StreetEasy Research Pipeline
Automated StreetEasy research pipeline for a Brooklyn real-estate operator, pulling matching listings into Google Sheets on a 2-week cadence for decision support.
Problem
Real estate firms spend hours every two weeks manually searching StreetEasy, copying listing data into spreadsheets, and formatting it for analysis. By the time the research is compiled, market conditions have shifted and the process starts again, creating a recurring time sink with no leverage.
Solution
A Python pipeline orchestrated through n8n that scrapes StreetEasy listings on a 2-week cadence. It extracts key data — price, location, square footage, days on market — normalizes it, and pushes the structured results directly into the client's existing Google Sheet. The client opens the same spreadsheet they've always used; it's automatically populated with current market data.
Results
Tech Stack
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