A distressed-property pipeline that runs every night on NYC open data and surfaces scored leads before competitors have finished their coffee.
Mizan buys distressed single-family homes in the Bronx. The deals are there, but finding them means cross-referencing tax records, scraping MLS listings, checking LLC ownership, and watching tax lien auction cycles. Every analyst the firm hired spent their first weeks learning to stitch the sources together, and the best deals closed before the research was complete.
The manual stack looked like this: open public data portals, pull CSVs, load spreadsheets, filter by hand, call listing agents, guess at severity. Hours of work for a list that was already stale by the time it reached a decision.
The Deal Pipeline runs overnight on NYC open data. It ingests PLUTO property records, tax lien auction schedules, LLC filings, block-level signals, and a set of private parameters. It filters for single-family distressed properties that match Mizan's buy-box, scores each one for severity and margin potential, and excludes LLC-owned properties that won't respond to direct outreach.
There is no spreadsheet layer. There is no scraping script that breaks on a page redesign. The entire analyst workflow, from data ingestion to ranked lead list, is rebuilt as one continuous system that produces a scored morning list without human input.
Every panel below is a live component of the real system, rendered here with sample data so the pattern is visible without exposing real deals.
"We used to burn a week trying to find properties this thing surfaces overnight. 357 in one run. I don't want to think about how many we were missing before."
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