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Articles Tagged with: Agency Edge

New Refinance Lag Functionality Affords RiskSpan Users Flexibility in Higher Rate Environments 

ARLINGTON, Va., September 29, 2022 — RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced that users of its award-winning Edge Platform can now fine-tune the assumed time lag between a rate-incentivized borrower’s decision to refinance and ultimate payoff. Getting this time lag right unveils a more accurate understanding of the rate incentive that borrowers responded to and thus better predictions of coming prepayments. 

The recent run-up in interest rates has caused the number of rate-incentivized mortgage refinancings to fall precipitously. Newfound operational capacity at many lenders, created by this drop in volume, means that new mortgages can now be closed in fewer days than were necessary at the height of the refi boom. This “lag time” between when a mortgage borrower becomes in-the-money to refinance and when the loan actually closes is an important consideration for MBS traders and analysts seeking to model and predict prepayment performance. 

Rather than confining MBS traders to a single, pre-set lag time assumption of 42 days, users of the Edge Platform’s Historical Performance module can now adjust the lag assumption when building their S-curves to better reflect their view of current market conditions. Using the module’s new Input section for Agency datasets, traders and analysts can further refine their approach to computing refi incentive by selecting the prevailing mortgage rate measure for any given sector (e.g., FH 30Y PMMS, MBA FH 30Y, FH 15Y PMMS and FH 5/1 PMMS) and adjusting the lag time to anywhere from zero to 99 days.   

Comprehensive details of this and other new capabilities are available by requesting a no-obligation live demo below or at


This new functionality is the latest in a series of enhancements that is making the Edge Platform increasingly indispensable for Agency MBS traders and investors.  


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 

Media contact: Timothy Willis


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 

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

Media contact: Timothy Willis 

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

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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

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

Traditional MSR valuation 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.


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.

    Source: 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.

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.

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.

EDGE: QM vs Non-QM Prepayments

Prepayment speeds for qualified mortgages (QM loans) have anecdotally been faster than non-QM loans. For various reasons, the data necessary to analyze interest rate incentive response has not been readily available for these categories of mortgages.

In order to facilitate the generation of traditional refinancing curves (S-curves) over the last year, we have normalized data to improve the differentiation of QM versus non-QM loans within non-agency securities.

Additionally, we isolated the population to remove prepay impact from loan balance and seasoning.

The analysis below was performed on securitized loans with 9 to 36 months of seasoning and an original balance between 200k and 500k. S-curves were generated for observation periods from January 2016 through July 2021.

Results are shown in the table and chart below.

For this analysis, refinance incentive was calculated as the difference between mortgage note rate and the 6-week lagged Freddie Mac primary mortgage market survey (PMMS) rate. Non-QM borrowers would not be able to easily refi into a conventional mortgage. We further analyzed the data by examining prepayments speeds for QM and non-QM loans at different level of SATO. SATO, the spread at origination, is calculated as the difference between mortgage note rate and the prevailing PMMS rate at time of loan’s origination.

Using empirical data maintained by RiskSpan, it can be seen the refinance response for QM loans remains significantly faster than Non-QM loans.

Using Edge, RiskSpan’s data analytics platform, we can examine any loan characteristic and generate S-curves, aging curves, and time series. If you are interested in performing historical analysis on securitized loan data, please contact us for a free demonstration.

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