Fotocasa Scraper & Spanish Real Estate Data API

Harness Spanish Real Estate Intelligence with a Managed Fotocasa Scraper

Fotocasa (part of the Adevinta group alongside Habitaclia and InfoJobs) is one of Spain's two dominant real estate portals, hosting over 1.5 million active residential, commercial, and land listings. For international property developers, PropTech valuation startups, holiday rental operators, and real estate investment trusts (REITs / SOCIMIs), Fotocasa provides indispensable insights into market trends, price per square meter (€/m²) trajectories, and neighborhood-level liquidity.

Whether you need to monitor urban core transactions in Madrid (Salamanca, Chamberí, Retiro) and Barcelona (Eixample, Sarrià-Sant Gervasi), track high-yield tourist assets along the Costa del Sol (Malaga, Marbella) and Balearic Islands (Mallorca, Ibiza), or capture emerging developments in Valencia and Seville, our managed Fotocasa web scraper delivers pristine, ready-to-analyze datasets on schedule.

Technical Challenges of Scraping Fotocasa & How We Solve Them

Scraping Fotocasa at enterprise scale requires navigating complex web architectures and strict anti-bot protections. Here is how our automated crawling infrastructure solves these hurdles:

Comprehensive Fotocasa Data Fields We Extract

Every dataset is cleaned, validated, deduplicated, and mapped to your preferred database schema:

Code Example: Extracting Fotocasa Listings with Python

Below is a Python demonstration using standard request headers and proxy routing to query Fotocasa search pages and parse structured property records:

# fotocasa_scraper.py - Extract Spanish property data via Python
import requests
import json
import pandas as pd

# Configure Spanish Residential Proxy
PROXIES = {
    "http": "http://user:pass@es-residential.proxyprovider.com:8000",
    "https": "http://user:pass@es-residential.proxyprovider.com:8000"
}

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36",
    "Accept-Language": "es-ES,es;q=0.9,en;q=0.8",
    "Accept": "application/json, text/plain, */*",
    "Referer": "https://www.fotocasa.es/es/comprar/viviendas/madrid-capital/todas-las-zonas/l"
}

def fetch_fotocasa_madrid(page=1):
    url = f"https://www.fotocasa.es/es/comprar/viviendas/madrid-capital/l/{page}"
    response = requests.get(url, headers=HEADERS, proxies=PROXIES, timeout=15)
    
    if response.status_code == 200:
        print(f"Successfully retrieved page {page} from Fotocasa Spain")
        # Parse hydration state or JSON payload
        return response.text
    else:
        print(f"Error fetching Fotocasa: HTTP {response.status_code}")
        return None

# For managed, continuous feeds with automatic anti-bot bypass,
# contact WebScrapingHub to deploy our turnkey pipeline.

Strategic Use Cases for Fotocasa Property Data

Frequently Asked Questions