How to Scrape ImmoScout24: Real Estate Data Guide
Learn how to scrape ImmoScout24, Germany's leading real estate platform. Extract property prices, listings, and leads for market analysis and investment.
Anti-Bot Protection Detected
- Akamai Bot Manager
- Advanced bot detection using device fingerprinting, behavior analysis, and machine learning. One of the most sophisticated anti-bot systems.
- DataDome
- Real-time bot detection with ML models. Analyzes device fingerprint, network signals, and behavioral patterns. Common on e-commerce sites.
- Cloudflare
- Enterprise-grade WAF and bot management. Uses JavaScript challenges, CAPTCHAs, and behavioral analysis. Requires browser automation with stealth settings.
- Google reCAPTCHA
- Google's CAPTCHA system. v2 requires user interaction, v3 runs silently with risk scoring. Can be solved with CAPTCHA services.
- Browser Fingerprinting
- Identifies bots through browser characteristics: canvas, WebGL, fonts, plugins. Requires spoofing or real browser profiles.
- Rate Limiting
- Limits requests per IP/session over time. Can be bypassed with rotating proxies, request delays, and distributed scraping.
About ImmoScout24
Learn what ImmoScout24 offers and what valuable data can be extracted from it.
ImmoScout24 is the dominant real estate marketplace in Germany, owned by Scout24 SE. It serves as a comprehensive platform where private individuals, real estate agents, and property developers list residential and commercial properties for rent or sale. The site attracts millions of users monthly, making it the primary source for property market data in the DACH region.
The platform contains a vast array of structured data including property prices, floor plans, neighborhood statistics, and historical listing information. Because it is the market leader, it provides the most accurate reflection of current market trends, supply and demand, and rental yields in major German cities like Berlin, Munich, and Hamburg.
Scraping this data is highly valuable for real estate investors, PropTech companies, and market analysts. It allows for automated price monitoring, competitive benchmarking, and the identification of undervalued investment opportunities. Additionally, it serves as a critical tool for lead generation by identifying active sellers and agencies within specific geographic regions.

Why Scrape ImmoScout24?
Discover the business value and use cases for extracting data from ImmoScout24.
Real-time monitoring of German rental price inflation and market shifts.
Identifying high-yield investment properties before they are discovered by the mass market.
Lead generation for moving services, renovation companies, and mortgage brokers.
Competitive benchmarking for real estate agencies to optimize their listing strategies.
Building historical datasets for predictive real estate valuation models.
Tracking 'Time on Market' to identify motivated sellers or overpriced listings.
Scraping Challenges
Technical challenges you may encounter when scraping ImmoScout24.
Aggressive bot detection via Akamai and Cloudflare on the web version.
Non-semantic HTML structure where multiple data points use identical CSS classes.
Sophisticated session-based tracking and browser fingerprinting to detect automation.
Heavy JavaScript requirements for dynamic content rendering and detail page interaction.
Frequent changes in UI and DOM selectors to break automated scraping scripts.
Scrape ImmoScout24 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 ImmoScout24. Just type it in plain language — no coding or selectors needed.
AI Extracts the Data
Our artificial intelligence navigates ImmoScout24, 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 ImmoScout24 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 ImmoScout24. Just type it in plain language — no coding or selectors needed.
- AI Extracts the Data: Our artificial intelligence navigates ImmoScout24, 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:
- Handles complex anti-bot measures like Akamai automatically without custom coding.
- Visual Point-and-Click selector identification handles complex and shifting DOM structures.
- Scheduled runs allow for tracking Time on Market and price changes for specific listings.
- Integrated proxy management to bypass IP blocks and region-based challenges automatically.
No-Code Web Scrapers for ImmoScout24
Point-and-click alternatives to AI-powered scraping
Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape ImmoScout24. 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 ImmoScout24
Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape ImmoScout24. 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
def scrape_immoscout(url):
# Headers are critical to avoid immediate blocks
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': 'de-DE,de;q=0.9,en-US;q=0.8'
}
try:
response = requests.get(url, headers=headers)
response.raise_for_status()
soup = BeautifulSoup(response.text, 'html.parser')
listings = []
# Target result list entries
for item in soup.select('.result-list-entry__data'):
title = item.select_one('.result-list-entry__brand-title')
price = item.select_one('.result-list-entry__primary-criterion:nth-child(1) dd')
listings.append({
'title': title.text.strip() if title else 'N/A',
'price': price.text.strip() if price else 'N/A'
})
return listings
except Exception as e:
return f'Error: {e}'
# Example search for Berlin apartments
results = scrape_immoscout('https://www.immobilienscout24.de/Suche/de/berlin/berlin/wohnung-mieten')
print(results)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 ImmoScout24 with Code
Python + Requests
import requests
from bs4 import BeautifulSoup
def scrape_immoscout(url):
# Headers are critical to avoid immediate blocks
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': 'de-DE,de;q=0.9,en-US;q=0.8'
}
try:
response = requests.get(url, headers=headers)
response.raise_for_status()
soup = BeautifulSoup(response.text, 'html.parser')
listings = []
