Is it legal to scrape Century 21?

Scraping publicly available real estate data is generally legal for research and analysis under precedents like the HiQ v. LinkedIn case. However, you must ensure you are not bypassing login walls, scraping private user data, or violating the site's Terms of Service in a way that damages their servers.

Does Century 21 have an official API?

Yes, Century 21 is part of Anywhere Real Estate, which provides an official API through the Anywhere Developers Portal. This API allows access to listing, agent, and office data, though it requires developer registration and approval.

How do I avoid the 403 Forbidden error?

The 403 error is typically triggered by CloudFront detecting automated traffic. To avoid this, use residential proxies, implement TLS fingerprinting mimicry, and ensure your headers exactly match those of a modern web browser.

What proxies are best for Century 21?

Residential rotating proxies are the most effective because they provide IP addresses from real home networks. Data center proxies are easily identified and blacklisted by real estate platforms like Century 21.

Can I scrape international listings?

Yes, Century 21 has a global presence, and its website allows you to filter listings by country. You may need to use localized proxies (e.g., French proxies for French listings) to ensure the site serves the correct regional content.

How often is the data updated?

Century 21 updates its listing data continuously throughout the day as new properties are added to the MLS. For most market research purposes, a daily scrape (every 24 hours) is sufficient to capture new listings and price changes.

Do I need to use a headless browser?

While basic data can sometimes be fetched via requests, a headless browser like Playwright or Puppeteer is highly recommended. This is because Century 21 uses JavaScript to render many of its dynamic components and search results.

What format can I export the data into?

Using scraping tools or custom scripts, you can export the data into structured formats like CSV, JSON, or Excel. Many professionals also use webhooks to feed the scraped property data directly into their CRM or SQL databases.

How to Scrape Century 21: A Technical Real Estate Guide

Scrape Century 21 for property listings, prices, and agent data. Master CloudFront bypass, residential proxies, and automated extraction at scale.

Start Scraping Free

century21.comHard

Coverage:GlobalUnited StatesCanadaEuropeAsia-PacificLatin America

Available Data10 fields

TitlePriceLocationDescriptionImagesSeller InfoContact InfoPosting DateCategoriesAttributes

All Extractable Fields

Property TitleListing PriceStreet AddressCityState/ProvinceZip CodeNumber of BedroomsNumber of BathroomsTotal Square FootageLot Size (Acres)Property TypeYear BuiltMLS NumberListing Agent NameBrokerage Office NameGeographic Coordinates (Lat/Long)Property DescriptionInterior/Exterior FeaturesImage URLs

Technical Requirements

JavaScript Required

No Login

Has Pagination

Official API Available

Anti-Bot Protection Detected

CloudFrontAWS WAFTLS FingerprintingIP BlockingRate Limiting

View API Documentation

About Century 21

Learn what Century 21 offers and what valuable data can be extracted from it.

The Global Real Estate Powerhouse

Century 21 Real Estate LLC is an American real estate agent franchise company and a subsidiary of Anywhere Real Estate Inc. (formerly Realogy). It is one of the most recognized brands in the industry, aggregating millions of residential, commercial, and luxury listings across dozens of countries. The platform serves as a standardized portal for buyers and sellers, offering deep technical data on every property.

High-Value Listing Data

The website contains a massive volume of structured property information. For data enthusiasts, it provides access to MLS numbers, historical price points, specific property attributes like square footage and year built, and direct agent contact details. This data is highly valuable for building real estate apps, lead generation databases, and market intelligence tools.

Why Scrape Century 21?

Scraping this site allows for large-scale comparative market analysis that is impossible to perform manually. By extracting global listing data, users can track international migration trends, monitor price fluctuations in high-growth markets, and identify undervalued investment opportunities before they go mainstream.

Why Scrape Century 21?

Discover the business value and use cases for extracting data from Century 21.

Real-time monitoring of property price fluctuations in local and global markets.

Identification of undervalued investment opportunities for house flipping or rental portfolios.

Generation of high-quality leads for mortgage brokers, insurance agents, and movers.

Aggregation of historical property data for training appraisal and valuation AI models.

Competitive analysis of brokerage performance and regional market share trends.

Scraping Challenges

Technical challenges you may encounter when scraping Century 21.

Aggressive 403 Forbidden errors caused by CloudFront bot protection layers.

Dynamic content rendering that requires full JavaScript execution for data visibility.

Sophisticated browser fingerprinting that detects standard headless browser signatures.

Strict rate limiting that necessitates the use of premium residential proxy networks.

Scrape Century 21 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 Century 21. Just type it in plain language — no coding or selectors needed.

AI Extracts the Data

Our artificial intelligence navigates Century 21, 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

Automatically handles residential proxy rotation to prevent IP-based blacklisting.

Simulates human-like browsing behavior to bypass CloudFront and WAF detection.

Built-in JavaScript rendering removes the need for complex Selenium or Playwright code.

Scheduled execution allows for consistent daily or weekly property data updates.

