How to Scrape Price History from Zillow

Welcome to our blog post on how to scrape price history from Zillow! If you’re a real estate enthusiast or investor, you know the importance of having access to accurate and up-to-date pricing information. Zillow is a popular online platform that provides valuable data on property prices, and being able to scrape this information can be incredibly beneficial.

In this blog post, we will walk you through the process of scraping price history from Zillow. We will start by understanding the basics of web scraping and why it is useful for extracting data from Zillow. Then, we will guide you through setting up your environment and installing the necessary tools and libraries for web scraping.

Next, we will dive into the specifics of navigating Zillow’s price history pages. We will explore the URL structure of Zillow and identify the data we want to scrape, which is the price history. We will also show you how to use developer tools to inspect the web page and identify the elements we need to extract.

Once we have a clear understanding of the web page structure, we will move on to writing the web scraping script for Zillow price history. We will demonstrate how to access the web page using Python and parse the HTML to extract the desired data. Additionally, we will discuss the best practices for storing the scraped data for future use.

We also understand the importance of responsible and ethical web scraping. Therefore, we will address the considerations for scraping data from Zillow, including understanding Zillow’s robots.txt and terms of service. We will show you how to implement delays between requests to avoid overloading the website and handle potential errors and exceptions that may arise during the scraping process.

By the end of this blog post, you will have a comprehensive understanding of how to scrape price history from Zillow. You will be equipped with the necessary knowledge and tools to extract valuable data for your real estate endeavors. So, let’s dive in and unlock the power of web scraping for Zillow’s price history!

Understanding the Basics: What is Web Scraping and Why Use it for Zillow

Web scraping is the process of extracting data from websites by using automated scripts or bots. It involves fetching the HTML code of a web page and then parsing it to extract specific information. Web scraping has become increasingly popular due to its ability to gather large amounts of data quickly and efficiently.

When it comes to Zillow, web scraping can be a valuable tool for real estate professionals, investors, or anyone interested in tracking property prices. Zillow provides a wealth of information about properties, including their historical price data. By scraping this price history, you can gain insights into market trends, analyze property values, and make more informed decisions.

Here are a few key reasons why web scraping is useful for extracting price history from Zillow:

  1. Access to Historical Data: Zillow’s price history provides a detailed record of the changes in property prices over time. By scraping this data, you can access historical information that may not be readily available elsewhere.

  2. Market Analysis: Analyzing price history can help you understand market trends, identify patterns, and make predictions about future property values. By scraping Zillow’s price history, you can gather a large dataset for comprehensive market analysis.

  3. Comparative Analysis: Web scraping allows you to compare the price history of different properties or neighborhoods. This information can be valuable for investors looking to identify areas with potential growth or find undervalued properties.

  4. Tracking Property Values: By regularly scraping Zillow’s price history, you can track changes in property values for specific locations. This can be particularly useful for homeowners, real estate agents, or investors who want to stay informed about the market.

  5. Automated Data Collection: Web scraping automates the process of collecting data, eliminating the need for manual data entry or searching through multiple listings. This saves time and effort, allowing you to focus on analyzing the data rather than collecting it.

Web scraping empowers you with the ability to gather and analyze large volumes of data efficiently, making it an invaluable tool for understanding Zillow’s price history. However, it is important to note that web scraping should be done responsibly and in compliance with the website’s terms of service. In the following sections, we will explore how to set up your environment for web scraping and navigate Zillow’s price history pages to extract the desired data.

Setting Up Your Environment for Web Scraping

To begin scraping price history from Zillow, you need to set up your environment with the necessary tools and libraries. This section will guide you through the steps required to prepare your environment for web scraping.

Understanding the Required Tools and Libraries

Before diving into the setup process, it’s essential to understand the tools and libraries you’ll need for web scraping. Here are the key components:

  1. Python: Python is a popular programming language for web scraping due to its simplicity and extensive range of libraries. We will be using Python for our web scraping script.

  2. Web Scraping Libraries: There are several libraries available in Python that simplify the web scraping process. The most commonly used libraries include BeautifulSoup and Scrapy. We will be using BeautifulSoup for this tutorial due to its simplicity and ease of use.

  3. Web Browser: You will need a web browser to access Zillow and inspect the elements you want to scrape. Popular web browsers like Google Chrome or Mozilla Firefox will work fine.

