Technology has dramatically reshaped the way that consumers and agents alike experience the process of buying or selling a home. The internet, cell phones, mobile devices and digital photo and video tools have played a prominent role in this revolutionary shift.

Now the tech landscape is experiencing another transition as big data takes on a more significant role in how agents approach the housing market in their area. Many agents and brokers are already analyzing big data to determine homebuyer patterns and behaviors using a host of applications often categorized as predictive analytics.

The real estate industry is prepared to embrace predictive analytics. A 2017 survey from Imprev Thought Leadership on what real estate tools will look like in 2022 revealed that two-thirds of real estate executives surveyed prefer predictive analytics, big data and marketing automation as potential investments over augmented or virtual reality and artificial intelligence applications. Predictive analytics was rated the best technology for real estate brokerages by 74 percent of executives surveyed. They based their conclusions on the technology’s ability to perform analytical tasks on targeted markets.

Putting data to work

Leo Pareja, founder and CEO of Remine, started the predictive analytics company after creating software for his own real estate business to make it easier to access the data available to agents.

“Predictive analytics is one subset of the byproduct of being able to harness big data,” Pareja said. “What that means to me is taking lots of different things and making it easy to gain insight from them. Instead of having to go to my MLS system and then a public record system and or my county website, I can actually see it all compiled in one place.”

Agents can use big data to extract a broad range of useful information. Big data and traditional analysis tools can help determine when a neighborhood started getting hot and point to factors that might have driven the shift, such as improved amenities, new businesses or an influx of homebuyers belonging to a particular demographic. In this way, past data gives insights into what homebuyers might be looking for and the types of areas that are likely to attract their attention.

“You can look at historical trends and see which neighborhoods depreciate, and the rate of appreciation, based on the proximity to city, based on the proximity to transportation and various universities,” says Ryan Wilson, owner and Realtor at Wilson Group Real Estate, describing the predictive data tools he and his agents use. “One example there would be Somerville. Ten to 15 years ago it was extremely affordable. Now, it’s one of the higher priced communities surrounding the city. I think it’s harder to predict who’s going to be moving into the neighborhood. It’s easier to predict when the sellers will be leaving the neighborhood based on public record data of the length of time they’ve been in the property and taking into consideration averages for those neighborhoods or cities.”

More from this issue:

• Big data solutions for housing’s most pressing problems
• Survey: Predictive analytics and real estate

Keller Williams is making a big push to provide agents access to data and technology that can give them a streamlined means of approaching their business. The company’s Command platform seeks to provide them with all the tools they need in one place, layering artificial intelligence on top. “That’s where all the AI predictive analytics comes in,” said Keller Williams chief product officer Neil Dholakia. “You first have to have a view of everything that’s going on. Once you have that, you then you add the intelligence.”

Dholakia said many new tech products are designed to capitalize on all the information that’s generated in the typical real estate transaction. “We have a tremendous amount of data exhaust that that our agents produce just in their day-to-day activities,” he said. “That exhaust can fall on the floor and just blow away in the wind. Or if they’re using our tools, we can capture that exhaust and make some meaning out of it for them.”

But Dholakia also noted that in order to make big data products work, they have to be widely adopted by real estate professionals: “And so that’s really our challenge. A gap we need to fill is working with our agents to understand the advantages of using the systems that we’re providing to them so that they generate the data to their benefit.”

Finding the needle in a haystack

Predictive analytics can also help agents target potential clients more accurately. Avi Gupta, president and CEO of SmartZip, helped start the predictive analytics company in 2008 to provide tangible data solutions to agents. SmartZip’s offerings include SmartTargeting, which uses data to identify homeowners who may be considering selling.

“When you’re talking about sellers, they do not find their Realtor on the web,” Gupta said. “They go through different relationships. It’s people they have worked with before. This allows these Realtors to get in front of those sellers before they’ve already made up their minds. Otherwise, if they just wait for sellers to call them, it will never happen. By being proactive and preemptive, they can actually get in front of people that are most likely to sell and be one of the first or second agents to be interviewed when the seller is ready to list their home.”

It’s not as though predictive analytics is something that’s completely new to users. Many people use the technology every day without noticing. They use Google Maps to figure out the best way to get to their destination, or Amazon suggestions to guide their shopping. In real estate, predictive analytics can be used to help an agent be more proactive in prospecting for clients. Finding a way to get in front of people who are looking to buy or sell in the near future is vital when many clients choose the first or second agent they interview.

“It allows a Realtor to understand who is three or four times more likely to sell than others,” Gupta said. “That allows them to focus their marketing, their prospecting and their time and energy on a small subset of people as opposed to mass marketing to everybody. But at the end of the day the agent still has to build relationships with those people.”

Bigger, better analytics applications

By finding novel ways to use data that’s already broadly available, these companies can help agents become more efficient in their outreach efforts and in their marketing budgets. Instead of using a scattered, ad hoc approach, they can hone in on the types of clients who are looking for homes in the areas where those agents typically work.

“We have a tremendous amount of data exhaust that our agents produce just in their day-to-day activities,” Dholakia said. “That exhaust can fall on the floor and just blow away in the wind. Or if they’re using our tools, we can capture that exhaust and make some meaning out of it for them. And so that’s really our challenge. A gap we need to fill is working with our agents to understand the advantages of using the systems that we’re providing to them so that they generate the data to their benefit.”

Wilson appreciates ease of use in tech tools and advocates in favor of learning how to use a few tools well. He makes heavy use of the Brivity CRM platform in his marketing efforts and when deciding what neighborhoods to farm and which homeowners to target as likely sellers.

“When I’m looking at a tech company in general, I look at their past successes, who is currently using the platform and how it can most simply integrate to the current systems and technology we’re currently using,” Wilson said. “After the quick ramp up and getting these programs up and running, if it does not make things easier and simplify your day-to-day business, in my opinion it is not a useful tool. With so many different technology options out there, the tough thing is not choosing every shiny object.”

Agents can drill deeply into data to uncover patterns of consumer behavior that can be helpful to them in their business. Analytics tools used by social media companies such as Facebook are already sophisticated enough to send users targeted ads based on their likes, their posts and their browsing habits while they’re connected. Likewise, Amazon turns searches into opportunities to put products in front of online shoppers.

That doesn’t mean real estate agents are in danger of being replaced by AI systems and predictive analytics algorithms, though.

“I think the chances that a home seller will basically choose an agent they’ve never met are pretty slim,” Gupta said. “It’s a way to hone in on the right people, but it’s not the end all because the agent still have to go build that relationship through whatever means they choose.”

Pareja agrees that developing relationships and having those face-to-face interactions will remain the most significant means of agents finding new clients.

“I don’t think technology will ever replace the real estate agent, at least in my lifespan,” Pareja said. “I do think that the agents who adopt tools and technology will 100 percent replace agents that don’t. They’re just a tool. Just because you purchase one of these tools or your MLS buys it for you or whatever, you still have to work. You still have to either make the phone calls, go to the meetings, do the mailers and all the activities that are required of your profession. We’re just trying to give you an edge.”