Few industries have seen the enormous amount of consumer data real estate professionals have at their fingertips. Every day, potential homebuyers and renters leave behind a plethora of online breadcrumbs that signal their intended next moves. The only challenge is, it’s incredibly difficult to gather all these breadcrumbs and even harder to trace a trail from them. Increasingly, there is more and more frustrated with the disconnect between the availability of data and the difficulty of harnessing it for quick, actionable insights. Real estate analysts are noticing that it is not raw data which creates value, but the ability to extract patterns and forecast predictions to design new business strategies.
Capacity of non-traditional data
The reason for this problem is simple: conventional analytical methods and data sources are too slow, manually intensive, exhausting, and expensive. Using traditional methods makes it challenging to draw clear hypotheses and build robust business cases that drive bottom-line impact. Consider that analysts must still sift through tens of millions of records or data points to discern clear patterns and place their bets with few supporting tools to help glean insights from the information gathered. By the time an investor can collect, compile, clean, and process the data needed to distill actionable takeaways, the best opportunities are gone – taken by faster competitors in the open market.
The rise of advanced technology
Due to this major roadblock, professionals in the real estate industry are increasingly looking towards advanced data science systems to drive insight and help stakeholders use data to drive forward-looking decisions. Machine learning algorithms, for example, make it significantly easier to aggregate and interpret disparate sources of data of various types, instead of forcing analysts to clean data extensively and then loading it into traditional statistical computations. Technology services, platforms, and solutions also exist for automating the data collection process by offering APIs and connecting various cleaned databases for analysts to load into machine learning systems.