How to Scrape Real Estate Web Data with Python

Homes appear. Homes disappear. Prices spike overnight. The market doesn’t pause—and opportunities slip through fingers faster than you think. What if you could capture it all—automatically, accurately, in real time? That’s the power of web scraping. Once you master the right tools and logic, it’s not just feasible—it’s transformative.
Web scraping real estate isn’t simply about pulling numbers. It’s about extracting actionable insights. Monitor trends, compare properties, identify investment opportunities, or build analytics dashboards that tell you what’s happening before others even notice. It saves hours, sharpens decision-making, and gives you a serious edge.

How to Use Python to Scrape Real Estate Web Data

Zillow offers a perfect case study. Dynamic, protected, but rich with insights if approached responsibly. We’ll leverage requests, BeautifulSoup, Selenium, and proxies.

Step 1: Configure Your Python Environment

Install the tools you need:

pip install requests beautifulsoup4 selenium pandas undetected-chromedriver

Ensure your ChromeDriver version matches your browser—critical for dynamic pages.

Step 2: Review the HTML

Navigate to Zillow for a target city.
Right-click a listing → Inspect (F12).
Locate the wrapper container, often <ul class="photo-cards">.
Listings generally sit in <li> or <article> tags. Track:
Address
Price
Bedrooms
Square footage
Class names are your roadmap—note them carefully.

Step 3: Leverage Proxies to Avoid Detection

Zillow aggressively blocks scrapers. Simulate real users with proxies and headers:

proxies = {
    "http": "http://your_proxy:port",
    "https": "http://your_proxy:port"
}

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
    "Accept-Language": "en-US,en;q=0.9"
}

Residential proxies are ideal—they make anti-bot systems believe you’re human.

Step 4: Extract Listings

Dynamic pages demand Selenium. Example setup:

import undetected_chromedriver as uc
from bs4 import BeautifulSoup
import time

options = uc.ChromeOptions()
options.add_argument('--disable-gpu')
options.add_argument('--no-sandbox')

driver = uc.Chrome(options=options)
driver.get("https://www.zillow.com/homes/for_sale/Los-Angeles,-CA/")
time.sleep(10)  # Let JavaScript fully load

soup = BeautifulSoup(driver.page_source, 'html.parser')
cards = soup.find_all("a", {"data-test": "property-card-link"})

for card in cards:
    try:
        address = card.find("address").text.strip()
        parent = card.find_parent("div", class_="property-card-data")
        price_tag = parent.find("span", {"data-test": "property-card-price"}) if parent else None
        price = price_tag.text.strip() if price_tag else "N/A"
        print(address, price)
    except Exception:
        continue

driver.quit()

If JavaScript challenges appear, run headful mode and complete them manually.

Step 5: Deal with Pagination

Zillow uses dynamic pagination. Loop pages like this:

for page in range(1, 4):
    paginated_url = f"https://www.zillow.com/homes/for_sale/Los-Angeles,-CA/{page}_p/"
    driver.get(paginated_url)
    time.sleep(5)
    soup = BeautifulSoup(driver.page_source, 'html.parser')

Step 6: Clean and Format Data

Pandas makes your dataset structured and analyzable:

import pandas as pd

data = [
    {"address": "123 Main St", "price": "$1,200,000"},
    {"address": "456 Sunset Blvd", "price": "$950,000"},
]

df = pd.DataFrame(data)
df['price'] = df['price'].str.replace(r'[^\d]', '', regex=True).astype(int)

Step 7: Output Your Data

Save your structured data efficiently:

# CSV
df.to_csv('zillow_listings.csv', index=False)

# JSON
df.to_json('zillow_listings.json', orient='records')

Wrapping Up

Scraping real estate web data goes beyond coding—it requires strategy. Identify your targets, monitor listings, and manage pagination efficiently. Clean, format, store, and analyze the data with accuracy.
Using proxies helps reduce blocks, and focusing on public data ensures compliance. Always respect the Terms of Service. By following these practices, you can stay ahead of the market and make informed, data-driven decisions.