Search-Driven Real Estate Listings and Market Analytics Platform

Added Jun 2025 3 design docs

Real estate moves fast, and both agents and buyers need more than static listings: they need instant search across thousands of properties and analytics that reveal how markets are moving. This project builds a listings platform where search speed and data insight are the core product. The intern implements a Flask and Python backend with PostgreSQL as the system of record for properties, agents, buyers, and transactions, then integrates Elasticsearch so full-text and filtered property search returns results in milliseconds even at scale. JWT authentication and role-based access separate agents, buyers, and administrators; agents manage listings and upload property images, buyers search and save properties, and admins oversee the platform. Analytics views aggregate listing and pricing data into market trends. The frontend combines React and TypeScript with Tailwind CSS for a responsive, filter-rich search experience. The project teaches the intern to run a dual-store architecture, keeping a relational database and a search index in sync, a pattern used across serious marketplace products, and to layer authorization and analytics on top. It is a demanding, production-shaped build that shows mastery of fullstack engineering beyond simple CRUD.

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