AI-Driven Real Estate CMA Automation

I want to build an end-to-end Comparative Market Analysis platform that automatically pulls fresh data from our MLS feed, zeroes-in on a specified ZIP code, and selects the most relevant comparables for each subject property. The first milestone is the engine that finds and ranks similar homes; it must weigh location, recent sale price and date, home size, bed/bath count, lot size, property type, condition, and local amenities to produce an accurate shortlist.

Once the comp-selection logic is solid, I’ll connect an AI agent so the system can draft narrative summaries, pricing recommendations, and branded reports we can hand directly to clients. Think Python or Node back-end, MLS API integration, vector search or traditional scoring for similarity, and an OpenAI (or equivalent) layer for natural-language output—all wired into a web dashboard my team can use daily.

Key deliverables
• Zip-code based comp-selection module pulling live MLS data
• Weighted scoring model reflecting the criteria above
• AI-generated summary and pricing recommendation template
• Simple dashboard with file export (PDF/Word) for finished CMA reports
• Deployment script or CI pipeline so we can keep iterating each month

Acceptance criteria
1. On a test ZIP code I provide, the system returns at least five high-quality comparables ranked by relevance.
2. Generated report passes a spot-check for factual accuracy against MLS records.
3. Turnaround from property address input to finished report is under two minutes.

This is an ongoing role paid monthly, so clean, well-documented code and an architecture that can grow into additional ZIP codes and features matter just as much as the initial build. If you have solid experience with MLS integrations and AI text generation, let’s get started right away.