The Challenge
A US-based real estate data provider needed to scale their valuation engine. Their existing process involved manual appraisals or simple linear regression models that failed to capture market nuances. They needed a system that could ingest millions of data points—from school ratings to crime statistics—and output a fair market value in milliseconds.
Our Solution
We built a massively parallel data pipeline and a customized Gradient Boosting model.
- Big Data Processing: Used Apache Spark on Databricks to process 30 million+ records daily.
- Feature Engineering: Created over 200 features, including geospatial data and historical price trends.
- API Delivery: Wrapped the model in a high-performance REST API with < 100ms latency.
The Outcome
The Automated Valuation Model (AVM) is now the core product of the company, serving thousands of API requests per second from mortgage lenders and real estate portals.
Key Metrics
- 100% Automation of valuation reports.
- 98% Accuracy against final sale price.
- 30M+ Properties valued daily.