Start Scraping Free

No credit card requiredFree tier availableNo setup needed

No-Code Web Scrapers for Century 21

Point-and-click alternatives to AI-powered scraping

Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape Century 21. 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

# Custom headers are mandatory to bypass basic AWS WAF checks
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
    'Accept-Language': 'en-US,en;q=0.9',
    'Referer': 'https://www.century21.com/'
}

def scrape_c21(url):
    try:
        # Requests will often fail with 403 without high-quality proxies
        response = requests.get(url, headers=headers, timeout=15)
        if response.status_code == 200:
            soup = BeautifulSoup(response.text, 'html.parser')
            # Selectors target common property card elements
            listings = soup.select('.property-card')
            for item in listings:
                price = item.select_one('.property-card-price').get_text(strip=True)
                addr = item.select_one('.property-address').get_text(strip=True)
                print(f'Price: {price} | Address: {addr}')
        else:
            print(f'Blocked: {response.status_code}')
    except Exception as e:
        print(f'Error: {e}')

scrape_c21('https://www.century21.com/real-estate/new-york-ny/LCNYNEWYORK/')

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

from playwright.sync_api import sync_playwright

def scrape_with_playwright():
    with sync_playwright() as p:
        # Launching with stealth-like parameters is recommended
        browser = p.chromium.launch(headless=True)
        context = browser.new_context(user_agent='Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)')
        page = context.new_page()
        
        # Navigate to a search result page
        page.goto('https://www.century21.com/real-estate/los-angeles-ca/LCCALOSANGELES/')
        
        # Wait for the property grid to load via JavaScript
        page.wait_for_selector('.property-card')
        
        cards = page.query_selector_all('.property-card')
        for card in cards:
            price = card.query_selector('.property-card-price').inner_text()
            address = card.query_selector('.property-address').inner_text()
            print({'price': price, 'address': address})
            
        browser.close()

scrape_with_playwright()

When to Use

Use when content loads dynamically via JavaScript, or when you need to interact with the page (clicks, scrolls, form fills). Handles modern anti-bot detection better.

Advantages

●Executes JavaScript like a real browser
●Handles SPAs and dynamic content
●Better anti-bot evasion with stealth plugins
●Can take screenshots and PDFs

Limitations

●Slower than HTTP requests
●Higher memory/CPU usage
●More complex to set up

import scrapy

class Century21Spider(scrapy.Spider):
    name = 'c21_spider'
    start_urls = ['https://www.century21.com/real-estate/miami-fl/LCCFMIAMI/']

    def parse(self, response):
        # Iterate through property containers using CSS selectors
        for listing in response.css('.property-card'):
            yield {
                'address': listing.css('.property-address::text').get().strip(),
                'price': listing.css('.property-card-price::text').get().strip(),
                'url': response.urljoin(listing.css('a.card-anchor::attr(href)').get())
            }
        
        # Follow next page links in the pagination bar
        next_page = response.css('a.next-page::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

When to Use

Ideal for large-scale crawling projects that need to scrape thousands of pages. Built-in support for rate limiting, retries, and data pipelines.

Advantages

●Built for scale (millions of pages)
●Automatic request throttling
●Built-in data export pipelines
●Middleware system for proxies/headers

Limitations

●Steeper learning curve
●Overkill for small projects
●No native JavaScript rendering

const puppeteer = require('puppeteer');

async function scrapeCentury21() {
  const browser = await puppeteer.launch({ headless: true });
  const page = await browser.newPage();
  
  // Set a realistic viewport
  await page.setViewport({ width: 1280, height: 800 });
  
  await page.goto('https://www.century21.com/real-estate/chicago-il/LCCICHICAGO/');
  
  // Wait for React components to render the listings
  await page.waitForSelector('.property-card');
  
  const properties = await page.evaluate(() => {
    return Array.from(document.querySelectorAll('.property-card')).map(el => ({
      price: el.querySelector('.property-card-price')?.innerText,
      address: el.querySelector('.property-address')?.innerText,
      beds: el.querySelector('.property-beds')?.innerText
    }));
  });
  
  console.log(properties);
  await browser.close();
}

scrapeCentury21();

When to Use

Choose this if you're in a Node.js/JavaScript ecosystem or need tight integration with frontend tools. Similar capabilities to Playwright.

Advantages

●Native JavaScript/TypeScript support
●Chrome DevTools Protocol access
●Large ecosystem and community
●Good for JS-heavy projects

Limitations

●Chrome-only (vs Playwright's multi-browser)
●Similar overhead to Playwright
●Less mature stealth options

How to Scrape Century 21 with Code

Python + Requests

import requests
from bs4 import BeautifulSoup

# Custom headers are mandatory to bypass basic AWS WAF checks
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
    'Accept-Language': 'en-US,en;q=0.9',
    'Referer': 'https://www.century21.com/'
}

def scrape_c21(url):
    try:
        # Requests will often fail with 403 without high-quality proxies
        response = requests.get(url, headers=headers, timeout=15)
        if response.status_code == 200:
            soup = BeautifulSoup(response.text, 'html.parser')
            # Selectors target common property card elements
            listings = soup.select('.property-card')
            for item in listings:
                price = item.select_one('.property-card-price').get_text(strip=True)
                addr = item.select_one('.property-address').get_text(strip=True)
                print(f'Price: {price} | Address: {addr}')
        else:
            print(f'Blocked: {response.status_code}')
    except Exception as e:
        print(f'Error: {e}')

scrape_c21('https://www.century21.com/real-estate/new-york-ny/LCNYNEWYORK/')

