- 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.
- 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.
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.
RiskSpan’s MSR platform incorporates the full range of input parameters necessary to fully characterize the positive and negative cash flows arising from servicing
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.
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.
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.
Positive cash flows include the servicing and other fees collected directly from borrowers as well as various types of ancillary and float income
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 look at here 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.