There are few superlative statements that most business experts and analysts can agree on. But here's one: Artificial intelligence is the most impactful technological advancement of the short century so far. You can feel its reach across several industries, practices, methodologies and facets of daily life.
And nowhere is AI's presence more acutely felt than in the real estate industry. A decade ago, the real estate industry was a mish-mash of manual processes and analog tools, unaffected by growing consumer cynicism. Now, real estate is being transformed into a lean, mean, consumer-centric outfit that puts its data to excellent use.
Here are five use cases illustrating how AI is transforming the real estate market.
Matchmaking Algorithms Handing Negotiating Power Back to Consumers
The most exciting use case for AI in the real estate industry is Nobul, an open digital marketplace leveraging an AI-powered algorithm to "matchmake" consumers with real estate agents.
But Nobul isn't the average lead generation platform aimed at aiding agents. It flips the script, placing power in consumers' hands. CEO Regan McGee spoke to Bloomberg: "We're building a full ecosystem - end-to-end real estate, and consumer-centric. Buyers and sellers never pay us anything, and they never see an ad." Instead, consumers can utilize Nobul's proprietary AI algorithm to see transparent information about relevant agents, including transaction histories, pricing, language, location and review summaries. It's a compelling case of how real estate data can benefit consumers - not just practitioners.
"Similar Listings" and the Rise of Recommendation Relevance
It's hard to imagine a time before AI-powered recommendations. The practice is pervasive in eCommerce (think of the "you might also like" tabs on a shopping website). But it's also reshaping how consumers hunt for homes.
Essentially, listings sites track user data like clicks, views and engagement to build out profiles that an AI algorithm can reference when combing through MLS metadata. Provided the site sources its user data ethically and responsibly, the process can be a boon to consumers, who can discover properties they otherwise might have missed.
Accurate, Streamlined Property Valuation and Analysis
Accurate, efficient property valuation was never a strong suit for real estate practitioners prior to the tech era. If you wanted to be accurate, you needed to allot too much time; if you wanted to be efficient, you needed to sacrifice accuracy.
AI lets analysts and agents have it both ways. Companies like Veros leverage AI and predictive analytics to offer accurate valuations and risk analyses for investors. Meanwhile, several companies (including Realtor.com) use AI to execute advanced property analysis for consumers.
Predictive Maintenance in the CRE Landscape
Mostly, we wanted to limit this article's research to residential real estate (what most readers know and care about). But the final use case to note is in commercial real estate (CRE), though it has implications for residential renters: Predictive maintenance.
AI-driven predictive maintenance is an emerging concept for many property managers. According to Entrepreneur, "By analyzing sensor data from properties, AI could estimate exactly when care might be required and even go ahead and schedule the work itself." This proactive approach to residential maintenance prevents disruptive equipment failure and reduces service downtime for renters, creating a more convenient tenant experience.
As AI matures - and as the real estate industry warms up to embracing technology wholesale - you can expect to see even more compelling use cases for artificial intelligence in real estate. For now, the four sections above represent the most significant ways AI is transforming how we buy, sell and think about real estate.
