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Vytautas Kirjazovas

Nov 06, 2019 6 min read

In the eyes of many, real estate investments deliver the most significant value, resulting in the highest returns while keeping the risk levels at the minimum. However, it is a genuinely competitive market, and a lot of factors influence the potential investment opportunities and returns. 

Fortunately, it is possible to analyze essential influences in the real estate industry and bet on data-backed decisions, thanks to data scraping practice. In fact, the majority of switched-on market players are already performing real estate data scraping for evaluating property value, observing vacancy rates, assessing rental yields, forecasting market direction, to name a few. 

In this article, we will define real estate data scraping and data mining. Secondly, we will explore several use cases of how this practice is already making a positive impact on the industry. Lastly, we will discuss the essential tools and solutions that are paramount for successful real estate data scraping.

Real estate data scraping

Data scraping (also called web scraping, web harvesting or web data extraction) refers to extracting publicly available data of the web. The identification and retrieval of data from the internet are performed by web crawlers/scrapers, i.e. automated software scripts that surf the web in a structured approach. 

Real estate data scraping

Once the pre-defined data points are identified, the extraction of data (web scraping) process commences. In this stage, web scraping focuses on gathering unstructured data from numerous online sources, aggregating and transforming it into structured datasets for later analysis stage.

In the real estate industry, the most common extracted data fields are real estate listings, buyers and sellers information, real estate agents contact details, in-depth building details, pictures, price or rent data, etc. 

Also, a more advanced approach can be taken to gather such data as crime and safety statistics, monitoring foreclosure and auctions listings, urban planning and construction permits, and so on. 

Real estate data mining

Real estate data mining refers to a stage of investigating a vast amount of aggregated data to generate actionable insights. Typically, highly specialized tools alongside algorithms are employed to identify patterns and trends. Once completed, this kind of analyzed information aids in deciding the best times to buy or sell, forecast the marketplace direction, adjust pricing strategies for the best ROI, identifying further opportunities in the industry, to name a few.

Technological advancement impact

In the last few decades, technology advancements played a huge part in transforming the processes and policies of the real estate industry. Long gone are the days of discovering houses through a yard sign, and it is crucial for market players to adjust to a fast-paced competitive environment to stay relevant.

For instance, a survey by the National Association of Realtors found out that 51% of real estate transactions start off on the internet. Furthermore, according to the report, real estate agents cite the primary tech tools that provide the highest quality leads as social media (47%), Multiple Listing Sites (32%), brokerage’s and listing aggregator’s websites (29%). More surprisingly, almost half (48%) of surveyed real estate firms named the biggest challenge in the industry to keep up with the pace of technology. 

Taking into this into account, it is evident that technology advancements will further influence market dynamics and stakeholders, and only those who will be able to adapt to changing market conditions will be able to claim a piece of market share.

Use cases

Here are only several already proven real estate data scraping use cases:

  • Real estate aggregators heavily rely on the web scraping practice to gather vast amounts of real-time intelligence from numerous data sources and display it on their one-stop-shop website for consumers’ benefit. 
  • Forecasting market direction is a must for all market players, be it investors, realtors, or brokerages. Hence, real estate data scraping aids in collecting historical and current intelligence on properties, value, sales cycles, etc. The analyzed data provides data-backed predictions on how the market is going to perform, which consequently allows market players to adjust their tactics and strategies.
  • Monitoring competition is yet another crucial aspect in this highly competitive market. Thanks to data scraping, it is possible to swiftly collect real-time pricing intelligence data and promptly react to pricing changes to be price competitive. Ultimately, this knowledge contributes to winning the customer or business.
  • Monitoring vacancy rates by data scraping practice helps to analyze positive and negative rental growth market cycles and provides data-backed insights for investment opportunities and the best ROI to real estate agents.
  • Product and service development is heavily backed by the web scraping practice. It allows to gather actionable knowledge and identify opportunities within the market. For instance, OpenDoor, which is now valued at an astonishing $2 billion mark, leveraged data scraping practice, creating algorithms to provide “instant offers” for homeowners seeking to sell their property quickly.

This is only the tip of the iceberg of how web scraping is already transforming the real estate industry. And, as the role of data scraping in the real estate industry is a relatively new concept, it is a given that there will be more use cases identified in the future, especially when collecting alternative data that has a direct impact on the industry.

Tools and solutions for real estate data scraping

Now, collecting this vast amount of data from various online sources is no easy task. Web crawling and scraping is a genuinely challenging practice that requires expert knowledge and resources. Typically, there are two following approaches to get about real estate data scraping:

  1. Building an in-house data extraction mechanism that will have to be supported with proxies. In short, proxies are an essential part of web scraping process as it allows to collect data under numerous IP address from the desired public data sources successfully.  
  2. Outsourcing data extraction tools, such as our Real-Time Crawler or Web Scraper, which eliminates the data gathering process. This approach allows concentrating on already collected and structured data to draw actionable insights.

It’s a wrap

Hopefully, by now, you have a decent understanding of how the real estate industry is being revolutionized by data scraping and mining practice. It is apparent that those who will adapt to never-ending technological advancements and harness data will have the best chance to set the pace in the industry.

If you have any further questions or would like to get a consultation, feel free to leave a comment below, drop us a line via live chat, or email us at [email protected]oxylabs.io.

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About Vytautas Kirjazovas

Vytautas Kirjazovas is a Content Manager at Oxylabs, and he places a strong personal interest in technology due to its magnifying potential to make everyday business processes easier and more efficient. Vytautas is fascinated by new digital tools and approaches, in particular, for web data harvesting purposes, so feel free to drop him a message if you have any questions on this topic. He appreciates a tasty meal, enjoys travelling and writing about himself in the third person.