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Articles Tagged with: Independent Mortgage Banks

Top 10 National Mortgage Servicer: MSR Pricing Model Review, Analysis and Enhancements

One of the nation’s leading mortgage lenders had recently acquired several large MSR portfolios and required assistance reviewing, documenting and recommending enhancements to the underlying assumptions of the model used to price the MSR portfolios at acquisition.

Requiring review and documentation included collateral assumptions, cost and revenue assumptions, and prepayment (CDR/CRR/CPR) assumptions.

The Solution

RiskSpan comprehensively analyzed the cash flow impact of each major assumption (e.g., CDR/CRR/CPR, servicing advances, fees, cost) — the collateral assumptions in the model as well as documented forecast vs. actual outcomes.

RiskSpan worked in concert with the servicer’s finance and pricing teams to collect and analyze roll rates and to forecast actual loan-level data around losses, servicing advances, servicing fees, ancillary fees, PIF, and scheduled principal payments.  

Deliverables 

A comprehensive pricing model validation report that included the following:

  • Consolidated CDR-, CRR-, CPR-related pricing model data, including balance, delinquency status, recapture, scheduled payments, default, etc. for all acquired portfolios. The resulting dataset could be used both for deal tracking and pricing model validation 
  • Documentation of the calculation and location of pricing model fields.
  • Reconciliation of the different methods for calculating CDR, CRR, and CPR.
  • Deep dives into model predictions of short sales and foreclosure turn-times
  • Loan-state transition model forecasts and comparison of the model variables between two version of the forecast, including shift analyses.
  • Drivers of forecast variance. 
  • Identification of dials responsible for short sale and foreclosure turn forecast shifting.
  • SAS-based streamlined process for comparing model variables for sub-segment and sub-models in loan state
  • Transition Model:  Incorporation of actual and forecast into pricing models to compare with original pricing model cash flow results for acquired portfolios
  • Creation and standardization of the pricing model validation report output.
  • Automation of reporting.  
  • Improvement of the process by creating a calculation template that could be easily replicated for other portfolios. 
  • Documentation of the validation process and comprehensive review of the validation results with the servicer’s risk team, finance team and pricing team management.

Residential Mortgage REIT: End to End Loan Data Management and Analytics

An inflexible, locally installed risk management system with dated technology required a large IT staff to support it and was incurring high internal maintenance costs.

Absent a single solution, the use of multiple vendors for pricing and risk analytics, prepay/credit models and data storage created inefficiencies in workflow and an administrative burden to maintain.

Inconsistent data and QC across the various sources was also creating a number of data integrity issues.

The Solution

An end-to-end data and risk management solution. The REIT implemented RiskSpan’s Edge Platform, which provides value, cost and operational efficiencies.

  • Scalable, cloud-native technology
  • Increased flexibility to run analytics at loan level; additional interactive / ad-hoc analytics
  • Reliable, accurate data with more frequent updates

Deliverables 

Consolidating from five vendors down to a single platform enabled the REIT to streamline workflows and automate processes, resulting in a 32% annual cost savings and 46% fewer resources required for maintenance.


GSE: Earnings Forecasting Framework Development

A $100+ billion government-sponsored enterprise with more than $3 trillion in assets sought to develop an end-to-end earnings forecast framework to project and stress-test the future performance of its loan portfolio. The comprehensive framework needed to draw data from a combination of unintegrated systems to compute earnings, capital management requirements and other ad hoc reporting under a variety of internal and regulatory (i.e., DFAST) stress scenarios. 

Computing the required metrics required cross-functional team coordination, proper data governance, and a reliable audit trail, all of which were posing a challenge.  

The Solution

RiskSpan addressed these needs via three interdependent workstreams: 

Data Preparation

RiskSpan consolidated multiple data sources required by the earnings forecast framework. These included: 

  • Macroeconomic drivers, including interest rates and unemployment rate 
  • Book profile, including up-to-date snapshots of the portfolio’s performance data 
  • Modeling assumptions, including portfolio performance history and other asset characteristics 

Model Simulation

Because the portfolio in question consisted principally of mortgage assets, RiskSpan incorporated more than 20 models into the framework, including (among others): 

  • Prepayment Model 
  • Default Model 
  • Delinquency Model 
  • Acquisition Model: Future loans 
  • Severity Model  
  • Cash Flow Model 

Business Calculations and Reporting

Using the data and models above, RiskSpan incorporated the following outputs into the earnings forecast framework: 

  • Non-performing asset treatment 
  • When to charge-off delinquent loans 
  • Projected loan losses under FAS114/CECL  
  • Revenue Forecasts 
  • Capital Forecast 

Client Benefits

The earnings forecast framework RiskSpan developed represented a significant improvement over the client’s previous system of disconnected data, unintegrated models, and error-prone workarounds. Benefits of the new system included:  

  • User Interface – Improved process for managing loan lifecycles and GUI-based process execution  
  • Data Lineage – Implemented necessary constraints to ensure forecasting processes are executed in sequence and are repeatable. Created a predefined, dynamic output lineage tree (UI-accessible) to build robust data flow sequence used to facilitate what-if scenario analysis. 
  • Run Management – Assigned a unique run ID to every execution to ensure individual users across the institution can track and reuse execution results 
  • Audit Trail – Designed logging of forecasting run details to trace attributes such as version changes (Version control system – GIT, SVN), timestamp, run owner, and inputs used (MySQL/Oracle Databases for logging)  
  • Identity Access Management – User IDs and access is now managed administratively. Metadata is captured via user actions through the framework for audit purposes. Role-based restrictions now ensure data and forecasting features are limited to only those who require such permissions 
  • Golden Configuration – Implemented execution-specific parameters passed to models during runtime. These parameters are stored, enabling any past model result to be reproduced if needed 
  • Data Masking – Encrypted personally identifiable information at-rest and in transit 
  • Data Management – Execution logs and model/report outputs are stored to the database and file systems 
  • Comprehensive User and Technical Documentation – RiskSpan created audit-ready documentation tied to logic changes and execution. This included source-to-target mapping documentation and enterprise-grade catalogs and data dictionaries. Documentation also included: 
      • Vision Document 
      • User Guides 
      • Testing Evidence 
      • Feature Traceability Matrix 

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