How to Scrape Realtor.com | 2026 Comprehensive Scraping Guide
Learn how to scrape Realtor.com property listings, prices, and agent data. Discover techniques to bypass Cloudflare and extract U.S. real estate data at scale.
Anti-Bot Protection Detected
- Cloudflare
- Enterprise-grade WAF and bot management. Uses JavaScript challenges, CAPTCHAs, and behavioral analysis. Requires browser automation with stealth settings.
- DataDome
- Real-time bot detection with ML models. Analyzes device fingerprint, network signals, and behavioral patterns. Common on e-commerce sites.
- Google reCAPTCHA
- Google's CAPTCHA system. v2 requires user interaction, v3 runs silently with risk scoring. Can be solved with CAPTCHA services.
- Rate Limiting
- Limits requests per IP/session over time. Can be bypassed with rotating proxies, request delays, and distributed scraping.
- IP Blocking
- Blocks known datacenter IPs and flagged addresses. Requires residential or mobile proxies to circumvent effectively.
- Browser Fingerprinting
- Identifies bots through browser characteristics: canvas, WebGL, fonts, plugins. Requires spoofing or real browser profiles.
About Realtor.com
Learn what Realtor.com offers and what valuable data can be extracted from it.
The Power of Realtor.com Data
Realtor.com is a leading real estate platform operated by Move, Inc., providing one of the most accurate and up-to-date databases of property listings in the United States. Because it maintains direct relationships with over 800 local Multiple Listing Services (MLS), it offers nearly 99% coverage of available listings, often updated every 15 minutes. This makes it a goldmine for professionals seeking the most current market information.
Comprehensive Property Insights
The platform goes beyond simple price and bedroom counts. It includes deep historical data, such as property tax records, neighborhood safety ratings, school district details, and estimated monthly payments. For real estate investors and market analysts, this granular level of data is essential for accurate property valuation and trend forecasting.
Why Businesses Scrape Realtor.com
Scraping this website allows companies to automate the collection of thousands of listings that would be impossible to gather manually. Whether it's for building a competitive mortgage calculator, identifying 'fix-and-flip' opportunities, or monitoring brokerage performance, the structured data extracted from Realtor.com serves as a foundational asset for high-level real estate intelligence.

Why Scrape Realtor.com?
Discover the business value and use cases for extracting data from Realtor.com.
Perform real-time market trend analysis across U.S. zip codes
Identify investment-ready properties meeting specific ROI criteria
Generate high-quality leads for mortgage brokers and home insurance providers
Analyze historical price fluctuations for accurate property appraisals
Monitor competitor brokerage inventory and listing performance
Aggregate comprehensive neighborhood and school data for relocation services
Scraping Challenges
Technical challenges you may encounter when scraping Realtor.com.
Aggressive Cloudflare challenges requiring advanced JS execution
Deeply nested React components with dynamic class names that change frequently
Strict rate limiting that results in rapid IP blacklisting without proxies
Regional geo-fencing that prioritizes U.S.-based IP addresses
Bot detection patterns that track mouse movements and user behavior
Scrape Realtor.com with AI
No coding required. Extract data in minutes with AI-powered automation.
How It Works
Describe What You Need
Tell the AI what data you want to extract from Realtor.com. Just type it in plain language — no coding or selectors needed.
AI Extracts the Data
Our artificial intelligence navigates Realtor.com, handles dynamic content, and extracts exactly what you asked for.
Get Your Data
Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.
Why Use AI for Scraping
AI makes it easy to scrape Realtor.com without writing any code. Our AI-powered platform uses artificial intelligence to understand what data you want — just describe it in plain language and the AI extracts it automatically.
How to scrape with AI:
- Describe What You Need: Tell the AI what data you want to extract from Realtor.com. Just type it in plain language — no coding or selectors needed.
- AI Extracts the Data: Our artificial intelligence navigates Realtor.com, handles dynamic content, and extracts exactly what you asked for.
- Get Your Data: Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.
Why use AI for scraping:
- Bypasses Cloudflare and DataDome without complex custom code
- Visual selector tool handles dynamic React class names effortlessly
- Cloud-based infrastructure prevents your local IP from being blocked
- Built-in scheduler allows for automatic daily market data refreshes
- Direct integration to export data into Google Sheets or via Webhooks
No-Code Web Scrapers for Realtor.com
Point-and-click alternatives to AI-powered scraping
Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape Realtor.com. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.
