Fraud Detection · Marketing Personalization · Real-time Analytics
Housing.com needed a better way to support real estate pricing intelligence, personalization, and fraud-aware demand forecasting because rapidly growing property, buyer, and renter data stayed stuck in silos, which weakened pricing accuracy, personalization, and fraud detection.
Reported impact
Housing.com improved real estate pricing intelligence, personalization, and fraud-aware demand forecasting with measurable results including 10%–15%, 20% reduction in time to insight, and 50% increase in team productivity, while giving teams a faster path from governed data to production decisions.
Housing.com uses Databricks Data + AI Platform to support real estate pricing intelligence, personalization, and fraud-aware demand forecasting. The implementation replaced a legacy state where rapidly growing property, buyer, and renter data stayed stuck in silos, which weakened pricing accuracy, personalization, and fraud detection. Property listings and buyer and renter behavior move through production data pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog and Delta Sharing governs access, lineage, and compliance across sensitive datasets. MLflow operationalizes models, agents, and analytics that deliver pricing predictions and demand forecasts. Housing.
Property listings and buyer and renter behavior land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI or analytics services deliver pricing predictions and demand forecasts.
Even before “big data,” data had always been important in real estate, encompassing everything from property and homeowner/renter information to data about finances, taxes and trends. As real estate continues to mature d
It has been much easier on our teams because now, there’s only one definition of the data, and it’s also not getting hidden in different silos.
The cost of storage wasn’t the problem — the cost of computing was way more, compared to other services. So, in moving away from our data warehouse, we actually saved 50% in total costs.
This led the business further away from their cloud data warehouse and closer to other options to cost-efficiently unify their data with analytics and ML. As Nikhil Sikka, Sr. Engineering Manager at Housing.com, further
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Traceability
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