How to Extract Idealista Real Estate Data Without Getting Blocked

Scraping Idealista for property listings sounds straightforward—until you hit DataDome's wall. Here's what really happens: you write a script, run it twice, and boom—CAPTCHA. Your scraper's dead. But here's the thing: with the right approach, you can pull thousands of listings without breaking a sweat. This guide shows you how to bypass anti-bot systems and extract clean real estate data efficiently.

So you want Idealista's real estate data. Makes sense—millions of property listings across Southern Europe, market insights waiting to be analyzed, competitor prices sitting right there. The problem? DataDome doesn't want you having it.

Let me walk you through what actually works. No theory, no fluff—just the practical steps that get you from blocked requests to clean JSON files full of property data.

What You're Up Against: DataDome's Detection System

DataDome isn't your average bot blocker. They've built a machine learning system that reads your scraper like a book. Every request you make gets a "trust score"—basically their guess at whether you're human or bot.

Here's how they catch you:

Browser fingerprinting checks your browser version, screen resolution, IP address. If they see the same fingerprint hammering their servers? Red flag.

Behavioral analysis watches how you move. Real people don't scroll at perfectly consistent speeds. They don't click with millisecond precision. Bots do.

IP monitoring tracks where requests come from. One IP making 100 requests per minute? That's not a person browsing apartments.

HTTP headers reveal what you're really using. Standard Selenium leaves traces in headers that scream "automated browser."

The result? Your basic scraping script triggers alarms before you've scraped your second page.

Why Your Basic Selenium Script Gets Caught

Let's say you write a standard Selenium scraper. You set it to headless mode, point it at Idealista's listings, and hit run. Here's what happens:

python
from selenium import webdriver
chrome_options = Options()
chrome_options.add_argument("--headless")
driver = webdriver.Chrome(options=chrome_options)
driver.get("https://www.idealista.com/venta-viviendas/alcala-de-henares-madrid/")

DataDome sees this immediately. Headless Chrome has a different fingerprint than regular Chrome. Your script loads pages with robotic timing. No mouse movements, no pauses, no human hesitation. Within two requests, you're staring at a CAPTCHA page.

The data's sitting right there in the HTML—property titles in <a class="item-link"> tags, prices in <span class="item-price">, bedroom counts in <span class="item-detail">. But you can't reach it. DataDome's blocked the gate.

The Solution: Handling Anti-Bot Systems Properly

Building your own proxy rotation system takes weeks. Maintaining it takes constant attention as detection methods evolve. The smarter move? Use infrastructure built specifically for this problem.

When you're facing sophisticated protection systems like DataDome, having the right tools means the difference between spending weeks on infrastructure versus getting data today. 👉 This anti-detection API handles proxy rotation, CAPTCHA solving, and browser fingerprinting automatically, so you can focus on extracting the data you actually need.

Here's what working code looks like:

python
import requests
from bs4 import BeautifulSoup
import json

API_KEY = "your_api_key_here"
url = "https://www.idealista.com/venta-viviendas/alcala-de-henares-madrid/"

payload = {
"api_key": API_KEY,
"url": url,
"render": True
}

response = requests.get("http://api.scraperapi.com", params=payload)
soup = BeautifulSoup(response.text, 'lxml')

That render: True parameter? It loads the page with JavaScript execution, just like a real browser. But without the fingerprints that get detected.

Extracting the Actual Data

Once you've got the HTML, parsing it is straightforward. Each property listing sits in an <article class="item"> tag. Inside, you'll find:

python
house_listings = soup.find_all("article", class_="item")
extracted_data = []

for listing in house_listings:
description_elem = listing.find("div", class_="item-description")
description = description_elem.get_text(strip=True) if description_elem else "nil"

item_details = listing.find_all("span", class_="item-detail")

bedrooms = item_details[0].get_text(strip=True) if len(item_details) > 0 else "nil"

area = item_details[1].get_text(strip=True) if len(item_details) > 1 else "nil"

listing_info = {

    'Title': listing.find("a", class_="item-link").get("title", "nil"),

    'Price': listing.find("span", class_="item-price").get_text(strip=True),

    'Bedrooms': bedrooms,

    'Area': area,

    'Description': description

}

extracted_data.append(listing_info)

Save it to JSON:

python
from datetime import datetime

timestamp = datetime.now().strftime("%Y%m%d%H%M")
filename = f"idealista_data_{timestamp}.json"

with open(filename, "w", encoding="utf-8") as f:
json.dump(extracted_data, f, ensure_ascii=False, indent=2)

Run this script. You'll get clean data like:

json
{
"Title": "Dúplex en Chorrillo, Alcalá de Henares",
"Price": "314.900€",
"Bedrooms": "3 hab.",
"Area": "178 m²",
"Description": "Maravilloso ático-dúplex en una de las mejores zonas..."
}

No CAPTCHA. No blocks. Just data.

What You've Actually Accomplished

You now know how to:

• Pull real estate data from Idealista's protected pages

• Bypass DataDome's multi-layered detection without maintaining complex infrastructure

• Parse property listings into clean, structured JSON

• Scale your scraping without getting blocked

The key insight? Modern web scraping isn't about writing clever code to trick detection systems. It's about using tools purpose-built for bypassing anti-bot measures while you focus on data extraction. Systems like 👉 this scraping API handle the hard parts—proxy rotation, browser fingerprinting, CAPTCHA solving—so your code stays simple and reliable.

Bottom line: Idealista's data is accessible if you approach it right. Skip the weeks of debugging headless browsers and building proxy pools. Use proper anti-detection infrastructure, keep your parsing logic clean, and you'll have thousands of property listings flowing into your database while others are still stuck on page two.