One of the things that makes modeling loan and MSR cash flows hard is appropriately applying assumptions to individual loans. Creating appropriate assumptions for each loan or MSR segment is crucial to estimating realistic performance scenarios, stress testing, hedging, and valuation. However, manually creating and maintaining such assumptions can be time-consuming, error-prone, and inconsistent across different segments and portfolios.
Fortunately, hidden among some of the Edge Platform’s better-known features is a powerful and flexible way of running loan-level analytics on a portfolio using the Platform’s segment builder and loan model assumptions features.
These sometimes-overlooked features allow users to create and apply granular and customized modeling assumptions to a particular loan portfolio, based on its various, unique loan characteristics. Assumptions can be saved and reused for future analysis on different loans tapes. This feature allows clients to effectively build and manage a complex system of models adjustment and tuners for granular sub-segments.
Applying the segment builder and loan model assumptions features, loan investors can:
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- Decouple how they run and aggregate results from how they assign modeling assumptions, and seamlessly assign different assumptions to various segments of the portfolio, based on user-defined criteria and preferences. For example, investors can assign different prepayment, default, and severity assumptions to loans based on their state, LTV, UPB, occupancy, purpose, delinquency status, loan type, collateral features, or virtually any other loan characteristic.
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- Choose from a variety of models and inputs, including RiskSpan models and vector inputs for things like CPR and CDR. Investors can define their own vector inputs as an aging curves by loan age or based on the forecast month, and apply them to different segments of the portfolio. For example, they can define their own CDR and CPR curves for consumer or C&I loans, based on the age of the loans.
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- Set up and save modeling assumptions one time, and then reference them over and over again whenever new loan tapes are uploaded. This saves time and effort and ensures consistency and accuracy in the analysis.
This hidden feature enables investors to customize their analysis and projections for different asset classes and scenarios, and to leverage the Edge Platform’s embedded cash flow, prepayment and credit models without compromising the granularity and accuracy of the results. Users can create and save multiple sets of loan model assumptions that include either static inputs, aging curves, or RiskSpan models, and apply them to any loan tape they upload and run in the forecasting UI.
Contact us and request a free demo or trial to learn more about how to use these and other exciting hidden (and non-hidden) features and how they can enhance your loan analytics.
