A Complete Guide on How to Scrape Real Estate Data!


Real estate is one of the most profitable businesses as you get into it. It entirely depends on successful long-term decisions. Unlike every other industry domain, the real estate market is highly competitive. Every country has hundreds of real estate companies, each trying to succeed in the marketplace. To ensure that your real estate company stays ahead of the competition, you only need the best quality and quantity of information.

Type of Data that Influence the Real Estate Sector

In the real estate business, the type of data includes:

  • Real estate listings
  • Descriptions
  • Buyers’ and sellers' profiles
  • Detailed information on real estate agents

Hence, you require a comprehensive database to ensure superior quality and quantity of information. This database provides high-quality and actionable data to the clients, helping you make the right and educated decisions to create a better business.

Scraping real estate data will ensure you possess a large volume of reliable and accurate data. This data can enhance your business to real utmost benefit.

iWeb data Scraping, we can scrape real estate data including agencies, names, email addresses, contact numbers, locations, titles, postcodes, and more.

Every Real Estate Business Needs Scraping Solutions

Web scraping is sorting from a vast pool of data, filtering the user's searches, and then displaying a list of relevant information. Regarding realtors, this is an on-the-go method for structured property listings. As the real estate industry rapidly grows, businesses must perform their best to stay caught up in the competition. Realtors can quickly assess sales and prospective buyers by scraping property data from real estate. This information is processed and stored in a database for potential business use.


Scraping real estate agents is another crucial aspect of finding the number of agents available in a particular area. It helps buyers to collect information on the property agents, connect with the right ones, and make informed decisions.

The real estate web scraping efforts provide the information on the below parameters such as

  • Property type
  • Area
  • Property Site
  • Amenities
  • Sale price
  • Monthly rent
  • Property agent, etc.

Features and Advantages

Property Value Tracking: Web scraping for the value of a similar property helps set a good value for your own, allows the buyers to get fair prices on the property and gives you lucrative benefits.

Helps in Making Suitable Investments: When investing in properties, investors must make decisions based on qualitative evidence rather than any incomplete knowledge. Collecting property data is essential for doing investment research.

Rental Efficiency: While investing in the property, rental income is the primary factor to consider. By scraping real estate data, you will quickly decide which property has the best rental income.

Another a significant aspect of real estate scraping is that it helps extract the property agent’s information from the internet. With scraping, you can access several tools like agents’ websites, directories, groups, discussion boards—agent profiles, etc.

Scraping Real Estate Data

Required Tools

To make an HTTP request, we will first install the got package and the jsdom to parse the HTML returned from the request.


Now, set the API request parameters.


Make the request


Now, the page returns to the HTML format

Inspect the Source Code

We will explore the page using the Developer Tools and search for exciting elements. For inspecting, right-click on the component and then select the Inspect option.


The above image shows that the property element has a class of . Some basic details of the listing are available within the component .

Parsing the HTML

For manipulating the request result, we will first parse it because the result obtained is in the HTML format. JSDOM is perfect for it.


Processing the Results

We will search for the elements with class. Within each listing, we will look for the corresponding factors like address, size, beds, baths, etc. The code looks like this:


Finally, you will end up with all the data like this. Use it as per your needs.


Let’s take the example of scraping all real estate agents.


All the agents are available in the .xlxs for each zip code. The output will be:


Conclusion: With the above-detailed information, we hope you can scrape the real estate data to accumulate and analyze it for business purposes.

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