How to Scrape Code and No Code Approaches

This article outlines a few methods to scrape This could effectively export real estate data from to Excel or other formats for easier access and use.

There are two methods to scrape

  1. Scraping in Python or JavaScript
  2. Using the ScrapeHero Cloud, Realtor Scraper, a no-code tool

Building a Scraper in Python/JavaScript

In this section, we will guide you on how to scrape using either Python or JavaScript. We will utilize the browser automation framework called Playwright to emulate browser behavior in our code.

One of the key advantages of this approach is its ability to bypass common blocks often put in place to prevent scraping. However, familiarity with the Playwright API is necessary to use it effectively.

You could also use Python Requests, LXML, or Beautiful Soup to build a scraper without using a browser or a browser automation library. However, bypassing the anti-scraping mechanisms put in place can be challenging and is beyond the scope of this article.

Here are the steps to scrape real estate data from Realtor using Playwright:

Step 1: Choose either Python or JavaScript as your programming language.
Step 2: Install Playwright for your preferred language:

Step 3: Write your code to emulate browser behavior and extract the desired data from using the Playwright API. You can use the code provided below:

This code shows how to scrape using the Playwright library in Python and JavaScript.
The corresponding scripts have two main functions, namely:

  1. run function: This function takes a Playwright instance as an input and performs the scraping process. The function launches a Chromium browser instance, navigates to, fills in a search query, clicks the search button, and waits for the results to be displayed on the page.
    The extract_data function is then called to extract the listings details and store the data in a HomesData.json file.
  2. extract_data function: This function takes a Playwright page object as input and returns a list of dictionaries containing the listing details. The details include each listing’s price, number of beds and baths, square footage, lot size, and address.

Finally, the main function uses the async_playwright context manager to execute the run function. A JSON file containing the listings of the script you just executed will be created.

Step 4: Run your code and collect the scraped data from

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Using No-Code Realtor Scraper by ScrapeHero Cloud

The Realtor Scraper by ScrapeHero Cloud is a convenient method for scraping real estate data from It provides an easy, no-code method for scraping data, making it accessible for individuals with limited technical skills.

This section will guide you through setting up and using the Realtor scraper.

  1. Sign up or log in to your ScrapeHero Cloud account.
  2. Go to the Realtor scraper by ScrapeHero Cloud in the marketplace.
    Note: The ScrapeHero Cloud’s Realtor Scraper falls under the premium scrapers category that does not include a free tier. To access this scraper, the user should be subscribed to a paid plan.
  3. Add the scraper to your account. (Don’t forget to verify your email if you haven’t already.)
  4. You need to add the search results URL of a certain place to start the scraper. If it’s just a single query, enter it in the field provided and choose the number of pages to scrape.
    For instance, if you have to scrape all the listings from Texas City, copy the URL:  to scrape, first input the location of choiceto scrape, copy search results URL into ScrapeHero Cloud crawler
  5. To scrape results for multiple queries, switch to Advance Mode, and in the Input tab, add the Search results URL to the SearchQuery field and save the settings.
  6. To start the scraper, click on the Gather Data button.
  7. The scraper will start fetching data for your queries, and you can track its progress under the Jobs tab.
  8. Once finished, you can view or download the data from the same.
  9. You can also export the real estate data from into an Excel spreadsheet from here. Click on the Download Data, select “Excel,” and open the downloaded file using Microsoft Excel.

Uses cases of Real Estate Data from

If you’re unsure as to why you should scrape, here are a few use cases where this data would be helpful:

Real-Time Property Valuation

By scraping, you can gather historical data like property age, location, and condition. This helps create precise pricing models that respond in real time, aiding buyers and sellers in making informed decisions.

Buyer-Seller Matching

With the right data, realtors can match buyers with properties that meet their needs—like budget, location, and preferences. data can help real-estate companies target their efforts towards buyers genuinely interested in their offer, thus saving time and resources.

Market Trend Analysis

The data can show which neighborhoods are becoming popular, what amenities are essential, and how human mobility and demographics affect property values. With data scraped from, businesses can keep a finger on the market’s pulse, adapting quicker to shifts and changes.

Future Growth Prediction

The real estate market patterns are based on various factors such as economic conditions, local policies, and interest rates. You can pick up on these patterns by scraping and analyzing data from platforms like This helps forecast which areas are likely to grow, allowing investors and developers to make proactive choices.

Cost Efficiency in Development

With the right data, real estate companies can optimize land acquisition costs and minimize waste during construction. Data from can provide valuable insights into property and material costs, ensuring that developers make smart spending choices.

Frequently Asked Questions scraping refers to extracting real estate data from the real estate listings available on This process allows for systematically collecting housing data displayed on this prominent online platform.

To scrape, you can either build a scraper using Python or JavaScript or if you don’t want to code, you can use the Realtor Scraper from ScrapeHero Cloud.

Legality depends on the legal jurisdiction, i.e., laws specific to the country and the locality. Gathering or scraping publicly available information is not illegal.
Generally, Web scraping is legal if you are scraping publicly available data.
Please refer to our Legal Page to learn more about the legality of web scraping.
Legal information

To scrape housing and apartment data, use pre-built scrapers such as the Zillow Scraper, Realtor Scraper, Trulia Scraper, etc., from ScrapeHero Cloud, or you can scrape listings manually from real estate platforms using Python or JavaScript.

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