Python + Playwright

from playwright.sync_api import sync_playwright

def scrape_with_playwright():
    with sync_playwright() as p:
        # Launching with stealth-like parameters is recommended
        browser = p.chromium.launch(headless=True)
        context = browser.new_context(user_agent='Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)')
        page = context.new_page()
        
        # Navigate to a search result page
        page.goto('https://www.century21.com/real-estate/los-angeles-ca/LCCALOSANGELES/')
        
        # Wait for the property grid to load via JavaScript
        page.wait_for_selector('.property-card')
        
        cards = page.query_selector_all('.property-card')
        for card in cards:
            price = card.query_selector('.property-card-price').inner_text()
            address = card.query_selector('.property-address').inner_text()
            print({'price': price, 'address': address})
            
        browser.close()

scrape_with_playwright()

Python + Scrapy

import scrapy

class Century21Spider(scrapy.Spider):
    name = 'c21_spider'
    start_urls = ['https://www.century21.com/real-estate/miami-fl/LCCFMIAMI/']

    def parse(self, response):
        # Iterate through property containers using CSS selectors
        for listing in response.css('.property-card'):
            yield {
                'address': listing.css('.property-address::text').get().strip(),
                'price': listing.css('.property-card-price::text').get().strip(),
                'url': response.urljoin(listing.css('a.card-anchor::attr(href)').get())
            }
        
        # Follow next page links in the pagination bar
        next_page = response.css('a.next-page::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

Node.js + Puppeteer

const puppeteer = require('puppeteer');

async function scrapeCentury21() {
  const browser = await puppeteer.launch({ headless: true });
  const page = await browser.newPage();
  
  // Set a realistic viewport
  await page.setViewport({ width: 1280, height: 800 });
  
  await page.goto('https://www.century21.com/real-estate/chicago-il/LCCICHICAGO/');
  
  // Wait for React components to render the listings
  await page.waitForSelector('.property-card');
  
  const properties = await page.evaluate(() => {
    return Array.from(document.querySelectorAll('.property-card')).map(el => ({
      price: el.querySelector('.property-card-price')?.innerText,
      address: el.querySelector('.property-address')?.innerText,
      beds: el.querySelector('.property-beds')?.innerText
    }));
  });
  
  console.log(properties);
  await browser.close();
}

scrapeCentury21();

What You Can Do With Century 21 Data

Explore practical applications and insights from Century 21 data.

Comparative Market Analysis

Real estate firms can monitor local competitor listings to ensure their own inventory is priced accurately against the market average.

How to implement:

1Extract prices and square footage for a specific zip code.
2Calculate the average price-per-square-foot for active listings.
3Generate automated reports for agents to adjust listing prices.

Use Automatio to extract data from Century 21 and build these applications without writing code.

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

Get Started Free

Pro Tips for Scraping Century 21

Expert advice for successfully extracting data from Century 21.

Always use rotating residential proxies; Century 21's CloudFront firewall aggressively blocks data center IP ranges.

Implement randomized sleep timers between 5-15 seconds to avoid triggering behavior-based rate limiting.

Use the official Anywhere Developers Portal if you require high-volume listing data, as it is the most stable source.

Target the site's XML sitemap index to find direct property URLs and bypass complex search-page pagination.

Monitor the HTML structure frequently; real estate sites often update CSS classes during seasonal platform refreshes.

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

Mohammed Ibrahim

CEO, qannas.pro

Ben Bressington

CTO, AiChatSolutions

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.

Related Web Scraping

Frequently Asked Questions About Century 21

Find answers to common questions about Century 21

How to Scrape Century 21: A Technical Real Estate Guide

About Century 21

The Global Real Estate Powerhouse

High-Value Listing Data

Why Scrape Century 21?

Why Scrape Century 21?

Scraping Challenges

Scrape Century 21 with AI

How It Works

Why Use AI for Scraping

How to scrape with AI:

Why use AI for scraping:

No-Code Web Scrapers for Century 21

Typical Workflow with No-Code Tools

Common Challenges

No-Code Web Scrapers for Century 21

Typical Workflow with No-Code Tools

Common Challenges

Code Examples

How to Scrape Century 21 with Code

Python + Requests

Python + Playwright

Python + Scrapy

Node.js + Puppeteer

What You Can Do With Century 21 Data

Comparative Market Analysis

Investor Lead Sourcing

Mortgage Lead Generation

AI Valuation Training

Brokerage Performance Tracking

What You Can Do With Century 21 Data

Supercharge your workflow with AI Automation

Pro Tips for Scraping Century 21

What Our Users Say

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Frequently Asked Questions About Century 21

Is it legal to scrape Century 21?

Does Century 21 have an official API?

How do I avoid the 403 Forbidden error?

What proxies are best for Century 21?

Can I scrape international listings?

How often is the data updated?

Do I need to use a headless browser?

What format can I export the data into?