Build, validate, and document production-grade mortgage credit risk models in R from raw Freddie Mac loan tapes to audit-ready Quarto reports.
Estimate PD, LGD, and EAD for fixed-rate and ARM mortgages - and defend every assumption to a regulator using SR 11-7-compliant documentation.
Master the full credit risk analyst workflow: scorecard development, CECL reserves, fair lending testing, and ongoing model monitoring - all coded in R.
Go from install.packages("tidyverse") to a complete model risk management package - covering glmnet, survival, xgboost, scorecard, and Quarto along the way.
The only mortgage credit risk course that uses real Freddie Mac loan-level data end-to-end with every method implemented in R, not pseudocode.
Requirements
Any prior exposure to R or another data language (Python, SAS, SQL) - we'll teach the R idioms but not how to think in code.
A statistics course at some point - undergraduate stats is plenty.
General awareness of consumer lending - you don't need to be a mortgage expert, but terms like LTV and FICO shouldn't be mysteries.
A laptop with 16 GB + RAM. R, RStudio, and Git are all free.
Description
This course takes you from R-comfortable analyst to building fully validated, production-grade mortgage credit risk models - the same models used by banks, GSEs, fintech mortgage lenders, and the regulators who examine them. Across 15 modules, 149 lectures, and 12 end-to-end capstone projects, you will construct a complete PD/LGD/EAD/CECL modeling framework - covering both fixed-rate and ARM mortgages - using real Freddie Mac Single-Family Loan-Level data.You won't just learn about mortgage credit risk modeling - you will build every component yourself in R, step-by-step, with a practitioner guiding you through the exact workflow used in industry and reviewed by independent validation and regulatory examiners.What You'll LearnSet up a professional R environment with renv-locked reproducibility, project structure, and Git-based version control for sensitive loan dataIngest, clean, and join Freddie Mac origination and performance files, HMDA, FHFA HPI, and FRED macro data into a unified loan-month panelMaster mortgage mathematics including amortization, prepayment, curtailment, LTV trajectory, and ARM mechanics - caps, floors, reset schedules, payment shock, and SOFR forward curvesBuild a full Probability of Default scorecard using WoE/IV, logistic regression, monotonic binning, and points-to-double-the-odds scalingDevelop survival-based and competing-risks PD models - Kaplan-Meier, Cox proportional hazards, parametric Weibull, and Fine-Gray for prepayment vs. default at ARM resetBuild Loss Given Default models using beta regression, two-stage default-flag-plus-severity, and LTV-based curves with HPI stress sensitivityImplement Exposure at Default using amortization schedules and curtailment behaviorConstruct lifetime CECL reserves with vintage loss curves, macro-linked satellite models, scenario blending, and Q-factor frameworkBuild modern machine learning models - XGB =oost, random forests - with SHAP explainability and adverse action reason code generationConduct fair lending analysis using HMDA: denial rate disparities, rate spread analysis, redlining tests, BISG proxies, and disparate impact regressionProduce audit-ready documentation in Quarto: Model Development Documents, Validation Reports, Ongoing Monitoring Reports, and CECL disclosuresWho This Course Is ForMortgage credit risk analysts, model developers, and model validatorsBank, credit union, GSE, and fintech mortgage professionalsData scientists transitioning into mortgage or consumer credit riskAudit and Model Risk Management staff supporting mortgage portfoliosCareer-changers preparing for risk, analytics, or model development roles in mortgage lendingModule 0 covers the R toolchain, RStudio configuration, package management, and Git basics from a practitioner's angle - but you should be comfortable with basic R syntax, data manipulation, and undergraduate-level statistics. This is a practitioner course, not an introduction to programming.Hands-On Projects You Will BuildBy the end of the course, you will have created:A reproducible RStudio project with Freddie Mac data, renv-locked dependencies, and Git historyA fully automated EDA report with roll rates, vintage curves, and product-segmented analysisAn ARM payment shock simulator with SOFR-linked reset schedulesA validated PD scorecard with KS, Gini, AUROC, calibration, and OOT/OOS validationA survival-based lifetime PD model with competing-risks treatment of prepaymentA two-stage LGD model with HPI stress sensitivityA complete CECL engine with three-scenario blending and back-testingAn XGB oost challenger model with SHAP explanations and reason codesA full HMDA-based fair lending analysis with disparate impact testing and remediationAn audit-ready Model Development Document, Validation Report, and PSI/CSI monitoring dashboardThree complete capstone projects spanning scorecard development, ARM-segmented CECL, and ML with fair lendingEvery module ends with a tangible, portfolio-ready artifact. Every section ends with a hands-on lab. Every capstone ends with a complete documentation package.Course StructureThe course is organized into five phases across 15 modules:Foundation (Modules 0, 1, 2, 2A) - R toolchain, mortgage credit risk fundamentals, data ingestion, and Quarto reportingMortgage Mechanics (Module 3) - fixed-rate amortization, ARM caps and floors, payment shock, and reset schedulesCore Models (Modules 4, 5, 6, 7) - scorecards, PD with survival analysis, LGD, EAD, and prepaymentApplication (Modules 8, 9, 10) - CECL, portfolio monitoring, and machine learning with explainabilityGovernance (Modules 11, 12, 13) - fair lending, model risk management, and three end-to-end capstonesEvery lecture includes a complete instructor script, branded slide deck, runnable R code, exercises, knowledge checks, and session notes - produced and quality-controlled to a single consistent standard.Why This Course Is DifferentMost credit risk courses teach theory on toy data. Most R courses don't touch regulation. This course does both - using real Freddie Mac single-family loan-level data, including the 2004–2007 ARM crisis cohort, with every method implemented in production-quality R code and every output framed for SR 11-7-compliant model documentation.You'll learn not just how to build mortgage credit risk models, but how to defend them in independent validation, document them for regulatory examination, monitor them in production, and remediate them when fair lending or stability issues surface.By the end, you'll be able to independently develop, validate, document, and govern mortgage credit risk models in a real-world environment - for fixed-rate and ARM products, across PD, LGD, EAD, and CECL, with the regulatory and fair lending lens that examiners actually apply.
Mid-career risk analysts, model developers, model validators, and audit/MRM staff at banks, credit unions, GSEs, or fintech mortgage lenders. New graduates with strong stats and R skills can succeed but should expect to put in extra time on the regulatory modules.


https://rapidgator.net/file/faf27c24596c7cf7a46ffac63c6bcc1a/Mortgage_Credit_Risk_Modeling_In_R.rar.html