Web data collection is not peculiar to ecommerce businesses as data is critical across various fields. Real estate, one of the most versatile and profitable industries, also relies on data. Choosing to buy, rent, or sell properties involves gathering data for comparative analysis based on your preferences, such as price, location, house model, and more. Therefore, real estate business owners, as well as customers, depend on updated data to make informed decisions.

There are various sources of real estate data, but this article will focus on Zillow- one of the leading real estate marketplaces. Manually copying data from the platform is not an efficient or sustainable process. Therefore, this article will examine how Zillow is scraped with Python, how the platform works, the challenges to data collection, how NetNut optimizes the process and other frequently asked questions.

Let us dive in!

About Zillow

Zillow is a leading online real estate marketplace where you can find a wealth of information related to buying, selling, renting, or loaning a house or land. The primary purpose of Zillow is to provide investors with access to all the necessary information required to make an informed decision regarding real estate. 

As someone looking to purchase real estate, you can find information about sellers to avoid conducting business with individuals or brands with questionable reputations. On the other hand, sellers can list their properties on the marketplace to reach a wide range of potential buyers. 

Use Cases of Data Obtained from Scraping ZillowUse Cases of Data Obtained from Scraping Zillow

Data obtained from scraping Zillow can be applied for various purposes, including:

Real estate market research

One of the most common applications of data from Zillow is gathering insights into the real estate market. It allows you to identify areas with high demand, available properties, ongoing constructions in various areas, insurance, mortgage range, and more. In addition, it informs your expectations about the real estate market near you. 


Collecting data from Zillow allows you to pull specific data that can be used for targeted advertisement. It provides access to region-specific real estate data, including average prices, location safety, and the perceived economic status of the people living in the area. Therefore, a comprehensive understanding of these factors helps real estate agents generate targeted ads. In addition, it ensures they have enough information to cater to the needs of their client- the home buyers.

Real estate market forecast

Historical data from Zillow, as well as real-time data, arm you with the necessary information to predict future trends. Trend analysis is only possible when you have access to old data. It provides insight into the market cycle, industry trends, lowest prices, and period of highest sales. Subsequently, data from Zillow provides a holistic approach to predict future trends and make strategic plans to optimize sales.

Pice monitoring

There are several real estate agents, so you need to keep your prices competitive. Scraping allows you to access the prices of your competitors and adjust yours accordingly. Buyers are consistently looking for the less expensive options. Therefore, real estate sellers can either decrease the price of properties to attract more customers or increase it to reflect higher quality. 

Buyer sentiment analysis

Understanding buyer sentiment is crucial in informing real estate agents of business strategies. Therefore, agents can collect home buyers’ reviews from Zillow to evaluate customer satisfaction. As a result, they can understand the neighborhood requirements- if the property will be used as an office, residential space, school, recreation space, and others. 

In addition, data collection highlights the most valuable features of a property, such as size, amenities, closeness to shopping malls, and others. Gathering data to understand sentiment analysis reveals the relationship between real estate agents and homeowners. Subsequently, collecting data from Zillow is useful in understanding customer expectations and optimizing marketing strategies to increase sales.

Scraping Zillow with Python

Scraping Zillow with Python is a way to automate the process of extracting data from the platform. Now, you may wonder why Python is among all other programming languages. Python is an easy-to-use programming and scripting language that comes with features that optimize the process of building a Zillow scraper.

In this section, we shall examine why Python is an ideal choice for scraping Zillow and the steps involved.

Scraping Zillow involves using Python to write a script that can effectively retrieve the data. Here are some reasons why Python is one of the best languages for web scraping:

Simple syntax

One of the characteristics of Python that makes it an excellent option for web scraping is its simple syntax. Python is easy to understand, so it is quite simple to use. In other words, the language is less messy to use because it does not require curly braces or semicolons. Subsequently, Python’s readability and simplicity make it an ideal choice to build a Zillow scraper.