# Target result list entries
for item in soup.select('.result-list-entry__data'):
title = item.select_one('.result-list-entry__brand-title')
price = item.select_one('.result-list-entry__primary-criterion:nth-child(1) dd')
listings.append({
'title': title.text.strip() if title else 'N/A',
'price': price.text.strip() if price else 'N/A'
})
return listings
except Exception as e:
return f'Error: {e}'
# Example search for Berlin apartments
results = scrape_immoscout('https://www.immobilienscout24.de/Suche/de/berlin/berlin/wohnung-mieten')
print(results)Python + Playwright
from playwright.sync_api import sync_playwright
def run():
with sync_playwright() as p:
# Launching with stealth-like configurations
browser = p.chromium.launch(headless=True)
context = browser.new_context(
user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/120.0.0.0 Safari/537.36',
locale='de-DE'
)
page = context.new_page()
# Navigate to search results
page.goto('https://www.immobilienscout24.de/Suche/de/berlin/berlin/wohnung-mieten', wait_until='networkidle')
# Wait for listings to render
page.wait_for_selector('.result-list-entry__data')
# Extract titles using locators
titles = page.locator('.result-list-entry__brand-title').all_inner_texts()
for title in titles:
print(f'Listing found: {title}')
browser.close()
run()Python + Scrapy
import scrapy
class ImmoSpider(scrapy.Spider):
name = 'immoscout'
start_urls = ['https://www.immobilienscout24.de/Suche/de/berlin/berlin/wohnung-mieten']
def parse(self, response):
# Loop through each property listing container
for listing in response.css('.result-list-entry__data'):
yield {
'title': listing.css('.result-list-entry__brand-title::text').get(),
'price': listing.css('.result-list-entry__primary-criterion:nth-child(1) dd::text').get(),
'rooms': listing.css('.result-list-entry__primary-criterion:nth-child(3) dd::text').get(),
'area': listing.css('.result-list-entry__primary-criterion:nth-child(2) dd::text').get(),
}
# Handle pagination by finding the 'Next' button
next_page = response.css('a[data-is24-test="pagination-next"]::attr(href)').get()
if next_page:
yield response.follow(next_page, self.parse)Node.js + Puppeteer
const puppeteer = require('puppeteer-extra');
const StealthPlugin = require('puppeteer-extra-plugin-stealth');
puppeteer.use(StealthPlugin());
(async () => {
const browser = await puppeteer.launch({ headless: true });
const page = await browser.newPage();
// Mimic a real German user
await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36');
await page.goto('https://www.immobilienscout24.de/Suche/de/berlin/berlin/wohnung-mieten');
// Evaluation in the browser context
const results = await page.evaluate(() => {
const items = Array.from(document.querySelectorAll('.result-list-entry__brand-title'));
return items.map(item => item.textContent.trim());
});
console.log('Titles found:', results);
await browser.close();
})();What You Can Do With ImmoScout24 Data
Explore practical applications and insights from ImmoScout24 data.
Real Estate Market Trend Analysis
Analyze price fluctuations and inventory levels over time to predict market movements in major German cities.
How to implement:
- 1Scrape rental listings across major cities daily.
- 2Store data in a time-series database.
- 3Calculate average price per square meter per district.
- 4Visualize trends to identify emerging neighborhoods.
Use Automatio to extract data from ImmoScout24 and build these applications without writing code.
What You Can Do With ImmoScout24 Data
- Real Estate Market Trend Analysis
Analyze price fluctuations and inventory levels over time to predict market movements in major German cities.
- Scrape rental listings across major cities daily.
- Store data in a time-series database.
- Calculate average price per square meter per district.
- Visualize trends to identify emerging neighborhoods.
- Investment Yield Calculator
Identify properties with the highest potential ROI by comparing sales and rental data for similar units.
- Scrape both sales and rental listings for specific ZIP codes.
- Match property types and sizes across both datasets.
- Calculate annual rental income versus purchase price.
- Filter for outliers where rental yields exceed market averages.
- Lead Generation for Relocation Services
Identify high-intent movers to offer targeted moving, cleaning, and renovation services.
- Monitor for newly posted rental listings by private individuals.
- Extract property size and location details.
- Identify properties with upcoming availability dates.
- Automate outreach with service offers based on the move-in timeline.
- Competitive Portfolio Monitoring
Track the inventory, vacancy rates, and pricing strategy of rival real estate agencies.
- Filter scraped listings by specific agency names or IDs.
- Track how long listings stay active (Time on Market).
- Monitor for frequent price reductions on their inventory.
- Benchmark your agency's pricing against their active listings.
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 ImmoScout24
Expert advice for successfully extracting data from ImmoScout24.
Use residential proxies with German geo-location (DE) to avoid region-based blocks from Akamai.
Attempt to reverse engineer the mobile app API (JSON over HTTPS) as it often lacks the heavy web-based protection.
Implement random sleep intervals between 5 and 15 seconds to simulate human browsing patterns.
Scrape during off-peak hours (midnight to 5 AM CET) to minimize server load and detection sensitivity.
Clean your data by removing currency symbols (€) and converting German decimal commas to periods for numeric analysis.
Monitor the 'exposed' data in the page source; sometimes raw JSON is embedded in a <script> tag which is easier to parse.
Testimonials
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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.
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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 ImmoScout24
Find answers to common questions about ImmoScout24