Typical Workflow with No-Code Tools
Common Challenges
Learning curve
Understanding selectors and extraction logic takes time
Selectors break
Website changes can break your entire workflow
Dynamic content issues
JavaScript-heavy sites often require complex workarounds
CAPTCHA limitations
Most tools require manual intervention for CAPTCHAs
IP blocking
Aggressive scraping can get your IP banned
No-Code Web Scrapers for Realtor.com
Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape Realtor.com. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.
Typical Workflow with No-Code Tools
- Install browser extension or sign up for the platform
- Navigate to the target website and open the tool
- Point-and-click to select data elements you want to extract
- Configure CSS selectors for each data field
- Set up pagination rules to scrape multiple pages
- Handle CAPTCHAs (often requires manual solving)
- Configure scheduling for automated runs
- Export data to CSV, JSON, or connect via API
Common Challenges
- Learning curve: Understanding selectors and extraction logic takes time
- Selectors break: Website changes can break your entire workflow
- Dynamic content issues: JavaScript-heavy sites often require complex workarounds
- CAPTCHA limitations: Most tools require manual intervention for CAPTCHAs
- IP blocking: Aggressive scraping can get your IP banned
Code Examples
import requests
from bs4 import BeautifulSoup
# Note: Realtor.com uses aggressive Cloudflare. Simple requests often fail.
url = "https://www.realtor.com/realestateandhomes-search/New-York_NY"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
"Accept-Language": "en-US,en;q=0.9"
}
try:
response = requests.get(url, headers=headers, timeout=15)
# Check if we got through the anti-bot
if response.status_code == 200:
soup = BeautifulSoup(response.text, 'html.parser')
# Target property cards based on common data attributes
prices = soup.select('span[data-label="pc-price"]')
for price in prices:
print(f"Property Price: {price.text}")
else:
print(f"Blocked or Error: Status code {response.status_code}")
except Exception as e:
print(f"Connection failed: {e}")When to Use
Best for static HTML pages where content is loaded server-side. The fastest and simplest approach when JavaScript rendering isn't required.
Advantages
- ●Fastest execution (no browser overhead)
- ●Lowest resource consumption
- ●Easy to parallelize with asyncio
- ●Great for APIs and static pages
Limitations
- ●Cannot execute JavaScript
- ●Fails on SPAs and dynamic content
- ●May struggle with complex anti-bot systems
How to Scrape Realtor.com with Code
Python + Requests
import requests
from bs4 import BeautifulSoup
# Note: Realtor.com uses aggressive Cloudflare. Simple requests often fail.
url = "https://www.realtor.com/realestateandhomes-search/New-York_NY"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
"Accept-Language": "en-US,en;q=0.9"
}
try:
response = requests.get(url, headers=headers, timeout=15)
# Check if we got through the anti-bot
if response.status_code == 200:
soup = BeautifulSoup(response.text, 'html.parser')
# Target property cards based on common data attributes
prices = soup.select('span[data-label="pc-price"]')
for price in prices:
print(f"Property Price: {price.text}")
else:
print(f"Blocked or Error: Status code {response.status_code}")
except Exception as e:
print(f"Connection failed: {e}")Python + Playwright
from playwright.sync_api import sync_playwright
def scrape_realtor():
with sync_playwright() as p:
# Launching with stealth-like settings
browser = p.chromium.launch(headless=True)
context = browser.new_context(user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) ...")
page = context.new_page()
print("Navigating to Realtor.com...")
page.goto("https://www.realtor.com/realestateandhomes-search/Austin_TX", wait_until="networkidle")
# Wait for property card selectors to load via JS
page.wait_for_selector('div[data-testid="property-card"]')
listings = page.query_selector_all('div[data-testid="property-card"]')
for item in listings:
price = item.query_selector('[data-label="pc-price"]').inner_text()
address = item.query_selector('[data-label="pc-address"]').inner_text()
print(f"Listing: {address} - Price: {price}")
browser.close()
scrape_realtor()Python + Scrapy
import scrapy
class RealtorSpider(scrapy.Spider):
name = 'realtor_spider'
start_urls = ['https://www.realtor.com/realestateandhomes-search/Miami_FL']
def parse(self, response):
# Extracting data using CSS selectors
for property in response.css('div[data-testid="property-card"]'):
yield {
'price': property.css('span[data-label="pc-price"]::text').get(),
'address': property.css('div[data-label="pc-address"]::text').get(),
'beds': property.css('li[data-label="pc-meta-beds"] span::text').get()
}
# Simple pagination handling
next_page = response.css('a[aria-label="Go to next page"]::attr(href)').get()
if next_page:
yield response.follow(next_page, self.parse)Node.js + Puppeteer
const puppeteer = require('puppeteer');
(async () => {
const browser = await puppeteer.launch({ headless: true });
const page = await browser.newPage();
// Set high-level headers to mimic a real user
await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36');
console.log('Visiting Realtor.com...');
await page.goto('https://www.realtor.com/realestateandhomes-search/Chicago_IL', { waitUntil: 'domcontentloaded' });
// Wait for the price elements to be visible
await page.waitForSelector('.pc-price');
const results = await page.evaluate(() => {
const prices = Array.from(document.querySelectorAll('.pc-price'));
return prices.map(p => p.innerText);
});
console.log('Extracted Prices:', results);
await browser.close();
})();What You Can Do With Realtor.com Data
Explore practical applications and insights from Realtor.com data.