Get more with less

When writing a script to automate repetitive activities like scraping Zillow, it is crucial to bear in mind that its effectiveness is based on the code. In addition to spending a greater portion of time writing codes, Python allows you to write small code to perform large tasks. 

Web scraping libraries

Python stands out as a scripting language due to its large collection of libraries. These libraries serve various purposes in extracting, parsing, and storing data from Zillow. For example, a requests package is used to send an HTTP request to Zillow. In addition, these libraries also support various web scraping methods, including XPath expression and CSS selectors.

Dynamic coding

Another unique feature of the Python language is it saves time. You don’t have to spend several hours writing long codes or defining data types for variables. Instead, you can directly use the variable within the code wherever it is needed.

Active Community

Python has a large, dynamic, and active community. You can join any of the forums on the various social media platforms. So, when you are stuck or need clarification about a concept, all you need to do is ask. You will receive opinions from those who are experts or have once been in that situation.

How does scraping Zillow with Python Work?How does scraping Zillow with Python Work?

Here is a step-by-step guide to scraping Zillow with Python:

Step 1: Download Python and set the environment

When working with Python to build a web scraper, the first thing you need to do is download Python. Be sure to get the latest version from the official website. In addition, you may also need to set up a virtual environment with the aid of an integrated development environment (IDE).

PyCharm is a powerful IDE designed for Python programming. It has a user-friendly interface and built-in debugging tools, which are necessary to deal with errors that may be present in your code. PyCharm stands out with its smart feature called intelligent code completion. 

Visual Studio Code is another popular IDE that is versatile and has features like Git Integration, syntax highlighting, and debugging. Meanwhile, Jupyter Notebooks are suitable for code development and analysis.

Step 2: Install and import Python libraries

There are several Python web scraping libraries that you can leverage for scraping Zillow. However, for the sake of this article, we shall focus on some libraries, including Requests, BeautifulSoup, and Playwright. 

The Requests package is responsible for sending HTTP requests to Zillow to download the raw HTML element. One unique feature of the request package is that once you implement the GET command, you can collect data on the website via the content property on the response generated. In addition, it supports critical API and its functionalities, including GET, DELETE, POST, and PUT.

BeautifulSoup is a powerful parsing library that extracts the data from the raw HTML obtained via the request library. In addition, it provides all the tools you need to structure and modify the parse tree to extract data from XML and HTML elements. 

On the other hand, Playwright is a versatile library that allows you to interact with browsers and automate tasks. In addition, it offers a unified interface that supports headless mode and automation features. 

To import the libraries:

pip3 install beautifulsoup4

pip3 install requests

pip3 install pandas

pip3 install playwright

Step 3: Examine the structure of the Zillow website

Once you have installed and imported the necessary software, you need to understand the structure of Zillow. You can do this by inspecting the website when you visit Zillow’s homepage. There is a search bar that displays properties, their locations, prices, and other details. 

Therefore, to understand the HTML structure of Zillow, enter a city or ZIP code into the search bar and press enter. Right-click on a property card and click inspect to open the developer tools. At this stage, you can analyze the HTML structure to identify the tags and attributes of the data you want to extract.

Step 4: Identify key data points

Scraping Zillow involves identifying the exact information you want to scrape. Zillow stands out because it offers an extensive range of information that allows you to analyze and compare different listings, pricing trends, and other useful factors. Here are some data points you can access on Zillow:

  • The location of the property includes the state, city, street name, and house number.
  • The price of the property: this provides insights into the market value of the real estate.
  • The type of Real Estate: this provides information on the property to help you determine if it is a condo apartment and if it is what you are looking for.
  • Number of bedrooms and bathrooms in the property.
  • The size of the property.
  • The age of the property provides an insight into when the property was constructed.

Step 4: Build the scraper

You should already have a folder where you can store your software and save the code for building the scraper. 

Once you have identified the key points in the data you want to extract, you are ready to build your scraper. It involves using the Request Python library to make HTTP requests to Zillow, while BeautifulSoup caters to parsing the HTML content to effectively retrieve the data. 

