Switching from the Universal Scraper API to dedicated Zillow Scraper APIs simplifies data extraction while reducing development overhead and ongoing maintenance. This guide provides a step-by-step migration path from the Universal Scraper API to using specialized APIs that deliver clean, structured data with minimal code.
In this guide, you’ll learn:
When extracting property data from Zillow using the Universal Scraper API, you need to make HTTP requests to specific property URLs and manually process the returned HTML.
To extract data from a Zillow property page, you need to set up proper parameters for the Universal Scraper API:
This script sends a request to the Universal Scraper API with the necessary parameters to retrieve property data from Zillow.
Once the raw HTML is retrieved, you’ll need to parse the page using BeautifulSoup to extract relevant information.
This function converts the raw property data into a usable format.
Once parsed, the data can be stored for later analysis:
This function saves the data into a CSV file for easy access and analysis.
Here’s the complete Python script that fetches, processes, and stores Zillow property data using the Universal Scraper API:
The dedicated Zillow Scraper APIs provide structured, ready-to-use real estate data through two specialized endpoints, the Zillow Property Data API and the Zillow Discovery API. These APIs offer several advantages over the Universal Scraper API:
js_render
, premium_proxy
, or others.The Zillow Property Data API returns valuable data points such as precise location coordinates, address, price, tax rates, property dimensions, agent details, etc., all in a standardized JSON format that’s immediately usable in your applications.
Here’s the updated code using the Zillow Property Data API:
When you run the code, you’ll get an output CSV file with all the data points:
Congratulations! 🎉 You’ve successfully upgraded to using an API that delivers clean, structured property data ready for immediate use.
Now, let’s explore how the Zillow Discovery API can help you search for properties and retrieve multiple listings with similar ease.
The Zillow Discovery API enables property searching with results that include essential details like property addresses, prices with currency symbols, bedroom/bathroom counts, listing status, property types, direct links to property pages, etc.
The API also handles pagination, making it easy to navigate through multiple pages of results.
When you run the code, you’ll get a CSV file containing property listings.
The benefits of migrating from the Universal Scraper API to the dedicated Zillow Scraper API extend beyond simplified code. It offers maintenance-free operation as ZenRows handles all Zillow website changes, provides more reliable performance with consistent response times, and enhances data coverage with specialized fields not available through the Universal Scraper API, where you need to maintain parameters.
By following this guide, you have successfully upgraded to using APIs that deliver clean, structured property data ready for immediate use, allowing you to build scalable real estate data applications without worrying about the complexities of web scraping or HTML parsing.
Switching from the Universal Scraper API to dedicated Zillow Scraper APIs simplifies data extraction while reducing development overhead and ongoing maintenance. This guide provides a step-by-step migration path from the Universal Scraper API to using specialized APIs that deliver clean, structured data with minimal code.
In this guide, you’ll learn:
When extracting property data from Zillow using the Universal Scraper API, you need to make HTTP requests to specific property URLs and manually process the returned HTML.
To extract data from a Zillow property page, you need to set up proper parameters for the Universal Scraper API:
This script sends a request to the Universal Scraper API with the necessary parameters to retrieve property data from Zillow.
Once the raw HTML is retrieved, you’ll need to parse the page using BeautifulSoup to extract relevant information.
This function converts the raw property data into a usable format.
Once parsed, the data can be stored for later analysis:
This function saves the data into a CSV file for easy access and analysis.
Here’s the complete Python script that fetches, processes, and stores Zillow property data using the Universal Scraper API:
The dedicated Zillow Scraper APIs provide structured, ready-to-use real estate data through two specialized endpoints, the Zillow Property Data API and the Zillow Discovery API. These APIs offer several advantages over the Universal Scraper API:
js_render
, premium_proxy
, or others.The Zillow Property Data API returns valuable data points such as precise location coordinates, address, price, tax rates, property dimensions, agent details, etc., all in a standardized JSON format that’s immediately usable in your applications.
Here’s the updated code using the Zillow Property Data API:
When you run the code, you’ll get an output CSV file with all the data points:
Congratulations! 🎉 You’ve successfully upgraded to using an API that delivers clean, structured property data ready for immediate use.
Now, let’s explore how the Zillow Discovery API can help you search for properties and retrieve multiple listings with similar ease.
The Zillow Discovery API enables property searching with results that include essential details like property addresses, prices with currency symbols, bedroom/bathroom counts, listing status, property types, direct links to property pages, etc.
The API also handles pagination, making it easy to navigate through multiple pages of results.
When you run the code, you’ll get a CSV file containing property listings.
The benefits of migrating from the Universal Scraper API to the dedicated Zillow Scraper API extend beyond simplified code. It offers maintenance-free operation as ZenRows handles all Zillow website changes, provides more reliable performance with consistent response times, and enhances data coverage with specialized fields not available through the Universal Scraper API, where you need to maintain parameters.
By following this guide, you have successfully upgraded to using APIs that deliver clean, structured property data ready for immediate use, allowing you to build scalable real estate data applications without worrying about the complexities of web scraping or HTML parsing.