How to avoid scraping outdated or removed listings on Zoopla?

Scraping outdated or removed listings on a website like Zoopla can become a challenge, especially since these listings can be detrimental to data quality and analysis. To avoid scraping outdated or removed listings, you can follow these strategies:

  1. Check if the Listing is Active: Before scraping the details of a listing, check if there are any indicators that the listing is no longer active. This can include messages like "Listing removed" or "This property is no longer available".

  2. Last Updated Timestamp: Look for a timestamp on the listing page that tells you when the listing was last updated. You can compare this timestamp to your own record of last visit to determine if the listing is fresh.

  3. HTTP Status Codes: Monitor HTTP status codes when accessing pages. A 404 Not Found or a 410 Gone status code indicates that a listing no longer exists.

  4. Robots.txt: Always check the robots.txt file of Zoopla to ensure that you are allowed to scrape the given pages.

  5. API: If Zoopla offers an API, consider using it instead of scraping the website directly. APIs often provide more reliable and up-to-date data.

  6. Set Up a Regular Scraping Schedule: Regularly scrape listings to keep your data fresh and identify any listings that have been removed or updated since your last scrape.

  7. Database Checks: If you're storing data in a database, periodically check for listings that haven't been updated in a while and flag them for review.

  8. User Feedback: If your application users can interact with the listings, allow them to report outdated or removed listings.

  9. Content Change Detection: Use algorithms to detect significant changes in the content of a listing, which might indicate that it's been removed or changed.

  10. Headless Browsers: Some websites may display different content for bots compared to browsers. Using a headless browser like Puppeteer (Node.js) or Selenium (Python) can help mimic a real user's interaction.

Here's a simple Python example using requests and BeautifulSoup to check if a listing is active before scraping:

import requests
from bs4 import BeautifulSoup

def is_listing_active(url):
    page = requests.get(url)
    if page.status_code == 404 or page.status_code == 410:
        return False  # Listing is not found or gone

    soup = BeautifulSoup(page.content, 'html.parser')
    # Look for specific element or class that indicates the listing is inactive
    inactive_indicator = soup.find('div', class_='inactive-listing-message')
    return inactive_indicator is None

listing_url = ''
if is_listing_active(listing_url):
    print('Listing is active, proceed to scrape.')
    print('Listing is not active. Do not scrape.')

And here's a JavaScript example using Puppeteer to check if a listing is active:

const puppeteer = require('puppeteer');

async function isListingActive(url) {
    const browser = await puppeteer.launch();
    const page = await browser.newPage();
    const response = await page.goto(url);

    if (response.status() === 404 || response.status() === 410) {
        await browser.close();
        return false;  // Listing is not found or gone

    const inactiveIndicator = await page.$('.inactive-listing-message');
    await browser.close();
    return inactiveIndicator === null;

const listingUrl = '';
isListingActive(listingUrl).then(active => {
    if (active) {
        console.log('Listing is active, proceed to scrape.');
    } else {
        console.log('Listing is not active. Do not scrape.');

Remember that web scraping should always be done in compliance with the website's terms of service, and access should be responsible to avoid overloading the server.

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