Property Investment Identification
Investors use scraped data to find properties listed below the median neighborhood price-per-square-foot.
How to implement:
- 1Scrape all active listings in a specific county or city
- 2Calculate the average price-per-square-foot for different property types
- 3Flag listings that fall 20% below the average for manual inspection
- 4Export results to a CRM for immediate agent outreach
Use Automatio to extract data from Realtor.com and build these applications without writing code.
What You Can Do With Realtor.com Data
- Property Investment Identification
Investors use scraped data to find properties listed below the median neighborhood price-per-square-foot.
- Scrape all active listings in a specific county or city
- Calculate the average price-per-square-foot for different property types
- Flag listings that fall 20% below the average for manual inspection
- Export results to a CRM for immediate agent outreach
- Mortgage Lead Generation
Lenders identify new listings to offer financing options to prospective buyers or listing agents.
- Monitor Realtor.com for 'Just Listed' homes in target zip codes
- Extract the listing price and estimated monthly payment
- Match listings with agent contact information for partnership outreach
- Automate a daily report of new high-value properties for sales teams
- Competitive Market Analysis (CMA)
Real estate agents generate reports comparing their listings against similar active properties in the area.
- Scrape property details including beds, baths, and square footage for a 1-mile radius
- Extract 'Days on Market' to analyze how fast similar homes are selling
- Compare listing prices versus historical sold prices in the same neighborhood
- Visualize the data in a dashboard to help clients set the perfect list price
- Rental Yield Forecasting
Analyze the relationship between purchase prices and rental rates to calculate potential ROI.
- Scrape both 'For Sale' and 'For Rent' listings across the same zip codes
- Map sales prices to average monthly rental income for specific property sizes
- Calculate the gross rental yield for various neighborhoods
- Identify emerging markets where rental demand outpaces property price growth
Supercharge your workflow with AI Automation
Automatio combines the power of AI agents, web automation, and smart integrations to help you accomplish more in less time.
Pro Tips for Scraping Realtor.com
Expert advice for successfully extracting data from Realtor.com.
Use high-quality residential rotating proxies to avoid rapid IP bans from DataDome.
Always set a realistic User-Agent and include standard browser headers like Accept-Language.
Implement random sleep intervals between 3 to 10 seconds to mimic natural human browsing.
Target the site's JSON-LD scripts found in the HTML for structured data without parsing complex CSS.
Check the robots.txt file at realtor.com/robots.txt to understand their official crawling policies.
Use headless browsers (Playwright/Puppeteer) rather than simple HTTP requests to handle JS challenges.
Testimonials
What Our Users Say
Join thousands of satisfied users who have transformed their workflow
Jonathan Kogan
Co-Founder/CEO, rpatools.io
Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.
Mohammed Ibrahim
CEO, qannas.pro
I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!
Ben Bressington
CTO, AiChatSolutions
Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!
Sarah Chen
Head of Growth, ScaleUp Labs
We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.
David Park
Founder, DataDriven.io
The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!
Emily Rodriguez
Marketing Director, GrowthMetrics
Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.
Jonathan Kogan
Co-Founder/CEO, rpatools.io
Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.
Mohammed Ibrahim
CEO, qannas.pro
I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!
Ben Bressington
CTO, AiChatSolutions
Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!
Sarah Chen
Head of Growth, ScaleUp Labs
We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.
David Park
Founder, DataDriven.io
The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!
Emily Rodriguez
Marketing Director, GrowthMetrics
Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.
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Frequently Asked Questions About Realtor.com
Find answers to common questions about Realtor.com