Unique Challenges and Opportunities in Working with Residential Real Estate Data
summary
Residential real estate data is messy and nonstandard across counties, so the team builds pipelines that scan public records, run YOLO object detection to pick relevant pages, then do OCR on selected pages and NLP to extract tokens and roles; computer vision uses CNN + k-means embeddings to auto-classify room types and condition. The system parses hundreds of thousands of documents in hours, runs on cloud, and supports clustering ~200k active flippers for marketing and risk signals.
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chapters (6)
- 0:00Start
- 1:20Business Context
- 1:59Challenges and Opportunities: data quality
- 3:25Challenges and Opportunities: unstructured data
- 6:31Parsing documents at scale
- 8:41Auto-clustering room types
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