Installing the Necessary Dependencies

Once you have Python and a web browser installed, you need to install the required dependencies for web scraping. Here’s how you can set up your environment:

  1. Install Python: If you don’t have Python installed, visit the official Python website ( and download the latest version suitable for your operating system. Follow the installation instructions provided.

  2. Install BeautifulSoup: Open your command prompt or terminal and run the following command to install BeautifulSoup:

pip install beautifulsoup4

  1. Install Requests: The Requests library is used to send HTTP requests and retrieve web page content. Install it by running the following command:

pip install requests

  1. Install Other Libraries: Depending on your specific needs, you may require additional libraries for data manipulation, visualization, or storage. Install them as needed using the pip command.

Creating Your First Python Web Scraping Script

Now that your environment is set up, it’s time to create your first Python web scraping script. Open your preferred code editor or IDE and create a new Python file.

In the next section, we will explore how to navigate Zillow’s price history pages and identify the data elements we want to scrape.

Navigating Zillow’s Price History Pages

Navigating Zillow’s price history pages is a crucial step in scraping the desired data. In this section, we will explore the URL structure of Zillow, identify the specific data we want to scrape (price history), and learn how to inspect the web page using developer tools.

Understanding Zillow’s URL Structure

To scrape price history from Zillow, it’s important to understand the URL structure that Zillow uses for its property listings and price history pages. The URL typically consists of several parameters that can be modified to retrieve specific information.

For example, a typical Zillow property listing URL looks like this:

To access the price history of a property, Zillow appends the string _zpid to the end of the URL, like this:

Understanding this URL structure will allow us to programmatically generate the URLs for scraping price history.

Identifying the Data to Scrape: Price History

The primary data we want to scrape from Zillow is the price history of properties. Price history provides valuable insights into how a property’s value has changed over time. It includes information such as the date of sale, sale price, and any price reductions or increases.

By inspecting Zillow’s price history page, we can identify the HTML elements that contain the relevant data. This will help us extract the price history information accurately using web scraping techniques.

Using Developer Tools to Inspect the Web Page

To inspect the web page and identify the elements containing the price history data, we can use the developer tools available in modern web browsers. Here’s how you can access the developer tools in Google Chrome:

  1. Open the property listing page on Zillow in your Chrome browser.

  2. Right-click on any element on the page and select “Inspect” from the context menu. This will open the Chrome Developer Tools panel.

  3. In the Developer Tools panel, you will see the HTML code of the web page. Use the mouse pointer to hover over different elements, and the corresponding HTML code will be highlighted in the panel.

  4. Locate the HTML elements that contain the price history information. Look for specific tags, classes, or IDs that can help identify these elements.

By inspecting the web page, you can identify the specific HTML elements that contain the price history data. This information will be crucial when we start writing the web scraping script in the next section.

In the upcoming section, we will delve into writing the web scraping script to access the web page and extract the price history data from Zillow.

Writing the Web Scraping Script for Zillow Price History

Now that we understand the URL structure of Zillow and have identified the data we want to scrape (price history), it’s time to write the web scraping script to extract the desired information. In this section, we will cover the steps involved in accessing the web page, parsing the HTML to extract price history, and storing the scraped data.

Accessing the Web Page with Python

To access the web page and retrieve its HTML content, we will be using the Requests library in Python. Here’s an example of how you can fetch the web page content:

import requests

url = “”
response = requests.get(url)

if response.status_code == 200:
html_content = response.text
print(“Failed to retrieve the web page”)

In this example, we use the requests.get() method to send an HTTP GET request to the specified URL. If the request is successful (status code 200), we store the HTML content in the html_content variable.

Parsing the Web Page to Extract Price History

Once we have obtained the HTML content of the web page, we need to parse it to extract the price history data. For this purpose, we will be using the BeautifulSoup library, which provides a convenient way to navigate and search through HTML documents.

Here’s an example of how you can parse the HTML and extract the price history using BeautifulSoup:

from bs4 import BeautifulSoup

Assuming we have the HTML content stored in the variable ‘html_content’

soup = BeautifulSoup(html_content, “html.parser”)

Find the HTML element(s) containing the price history

price_history_elements = soup.find_all(“div”, class_=”price-history-container”)

Extract the price history data from the HTML elements

for element in price_history_elements:
# Extract specific information such as date, price, etc.
# Perform any necessary data manipulation or cleaning
# Store the extracted data in a suitable data structure

In this example, we use the find_all() method of BeautifulSoup to locate the HTML elements that contain the price history data. We specify the tag name (div) and the class attribute (price-history-container) to narrow down the search.

