Overview
A proptech startup wanted a machine learning engine to predict property valuations across major metropolitan markets, powering their pricing recommendations for agents and buyers.
The Challenge
Accurate property valuation required analyzing hundreds of variables including location, amenities, market trends, and comparable sales. Existing models had 70-75% accuracy, which was insufficient for client confidence.
Our Solution
We built an ensemble ML model combining gradient boosting and neural networks, trained on 5 million+ property records. A FastAPI backend serves predictions via API, and a React dashboard visualizes market trends and individual property analyses.
Development Process
Data collection and feature engineering
Model experimentation and selection
Ensemble model training and validation
API development with FastAPI
Dashboard development and deployment
Results
94% prediction accuracy achieved
Covering 15 major metropolitan markets
Sub-second prediction response time
Adopted by 200+ real estate agents
20% improvement over previous model accuracy
Technologies Used
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