Unlock Zillow Data: A Step-by-Step Guide to Scrape Zillow Info

Up-to-date data can make all the difference in real estate. Zillow, a leading online real estate platform, offers a treasure trove of information—property listings, prices, Zestimates, and market trends. Whether you’re a real estate agent tracking local markets, a developer building a property app, a researcher studying housing dynamics, or an investor hunting for deals, scraping Zillow can unlock the insights you need.

This guide walks you through two paths, overcoming anti-scraping measures using rotating residential proxies, and doing so responsibly.

• Beginner Path: A clear, seven-step HTML scraper using Requests & BeautifulSoup.
• Pro Path: A robust JSON-extraction solution with async concurrency via httpx.

What is Web Scraping and Why Scrape Zillow?

Web scraping uses automated scripts to extract data from websites. For Zillow, this means collecting details like property prices, addresses, square footage, and Zestimates (Zillow’s estimated market values).

Use Cases

Market Analysis: Track price trends and rent vs. sale fluctuations.

Lead Generation: Identify new or price-reduced listings for outreach.

Data Science: Build ML models on real estate features.

Competitive Insight: Map listing density and spot underserved areas.

Who Benefits?

Real Estate Agents: Monitoring price trends in specific neighborhoods.

Developers: Aggregating listings for apps or platforms.

Researchers: Analyzing housing market shifts over time.

Investors: Identifying undervalued properties for investment.

Scraping Zillow saves time compared to manual data collection and enables large-scale analysis, but it comes with technical and ethical challenges we’ll address.

Extracting Specific Data Points from Zillow

Zillow’s pages contain valuable data, but you need to pinpoint it. Common targets include:

• Price: .list-card-price elements.
• Address: <address> tags.
• Details: Beds, baths, square footage in .list-card-details.
• Photos: Image URLs in <img> tags within cards.
• Zestimate & ZPID: Found in embedded JSON (Pro Path).

Use your browser’s Developer Tools (Inspect → Elements) to confirm selectors. For pagination or filter parameters, adjust URLs (e.g., ?beds=2&price=500000-700000).

Ethics & Anti-Bot Basics

1. Respect Robots.txt & TOS: Check Zillow’s terms of service and robots.txt for compliance guidelines.

2. Avoid PII: Don’t collect sensitive data (e.g., owner names or contacts) without consent.

3. Rate-Limit: Keep requests ≤60 per minute; randomize headers to mimic human behavior.

4. Rotate Proxies: Use GoProxy’s rotating residential proxies to mask your scraper and avoid blocks.

5. Use Delays: Add time.sleep(1–3) between requests to simulate natural browsing.

6. Set User Agents: Mimic a real browser (e.g., Chrome) to reduce detection risks.

Responsible scraping reduces risks and respects Zillow’s server.

GoProxy Setup (All Paths)

Configure your proxy once and reuse it everywhere:

python

 

GOPROXY_USER     = "your_username"

GOPROXY_PASS     = "your_password"

GOPROXY_ENDPOINT = "proxy.goproxy.com:8000"  # Single rotating endpoint

 

proxies = {

    "http":  f"http://{GOPROXY_USER}:{GOPROXY_PASS}@{GOPROXY_ENDPOINT}",

    "https": f"http://{GOPROXY_USER}:{GOPROXY_PASS}@{GOPROXY_ENDPOINT}",

}

 

HEADERS = {

    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"

}

Sign up for GoProxy, grab your credentials, and route requests through their proxies for seamless IP rotation.

Beginner Path: HTML Parsing with BeautifulSoup

A minimal scraper you can write and run in minutes. Here’s how:

1. Install Python & Libraries

Ensure Python 3.7+ is installed. In your terminal, run:

bash

 

pip install requests beautifulsoup4

2. Test Your Proxy Connection

python

 

import requests

 

url = "https://www.zillow.com/homes/San-Francisco_rb"

resp = requests.get(url, headers=HEADERS, proxies=proxies, timeout=10)

print("Status code:", resp.status_code)  # Expect 200

3. Load & Parse the HTML

python

 

from bs4 import BeautifulSoup

 

soup = BeautifulSoup(resp.text, "html.parser")

cards = soup.select("ul.photo-cards li article")

print("Listings found:", len(cards))

4. Extract Listing Details

python

 

listings = []

for card in cards:

    price_tag   = card.select_one(".list-card-price")

    address_tag = card.select_one("address")

    link_tag    = card.select_one("a.list-card-link")

 

    price   = price_tag.get_text(strip=True)   if price_tag else "N/A"

    address = address_tag.get_text(strip=True) if address_tag else "N/A"

    url     = link_tag["href"]                 if link_tag else "N/A"

 

    listings.append({"price": price, "address": address, "url": url})

Editor’s Tip: Always check for None in case a selector fails.

