How to Scrape Funda Real Estate Listings with Python?

Looking for a Funda scraper in Python? The Dutch real estate market moves fast. Whether you're tracking price trends, monitoring competitor listings, or building a data pipeline, this Funda scraper Python guide shows you how to get structured property data at scale using ScrapingBot's API.

1. Why scrape Funda?

Funda is the first real estate platform in the Netherlands, with over 30,000 active listings at any given time. It's used by buyers, sellers, agents, and investors alike. Scraping it gives you access to:

• Price history and market trends by neighborhood
• Listing density and time-on-market data
• Rental vs. sale inventory ratios
• Competitor agent activity

2. Technical challenges

Before diving into the code, it's important to understand why scraping Funda isn't as simple as a basic HTTP request:

• Anti-bot protection — Funda detects headless browsers and blocks requests with suspicious headers.
• JavaScript rendering — Listing data is loaded dynamically, so basic HTTP requests won't capture it.
• IP rate limiting — Too many requests from one IP triggers temporary bans.
• Pagination — Search results span hundreds of pages that need systematic traversal.

3. How ScrapingBot handles them

ScrapingBot's Real Estate API abstracts all of this complexity: it rotates IPs automatically, renders JavaScript, and returns clean structured JSON — no browser automation needed on your end.

4. Step-by-step: build your Funda scraper in Python

Install the library

pip install requests

Basic request

import requests

# Your ScrapingBot credentials
USERNAME = "your_username"
API_KEY  = "your_api_key"

# The Funda listing you want to scrape
TARGET_URL = "https://www.funda.nl/koop/amsterdam/huis-12345678/"

def scrape_funda(url):
    api_url = "https://api.scraping-bot.io/scrape/real-estate"
    payload = {"url": url}

    response = requests.post(
        api_url,
        json=payload,
        auth=(USERNAME, API_KEY)
    )

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Error {response.status_code}: {response.text}")

data = scrape_funda(TARGET_URL)
print(data)

Scraping multiple listings (with pagination)

import requests, time

BASE_SEARCH = "https://www.funda.nl/koop/amsterdam/p{page}/"

def scrape_pages(n_pages=5):
    results = []
    for page in range(1, n_pages + 1):
        url = BASE_SEARCH.format(page=page)
        data = scrape_funda(url)
        results.extend(data.get("listings", []))
        time.sleep(1)  # polite delay
    return results

listings = scrape_pages(n_pages=3)
print(f"Collected {len(listings)} listings")

5. Sample output data

Here's what a typical response looks like for a single Funda listing:

6. Going further

Once you have the raw data from your Funda scraper Python script, you can pipe it into a CSV with pandas, store it in a PostgreSQL database, or feed it into a price-trend dashboard. ScrapingBot also supports Airbnb, Zillow, and Rightmove with the same API interface.