Let us consider a case where we are trying to extract data related to houses for sale in California. You need to provide the web address for the search result page –

To extract the data you need, create a file and name it- and use the following code:

 import requests

from bs4 import BeautifulSoup


url = ‘’


headers = {

    ‘User-Agent’: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3’}


response = requests.get(url, headers=headers)

soup = BeautifulSoup(response.content, ‘html.parser’)


listings = []


for listing in soup.find_all(‘div’, {‘class’: ‘property-card-data’}):

    result = {}

    result[‘address’] = listing.find(‘address’, {‘data-test’: ‘property-card-addr’}).get_text().strip()

    result[‘price’] = listing.find(‘span’, {‘data-test’: ‘property-card-price’}).get_text().strip()

    details_list = listing.find(‘ul’, {‘class’: ‘dmDolk’})

    details = details_list.find_all(‘li’) if details_list else []

    result[‘bedrooms’] = details[0].get_text().strip() if len(details) > 0 else ”

    result[‘sqft’] = details[2].get_text().strip() if len(details) > 2 else ”

    type_div = listing.find(‘div’, {‘class’: ‘gxlfal’})

    result[‘type’] =  type_div.get_text().split(“-“)[1].strip() if type_div else ”





Save the code, and you will be ready to initiate the Zillow scraper. 

Step 5: Save the data

Once the data has been extracted, you can save it either in a JSON or CSV file. Saving the data in the folder you created earlier is essential to ensure you can easily access it. After proper data storage, you can move on with sorting, analysis, and interpretation. 

To save the data, you need to import the json and panda libraries at the top of your file. 

import pandas as pd

import json

Add this code to the end of the file to save in CSV or JSON format:

#Write data to csv

df = pd.DataFrame(listings)

df.to_csv(‘listings.csv’, index=False)

print(‘Data written to CSV file’)


#Write data to Json file

with open(‘listings.json’, ‘w’) as f:

    json.dump(listings, f)

print(‘Data written to Json file’)

Challenges Associated With Scraping Zillow

Extracting data from any website, including Zillow, comes with some challenges. Here are some of the limitations associated with scraping data from Zillow:

Dynamic content

Zillow loads data dynamically using JavaScript; this becomes a limitation for a web scraper built with a simple Request and parsing library. Subsequently, the HTTP request will be unable to download the HTML elements of the dynamic pages. As a result, the extracted data may be inaccurate or incomplete because some of the content is loaded after the initial page load. 


CAPTCHAs are tests designed to tell humans apart from bots. They are a leading anti-scraping measure implemented by websites like Zillow. Subsequently, they prevent the automated bot from scraping the page, which could trigger an IP ban.

Rate limiting

The concept of rate limiting implies that the website employs some strategies to restrict the number of requests from a single user. This is especially true when you are using API to collect data from Zillow. As a result, you can only send a limited amount of requests within a timeframe.

IP block

A significant limitation of scraping Zillow is the high chance of IP blocks. This may occur due to browser fingerprinting, which is used to identify your device. When you send too many requests within a short time, your IP address may be temporarily banned or blocked. Likewise, geographical restrictions can trigger IP blocks.

Best Practices for Extracting Data from ZillowBest Practices for Extracting Data from Zillow

Although there are several challenges associated with extracting data from Zillow, here are a few practices to optimize the process:

Review robots.txt file/ terms of service

One of the best practices for extracting data from Zillow is to review the robots.txt file. This gives you a good understanding of the parts you are allowed to scrape. You can take it a step further by reading the terms of service page. Bear in mind that if you violate the instructions, it could lead to legal consequences. 

Use a headless browser

Since Zillow relies on JavaScript, a useful tip for effective scraping is to use a headless browser. Headless browsers like Selenium and Puppeteer can be employed to execute JavaScript and scrape data from Zillow.

Use authentic User-agent

The website can determine the activities of a bot via the user agent. Therefore, it becomes necessary to use authentic user agent strings. However, you need to rotate it to reduce the chances of being detected, as this makes the browser think the request is coming from different browsers.

