Zillow is one of the most popular real estate websites in the United States, providing a wealth of data on home listings across the country. For real estate professionals, investors, researchers and others, scraping Zillow data can be extremely valuable. However, Zillow employs anti-scraping measures to prevent automation and large-scale data extraction.
In this comprehensive guide, we‘ll discuss the best tools and methods for scraping Zillow data in 2024, including both ready-made scrapers and do-it-yourself approaches. We‘ll also explain what web scraping is, how it works on Zillow, and why using proxies is so important for effective Zillow data extraction.
Contents
Overview: Best Zillow Scrapers 2024
Here are the top Zillow scrapers available today:
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ScraperAPI – The best overall Zillow scraper, with smart visual tools, high success rates and excellent customer support. Our top recommendation.
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Octoparse – A great desktop scraping tool for Windows, with a free trial available. Easy to use for beginners.
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BrightData – Enterprise-level scraper with high scalability and compliance standards. Integrates well with proxy networks.
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Apify – Cloud-based scraping for developers, with handy Zillow API integration. Free trial offered.
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Oxylabs – Focus on proxies and geographic targeting. Good for accessing restricted data.
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WebHarvy – A long-time scraping leader, WebHarvy works well for niche Zillow scraping needs.
Below, we‘ll explore these top Zillow scraping tools in more detail, along with key features and suitable use cases. But first, let‘s get into what web scraping is and how it works with the Zillow platform.
What is Zillow Scraping?
Zillow scraping refers to extracting data from Zillow.com through automated tools rather than manual searching and copying. This allows you to harvest large volumes of listing data across cities, states or the entire USA.
Potential data points include:
- Property addresses
- Listing prices
- Home details (bedrooms, bathrooms, sq footage)
- Listing descriptions
- Photos
- Neighborhood demographics
- Historical price data
- Zestimates
- Agent contact information
This data can be extremely valuable for:
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Real estate investors – Identify promising investment properties, trends and opportunities.
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Market researchers – Analyze real estate markets, price changes and demand factors.
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App/tool developers – Populate apps and sites with comprehensive real estate data.
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Data scientists – Run statistical analyses, train machine learning models and more.
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Agents/brokers – Research comps and gather lead info by geography.
Manual copying would be hugely time consuming given the vast amount of listing data on Zillow. Web scraping solves this problem through automation.
How Does Zillow Scraping Work?
While the details vary by tool, Zillow scraping generally follows a 3-step process:
1. Configure the Scraper
The first step is configuring your web scraper‘s settings and parameters:
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Locations – City, state, zip code or other areas to target.
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Search filters – Beds, baths, home types, price range, etc.
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Number of pages – How many search result pages per location.
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Data types – Which data fields/points to extract.
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Output format – CSV, JSON, Excel, etc.
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Scraping mode – Browser automation vs API access. Some tools support both.
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Proxy settings – Rotating proxies are highly recommended.
2. Execute the Scrape
Once configured, the scraper will programmatically search Zillow as if manually browsing the site. Depending on settings, it may:
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Loop through filters to retrieve varied listings.
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Iterate through multiple pages of search results.
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Expand each listing to harvest all data points.
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Store data extracted from each page.
Scraping follows the site‘s layout but is much faster than human searching. Thousands of listings can be scraped in hours.
3. Output Structured Data
After scraping completes, the tool exports scraped data in the selected structured format like CSV/Excel:
| Address | Price | Beds | Baths | Sq.Ft. | Zestimate | … |
|---|---|---|---|---|---|---|
| 123 Main St, Springfield | $280,000 | 4 | 2 | 2,100 | $278,000 | … |
| 456 Oak Rd, Springfield | $325,000 | 3 | 2 | 1,890 | $319,000 | … |
| 789 Elm Dr, Shelbyville | $185,000 | 2 | 1 | 1,260 | $183,000 | … |
This data can then be analyzed, cleaned and utilized as needed. Proxies are highly recommended for effective Zillow scraping, as we‘ll explore next.
Why Use Proxies for Zillow Scraping?
Zillow employs various anti-scraping mechanisms including rate limits and bot detection systems. Scraping from a single IP address is very likely to get blocked.
Proxies serve as intermediaries between your scraper and Zillow:
![diagram showing scraper –> proxy –> Zillow website, with proxy hiding scraper IP address from Zillow]
Here are 3 key benefits of using proxies for Zillow scraping:
1. Avoid IP Blocking and captchas
By routing your scraper through multiple proxy IP addresses, each Zillow search appears to come from a different user. This avoids detection as an automated bot.
2. Scale Data Extraction
Zillow limits users to around 500 requests per hour. Proxies allow you to parallelize requests across many IPs to extract data at scale.
3. Access Restricted Data
Proxies can spoof geographic IP locations, helping access Zillow content limited to certain regions.
High quality, constantly rotating residential proxies are ideal for Zillow scraping to maximize success and data yields.