5. Handle Pagination

Zillow splits results across pages. Here’s how you can scrape 1-3 pages:

python

 

import time

 

all_listings = []

base = "https://www.zillow.com/homes/San-Francisco_rb"

for page in range(1, 4):  # pages 1–3

    page_url = f"{base}{page}_p"

    resp = requests.get(page_url, headers=HEADERS, proxies=proxies, timeout=10)

    soup = BeautifulSoup(resp.text, "html.parser")

    cards = soup.select("ul.photo-cards li article")

    for card in cards:

        price = card.select_one(".list-card-price").get_text(strip=True)

        addr  = card.select_one("address").get_text(strip=True)

        link  = card.select_one("a.list-card-link")["href"]

        all_listings.append({"price": price, "address": addr, "url": link})

    time.sleep(3)

6. Save Your Data

Write your collected listings to a CSV file:

python

 

import csv

 

with open("zillow_listings.csv", "w", newline="", encoding="utf-8") as f:

    writer = csv.DictWriter(f, fieldnames=["price", "address", "url"])

    writer.writeheader()

    writer.writerows(all_listings)

 

print("Saved", len(all_listings), "listings.")

7. Verify & Tweak

Run on 1–2 pages first.

Adjust CSS selectors if Zillow’s HTML changes.

Increase delays if you see rate-limit responses.

Pro Path: JSON Extraction from <script> Tags

Parse Zillow’s embedded JSON for a more stable, data-rich approach.

1. Install Libraries

bash

 

pip install httpx jmespath

2. Single-Page JSON Scraper

python

 

import json

import jmespath

import httpx

 

def fetch_listings_json(url):

    with httpx.Client(proxies=proxies, headers=HEADERS, timeout=10) as client:

        r = client.get(url)

    text = r.text

    start = text.find('window.__INITIAL_STATE__ = ') + 27

    end   = text.find(';</script>', start)

    raw   = text[start:end]

    data  = json.loads(raw)

    return jmespath.search("searchResults.cat1.searchResults.listResults", data) or []

 

if __name__ == "__main__":

    listings = fetch_listings_json("https://www.zillow.com/homes/New-York_rb")

    for item in listings:

        print(item["zpid"], item["price"], item["addressStreet"])

Scaling Up: Async Concurrency with httpx

When you need thousands of listings quickly:

python

 

import asyncio

import json

import jmespath

import httpx

 

async def fetch(client, url):

    r = await client.get(url)

    text = r.text

    start = text.find('window.__INITIAL_STATE__ = ') + 27

    end   = text.find(';</script>', start)

    data  = json.loads(text[start:end])

    return jmespath.search("searchResults.cat1.searchResults.listResults", data) or []

 

async def main(urls):

    async with httpx.AsyncClient(proxies=proxies, headers=HEADERS, timeout=10) as client:

        tasks = [fetch(client, url) for url in urls]

        results = await asyncio.gather(*tasks)

    all_items = [item for sub in results for item in sub]

    print("Total listings scraped:", len(all_items))

 

if __name__ == "__main__":

    urls = [

        "https://www.zillow.com/homes/Los-Angeles_rb",

        "https://www.zillow.com/homes/Chicago_rb",

        # add more city or filter URLs

    ]

    asyncio.run(main(urls))

Troubleshooting Common Issues

Scraping isn’t always smooth. Here’s how to handle hiccups:

CAPTCHAs: Slow down requests or switch GoProxy IPs.

Blocked IPs: Increase proxy rotation frequency.

HTML/JSON Changes: Regularly re-inspect Zillow’s page and update selectors or paths.

JavaScript-Rendered Content: Use Selenium or Playwright if BeautifulSoup misses data.

Best Practices & Tips

Monitor Key Changes: Alert on missing JSON keys or empty results.

Dynamic Filters: Build URLs with query params (?beds=2&price=500000-700000) to focus your scrape.

Storage: Stream results into CSV, SQL, or a data lake for analysis.

Error Handling: Implement retries with exponential backoff; log errors without exposing sensitive details.

Why Choose GoProxy?

Compared to alternatives, GoProxy stands out for Zillow scraping:  

Rotating Residential IPs: Automatically rotate through 90 M+ real-home addresses to avoid blocks. 

High Reliability: Built for heavy scraping with automatic failover.

Ease of Use: Simple Python integration.

Scalability: Handles small tests to massive projects.

Cost-Effective: Offers a 7-day trial and unlimited plans.

Final Thoughts

Scraping Zillow with Python and GoProxy unlocks real estate insights—from price trends to investment opportunities. With Python, GoProxy, and ethical practices, you can build a reliable scraper tailored to your goals. Start small, refine your approach, and then scale up as needed.  

Ready to dive in? Register for GoProxy’s 7-day trial! Need more? Check out unlimited plans. Or skip the setup—contact GoProxy for custom scraping services. Tell us your target data, and we’ll deliver!