How to Scrape Using Code and No Code Approaches

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

There are three methods to scrape real estate data:

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

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Build Scraper in Python/JavaScript

In this section, we will guide you on how to scrape real estate data from 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 using Playwright:
Step 1: Choose 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 save_data function is then called to extract the real estate data and store the data in a JSON file named Homes_data.json.
  2. save_data function: This function takes a Playwright page object as input and returns a list of dictionaries containing listing details. The details include each listing’s price,number of bedrooms and bathrooms, square footage, address, etc.

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 would be created.

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

Using No-Code Scraper by ScrapeHero Cloud

The 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 the steps to set up and use the scraper.

  1. Sign up or log in to your ScrapeHero Cloud account.
  2. Go to the Scraper by ScrapeHero Cloud in the marketplace.
    Note:The ScrapeHero Cloud’s 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 for a particular location 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.input the search results page URL to scrape using ScrapeHero Cloud
  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 listings data into an Excel spreadsheet from here. Click on the Download Data, select “Excel,” and open the downloaded file using Microsoft Excel.

Uses cases of Web Scraping Real Estate Data

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

Real Estate Investment 

Use data to analyze property prices, historical value trends, and local features in specific zip codes. This data helps investors pinpoint lucrative investment locations, understand market dynamics, and make forecasts to reduce risk and maximize ROI.

Smart Home Buying 

Real Estate Data from aids potential homeowners in price comparison, value trend analysis, and amenity mapping (like schools, malls, parks). This equips buyers to make decisions that are cost-effective and align with their lifestyle.

Real Estate Professionals

Agents and brokers can utilize real estate data from for in-depth market analysis. Knowledge of average pricing, buyer patterns, and past sales enables agents to effectively match properties with buyers and price listings competitively.

Property Development 

Builders and developers can use data from to identify in-demand property features in targeted locations. For example, if solar-powered homes are trending in an area, developers can include these features in new projects.

Urban Planning 

Policy-makers can use data to inform decisions on zoning laws and housing policies. If the data indicates a shortage of affordable housing in an area, strategies can be developed to incentivize low-cost housing projects.

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 popular real estate website.

Real estate web scraping is the automated collection of property and consumer data from online real estate websites. This method provides detailed insights on available properties, buyer preferences, and agent reliability, storing it in a structured table format, such as spreadsheets or databases. The scraped data is instrumental for making investment decisions, optimizing pricing models, etc. in the real estate sector.

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:

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