Implement rate limiting in the Python code

Rate limiting ensures your activities are not easily identified as an automated program. Subsequently, sending too many requests within a short time frame will trigger Zillow’s anti-scraping measures. Therefore, you need to add delays in your Python script to imitate human activity.


Proxies act as an intermediary between your device and the internet. While there are several free proxies, you need to choose a reputable provider if you prioritize security and anonymity. Proxies help to bypass geographical restrictions and mask your IP address so you can avoid bans.

Optimizing Zillow Data Extraction with NetNut

One of the best practices for collecting data from Zillow is using proxies. NetNut is an industry-leading proxy service provider. With an extensive network of over 85 million rotating residential proxies in 200 countries and over 250,000 mobile IPS in over 100 countries, you can scrape data from any website with ease.

NetNut rotating residential proxies can mask your IP address so the website can only interact with the proxy IP. Subsequently, this can help you avoid IP bans, especially if you regularly rotate the proxies.

In addition, NetNut residential proxies help you bypass geographical restrictions as well as CAPTCHA. NetNut proxy solutions are integrated with a CAPTCHA-solving software that ensures your access to data is not inhibited by these tests. 

Alternatively, you can use our in-house solution- NetNut Scraper API, to collect data without worrying about building a Zillow scraper. Moreover, if you want to scrape data using your mobile device, NetNut also has a customized solution for you. NetNut’s Mobile Proxy uses real phone IPs for efficient web scraping and auto-rotates IPs for continuous data collection. 


This guide has examined step-by-step instructions on how scraping Zillow with Python works. Zillow is one of the biggest platforms in the real estate industry. Therefore, it holds a vast amount of data that can be leveraged by sellers or home buyers to make informed decisions.

Python remains one of the best languages for building a scraper. The scraper works by sending an HTTP request, parsing the data, and storing it on your preferred storage. Usually, the data obtained can be used for price monitoring, competition analysis, prediction of future trends, and more.

Some of the best practices for using the Python scraper are using proxies, implementing a rate limit on the code, and using a headless browser to load the dynamic content on Zillow. NetNut offers various proxy solutions that optimizes the efficiency of your Zillow scraping acclivities.

Kindly contact us if you have any questions regarding choosing the best proxy solution.

Frequently Asked Questions

What are the legal considerations associated with collecting data from Zillow?

First, Zillow’s database is protected by copyright. Therefore, extracting the data without appropriate permission may result in copyright infringement. Another legal consideration is the Computer Fraud and Abuse Act (CFAA), especially foe scraping Zillow while in the United States of America. 

In addition, data protection laws like GDPR frown upon the use of scrapers to collect personal and identifying information from others without explicit consent. Furthermore, the terms of service mention that you are not permitted to use automated programs to access the website or collect information from any user. 

What are the best practices for storing data collected from Zillow?

  • Choose the right storage location
  • Regularly backup your data
  • Generate an emergency structure to recover data in terms of sudden loss
  • Protect the data with 2-factor authentication
  • Comply with privacy laws
  • Store data in a format that is easy to read and analyze

What are some valuable data attributes to consider when scraping Zillow?

  • Property details, including address, price, status, and type
  • Property attributes like total area, lot size, number of bathrooms, year built, garage, basement
  • Financial information on real estate, like tax history
  • Images and videos of the property
  • Energy efficiency
  • Information regarding the neighborhood
Scraping Zillow (Step-by-step guide on how to scrape data from Zillow) 
Full Stack Developer
Stav Levi is a dynamic Full Stack Developer based in Tel Aviv, Israel, currently working at NetNut Proxy Network. In her role, she specializes in developing and maintaining intricate management systems, harnessing a diverse tech stack, including Node.js, JavaScript, TypeScript, React, Next.js, MySQL, Express, REST API, JSON, and more. Stav's expertise in full-stack development and web technologies makes her an invaluable contributor to her team.