US Mortgage Underwriting & Condo Project Review Hub

An automated evaluation system using custom credit tiers, debt risk brackets, and property criteria. This Streamlit application runs a full simulated pipeline to analyze loan applicants and property compliance.

🚀 Live Deployment: Launch Streamlit Web App

Created by srinivasta.

📝 Description

This repository contains an end-to-end automated loan evaluation and data generation simulation tool built for mortgage risk analysis. The project creates a programmatic framework to assess applicant reliability based on three key risk vectors: credit bureau tier thresholds, calculated debt-to-income (DTI) parameters, and Homeowners Association (HOA) reserve health indexes.

By modeling variables like automated auto-reject logic flags, legal risk rates for condo litigation, and baseline property checklists, the platform serves as an interactive decision-support layer for understanding how strict capital underwriting rules impact master pipeline loan closure ratios.

🏷️ Topics

streamlit mortgage-underwriting risk-assessment data-simulation credit-scoring debt-to-income condo-project-review fintech data-visualization python

✨ Features

• Interactive Parameters: View core risk evaluation parameters using an expandable dashboard panel.
• Live Configuration Sidebar: Set custom sample sizes and dynamic condo active litigation probability percentages.
• Underwriting Compliance Engine: Evaluates applicants based on Credit Score, Debt-to-Income (DTI), and Condo HOA Reserves with lawsuit checks.
• Visual Graphics Layer: Displays dynamic compliance distributions using customized matplotlib pie charts.
• Detailed Reports Table: Filters out and targets approved clients who are officially clear to close.
• Data Pipeline Export: Download the entire simulated data sheet directly as a clean CSV format file.

🛠️ System Requirements

• Python 3.8 or higher
• Streamlit
• Pandas
• Numpy
• Matplotlib

🚀 Getting Started

1. Installation

Clone the repository or save the source file, then install the required dependencies:

pip install streamlit pandas numpy matplotlib

2. Run the Application Locally

Start the Streamlit dashboard server from your command line terminal:

streamlit run app.py

📊 Core Compliance Rules

Credit Score Tiers

• Excellent (800 - 900): Instant Approval / Best Deals 🟢
• Very Good (740 - 799): Low Risk / Fast Approval 🟢
• Good (670 - 739): Standard Risk / Standard Rates 🟡
• Fair (580 - 669): High Risk / Strict Review 🟠
• Poor (Below 580): Severe Risk / Auto-Reject 🔴

Debt-to-Income (DTI) Brackets

• Excellent (Below 30.00%): Low Debt Risk 🟢
• Good (30.01% - 36.00%): Manageable Debt 🟡
• High Risk (36.01% - 43.00%): Borderline Strain 🟠
• Critical Danger (Over 43.00%): Too Much Debt / Auto-Reject 🔴

Condo Project Review Matrix

• PASSED: HOA Reserves $\ge$ 10% AND No Active Litigation 🟢
• REJECTED: HOA Reserves < 10% 🔴
• REJECTED: Dangerous Active Litigation Present 🔴
• REJECTED: Low Reserves & Active Lawsuit Combined 🔴

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Developed by srinivasta