A Comprehensive Guide to Scrape Zillow with Python

The real estate market is one of the most dynamic fields, where data scraping plays a major role not only for real estate business owners and agencies but also for regular customers. When we need to make the decision regarding buying or renting properties, the first thing we should do is a comparative analysis based on price, type of house, its size, location, etc.

Therefore, we’re going to scrape the leading real estate marketplace called Zillow. There are several paid Zillow data scrapers in the market that you can buy and use, but in this article, we are going to scrape Zillow with the help of Python. So, if you have some coding skills and do not want to pay the extra money, let’s move forward to learn how to download data from Zillow.

As we’ve mentioned above, if you have some coding skills and a bit of knowledge about web scraping, then you can develop your Zillow data scraper to extract the required data from Zillow. You can use any programming language to handle HTML files, but Python is widely used for developing scrapers. Some facts:

For scraping Zillow with Python, it is required to have Python 3 and Pip installed. Follow the instructions below for the purpose

• For Linux users: http://docs.python-guide.org/en/latest/starting/install3/linux/
• For Mac users: http://docs.python-guide.org/en/latest/starting/install3/osx/
• Windows users go here: https://www.scrapehero.com/how-to-install-python3-in-windows-10/

As we are using Python 3, it is also required to install the following packages for downloading and parsing the HTML code. Here are the package requirements:

• To install the packages, we need PIP – https://pip.pypa.io/en/stable/installing/
• To download the HTML content, we need Python Requests – http://docs.python-requests.org/en/master/user/install/
• To parse the HTML Tree Structure, it is required Python LXML – https://lxml.de/installation.html

We are going to search and scrape Zillow data based on a specific postal code: 02128.

The whole scraping process contains the following steps:

Let’s name the script zillow.py that will be used for the script name in a command line.

So, to get the newest listings, we should run an appropriate script to sort the relevant arguments for the specific zip code.

In the final step, a CSV file will be created in the same folder as the script.

In this part, we’ll just go through some useful insights that you can use while scraping Zillow.

For BeautifulSoup you need to install the required libraries, which can be done through the requirements.txt file. Just input the complete list in the file and run the pip install requirements.txt file.

Like many websites, Zillow also throws captchas. That’s why while deploying a request.get(url) function, it is required to add headers to the request function. See the below example:

To create variables, there are many ways to loop through URLs. Let’s try the simplest one. So, if you are planning to extract 5 pages’ data, you can create 5 soup variables and give them a unique title as in the below example.

To make the extracted data more readable, just make some formatting jobs. So, we are going to:

• Convert columns
• Rearrange columns
• Drop null rows

Download HTML pages using Python, parse them with LXML to extract the necessary data, and import them to CSV. If you don’t have coding skills, use online services or Data OX developers’ help.

Create API requests using Python or other development tools.

Create an account, get a free Zillow web services ID (access token) to access API, and make API calls with preferred developer tools.

Zillow gets data about the real estate properties at local municipal offices, the National Association of Realtors, and the State Department of Real Estate. All data is processed via MySQL Cluster.

Once you decide to scrape Zillow keep in mind that it uses anti-scraping techniques like captchas, IP blocking, and honeypot traps to prevent its data from scraping. Already skilled scraper builders can overcome them, but for newbies, it can be a challenge. At DataOx we are always happy to help you with professional advice regarding extracting real estate data or offer you a customized Zillow scraper that would meet your business needs.

Schedule a free consultation with our expert and find out how web scraping can help your real estate business grow.