How to Scrape Real Estate Web Data: A Python Tutorial

Real estate scraping can seem challenging, but once you start using the right tools and set up a fair logic, it’s not that difficult. Web scraping involves writing code that visits real estate websites, reads their HTML, and pulls out information.

When you scrape real estate listings, you can select what data points you want to get: property listings, photos, agent info, property price, or more. People typically do this to monitor the real estate market, track market trends, compare data, or develop tools for investment analysis.

Here, you will learn how web scraping real estate data works and why many use it to collect data, such as property listings, property prices, and additional data points.

Is It Legal to Scrape Real Estate Data?

Most major platforms, such as Zillow, Realtor, and Redfin, prohibit web scraping in their Terms of Service and restrict automatic bots. Instead, they encourage you to use their API or get official access to licensed data.

Here’s a quick guide on how you can check a website’s Terms of Service for scraping permissions:

• Find ’Terms’ or ’Legal’ at the bottom of the webpage.
• Search for ’scrape’ or ’bot’.
• If you see phrases such as ’no automated access’ or some other wording that prohibits scraping, then real estate web scraping on the website is disallowed.
• If you want to be 100% safe, consider licensed data, APIs, or some other deals they offer instead of web scraping real estate data.

However, there is a slight catch here. If you never log in, sign up, or otherwise explicitly accept the Terms of Service, you’re technically only accessing public data that’s not hidden behind a login or a paywall. And public data, technically speaking, is fair game.

Keep in mind that it remains a gray area and you should proceed with caution. It’s highly recommended to always consult with a legal professional first and do not take this blog post as legal advice.

Web Scraping Real Estate Data: Step-by-Step Tutorial

Here, you will find a complete Python-based tutorial on how to scrape real estate listings from Zillow.

Due to Zillow’s dynamic content and anti-bot measures, we’ll focus on responsible scraping practices using tools such as requests, BeautifulSoup, and Selenium, along with proxy integration and data storage best practices.

Step 1: Setting Up the Python Environment

Install the required libraries using pip:

pip install requests beautifulsoup4 selenium pandas undetected-chromedriver

If you’re using dynamic pages, you’ll also need the ChromeDriver (version must match your Chrome browser).

Step 2: Checking the HTML structure

• Open Zillow and search for a city (e.g., https://www.zillow.com/homes/for_sale/Los-Angeles,-CA/ )
• Right-click a listing and choose ’Inspect’ (or use F12).
• Find the container wrapping the listings. Common classes include:

<ul class="photo-cards"> ... </ul>

4. Inside, each property is often in a <li> or <article> tag. Check for fields like: - Address - Price - Bedrooms - Square footage

Take note of class names and structure.

Step 3: Implementing Proxies to Avoid Detection

Zillow blocks scrapers aggressively. Use proxies and headers to simulate human behavior. Here’s a sample setup:

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"
}

To achieve this, you can use our Zillow proxies and minimize the chances of getting blocked.

Step 4: Extracting Real Estate Data

Zillow loads data dynamically, so Selenium is the most reliable option for this task.

import undetected_chromedriver as uc
from bs4 import BeautifulSoup
import time

options = uc.ChromeOptions()
#options.add_argument('--headless')
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)  # Wait for full JS-rendered content

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()

You may run into a JS challenge preventing the scraper from working. The easiest solution is to run headful mode and complete the challenge to allow the scraper to access the HTML.

Step 5: Handling Pagination

Zillow paginates via dynamic JavaScript. To paginate effectively, do 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: Cleaning and Formatting Data

Use pandas to structure and clean the scraped data.

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: Storing Data

Save the structured data in CSV, JSON, or SQLite.

CSV

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

JSON

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

If you find this interesting, explore our related article covering more web scraping examples with tools and their overviews. If you don’t want to scrape Zillow, we also offer Property24 proxies and more solutions for other websites.

Conclusion

Now you know how to scrape real estate data. First, define your target real estate listings and data points. Then, use Python tools to collect data from property listings of your choice. Make sure you set your tools up to handle pagination efficiently and format the results in a desired structure.

If you want to minimize the chances of getting blocked, use residential proxies to change IPs frequently and make the anti-bot measures think that you’re a genuine user.

Follow these steps and you’ll be able to track real estate market trends and conduct investment analysis over time in a clean and structured way. Just make sure to respect the websites’ Terms of Service and don’t touch private data.

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