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How to Do Data Scraping for Real Estate as a Virtual Assistant.

Here's how you can do data scraping as a virtual assistant and make money on the side!

Data scraping is a great skill to have as a virtual assistant, and it is also very lucrative.  When working as a data scraper, you’re tasked to find and extract accurate data that can then be analyzed and used to inform business decisions.  

In this blog post, I will show you how you can do data scraping for real estate websites and leverage this skill to make money.

What is Data Scraping and Why Should I Care?

Data scraping is the process of extracting data from a website or database so that it can be improved and used in some way. When you scrape data, you get raw information that can form the basis of a database, spreadsheet or report. These reports can then be used in different ways by your client, from deciding which properties to promote to which agents to recruit for their businesses.

The data you scrape lets your clients do more with it than typical search engine results might allow.

Thus, data scraping is a handy skill to have when working with real estate websites. It can help you find relevant information about properties and makes data analysis and listing much easier!

Being great at web scraping allows you to make money online. For example, as a Real Estate Virtual Assistant, you could be making as high as $40 per hour scraping and cleaning data for your clients.

How to do data scraping for Real Estate as a VA

To scrape data from the web, you will need to work some sort of a script or a program that will automatically collect and compile the data into a format that is easier to analyze, use and draw meaningful conclusions.

You’ll be scraping data from popular real estate listing sites like Zillow, Trulia, and Redfin.

To scrape data in bulk from these sites, it will require a little more work on the front end. First, you’ll need to know or learn how to use scripting or a programming language such as Python, JavaScript (jQuery), Java to scrape these data and structure so that the output is in a format that can be easily imported into a database.

Identify the Parameters You’ll be Collecting

since data scraping can take a while to be processed, you may want to identify the parameters you’re required to collect. Only scrape the data points that your client will actually use.

Such data points could include:

  • Property name and description
  • Utilities such as electric company, natural gas company, water suppliers
  • Amenities in the area such as parking spaces, co-working spaces, restaurants, cafes
  • Building information such as age and year of construction
  • Location-specific data like zip code, municipality, or state; local driving distance from the property to other places
  • seller information and contact details

The client might specify other data points that you’ll need to collect, so before you run the script, let your client go through the enumerated data points and confirm that that’s exactly what they want.

Get the Listing URL from Real Estate Websites

The first step you need to take when scraping data using Python from a listing site is to visit a Real Estate website like Zillow or Trulia and search for the properties that you need. Be sure to grab the URL of the search query results pages. For example, you can search for properties available in a given area using a zipcode or city name.

Write and Execute Your Script

Once you have found some listings, take note of their URLs and create a script using Python that scrapes all the data points that you need from the query page.

  • When running your script for the first time, the results that you get may not look pretty, so you need to modify the script to clean up the output and present them in a tabular form instead of just in plain text.
  • To do this, you might need to use what is called a “pandas DataFrame,” which will allow for easier visualization of your data with various columns and formatting. “Pandas DataFrame” is a term for a data structure in Python that stores and organizes data.
  • Once the script is clean and your “pandas DataFrame” has been formatted properly, you will be able to save it as a .csv file or copy-paste it into your Excel document.
  • You can then improve your data within Excel by removing unnecessary columns, filling in missing details etc., to make them actionable.
  • Upload the cleanly formatted CSV file to Google Drive or Dropbox so that you can share it with your client without any issues of formatting errors.

Working with Python can sound intimidating for many beginners, but if you’ve been doing web-scraping for a while, you know that you can collect any data that you want from almost any website once you’ve mastered the program.

There are some web scraping software that you can use if you are not tech-savvy, but you still need some coding skills to work them properly.

You can learn how to scrape data on YouTube or take a dedicated course on Udemy, Skillshare and LinkedIn.

Conclusion

Data scraping for real estate websites could be a great option for virtual assistants who know how to code and have been grabbing and analyzing data for their own use for years.

If you’ve been working as a real estate virtual helping clients with data scraping, I would love to know how your experience has been. Drop a comment below.

How to do data scraping for real estate as a virtual assistant.
How to Do Data Scraping for Real Estate
How to Do Data Scraping for Real Estate
Mysson Victor
Mysson Victor
As an SEO freelance writer for hire, Mysson creates compelling content in online business, SaaS, and Digital Marketing niches for clients just like you. Hire Mysson for powerful business storytelling, crispy copy for service business or insightful articles that spark online conversations, attract links and boost conversions. Connect on LinkedIn @Mysson

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