Web Scraping Zoopla Real Estate Data using Python

Zoopla is one of the UK’s leading property portals, packed with real estate listings, rental data, and housing market insights. Whether you’re a developer building a property comparison tool or a researcher tracking market trends, accessing this data programmatically can be incredibly useful.

In this guide, we’ll show you how to scrape property listings from Zoopla using Python, covering everything from navigating pagination to extracting key details like price, location, and property type, all while being mindful of best scraping practices. If you’re just starting out with web scraping, I highly recommend reading Web Scraping with Python. It’s a great resource for building a strong foundation.

I assume that you have already installed Python on your machine. If not, you can download it from here. Now, create a folder by any name you like. I am naming the folder as zoopla.

Install these 3 libraries inside this folder.

• requests for making an HTTP connection with the target website.

• BeautifulSoup for parsing the raw data.

• Pandas for storing data in a CSV file.

Final step before moving to the coding part would be to sign up for a web scraping API. In this tutorial, we are going to use Scrapingdog.

Now, we should create a Python file where we can write the scraping code. I am naming the file as zoopla.py.

Before we start coding the scraper, take a moment to read Scrapingdog’s documentation; it’ll give you a clear idea of how we can use the API to scrape Zoopla at scale.

Once you have read the documentation, it is better to decide what information you want to extract from Zoopla in advance.

We are going to scrape the price, address, and description of the property. Let’s find the location of each data point in the DOM.

The pricing is stored inside the p tag with the class _64if862.

The description is stored inside the p tag with the class m6hnz63.

The address is located inside the address tag itself.

Each property is located inside a div tag with the class dkr2t86.

Let’s code now.

This is the basic Python code where we are making a GET request to the Scrapingdog API. Remember to use your own API key in the above code. Once you run the code, you should see 200 status code indicating a successful scrape.

The code is pretty simple, but let me explain to you the logic behind it.

• Finds all <div> elements with class "dkr2t86" and stores them in allData.

• Loops through each element in allData.

• Tries to extract the price from a <p> tag with class "_64if862". Sets to None if not found.

• Tries to extract the address from an <address> tag with class "m6hnz62". Sets to None if not found.

• Tries to extract the description from a <p> tag with class "m6hnz63". Sets to None if not found.

• Stores all extracted data in a dictionary called obj.

• Appends obj to the list l.

• Resets obj to an empty dictionary for the next loop.

• Prints the final list l containing all extracted records.

Once you run this code, you should see a beautiful JSON response like this.

When you click on the II page from the bottom of the page, the URL of the page becomes https://www.zoopla.co.uk/for-sale/property/london/?q=london&search_source=home&pn=2. As you can see, a page parameter by the name pn appears within the URL, which can be used for changing the page number.

We have to run a for loop that can automatically change the page number within the URL and make a separate API call on every iteration.

Using the pandas library, we can save this data to a CSV file. Let’s see how it can be done.

df = pd.DataFrame(l) df.to_csv('zoopla.csv', index=False, encoding='utf-8')

• Creates a DataFrame df from the list l.

• Saves the DataFrame to a CSV file named zoopla.csv.

• Disables the index column in the CSV using index=False.

Once you run it, you will find a CSV file by the name zoopla.csv.

You can, of course, scrape other details as well, but as of now, the code will look like this.

If you want structured data without writing parsing logic or using BeautifulSoup, you can use AI parsing instead. Just submit a simple query, and the data gets parsed for you, no custom code required. You can read more about this parameter over here.

This will generate a JSON response that looks like this.

• No code required — Easily extract structured data without writing any parsing logic.

• Faster implementation — Skip HTML structure analysis and get results instantly.

• Handles messy HTML — AI can intelligently extract data even from inconsistent or nested layouts.

• Reduces maintenance — No need to update scrapers when websites change minor HTML tags or classes.

• Ideal for non-devs — Makes web scraping accessible to users without programming experience.

• Cleaner output — Directly get structured JSON or CSV-ready data.

• Zoopla listings can be scraped to extract structured data like property prices, locations, descriptions, and key features for analysis.

• A Python setup using requests and parsing libraries allows you to collect and organize real-estate data efficiently.

• Search pages and pagination need to be handled properly to gather data across multiple result pages.

• Zoopla has anti-scraping protections, so headers, rate control, and smart request handling are important.

• Structured property data can be used for market research, price tracking, and real-estate intelligence.

Scraping property listings from Zoopla with Python and Scrapingdog opens up a world of possibilities for real estate analysis, price tracking, and market research. With libraries like requests, BeautifulSoup, or even automation tools like PuppeteerYou can extract valuable data at scale. Whether you're building a real estate dashboard or feeding data into an ML model, Python makes the process efficient and flexible.

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