Zoocasa Scraper: Smart Property Data Extraction for Competitive Research

import requests
from bs4 import BeautifulSoup

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
    "Accept-Language": "en-US,en;q=0.9",
    "Referer": "https://www.google.com/"
}

def scrape_zoocasa_listing(url):
    response = requests.get(url, headers=HEADERS, timeout=15)

    if response.status_code != 200:
        print("Request Failed:", response.status_code)
        return None

    soup = BeautifulSoup(response.text, "lxml")

    title = soup.find("h1")
    price = soup.find("span", {"class": "listing-price"})
    address = soup.find("div", {"class": "listing-address"})
    beds = soup.find("span", {"class": "beds-count"})
    baths = soup.find("span", {"class": "baths-count"})

    listing_data = {
        "Property Title": title.get_text(strip=True) if title else "N/A",
        "Listing Price": price.get_text(strip=True) if price else "N/A",
        "Property Address": address.get_text(strip=True) if address else "N/A",
        "Bedrooms": beds.get_text(strip=True) if beds else "N/A",
        "Bathrooms": baths.get_text(strip=True) if baths else "N/A",
        "Source URL": url
    }
    return listing_data

# Example Zoocasa listing URL
zoocasa_listing_url = "https://www.zoocasa.com/example-listing"
data = scrape_zoocasa_listing(zoocasa_listing_url)

if data:
    print(data)