The real estate industry is finally coming around to emerging technologies. Agents are embracing AI in innovative open marketplaces; some are adopting virtual reality and drone technology for immersive property marketing; and some forward-thinking practitioners have moved toward blockchain contract management.
But the most significant emerging technology to sweep through real estate in the past few years is big data analytics. In many ways, big data and real estate are a match made in heaven. This post serves as an introduction to big data in real estate investment analysis, and explores its benefits for consumers and investors.
Big data refers to data sets too large and complex to be stored, processed and analyzed by conventional software. It’s commonly defined by the “three V’s”:
These massive, diverse, fast data sets require cutting-edge analysis techniques like machine learning, artificial intelligence, and natural language processing.
Upon successfully applying these techniques, a user can glean rich insights from big data – in a process (perhaps obviously) known as “big data analytics.” To illustrate how it works, consider Amazon. The e-commerce giant practically made its fortune through big data analytics, combing through reams of data on user preferences and buying choices to offer personalized recommendations for each shopper.
As the real estate big data firm Predik puts it, the technology is all about “examining large and complex data sets to uncover hidden patterns, correlations, and other insights.”
On the topic of big data, tech innovator and real estate expert Regan McGee shared that, “The real estate industry is awash in data, yet we as a sector have not really embraced the tools available to leverage that flood of information. But, when the real estate market really started to heat up last year, we as an industry finally started to see the light and embrace the benefits that data analytics have to offer.”
Essentially, big data analytics allows investors and practitioners to pull insights from traditional and non-traditional data sets to paint a clearer picture of value. “Conventional analytical methods and data sources make it challenging to draw clear hypotheses and build robust business cases, ”writes McKinsey. But by roping in “variables related to non-traditional data, such as proximity to highly rated restaurants or changes in the number of nearby apparel stores,” big data “enables more accurate predictions” of value.
Investors can use these vivid insights to make smarter decisions about where to put their money, thereby mitigating risk in a traditionally high-risk endeavor. Meanwhile, consumers can use big data similarly, to glean insights on a neighbourhood’s ten-year potential, or to determine what a fair offer might be on a home.
The CFMOTO UForce 1000 is a remarkable utility vehicle, designed to handle a variety of…
A reliable way to keep food and drinks fresh is a must for road trips,…
Data handling, protection, and analysis expertise are crucial for accurate data leveraging in the dynamic…
A stable and secure family environment is crucial for a child’s healthy development and well-being.…
Your trade show booth display acts as your storefront for the entire event. Therefore, it…
Introduction People are interested in lab made diamonds, because they look good and last for…