The real estate industry is awash in data – transactional data, agent sales histories, housing market data, rental market data, mortgage and debt statistics, and non-traditional variables that influence real estate values, to name just a few sources.
At one time, the industry didn’t know what to do with its wealth of data. Traditional data-processing software was incapable of making sense of it all. And human practitioners, try as they might, couldn’t keep up with the deluge of new data incoming daily.
But recently, machine learning, AI algorithms and predictive analytics have unlocked the potential of these diverse data streams. Many industry spectators now believe that data will play a pivotal role in the industry’s future, aiding consumers, developers and investors.
Here’s what the future of data in real estate will look like.
Big Data Risk Assessment and Mitigation in CRE Development
Investors and developers have much to be thankful for with these recent advancements in data processing. According to McKinsey, developers and investors have started leveraging big data to pinpoint areas ripe for development – with startling accuracy.
Big data analytics pulls from traditional data like “concentration of schools in the neighborhood” and “average income levels in an area.” But it also ropes in non-traditional variables like “tone of Yelp reviews for nearby businesses” and “building energy consumption relative to other structures in the same zip code.” Together, these diverse data sets paint a clear picture for developers and investors of where the risks lie – and where golden opportunities exist.
Data-Driven Consumer Empowerment in Digital Marketplaces
We’re already seeing data put to work in helping consumers find the right real estate agent. The innovative digital marketplace Nobul uses a proprietary AI algorithm to comb through reams of data on real estate agents to recommend agents to consumers. The company picks through verified reviews, location, sales histories, preferred language, commission fees and more to develop an accurate profile on agents that consumers can use in their search for representation.
The enterprise demonstrates data’s potential in empowering real estate consumers. “Shopping for the right home is not one-size-fits-all, it is personal,” Nobul’s CEO Regan McGee said. In his interview with Superb Crew, he explains that “We aim to facilitate homebuyers’ ability to choose the agent best suited to their needs, and support them end-to-end throughout their real estate journey.”
Data’s Role in Fraud Detection and Scoring
For a long time, real estate fraud has been an unfortunate but largely unavoidable scourge in the industry. Recently, however, some innovative tech companies and fraud detection agencies are realizing that data analytics might be the key to eradicating it.
When fraudsters falsify materials like documents or signatures, they leave behind traces of their actions. While these traces are largely imperceptible to human detectors, AI algorithms can spot the data deviations instantaneously. And as fraudsters evolve, fraud detection companies can leverage machine learning and predictive analytics to evolve alongside – or even ahead of – malicious actors.
The three examples above represent the tip of the iceberg. Elsewhere in the industry, CRE property managers are using data to drive energy efficiency, mortgage companies are leveraging AI models for quick approvals, and appraisers are capitalizing on big data to create automated valuation models. The future of data in real estate looks bright – and it’s already underway.
