Scraping Zillow for Smarter Decisions

Zillow data offers significant value, whether you’re tracking real estate trends, analyzing rental properties, or making informed investment decisions. To access this wealth of information, scraping Zillow’s real estate data with Python is an effective solution.
In this guide, I will walk you through the process of scraping Zillow’s property listings. From installation to execution, you’ll learn how to extract valuable data using libraries like requests and lxml.

Getting Started with Essential Installations

Before we jump into scraping, make sure you’ve got Python set up and ready to go. You’ll need two libraries to get started:

pip install requests
pip install lxml

Once that's done, you’re all set for the next steps.

Step 1: Analyze Zillow's HTML Structure

To effectively scrape Zillow, you first need to understand the layout of the website. You can easily inspect this by opening any property listing and checking the elements you want to scrape—like the property title, rent estimate, or assessment price. You’ll need this information for the next steps.
For example, you might be interested in the following:
Title of the property
Rent estimate
Assessment price

Step 2: Make Your First Request

Now, let’s fetch the HTML content of a Zillow page. We’ll use Python’s requests library to send a GET request. To ensure that Zillow doesn’t block you, we’ll also set up request headers to simulate a real browser.
Here's a basic example:

import requests

# Define the target URL
url = "https://www.zillow.com/homedetails/1234-Main-St-Some-City-CA-90210/12345678_zpid/"

# Set up request headers
headers = {
    'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,/;q=0.8,application/signed-exchange;v=b3;q=0.7',
    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
}

response = requests.get(url, headers=headers)
response.raise_for_status()  # Ensure the request succeeded

Step 3: Process HTML Content

Once you have the page, it's time to extract useful data. To do this, we’ll use lxml, a library that makes parsing HTML and XML data easy. The fromstring function converts the HTML into a format that Python can work with.

from lxml import html

# Parse the response content
tree = html.fromstring(response.content)

Step 4: Extract Specific Data Points

Using XPath—a language for navigating through elements in an HTML document—you can easily extract specific pieces of data like the property title, rent estimate, and assessment price.

# Extract property title
title = tree.xpath('//h1[@class="property-title"]/text()')[0]

# Extract rent estimate price
rent_estimate = tree.xpath('//span[@class="rent-estimate"]/text()')[0]

# Extract assessment price
assessment_price = tree.xpath('//span[@class="assessment-price"]/text()')[0]

Step 5: Save

Once you’ve scraped the data, you'll want to store it for future analysis. A JSON file is an excellent format for this, as it keeps everything organized and easy to access later.

import json

# Store the extracted data
property_data = {
    'title': title,
    'rent_estimate': rent_estimate,
    'assessment_price': assessment_price
}

# Save data to a JSON file
with open('zillow_properties.json', 'w') as json_file:
    json.dump(property_data, json_file, indent=4)

print("Data saved to zillow_properties.json")

Step 6: Scrape Multiple URLs

Want to scrape more than one property? No problem. You can loop over multiple URLs and apply the same scraping process to each. Here’s how you can handle multiple listings:

# List of property URLs to scrape
urls = [
    "https://www.zillow.com/homedetails/1234-Main-St-Some-City-CA-90210/12345678_zpid/",
    "https://www.zillow.com/homedetails/5678-Another-St-Some-City-CA-90210/87654321_zpid/"
]

# List to hold all property data
all_properties = []

for url in urls:
    response = requests.get(url, headers=headers)
    tree = html.fromstring(response.content)

    title = tree.xpath('//h1[@class="property-title"]/text()')[0]
    rent_estimate = tree.xpath('//span[@class="rent-estimate"]/text()')[0]
    assessment_price = tree.xpath('//span[@class="assessment-price"]/text()')[0]

    property_data = {
        'title': title,
        'rent_estimate': rent_estimate,
        'assessment_price': assessment_price
    }

    all_properties.append(property_data)

# Save all data to a JSON file
with open('multiple_zillow_properties.json', 'w') as json_file:
    json.dump(all_properties, json_file, indent=4)

Best Practices for Scraping Zillow

When scraping websites like Zillow, it’s essential to be mindful of a few things:
1. Respect Robots.txt: Always check the website’s robots.txt file to ensure that you're not violating any scraping rules.
2. Use Proxies: Too many requests from one IP can get you blocked. Use proxies or rotate User-Agents to keep things smooth.
3. Rate Limiting: Space out your requests to avoid overwhelming the server and getting flagged.

Conclusion

With these steps, you can efficiently scrape Zillow data and start analyzing it for real estate insights. By combining Python's requests and lxml, you can automate data extraction more effectively. Whether you're building a portfolio of real estate data or tracking market trends, this skill will save you hours of manual work. Start today and explore the full potential of Zillow's property listings.