How to gather real estate listings using Scrapingbee for market analysis teams

If you work in a real estate market analysis team, you know the pain: you need fresh, reliable listings from public sites, but manual collection is a slog and most tools out there are either overpriced, unreliable, or both. If you can write a little Python, it’s not rocket science to get what you need yourself. You just need an API that’s not going to get you blocked every other request. That’s where Scrapingbee comes in.

Below is a blunt, step-by-step guide to getting real estate listings using Scrapingbee. No fluff, no hand-waving—just what works, where the headaches are, and how to avoid the worst of them.

1. Know What You’re After (and What’s Legal)

Before you even open your code editor, be clear on:

  • Which real estate sites? (Zillow, Realtor.com, Redfin, smaller local ones?)
  • What data? (Price, address, square footage, pictures, agent info?)
  • How much data? (Just new listings? Historical data? The whole city?)
  • How often? (One-off scrape, or weekly updates?)

Reality check:
Most big real estate sites don’t want you scraping, and some are aggressive about blocking bots. Some even spell out in their terms that you can’t do it. You’re responsible for making sure you’re not breaking the law or their terms of service. If you need data for commercial use (especially resale or redistribution), check with legal and see if there's a proper API or data feed you can pay for.

If you’re just doing internal research, you’re on firmer ground, but tread carefully.

2. Take a Quick Look at the Site

Before you start writing code, open the site you want to scrape and:

  • Browse a few listings.
  • Right-click and “Inspect” the page (or hit F12) to open DevTools.
  • Look at the HTML structure—are the listings in easy-to-pick-out <div>s or tables?
  • Check the network tab. Sometimes listing data comes from API calls in the background—way easier to work with than scraping rendered HTML.

Pro tip:
If the site loads listings as you scroll (infinite scroll), you might need to simulate scrolling or call the site’s backend APIs directly. If it’s just paged results, it’s simpler.

3. Set Up Your Scrapingbee Account

Go to Scrapingbee and sign up for an account. You’ll get an API key and a free trial (as of this writing). You’ll need the key for all requests.

Why Scrapingbee?

  • It handles headless browsers and proxies for you. That means fewer CAPTCHAs and blocks.
  • You don’t have to mess with ChromeDriver or a VPS to avoid being caught as a bot.
  • Their docs are straightforward, and the API is simple.

This isn’t a sales pitch—it just works better than most of the “plug and play” scraping tools, and you only pay for what you use.

4. Write a Simple Scraper in Python

You don’t need to build a full-blown app. Here’s how to get started:

a. Install the basics

You’ll want requests and BeautifulSoup. If you don’t have them:

bash pip install requests beautifulsoup4

b. Make your first Scrapingbee request

Here’s a minimal example to fetch a search results page:

python import requests

API_KEY = 'your_scrapingbee_api_key' target_url = 'https://www.example-realestate.com/search?city=YourCity'

params = { 'api_key': API_KEY, 'url': target_url, 'render_js': 'false' # set to true if the site needs JavaScript to load listings }

response = requests.get('https://app.scrapingbee.com/api/v1/', params=params) if response.status_code == 200: html = response.text print(html[:1000]) # Show a snippet else: print("Error:", response.status_code, response.text)

  • Set render_js to true if listings don’t show up in the raw HTML. This costs more credits but solves most “why is my data missing” problems.
  • Start with one page. Don’t hammer the site.

c. Parse the HTML

Once you have the HTML, extract what you need:

python from bs4 import BeautifulSoup

soup = BeautifulSoup(html, 'html.parser') listings = soup.find_all('div', class_='listing') # Adjust selector to fit the site

for listing in listings: price = listing.find('span', class_='price').get_text(strip=True) address = listing.find('span', class_='address').get_text(strip=True) print(price, address)

  • Every site has different HTML. You’ll need to tweak your selectors.
  • If the site uses weird class names like class="c1x2y3", be prepared for them to change frequently—sites do this to break scrapers.

5. Handle Pagination and Rate Limits

A single page isn’t enough. Most real estate sites paginate listings—sometimes with “Next” links, sometimes with page numbers in the URL.

  • Look at how the URL changes as you move through pages (e.g., ?page=2, offset=20, etc.).
  • Loop through a reasonable number of pages:

python for page in range(1, 6): # First 5 pages target_url = f'https://www.example-realestate.com/search?city=YourCity&page={page}' # ...same code as above...

Don’t go nuts:
If you fire off 50 requests in 10 seconds, you’ll get blocked fast—even with Scrapingbee. Add a time.sleep(2) between requests.

Scrapingbee tip:
You pay per request, so don’t scrape useless pages. If you only need new listings, just grab the first page or filter by “posted in last 24 hours.”

6. Clean Up and Save Your Data

Dumping to CSV works for most teams:

python import csv

with open('listings.csv', 'w', newline='') as f: writer = csv.writer(f) writer.writerow(['Price', 'Address']) for listing in listings: price = listing.find('span', class_='price').get_text(strip=True) address = listing.find('span', class_='address').get_text(strip=True) writer.writerow([price, address])

If you need more fields, add them. If you want JSON, use json instead.

Don’t overcomplicate it:
Fancy databases are overkill for early experiments. Get a CSV working first.

7. Watch Out for Common Pitfalls

  • CAPTCHAs and Bot Detection:
    Even with Scrapingbee, some sites are aggressive. If you start seeing weird HTML or error messages, slow down your requests, rotate user agents, and try render_js=true. If you keep getting blocked, you may be out of luck—some sites just won’t play ball.

  • Data Consistency:
    HTML can change without warning. What worked yesterday might break tomorrow. Keep your scraper simple so it’s easy to fix.

  • Legal and Ethical Stuff:
    Don’t resell scraped data, and don’t scrape user info or anything behind a login. If you’re scraping lots of pages, respect robots.txt or at least don’t overload the site.

  • Cost:
    Scrapingbee charges per request. Rendering JavaScript costs more. Keep your scrapes focused and efficient.

8. Take It Further (But Only If You Need To)

If you’re comfortable, you can:

  • Schedule your script to run daily with cron or Windows Task Scheduler.
  • Store results in a simple database like SQLite.
  • Set up email alerts for new listings matching certain criteria.

But don’t build a giant system before you have a small one working. Most teams overthink this and end up maintaining a mess.

Final Thoughts: Start Simple, Iterate Fast

Web scraping for real estate market analysis isn’t magic. Most sites are a pain to work with, and the rules change constantly. Scrapingbee takes away a lot of the grunt work, but you still need to be ready for blockers, weird HTML, and limits on how much you can collect.

Start small. Get a CSV with 50 listings. If it works, automate a bit more. If it breaks, fix the selectors and try again. Don’t let “perfect” get in the way of “done.” And if you hit a wall, it’s usually not your code—it’s the site changing or blocking you. That’s just the game.

Good luck, and happy scraping.