#United States #Arlington #Non-QM Loans #RiskSpan #CoreLogic

Introduction

RiskSpan, a prominent player in the domain of trading, risk management, and data analytics, has recently announced an upgrade to its Non-QM Prepayment Model. This upgrade, branded as Version 3.11, represents a significant leap forward as it incorporates loan-level performance data from CoreLogic, enhancing the forecasting capabilities of prepayment metrics associated with non-QM loans and mortgage-backed securities (MBS).

New Model Features

The latest model introduces a sophisticated two-component framework designed to refine prepayment predictions:
1. Unified Turnover Model: This component captures the baseline prepayment trends relevant to various market conditions.
2. Refinance Model by Documentation Type: This unique aspect differentiates among various document styles, such as bank statement loans and debt service coverage ratio/investor loans. This allows for a more tailored modeling of borrower behavior depending on their documentation.

These components are grounded on robust data that covers loan performance from October 2019 to March 2024, which helps in overcoming the limited historical data issues often faced by non-QM lending.

Key Enhancements

The enhancements brought by this new model are notable:

• - Sensitivity Adjustments: It intelligently factors in the Spread at Origination (SATO) and burnout effects, enhancing the accuracy of prepayment behavior projections.
• - DSCR-Specific Adjustments: By considering prepayment penalties and adjusting for various documentation styles, the model provides more precise refinance calculations.

These modifications not only facilitate the assessment of non-QM prepayment risk but also allow market participants to optimize their portfolio strategies and enhance pricing approaches in the secondary market.

Insights from RiskSpan's Leadership

Divas Sanwal, Senior Managing Director at RiskSpan, expressed enthusiasm for the newly introduced model, stating that it provides an upgraded perspective on non-QM borrower behavior. By harnessing a wealth of data from CoreLogic along with Generalized System for Estates (GSE) resources, the model equips investors with essential insights for anticipating prepayment trends accurately. This capability fosters better-informed decision-making processes within the sector.

“Our latest model delivers a more precise view of non-QM borrower behavior, equipping market participants with the insights needed to manage risk effectively,” said Sanwal.

Conclusion

The newly unveiled Non-QM Prepayment Model is not just a simple update but an essential tool for investors dealing in non-QM loans and MBS. With its sophisticated approach to data analytics, RiskSpan is paving the way for improved risk management and decision-making in an evolving financial landscape. This model is now readily available for integration into RiskSpan's Analytics Platform, providing further resources for investors seeking efficiency and precision in their trading and risk management strategies.

For more information, visit RiskSpan's official site.