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

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.”

RiskSpan’s MSR engine integrates both prepayment and credit models, permitting the precise estimation of net cash flows to MSR owners

(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.” .).

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 www.paydayloanstennessee.com/cities/munford/ 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.

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.

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

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:

Once an analyst looks to incorporate credit performance into MSR valuation, the number of meaningful explanatory loan characteristics grows sharply. ” 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.

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