In a recent project, I scraped property data from Zillow.com, extracting detailed information for real estate analysis and investment purposes. The goal was to build a comprehensive dataset of properties with all relevant information for my client’s market research needs.
Project Details:
Fields Scraped:
- Property Price
- Address
- Number of Bedrooms & Bathrooms
- Property Size (Square Feet)
- Zestimate (Property Value Estimate)
- Property Description
- Additional relevant fields such as year built, lot size, and property type
How I Achieved It:
- I developed a custom Python script using libraries like BeautifulSoup and Selenium to automate the extraction of property data.
- To bypass Zillow’s anti-scraping mechanisms, I implemented proxy rotation, which allowed me to scrape large amounts of data without being blocked. This technique ensured continuous data collection while maintaining a high success rate.
- I also bypass Cloudflare protection where most of the data scraper gives up scraping.
- I handled pagination, data formatting, and error handling to ensure the scraped data was clean, structured, and ready for immediate use.
- The final data was delivered in a CSV format, providing my client with a valuable dataset for further analysis and decision-making.
This project helped my client identify potential investment properties, track pricing trends, and gain detailed insights into the real estate market. I can provide similar web scraping solutions for real estate or any other data-heavy industry.
Screenshots
Sample Data
If you’re looking for automated solutions to gather property or market data, I’d be happy to assist with your project. Please Contact Us.
