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.
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
- Recurring manual research eliminated
- Delivers structured data on a fixed cadence
- Automated recurring cadence
- Delivered into client's existing workflow
Built a Python scraping pipeline orchestrated through n8n that runs on a 2-week schedule. The scraper extracts key listing data (price, location, sqft, days on market), normalizes it, and pushes structured results directly into a shared Google Sheet via the Sheets API. Error handling and retry logic ensure reliable delivery. BeautifulSoup handles HTML parsing; n8n manages scheduling, error alerting, and the final Sheets write.
Tech Stack
- Python
- n8n
- Google Sheets API
- BeautifulSoup