA & Non-QM

  • Understand cohort-specific CPR trends relevant to your MSR sale or portfolio

  • Go beyond cohort-level valuation with loan-level, trading-quality prepayment and credit models

  • Spend less time cleaning and loading loan data

  • Customize MSR risk scenarios

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Model documentation

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RiskSpan’s Platform is the only integrated MSR solution offering granular loan-level historic performance analysis, smart loan mapping technology and innovative loan level MSR pricing models, robust scenario capabilities all built on a modern, fast, scalable, secure and user-friendly tech stack. 

Interactively Query & Filter Loan Data and Historical Performance Metrics

Quickly generate custom cohort stratifications to understand CPR trends relevant to your MSR sale or portfolio. Actively benchmark performance of Ginnie Mae, High-LTV, Low-FICO, or any other loan subset

(Direct Data Access via Snowflake Also Available)

Access Interactive Loans module: 

    • Create Composition reports
    • Query/filter loan data
    • Extract loan data for external constituents

Report on historical performance and filter performance metrics across dimensions:

    • Prepayment
    • Default
    • Recapture rates

Leverage suite of customized data visualization reports (via Tableau)

Outsource the Heavy Lifting of Consolidating and Mapping Servicer Data

  • Quickly load and map portfolios from different counterparties using AI-powered tape cracking

  • Apply machine learning model that accounts for past experience

  • Leverage RiskSpan’s recommended QC rules and backup values 

  • Access QC audit reports showing mapping choices and exceptions

Meet multiple bid deadlines, save hours of work, and improve your analytic precision by using Smart Mapper to load loan tapes

MSR Bid Analysis: Loan-Level MSR Prepayment Model

  • Trading-quality prepayment and credit models and MSR cash flow engin.

  • Forecasts include detailed MSR component cash flows, optionadjusted valuations

  • Results can be viewed across various pre-canned or custom segmentations

  • In-house best in class modeling team regularly updates model and is available to clients as a resource

  • Robust interactive model diagnostics available for back-testing

MSR Risk Analysis: Comprehensive Scenario Analysis and Customization Options

Run multiple interest rate and or pricing scenarios to better understand  range of possible MSR valuations

  • Flexible User Interface (UI) easily  incorporates custom model dials and MSR assumptions

  • Create and save custom interest rate scenarios aligned with your firm’s economic view¤

  • Multiple team members can view, and access analytics results through the Platform

  • Automated, overnight run process allows for on-time delivery of daily analytics run at the loan-level

  • Integrate overnight analytics results into your firm’s enterprise risk platform through Snowflake connectivity

Leverage modern technology and a managed service approach for reliable and timely daily MSR valuation and risk analytics 

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Residential Mortgage REIT: End to End Loan Data Management and Analytics

An inflexible, locally installed risk management system with dated technology required a large IT staff to support it and was incurring high internal maintenance costs.

Absent a single solution, the use of multiple vendors for pricing and risk analytics, prepay/credit models and data storage created inefficiencies in workflow and an administrative burden to maintain.

Inconsistent data and QC across the various sources was also creating a number of data integrity issues.

The Solution

An end-to-end data and risk management solution. The REIT implemented RiskSpan’s Edge Platform, which provides value, cost and operational efficiencies.

  • Scalable, cloud-native technology
  • Increased flexibility to run analytics at loan level; additional interactive / ad-hoc analytics
  • Reliable, accurate data with more frequent updates

Deliverables 

Consolidating from five vendors down to a single platform enabled the REIT to streamline workflows and automate processes, resulting in a 32% annual cost savings and 46% fewer resources required for maintenance.

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