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Big data is everywhere these days. Finance companies leverage big data to determine creditworthiness with startling accuracy; logistics companies utilize it to spot minute inefficiencies in their supply chain; even major sports teams use it to stack their roster with unconventional draft picks.

But for whatever reason, the real estate industry dragged its heels. “Real estate has traditionally been a late adopter in tools and technologies,” says Deloitte’s John D’Angelo. In the past, the industry preferred its mixture of manual processes and conventional, conservative data.

Realtors searched for properties based on the MLS’ limited data sets like square footage, demographic growth and date built. And by extension, homebuyers had to settle for a limited view of their options.

Thankfully, the industry is starting to realize big data’s big potential. New real estate tech innovators are applying AI, ML and predictive analytics to extract valuable non-traditional data. And they’re passing the benefits onto homebuyers.

Here are a few ways homebuyers can use big data to make an educated purchase.

Finding the Right Real Estate Agent

The first step in many homebuyers’ journeys is to secure a good real estate agent. But in the past, this search was fraught with incomplete information and purposeful opacity. Real estate agencies could obscure their sales histories, hide their commission rates and curate their online reviews, leaving consumers little recourse for properly vetting their real estate agents.

Then Nobul came along. The world’s first “real estate digital marketplace,” Nobul uses artificial intelligence to sift through reams of real estate agent data (sales histories, verified reviews, rates, languages spoken, etc.), allowing consumers to find agents aligned with their values and criteria. “We’re finally giving consumers power in this industry,” says Nobul CEO Regan McGee. “We’re helping bring more transparency, credibility, and accountability to the single biggest transaction of people’s lives.”

Getting Granular with Home Hunting

In the past, consumers could access only what their realtors could find through the local MLS. As mentioned, this conventional data was incomplete at best, and misleading at worst.

Now, buyers can get as granular as they like with their home hunting. They can tell the relative crime rates of each nearby intersection to a property. They can discern the tone of Yelp reviews for surrounding restaurants. They can determine the inflow of new families to a neighborhood relative to the inflow of retirees. As startup tech companies apply big data to proprietary listings services, the sky’s the limit for home hunter information.

Real-Time Market Reports

Real estate markets move at lightning speed, especially in hot, high-density areas. Before big data, consumers had to rely on their real estate agents to divine whether now was the right time to buy. (Too often, agents pushed buyers into sellers’ markets for a quick turnover).

Now, consumers can access real-time market conditions indicators. These indicators rope in conventional and non-conventional data to pinpoint the state of a market – i.e., whether it’s a buyer’s, seller’s or balanced market. Consumers can cross-reference these conditions against other data-driven resources, like average home price trends, to determine whether it’s the right time to buy.

Homebuyers can use big data throughout their real estate journey – from finding the perfect realtor to snagging their dream home. And best of all, these platforms tend to be free for consumers.