How to Scrape Zillow Data With Massive - A Beginner’s Guide

Zillow is one of the largest real estate websites in the U.S., offering a treasure trove of real estate listings, property prices, and market analytics. But scraping Zillow isn’t easy—its anti-bot defenses can quickly shut down your data extraction efforts.

This guide walks you through how to scrape Zillow property data effectively using Massive’s residential proxies and Python with Playwright. You’ll learn how to bypass detection, extract real estate data reliably, and scale your scraping workflow like a pro.‍

Why Scrape Zillow Real Estate Data?

Zillow real estate data is a goldmine for:

• Market Research: Analyze property listings, pricing trends, and neighborhood statistics to gain insights into real estate market dynamics.
• Investment Analysis: Examine historical price patterns and market indicators to assess potential investment opportunities.
• Location Analytics: Investigate neighborhood demographics, amenities, and property characteristics to support development planning.
• Economic Research: Monitor housing market trends, price indices, and regional economic indicators for academic studies or policy research.

Whether you’re a data analyst, a real estate investor, or a developer building automation tools, scraping data from Zillow can provide valuable insights into the real estate market.

The Problem: Zillow’s Anti-Scraping Measures

Scraping data from Zillow presents significant challenges due to its robust anti-bot systems:

• Human Verification Systems: Zillow employs "Press & Hold" verification and other methods to confirm that requests originate from actual users rather than automated systems.
• Rate Limiting: Zillow carefully monitors the frequency of page requests and may temporarily restrict access if too many requests are made in a short period.
• IP Blocking: Zillow may block access from IP addresses that reveal unusual activity patterns.
• Dynamic Site Updates: Zillow regularly updates its website structure and layout, requiring your scraper to be adaptable.

Here’s an example of what happens when a scraper gets blocked by human verification:

That’s where proxies—specifically residential proxies—come in.

Why Use Proxies to Scrape Zillow?

Proxies act as intermediaries between your scraper and the web. They’re essential for web scraping Zillow because they help:

• Avoiding IP Bans: Proxies allow you to rotate IP addresses, significantly reducing the risk of detection and blocking.
• Accessing Geo-Restricted Content: Some property listings and data are available only in certain regions. Proxies allow you to appear as though you're browsing from different locations.
• Bypassing Rate Limits: Zillow imposes limits on the number of requests from a single IP address. Proxies help distribute your requests across multiple IPs, allowing you to avoid these restrictions.

However, not all proxies are created equal.

Residential vs. Datacenter Proxies: What Works Best?

From experience, residential proxies outperform datacenter ones for scraping Zillow. Here’s why:

• Residential proxies use real IPs tied to actual ISPs and devices, making them appear as normal users.
• Datacenter proxies, while faster and cheaper, are easily detected by Zillow’s systems.

If your goal is consistent, scalable scraping without blocks, residential is the way to go.

Getting Started with Massive

Create your account at partners.joinmassive.com and choose a plan for your needs. After that, go to the Massive Dashboard to retrieve your proxy credentials (username and password).

Configuration Steps:

Visit the Quickstart section to customize your proxy settings:

1. Choose your preferred protocol (HTTP, HTTPS, or SOCKS5).
2. Select between rotating or sticky proxies.‍
3. Set geo-targeting preferences (country, state, city, ZIP, or ASN).

Once configured, you'll get a ready-to-use cURL command for your specific use case.

For advanced features like location-based targeting and sticky sessions, refer to the Massive Documentation. The docs provide step-by-step instructions for getting the most out of Massive Residential Proxies.

With this setup, you can use Massive Proxies to scrape Zillow product data from a specific region.

Building Zillow Scraper Using Massive Proxies

Let’s build a Zillow scraper using Playwright and Massive Proxies. Playwright automates browser interactions and effectively handles dynamic content, while proxies help to avoid detection and bypass restrictions.

We’ll scrape real estate listings for Chicago IL, extracting the following information for each property:

• Address
• Status (e.g., Active, Pending)
• Price
• Number of Bedrooms
• Number of Bathrooms
• Square Feet
• Listing Company
• Property URL

‍1. Set Up Your Environment

Start by creating a virtual environment and installing the required packages. You can also use Conda or Poetry if preferred.

python -m venv zillow_envsource 
zillow_env/bin/activate  # Windows: zillow_env\\Scripts\\activate
pip install playwright python-dotenv
playwright install

‍

Create a .env file to store your Massive proxy credentials securely.

PROXY_SERVER="your_proxy_server"
PROXY_USERNAME="your_username"
PROXY_PASSWORD="your_password"

‍‍

2. Configure Proxy and Browser Settings

Set up proxy credentials and block unnecessary assets to optimize performance and avoid detection.

class Config:
    PROXY_SERVER = os.getenv("PROXY_SERVER")
    PROXY_USERNAME = os.getenv("PROXY_USERNAME")
    PROXY_PASSWORD = os.getenv("PROXY_PASSWORD")
    BLOCKED_RESOURCES = ["stylesheet", "image", "media", "font", "imageset"]

‍

Blocking unnecessary resources like fonts and images speeds up the scraping process.