Once we have the price history elements, we can iterate over them and extract the desired information. Depending on the structure of the HTML and the specific data you want to scrape, you may need to further navigate the HTML tree or apply additional parsing techniques.

Storing the Scraped Data

After extracting the price history data, it’s important to store it for further analysis or future use. The choice of storage will depend on your specific requirements and preferences. Some common options include storing the data in CSV files, databases (e.g., SQLite or MySQL), or data structures such as lists or dictionaries in memory.

Here’s an example of how you can store the scraped data in a CSV file using the csv module in Python:

import csv

Assuming we have the extracted price history data stored in a list called ‘price_history_data’

csv_file = “price_history.csv”

with open(csv_file, “w”, newline=””) as file:
writer = csv.writer(file)
writer.writerow([“Date”, “Price”]) # Write the header row

for data in price_history_data:
    writer.writerow([data["date"], data["price"]])  # Write each row of data


In this example, we create a CSV file and write the price history data into it. The csv.writer object allows us to write rows into the file, with each row containing the date and price values.

Remember to adapt the storage approach based on your specific needs and the structure of the extracted data.

In the next section, we will address the importance of responsible and ethical web scraping practices when scraping data from Zillow.

Ensuring Responsible and Ethical Web Scraping

Ensuring responsible and ethical web scraping practices is crucial when scraping data from websites like Zillow. By following ethical guidelines and respecting the terms of service of the website, you can avoid legal issues and maintain a positive relationship with the website’s owners. In this section, we will discuss the considerations for responsible web scraping when extracting price history from Zillow.

Understanding Zillow’s Robots.txt and Terms of Service

Zillow, like many websites, has a robots.txt file that outlines the rules and guidelines for web crawlers and scrapers. The robots.txt file specifies which parts of the website are accessible to web crawlers and which are off-limits. It is important to review and respect the directives in Zillow’s robots.txt file to ensure responsible scraping.

Additionally, it is essential to familiarize yourself with Zillow’s terms of service. The terms of service outline the acceptable use of the website and any specific restrictions on data scraping. By adhering to these terms, you can ensure that your scraping activities are within legal boundaries.

Implementing Delay Between Requests

To avoid overloading the website’s servers and to be considerate of Zillow’s resources, it is best to implement a delay between your scraping requests. Rapid and frequent requests can put a strain on the website and may result in unintended consequences, such as IP blocking or disruption of service.

You can use the time module in Python to introduce delays between requests. For example:

import time

Make a request to Zillow

Delay for 2 seconds before making the next request


By adding a delay, you allow Zillow’s servers to handle requests from other users and reduce the likelihood of being identified as a bot or scraper.

Handling Potential Errors and Exceptions

During the scraping process, it is possible to encounter errors or exceptions. These can arise due to changes in the website’s structure, network issues, or other unforeseen circumstances. It is important to handle these errors gracefully to ensure the stability and reliability of your scraping script.

You can use exception handling techniques in Python to catch and handle errors. For example:

# Code to scrape Zillow's price history
# ...
except Exception as e:
# Handle the exception
print("An error occurred:", str(e))

By implementing proper error handling, you can prevent your scraping script from crashing and handle any unexpected situations that may arise during the scraping process.

Respecting Data Usage and Privacy

When scraping data from Zillow, it is crucial to respect data usage and privacy guidelines. Avoid scraping and storing personal or sensitive information that is not publicly available. Use the scraped data responsibly and in compliance with applicable laws and regulations.

Furthermore, it is recommended to avoid excessive or unnecessary scraping that may put a strain on Zillow’s servers or violate the terms of service. Only scrape the data you need for your intended purposes and avoid causing any disruption or inconvenience to Zillow or its users.

By following these responsible and ethical web scraping practices, you can ensure a positive scraping experience, maintain a good relationship with Zillow, and avoid any legal or ethical issues.

In conclusion, web scraping Zillow’s price history can provide valuable insights for real estate analysis and decision-making. However, it is essential to approach web scraping responsibly, respecting the website’s guidelines and terms of service. With the right approach, you can gather the desired data while maintaining ethical and legal standards.