Scrape Zillow Data Python Method: All You Need to Know

Zillow Scraper Python Usage as a Business Goal

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 apply a scrape Zillow data Python approach, which provides the ability to target the leading real estate marketplace called Zillow for property prices, listing types, locations, and more.

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.

Note: Zillow actively protects its data with multiple anti-bot layers, including CAPTCHAs, IP blocking, rate limiting, and JavaScript-rendered content. The approaches below cover the technical fundamentals. Always review Zillow’s Terms of Service and robots.txt before scraping.

Why Choose Python Zillow Scraper

As we have 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:

• BeautifulSoup and Scrapy are the most popular scraping-friendly frameworks based on Python.
• BeautifulSoup library provides a fast and highly effective data extraction.
• Python supports XPath.
• Great idioms are provided for searching, navigating, and modifying the parse tree.
• Other advanced web scraping libraries are available.

Setting Up Zillow Scraper: Python and LXML Methods

Python tools you will need

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 – installation
• To download the HTML content, we need Python Requests – installation of requests
• To parse the HTML Tree Structure, it is required Python LXML – lxml installation

Install all required packages with a single command:

pip install requests lxml pandas

Common steps

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

The whole scraping process contains the following steps:

1. Conduct a search on Zillow by inserting the postal code.
2. Get the search results URL: https://www.zillow.com/homes/for_sale/02128/
3. Download HTML code through Python Requests.
4. Parse the page through LXML.
5. Export the extracted data to a CSV file.

The whole scraping process contains the following steps:

Running the Zillow data scraper

Let’s name the script zillow.py The script accepts a zip code and sort order as command-line arguments.

import argparse

sort_help = """available sort orders are:
newest : Latest property listings
cheapest: Properties sorted by lowest price"""

if name == "main":
argparser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter
)
argparser.add_argument('zipcode', help='ZIP code to search')
argparser.add_argument(
'sort',
nargs='?',
help=sort_help,
default='newest'
)
args = argparser.parse_args()
zipcode = args.zipcode
sort = args.sort

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

python3 zillow.py 02128 newest

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

Setting Up Zillow Web Scraper: Python and BeautifulSoup Methods

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

Required libraries

Install the required libraries:

pip install requests beautifulsoup4 lxml pandas

Then import them at the top of your script:

import requests
from bs4 import BeautifulSoup
import pandas as pd

Bypassing CAPTCHAs

Like many websites, Zillow throws CAPTCHAs and blocks requests that do not look like they come from a real browser. The minimum requirement is a realistic User-Agent header and a referer value. A requests.Session() object also helps maintain consistent cookies across requests, which reduces detection.

import requests
from bs4 import BeautifulSoup

request_headers = {
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,/;q=0.8',
'accept-encoding': 'gzip, deflate, br',
'accept-language': 'en-US,en;q=0.8',
'upgrade-insecure-requests': '1',
'user-agent': (
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
'AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/120.0.0.0 Safari/537.36'
),
'referer': 'https://www.zillow.com/'
}

city = 'seattle-wa' # change this to the city you want
url = f'https://www.zillow.com/homes/for_sale/{city}/'

with requests.Session() as session:
session.headers.update(request_headers)
response = session.get(url)
print(response.status_code)

Important: Even with correct headers, Zillow may still return a CAPTCHA or block the request at scale. For high-volume scraping, rotating proxies are recommended. Datacenter IPs are quickly flagged.

Looping through URLs

To collect listings across multiple pages, loop through paginated URLs dynamically rather than creating separate variables for each page. The pattern below iterates through pages for a given city:

import requests
from bs4 import BeautifulSoup
import time

request_headers = {
'user-agent': (
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
'AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/120.0.0.0 Safari/537.36'
),
'referer': 'https://www.zillow.com/'
}

base_url = 'https://www.zillow.com/homes/for_sale/seattle-wa/{page}_p/'
soups = []

for page in range(1, 6): # pages 1 through 5
url = base_url.format(page=page)
response = requests.get(url, headers=request_headers)
if response.status_code == 200:
soups.append(BeautifulSoup(response.content, 'html.parser'))
time.sleep(2) # pause between requests to avoid rate limiting

Formatting data

Zillow’s current HTML uses data-test attributes to identify key elements reliably. The property price is in a <span> with data-test=”property-card-price”, and the address is in an <address> tag with data-test=”property-card-addr”. Use these stable selectors rather than class names —Zillow updates them frequently.

import requests
from bs4 import BeautifulSoup
import pandas as pd
request_headers = {
'user-agent': (
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
'AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/120.0.0.0 Safari/537.36'
),
'referer': 'https://www.zillow.com/'
}
url = 'https://www.zillow.com/homes/for_sale/02128/'
response = requests.get(url, headers=request_headers)
soup = BeautifulSoup(response.content, 'html.parser')
properties = []
for card in soup.find_all('div', {'class': 'property-card-data'}):
try:
address_el = card.find('address', {'data-test': 'property-card-addr'})
price_el = card.find('span', {'data-test': 'property-card-price'})
details = card.find_all('li')
address = address_el.get_text(strip=True) if address_el else '' price = price_el.get_text(strip=True) if price_el else '' beds = details[0].get_text(strip=True) if len(details) > 0 else '' baths = details[1].get_text(strip=True) if len(details) > 1 else '' sq_feet = details[2].get_text(strip=True) if len(details) > 2 else '' properties.append({ 'address': address, 'price': price, 'beds': beds, 'baths': baths, 'sq_feet': sq_feet }) except Exception: continue
df = pd.DataFrame(properties)
Drop rows where both address and price are empty
df = df[(df['address'] != '') | (df['price'] != '')]
Rearrange columns
df = df[['price', 'address', 'beds', 'baths', 'sq_feet']]
df.to_csv('zillow_listings.csv', index=False)
print(df)

Tip: Always use developer tools to analyze the current page structure before the scraping process. Zillow updates its HTML regularly. If property-card-data or data-test attributes stop returning results, re-inspect the live page to find updated selectors.

Zillow Web Scraper: DataOx Solutions

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.