‍

Here’s an example of how the page looks when resources are blocked:

‍

3. Launch Browser with Proxy Support

Here, we define a browser context that routes requests through Massive and filters unwanted content.

class AsyncZillowSearchScraper:
    def __init__(self, headless: bool = True):
        self.headless = headless
        self.playwright = None
        self.browser = None
        self.context = None

    async def __aenter__(self):
        self.playwright = await async_playwright().start()

        browser_config = {"headless": self.headless}
        if Config.PROXY_SERVER:
            browser_config["proxy"] = {
                "server": Config.PROXY_SERVER,
                "username": Config.PROXY_USERNAME,
                "password": Config.PROXY_PASSWORD,
            }
        self.browser = await self.playwright.chromium.launch(**browser_config)
        self.context = await self.browser.new_context()
        await self.context.route("**/*", self._route_handler)
        return self

    async def _route_handler(self, route):
        if route.request.resource_type in Config.BLOCKED_RESOURCES:
            await route.abort()
        else:
            await route.continue_()

‍

4. Extract Zillow Listing Data

Each property on Zillow is contained within a <li> tag. These <li> tags have a class that starts with ListItem, and each tag represents a single property listing. Inside these <li> tags, you’ll find all the key details about the property, such as the address, price, and property features

.

Here’s how these <li> tags are structured:

• The address is located inside a <address> tag with the attribute data-test="property-card-addr".
• The price is within a <span> tag with the attribute data-test="property-card-price".

Additional details like the number of bedrooms, bathrooms, and square footage are nested within <ul> lists.

Here's how we parse individual property listings:

class ListingParser:
    @staticmethod
    async def extract_listing_details(listing) -> Optional[Dict]:
        try:
            data_container = await listing.query_selector(
                'div[class*="property-card-data"]'
            )
            if not data_container:
                return None
            # Extract basic details

            details = {
                "address": None,
                "status": None,
                "price": None,
                "bedrooms": None,
                "bathrooms": None,
                "square_feet": None,
                "listing_company": None,
                "url": None,
            }

            # Get address

            if address_elem := await data_container.query_selector(
                'address[data-test="property-card-addr"]'
            ):
                details["address"] = ListingParser.clean_text(
                    await address_elem.text_content()
                )
            # Get price

            if price_elem := await data_container.query_selector(
                'span[data-test="property-card-price"]'
            ):
                details["price"] = ListingParser.clean_text(
                    await price_elem.text_content()
                )
            # Get property URL

            if url_elem := await data_container.query_selector(
                'a[data-test="property-card-link"]'
            ):
                if url := await url_elem.get_attribute("href"):
                    details["url"] = (
                        f"<https://www.zillow.com>{url}"
                        if not url.startswith("http")
                        else url
                    )
            # ...

            return details
        except Exception as e:
            logger.error(f"Error extracting listing details: {e}")
            return None

‍

5. Scroll & Paginate Through Results

Simulate scrolling to load more listings dynamically:

async def _scroll_and_get_listings(self, page):
    last_count = 0
    attempts = 0
    MAX_ATTEMPTS = 20

    while attempts < MAX_ATTEMPTS:
        listings = await page.query_selector_all('article[data-test="property-card"]')
        current_count = len(listings)

        if current_count == last_count:
            break
        last_count = current_count
        await page.evaluate("window.scrollBy(0, 500)")
        await asyncio.sleep(1)
        attempts += 1
    return listings

‍

Click the Next Page (>) button to navigate through other pages.

To go to the next page:

next_button = await page.query_selector('a[title="Next page"]:not([disabled])')
if next_button:
    await next_button.click()
    await asyncio.sleep(3)

‍

6. Save Extracted Data

Save your scraped Zillow data to a JSON file. You can also convert it to CSV later.

class ResultsSaver:
    @staticmethod
    def save_results(data, filename="zillow_listings.json"):
        with open(filename, "w", encoding="utf-8") as file:
            json.dump(data, file, indent=4, ensure_ascii=False)
        logger.info(f"Saved {len(data)} listings")

    @staticmethod
    def load_existing_results(filename="zillow_listings.json"):
        try:
            if os.path.exists(filename):
                with open(filename, "r", encoding="utf-8") as file:
                    return json.load(file)
        except Exception as e:
            logger.error(f"Error loading data: {e}")
        return []

‍

7. Execute the Scraper

Tie it all together in a main function to launch the scraper, extract data, and save the results.

async def main():
    search_url = "https://www.zillow.com/chicago-il/"
    max_pages = None  # Set to a number to limit pages

    async with AsyncZillowSearchScraper(headless=False) as scraper:
        results = await scraper.scrape_search_results(search_url, max_pages=max_pages)
    print(f"\\nTotal listings scraped: {len(results)}")
    if results:
        print("\\nSample listing:")
        print(json.dumps(results[0], indent=2, ensure_ascii=False))


if __name__ == "__main__":
    asyncio.run(main())

‍

Sample Output

Once you've successfully set up and run your Zillow scraper using Massive proxies, your output will look something like this:

[
    {
        "address": "722 N Trumbull Ave, Chicago, IL 60624",
        "status": "Active",
        "price": "$399,000",
        "bedrooms": "5",
        "bathrooms": "3",
        "square_feet": "2250",
        "listing_company": "REMAX LEGENDS",
        "url": "https://www.zillow.com/homedetails/722-N-Trumbull-Ave-Chicago-IL-60624/3810331_zpid/",
    }
]

The data is now structured and usable—perfect for real estate analytics, dashboards, or investment tools.

‍

You can access the complete code for scraping Zillow data using Massive proxies in GitHub Gist.

Conclusion

Scraping Zillow real estate data gives you an edge in understanding the market, tracking properties, and building automation tools. With Massive’s residential proxies, you can:

• Scrape data without getting blocked
• Target specific ZIP codes or cities
• Automate your Zillow search results workflow
• Extract clean, structured Zillow property data

 Ready to build your own Zillow data scraper? Sign up for Massive Proxies today.