Scraping Zillow with Python and BeautifulSoup
For developers and data scientists, scraping Zillow with Python provides maximum flexibility and control. The two most important Python libraries are Requests and BeautifulSoup.
Requests
The Requests library allows Python to send HTTP requests programatically to download web pages. For example:
import requests
url = ‘https://www.zillow.com/homes/for_sale/New-York-NY/‘
response = requests.get(url)
page_html = response.textThis provides the HTML content of the target Zillow search URL, which contains all the data we want to extract.
BeautifulSoup
BeautifulSoup parses HTML and helps locate specific elements within the page content. We can "soupify" the page HTML:
from bs4 import BeautifulSoup
page_soup = BeautifulSoup(page_html, ‘html.parser‘)Now we can use BeautifulSoup to extract data like listing titles:
listings = page_soup.find_all(‘a‘, {‘class‘: ‘list-card-link‘})
for listing in listings:
title = listing.find(‘address‘).text
print(title)
# Prints:
# 345 E 94th St #11C, New York, NY 10128
# 301 W 53rd St #3D, New York, NY 10019
# ...While simple in concept, successfully scraping at scale requires fine-tuning request patterns, proxy rotation, and other optimizations. Ready-made scraping services like ScraperAPI will be much easier for most users. But Python web scraping provides the deepest level of customization for those with the requisite skills.
Top Zillow Web Scrapers Compared
Now let‘s take a deeper look at the key features, pros and cons of the top tools for scraping Zillow in 2024:
ScraperAPI
![ScraperAPI logo]
Overview: ScraperAPI is our top recommendation for most users, offering a perfect blend of power, simplicity and reliability. Smart visual tools make configuring scrapes easy, while integrated proxies, CAPTCHA solving and AI optimize success rates.
Key Features:
- Visual workflow builder – no coding needed
- Integrated proxy manager
- Fast scraping speed
- 99.9% uptime and reliability
- CAPTCHA solving included
- AI tools to adapt to site changes
- Excellent customer support
Price: Starts at $49/mo
Best For: Real estate investors, researchers, startups, app developers and more. ScraperAPI is the easiest way for non-coders to scrape Zillow efficiently.
Octoparse
![Octoparse logo]
Overview: Octoparse is a popular Windows desktop web scraping tool, commonly used for scraping Zillow and other real estate sites. It has an intuitive visual interface and works well for small-to-medium scale scraping.
Key Features:
- Visual design – no coding required
- Good for beginners
- Windows desktop app
- Free trial available
- Output to CSV, Excel etc.
Price: Starts at $75/month
Best For: Casual users, small businesses. Octoparse is one of the most accessible scraping tools for Windows users.
BrightData
![BrightData logo]
Overview: BrightData is an enterprise-level web scraping solution focused on scalability, speed and compliance standards. Integrates well with proxy networks. Better suited for large-scale Zillow scraping.
Key Features:
- High scalability
- Fast scraping speed
- Integrates with all major proxies
- Advanced bot detection evasion
- Excellent compliance procedures
- 24/7 support.
Price: Custom quotes
Best For: Big data teams, researchers, hedge funds, etc. BrightData excels at large-scale, high-speed extraction scenarios.
Apify
![Apify logo]
Overview: Apify offers a cloud-based web scraping platform tailored towards developers. Its integrated Zillow API scraper provides a handy option for those with coding skills.
Key Features:
- Made for developers
- Zillow API integration
- Hosted cloud infrastructure
- Integrates with Python, NodeJS etc.
- Free trial available
Price: Starts at $49/month
Best For: Developers and data scientists who prefer the flexibility of API scraping over browser automation.
Oxylabs
![Oxylabs logo]
Overview: Oxylabs is a proxy-focused scraping solution best suited for targeted regional data extraction or accessing geo-restricted content on Zillow.
Key Features:
- Specializes in proxy management
- Geo-targeting capabilities
- Bypass regional restrictions
- Integrates with Python, NodeJS, etc.
- Headless browser automation
Price: Starts at $99/month
Best For: Scraping restricted or localized Zillow data through geo-targeted proxies.
WebHarvy
![WebHarvy logo]
Overview: WebHarvy is a long-time scraper focused on needs not covered by mass-market tools, including advanced javascript handling. Good for niche scraping cases.
Key Features:
- Advanced JS rendering
- Handles complex sites well
- Desktop Windows/Mac app
- Code-free scraping
- Free trial available
Price: $139 one-time purchase
Best For: Scraping complex sections of Zillow or other sites unaffected by mainstream tools.
Scraping Zillow Data: Final Thoughts
Scraping Zillow provides access to an immense dataset of nationwide real estate listings data. Both ready-made scraping tools and custom coding solutions are viable options, each with their own pros and cons.
For most users, we recommend starting with ScraperAPI. The combination of convenience and power is unbeatable. Apify and Octoparse are also excellent choices in the right circumstances.
No matter which approach you choose, utilizing proxies and optimizing scraping patterns is crucial for success when scraping Zillow data. With the right tools and techniques, you can extract huge value from Zillow‘s vast real estate database.
