How to Scrape Data from idealista: The Ultimate Guide

idealista is one of the most popular real estate listing websites in Spain, Portugal and Italy. It contains millions of listings for properties for sale and rent, making it an invaluable resource for those looking to buy a home or conduct market research. However, manually browsing through the immense number of listings can be incredibly time-consuming. That‘s where web scraping comes in.

In this ultimate guide, we‘ll walk you through the process of scraping data from idealista using Python and Selenium. You‘ll learn how to extract key information like property titles, prices, descriptions, and more. We‘ll also cover strategies for avoiding getting blocked by the website‘s anti-bot measures. Let‘s get started!

Why Scrape Data from idealista?

There are many reasons you might want to scrape data from a real estate website like idealista:

• Market research: Collect data on property prices, characteristics, and availability in different areas to inform investment decisions.
• Finding properties to buy: Automate the search process by scraping listings that match your criteria and getting notified of new matches.
• Analyzing trends: Track metrics like average price per square meter over time to understand market movements.
• Competitor analysis: See what properties other real estate companies and individual sellers are listing.

Whatever your motivation, scraping allows you to harness large amounts of publicly available data and derive valuable insights.

Challenges of Scraping idealista

Like many websites, idealista employs measures to prevent bots and scrapers from excessively accessing their pages. Some of the challenges you may encounter include:

• IP blocking: idealista may block IP addresses that make too many requests in a short period of time.
• CAPTCHAs: The site may present a CAPTCHA challenge to verify you are human, especially on the first page load.
• Dynamic content loading: Some data may be loaded dynamically via JavaScript, which can trip up basic HTML scrapers.

We‘ll show you strategies to work around these roadblocks as we build our scraper.

Overview of the Scraping Process

To scrape data from idealista, we‘ll use the following tools and libraries:

• Python: The programming language used to write the scraping script.
• Selenium: A browser automation tool that allows interacting with web pages, filling forms, clicking buttons, etc. We‘ll use it to navigate pages and extract data.
• undetected_chromedriver: A library that provides a Chrome webdriver with modifications to avoid triggering anti-bot detection.

The general process will be:

1. Set up a Python environment with the required dependencies
2. Use Selenium to navigate to a starting page (e.g. list of provinces)
3. Extract URLs to relevant sub-pages (e.g. municipalities)
4. Navigate to each of those sub-pages and extract data
5. Handle pagination to scrape all results
6. Format, clean and store the extracted data

Now let‘s set up our environment and start coding!

Setting Up the Environment

First, make sure you have Python 3.x installed. We‘ll be using Python 3.10 but any recent 3.x version should work.

Create a new directory for the project and a Python virtual environment:

mkdir idealista-scraper
cd idealista-scraper
python -m venv venv

Activate the virtual environment:

# On Windows
venv\Scripts\activate.bat

# On Unix/macOS 
source venv/bin/activate

The name of your active environment should appear before the terminal prompt.

Next install the required libraries:

pip install selenium undetected_chromedriver

That takes care of our setup! On to the fun part.

Scraping the List of Provinces

Create a new Python file, e.g. scraper.py. We‘ll start by importing the necessary modules and initializing a Chrome webdriver:

from selenium import webdriver
from selenium.webdriver.common.by import By
import undetected_chromedriver as uc

driver = uc.Chrome()

This launches a Chrome browser controlled by our script.

Next, we navigate to the idealista homepage and find the HTML element containing the list of provinces:

driver.get("https://www.idealista.com/")

provinces_div = driver.find_element(By.CLASS_NAME, ‘locations-list‘)
province_links = provinces_div.find_elements(By.TAG_NAME, ‘a‘)

Here we used Selenium‘s find_element and find_elements methods to locate elements by their HTML class and tag names respectively.

We can now extract the name and URL of each province and store them in a dictionary:

provinces = {}
for province in province_links:
    provinces[province.text] = province.get_attribute(‘href‘)

print(provinces)

This prints out something like:

{‘Álava‘: ‘https://www.idealista.com/venta-viviendas/alava/‘, ‘Albacete‘: ‘https://www.idealista.com/venta-viviendas/albacete-provincia/‘, ...}

Great, we‘ve got our list of provinces! Let‘s move on to scraping the municipalities.

Scraping Municipalities

The process for getting the municipalities in each province is very similar. We‘ll navigate to each province URL, find the municipality links, and extract their names and URLs.

First, let‘s define a function to handle scraping the municipalities:

def scrape_municipalities(province_url):
    driver.get(province_url)
    sleep(1)

    municipality_list = driver.find_element(By.ID, ‘location_list‘)
    municipality_links = municipality_list.find_elements(By.TAG_NAME, ‘a‘)

    municipalities = {}
    for municipality in municipality_links:
        municipalities[municipality.text] = municipality.get_attribute(‘href‘)

    return municipalities

This function takes a province URL, navigates to it, finds the list of municipalities, and returns a dictionary mapping municipality names to URLs.

Note the sleep(1) call – this pauses execution for 1 second, giving the page time to load before we start looking for elements. Adjust this delay as needed.

Let‘s update our provinces loop to call scrape_municipalities for each province:

provinces = {}
for province in province_links:
    province_name = province.text
    province_url = province.get_attribute(‘href‘)

    provinces[province_name] = {
        ‘url‘: province_url,
        ‘municipalities‘: scrape_municipalities(province_url)
    }

Now our provinces dictionary contains the list of municipalities for each province, ready for the next step.

