As real estate professionals, we’ve heard about 3-D tours and other technologies that are changing the way real estate is done these days.

One area flying under the radar that is likely to have a huge impact on the industry is predictive analytics.

Actually, a field known as predictive analysis contends that researchers can map out a sketch of what’s in the works. Predictive analysis uses sophisticated computer-based tools, such as data mining, to plot a path down the road.

The idea of using data in real estate isn’t new. For years, the industry has looked to tax records, comps, valuations or even local school, business and crime statistics for information.

As consumers demand more and more information, and more data being available on consumer behavior than ever before, the use of predictive analytics — being able to accurately show buyers and sellers what their home will be worth in the future, backed up by data science — has the potential to become a game-changer for agents.

According to a recent survey by Bellvue, Wash,-based Imprev, 65 percent of top real estate executives say they’re more likely to invest in predictive analytics and marketing automation, which involves accessing ad pitches in seconds; and 64 percent would put money into big data – tracking trends through reams of quickly scanned information.

Figuring things out going forward seems to excite top real estate executives, much more than the present “hot” technologies.
Predictive analytics will be the top emerging technology for real estate brokerages by 2022, according to three in four executive.

Big data will be big, but the need for integrated systems is cooling among what real estate companies use now.

According to Imprev CEO Renwick Congdon, real estate leaders are pragmatic when it comes to their view of the future.

“They clearly anticipate investing only in tech that can provide a hard ROI (return on investment), which means avoiding the latest fads,” he noted. “They’re under more pressure to deliver results and need proven marketing infrastructure to make that happen.”

Jeremy Sicklick, co-founder and CEO of HouseCanarym said one of the key opportunities predictive analytics provides in real estate is having the ability to know the property value of a home, and knowing where the value is going to go, to help consumers make better home-buying and -selling decisions.

HouseCanary’s home value reports also allow users to add or remove properties or property details to instantly adjust a home’s comparable value, for example, or add a bathroom or remodel the kitchen and see how that affects the home’s value.

“Being able to quantify and visualize it is where big data is going,” he said. “Being able to show people and arm people with the ‘why’ it’s occurring and explain it to someone who is trying to make a decision — and putting brokers and agents at the center of those decisions to help [buyers and sellers] see their options—that’s the opportunity.”

According to Mark Choey, co-founder and CTO of the innovative boutique firm Climb Real Estate in San Francisco, the importance to brokerages of collecting data on buyers and sellers is key today many tools available now to help the effort.

“I wouldn’t recommend hiring a data scientist,” he said. “There are a lot of tools out there to help you get started.”

John Murray, managing broker and president of Rockford, Ill.-based Key Realty, supports the idea of brokerages utilizing data tools available, rather than hiring a data scientist.

“Companies providing predictive analytics are emerging and some are better than others. If you don’t have the expertise, you can work with these companies,” he said. “Some bigger shops are hiring data scientists, but the majority of brokerages should focus on how to use the data and work with these companies to improve their data — then everyone wins.”

For agents, Murray noted that the key is how you use that data to find out what’s important to your clients.

In an industry where valuations have been called into question, Murray pointed out that it is vital that data be accurate to ensure the industry can properly interpret it for clients.

“Data is readily available and we have to be better at interpreting what it means,” he concluded “If we stick to being brokers and focus on customer service and restore our credibility with knowledge, it will help us recapture some of the credibility we’ve lost.”

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