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Real Estate: A Use Case for Web Scraping

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Web Scraping – Data has become the mainstream of every decision and the backbone of every thriving business. Its application spreads across literally every industry, including the real estate industry.

In the real estate industry, data can be used profitably by both the brands and the clients. The brands can use the data to understand customers’ pain points, establish a broader community outreach, and even protect their reputation.

Real Estate: A Use Case for Web Scraping

Web Scraping

Whereas clients can use real estate data to index listings, curate updates in real-time, and even learn all they want about a brand and properties.

Ironically, real estate has since become a use case for web scraping, and today we will see how web scraping affects real estate and how real estate, in turn, influences web scraping.

What Is Web Scraping?

Web scraping can be seen as the technique employed in gathering unstructured data from various sources at once.

This data can later be transformed into a structured and easy-to-use format such as a CSV or an Excel Spreadsheet before storing it in a local depository.

The process often involves going through millions and even billions of web pages each day. To make the job a lot easier, web scraping involves using scraping bots and proxy servers.

The scraping bots work automatically and with very minimal human input to scrape data from various data sources. At the same time, the proxy ensures that restrictions are removed and the process is as smooth as possible. Proxies also work fast in delivering and returning connections while ensuring the servers do not crash from too much traffic.

What Are Some Practical Usages of Web Scraping?

Web scraping is used in various industries and for different reasons. Although the general usage of web scraping is to collect data and make it available, below are some of the best practical usage of this process:

  • Expanding Business Borders

Brands used to be limited by their physical borders, but all that has changed with the widespread web scraping.

Today, a business based in one part of the world can have information from another side of the world and even extend their business to become global by simply using web scraping to collect as much data as is necessary.

  • Brand Protection

While the internet is, inarguably, one of the biggest blessings for brands, it can also become the instrument through which they lose their customers, revenue, and assets.

For instance, customers usually drop reviews and comments on different platforms on the internet. Sometimes, the most negative feedback can spread so wide that prospective customers and even old customers use them to judge the affected brand. This can dissuade those customers from patronizing the brand.

Therefore, brands use web scraping to check for negative feedback regularly and duly attend to them to avert any damages.

They also use web scraping to monitor their assets such as brand name, logo, patent, online to prevent theft and infringement.

  • Price Scraping

Knowing what prices your competitors are selling has become very important in a very competitive market. By having this information regularly, brands can determine whether or not they are selling the right way.

Failure to do this can cost you a loss of customers or a decline in profit margin. Web scraping is therefore used to collect prices from different eCommerce platforms so that brands can confirm and adjust appropriately.  

Aspects of Real Estate That Can Be Collected and Extracted

Webscraping has become increasingly popular and is used by real estate brands looking to succeed in the market. However, it is also used by clients and prospective clients to help them make better buying/renting decisions.

The following are some of the most valuable data that can be extracted in real estate:

  • Customers reviews, comments, and general feedback
  • Prices of buying and renting properties across different locations and from different platforms
  • Information about a property including type, location, price, size, City/State/Zip Code
  • Information about agents, vacancies, and listings

Real-World Examples of How Web-Scraping Can Benefit the Real Estate Market

Scraping real estate data can be as simple as using a custom web scraper to search through several data sources and provide you with the data you need.

A web scraper can be easily built with Python libraries such as lxml. The lxml library is a fast and powerful library that anyone can use, even a beginner with minimal experience in programming. You could become comfortable using the library after a short lxml tutorial. Read more on Oxylabs’ blog regarding the lxml tutorial in Python programming language.

Whichever tools you decide to use in scraping real estate data, below are some real-world examples that you may benefit from this process:

  • Availability of Updated Data

Data is good, but updated data is better. Every day more and more data is pumped into the internet so that yesterday’s data can easily become outdated today.

By scraping frequently, web-scraping makes updated data always available at your disposal. This update can then be used to do strategic planning that can move your business forward.

  • Business Proficiency

Another benefit of real estate data to brands is that it makes them more proficient in business.

For instance, through extracted data, brands can know what properties can be sold faster based on their type, location, and ROI.

Upon learning this, the brand can then provide better services for its customers.

  • Better Investment Decisions

This benefit is mainly for investors. Real estate investors can easily use qualitative, applicable, and experiential data to decide where to put their money for the best gains.

Making sound decisions this way minimizes risks and increases profitability. 

Conclusion

As it stands, there is no field in which data doesn’t have its application, and if data is growing in importance, so is web-scraping.

In real estate, web scraping can benefit both businesses and investors by relying on updated data to make the best decisions.

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