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, enabling rapid retraining of models.
Feature Engineering
Created over 200 features, including geospatial data, historical price trends, and neighborhood amenities.
API Delivery
Wrapped the model in a high-performance REST API with < 100ms latency, enabling real-time integration with client applications.
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. The system achieves 98% accuracy against final sale prices, outperforming traditional manual appraisals.