Scraping Property Listings

Finally, we get to the heart of it – scraping the actual property listings! For each municipality, we‘ll navigate to its URL, find all the listing elements, and extract the relevant data points.

Here‘s a function to handle scraping a single listing:

def scrape_listing(listing_element):
    title = listing_element.find_element(By.CLASS_NAME, ‘item-link‘).text
    subtitle = listing_element.find_element(By.CLASS_NAME, ‘item-detail-char‘).text
    price = listing_element.find_element(By.CLASS_NAME, ‘item-price‘).text
    description = listing_element.find_element(By.CLASS_NAME, ‘ellipsis‘).text
    url = listing_element.find_element(By.CLASS_NAME, ‘item-link‘).get_attribute(‘href‘)

    return {
        ‘title‘: title,
        ‘subtitle‘: subtitle, 
        ‘price‘: price,
        ‘description‘: description,
        ‘url‘: url
    }

And a function to scrape all the listings for a given municipality URL:

def scrape_listings(municipality_url):
    driver.get(municipality_url)
    sleep(1)

    listings = []
    listing_elements = driver.find_elements(By.CLASS_NAME, ‘item‘)
    for listing_element in listing_elements:
        listings.append(scrape_listing(listing_element))

    return listings        

Let‘s slot this into our existing code:

provinces = {}
for province in province_links:
    province_name = province.text
    province_url = province.get_attribute(‘href‘)

    municipalities = scrape_municipalities(province_url)

    for municipality_name, municipality_url in municipalities.items():
        listings = scrape_listings(municipality_url)
        municipalities[municipality_name] = {
            ‘url‘: municipality_url,
            ‘listings‘: listings
        }

    provinces[province_name] = {
        ‘url‘: province_url,
        ‘municipalities‘: municipalities
    }

Phew! We now have a complete hierarchy of provinces, municipalities, and property listings. But there‘s one more thing…

Handling Pagination

For municipalities with many listings, the results will be split across multiple pages. We need to handle this pagination to ensure we scrape all available listings.

The logic is:

1. Scrape the listings on the current page
2. Check if there is a "Next" button
3. If yes, click it and repeat from step 1
4. If no, we‘re done

Here‘s the updated scrape_listings function:

from selenium.common.exceptions import NoSuchElementException

def scrape_listings(municipality_url):
    driver.get(municipality_url)
    sleep(1)

    listings = []

    while True:
        listing_elements = driver.find_elements(By.CLASS_NAME, ‘item‘)
        for listing_element in listing_elements:
            listings.append(scrape_listing(listing_element))

        try:
            next_button = driver.find_element(By.XPATH, ‘//a[contains(@class, "next")]‘)
            next_button.click()
            sleep(1)
        except NoSuchElementException:
            break

    return listings

We use a while True loop to keep scraping until there‘s no "Next" button. The NoSuchElementException is caught to break the loop when we run out of pages.

Avoiding Blocking and CAPTCHAs

As mentioned earlier, idealista has some measures in place to prevent excessive automated access. Here are a few strategies to avoid triggering them:

• Use undetected_chromedriver as the webdriver. It includes some modifications to make the automated browser harder to distinguish from a human-controlled one.

• Introduce random delays between requests using time.sleep() and a random number generator. This makes the scraping pattern less predictable.

• Rotate IP addresses by using a proxy service. This prevents a single IP from making too many requests and getting blocked.

• If a CAPTCHA does appear, you can pause the script with an input() prompt to let yourself solve it manually before continuing. Not ideal for full automation, but fine for one-off scraping runs.

For a more robust solution to the IP rotation and CAPTCHA issues, consider using a service like ScrapingBee.

Using ScrapingBee to Handle Proxies and CAPTCHAs

ScrapingBee is a web scraping service that provides easy access to a large proxy pool and automatic CAPTCHA solving. Using it in your idealista scraper can significantly reduce the chances of getting blocked.

First, sign up for an account at ScrapingBee.com to get an API key. Then install the Python library:

pip install scrapingbee-sdk

Next, initialize the ScrapingBee client in your script:

from scrapingbee import ScrapingBeeClient

client = ScrapingBeeClient(api_key=‘YOUR_API_KEY‘)

Now, instead of fetching pages with Selenium‘s driver.get(), you can use ScrapingBee‘s client.get():

url = ‘https://www.idealista.com/‘
response = client.get(url, params={
    ‘premium_proxy‘: ‘true‘,
    ‘country_code‘: ‘es‘,
    ‘render_js‘: ‘false‘
})

html_content = response.content

This sends the HTTP request through ScrapingBee‘s proxy and CAPTCHA-solving service. You can then pass the returned HTML to Selenium for parsing, or use BeautifulSoup or another HTML parsing library.

Using ScrapingBee has a cost, but it can save you a lot of time and headache in dealing with anti-bot measures. They have a free tier to get started.

Conclusion

In this guide, we walked through the process of scraping property data from idealista using Python, Selenium, and undetected_chromedriver.

We covered:

• Navigating the website‘s structure to extract province, municipality and listing data
• Handling pagination to scrape all available results
• Strategies for avoiding IP blocking and CAPTCHAs
• Using ScrapingBee to simplify proxy rotation and CAPTCHA solving

Some additional considerations for a production scraper:

• Implement proper error handling and logging
• Save scraped data to persistent storage (e.g. database or JSON files)
• Respect robots.txt rules and limit scraping rate to avoid overwhelming the server
• Adapt the code to scrape other idealista sites like idealista.it and idealista.pt

With the techniques outlined here, you should be able to build a robust web scraper to extract valuable real estate data from idealista. Happy scraping!