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.

Coverage:United States
Available Data10 fields
TitlePriceLocationDescriptionImagesSeller InfoContact InfoPosting DateCategoriesAttributes
All Extractable Fields
Property TitleListing PricePrice HistoryProperty TypeYear BuiltBedroomsBathroomsTotal Square FootageLot SizeFull AddressNeighborhood NameSchool District InfoProperty Image URLsVirtual Tour LinksDays on MarketListing Agent NameBrokerage NameProperty Tax HistoryHOA FeesEstimated Monthly Payment
Technical Requirements
JavaScript Required
No Login
Has Pagination
No Official API
Anti-Bot Protection Detected
CloudflareDataDomereCAPTCHARate LimitingIP BlockingBrowser Fingerprinting

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.

About Realtor.com

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

1

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.

2

AI Extracts the Data

Our artificial intelligence navigates Realtor.com, handles dynamic content, and extracts exactly what you asked for.

3

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 credit card requiredFree tier availableNo setup needed

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:
  1. 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.
  2. AI Extracts the Data: Our artificial intelligence navigates Realtor.com, handles dynamic content, and extracts exactly what you asked for.
  3. 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

1
Install browser extension or sign up for the platform
2
Navigate to the target website and open the tool
3
Point-and-click to select data elements you want to extract
4
Configure CSS selectors for each data field
5
Set up pagination rules to scrape multiple pages
6
Handle CAPTCHAs (often requires manual solving)
7
Configure scheduling for automated runs
8
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

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
  1. Install browser extension or sign up for the platform
  2. Navigate to the target website and open the tool
  3. Point-and-click to select data elements you want to extract
  4. Configure CSS selectors for each data field
  5. Set up pagination rules to scrape multiple pages
  6. Handle CAPTCHAs (often requires manual solving)
  7. Configure scheduling for automated runs
  8. 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:

  1. 1Scrape all active listings in a specific county or city
  2. 2Calculate the average price-per-square-foot for different property types
  3. 3Flag listings that fall 20% below the average for manual inspection
  4. 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.

    1. Scrape all active listings in a specific county or city
    2. Calculate the average price-per-square-foot for different property types
    3. Flag listings that fall 20% below the average for manual inspection
    4. 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.

    1. Monitor Realtor.com for 'Just Listed' homes in target zip codes
    2. Extract the listing price and estimated monthly payment
    3. Match listings with agent contact information for partnership outreach
    4. 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.

    1. Scrape property details including beds, baths, and square footage for a 1-mile radius
    2. Extract 'Days on Market' to analyze how fast similar homes are selling
    3. Compare listing prices versus historical sold prices in the same neighborhood
    4. 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.

    1. Scrape both 'For Sale' and 'For Rent' listings across the same zip codes
    2. Map sales prices to average monthly rental income for specific property sizes
    3. Calculate the gross rental yield for various neighborhoods
    4. Identify emerging markets where rental demand outpaces property price growth
More than just prompts

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.

AI Agents
Web Automation
Smart Workflows

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

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

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

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

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

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

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

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

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

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

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

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

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.

Related Web Scraping

Frequently Asked Questions About Realtor.com

Find answers to common questions about Realtor.com