Zillow has a lot of useful data, including property listings, estimated home values, rental prices, and neighborhood details, and pulling it manually is not realistic at scale. Scraping Zillow lets you collect that data automatically, whether you are building a real estate dataset, tracking price trends, or doing market research. The challenge is that Zillow actively blocks scrapers, so a basic script with a static IP will get cut off quickly. The right setup, including rotating residential proxies, gets around that.
In this article, we'll explore how to scrape Zillow data reliably in 2026.
How to Scrape Zillow Without Getting Blocked
Zillow uses bot detection that flags repeated requests from the same IP, so a static scraper will get blocked fast. The fix is rotating residential proxies, which cycle through real household IPs and make your requests look like normal traffic.
Set up your proxy in Python with the Requests library by passing your Proxyon credentials into the proxies parameter. Every request will rotate automatically through a fresh IP. Pair that with a realistic user-agent header and a short delay between requests, and you will clear most of Zillow's detection without extra configuration.
If you are scraping at a higher volume, sticky sessions are worth considering. They let you hold the same IP across a few requests, which helps when navigating multiple pages without triggering session-based flags. You can verify your proxy setup is working correctly using Proxyon's tools before running your scraper.
Also Read: How to Use Proxies With Python Requests
Setting Up Your Scraper and Extracting Data
Start by installing the Requests and BeautifulSoup libraries. Requests handles the HTTP calls, and BeautifulSoup parses the HTML so you can pull out the fields you need.
Send a GET request to the Zillow search URL for your target city, with your proxy and headers in place. Once you get a response, pass the HTML into BeautifulSoup and locate the listing cards. From each card, you can extract the property address, listed price, number of bedrooms and bathrooms, and the square footage.
Zillow loads some of its data through JavaScript, so if your response is coming back empty, switch to Playwright instead of Requests. It renders the full page before you scrape, which solves most missing-data issues with minimal extra code.
Handling Pagination and Storing Your Results
Zillow splits results across multiple pages, so your scraper needs to handle pagination to collect more than the first batch of listings. The URL structure makes this straightforward, since Zillow appends a page-number parameter thatyou can increment in a loop.
Set a delay of around two to three seconds between page requests to avoid hitting rate limits. For storage, CSV works fine for small datasets. If you are collecting data regularly or at larger volumes, write it to a PostgreSQL or SQLite database instead so you can query and update records without managing flat files.
Also Read: How to Scrape Amazon Product Data
Final Thoughts
Scraping Zillow comes down to bypassing bot detection with rotating residential proxies, extracting the right fields from listing cards, and looping through pages without triggering rate limits. Residential proxies start at $1.75/GB with no subscription required. Deposit $5 and start scraping at Proxyon.
