A developer built a multi-agent AI system to automate real estate property analysis, mimicking how investors research neighborhoods, assess conditions, and calculate returns. The proof-of-concept tool uses specialized agents to evaluate market trends, property quality, and financial metrics like cap rates and cash flow, delivering investment grades from A+ to D in seconds.

Traditional property analysis requires juggling Zillow, GreatSchools, and spreadsheets, often leading to decision fatigue. This Python-based project demonstrates that agentic AI doesn't require complex frameworks. By isolating responsibilities—Market Analyzer, Property Evaluator, Financial Calculator, and Decision Engine—the system remains modular, testable, and easy to extend with real APIs.

The architecture relies on Python dataclasses for strict typing and the Rich library for a polished terminal interface. It calculates critical metrics including monthly cash flow, 5-year ROI, and break-even timelines. The Decision Engine weighs financial health most heavily (40%), ensuring mathematically sound recommendations before visualizing results with Matplotlib charts and JSON exports.