People often throw around terms like artificial intelligence (AI), machine learning (ML) and predictive analytics interchangeably in conversations about new real estate tech, but they don’t always really know what those terms mean.

  • Artificial intelligence (AI) is the broad umbrella term for intelligent systems driven by algorithms .
  • Machine learning, a fundamental branch of AI, enables machines to learn on their own.
  • Predictive analytics use past data to predict or estimate a future phenomenon.

People often throw around terms like artificial intelligence (AI), machine learning (ML), and predictive analytics interchangeably in conversations about new real estate tech.

We constantly hear things about:

  • AI-powered “bots” that serve as an agent’s personal assistant
  • Products that “learn” how to respond to leads
  • Predictive models used to identify who’s going to sell and when

These technologies hold enormous possibilities, which is why I think it’s important to understand what they can do and what they mean for the business of real estate. Agents and brokers will become more efficient and productive because of them.

Despite all the buzz, there’s a lot of confusion about what these things actually are, how they relate to one another and how they can be put to work.

You may have wanted to ask, “well, what is that?” or “are those really the same thing?” Even a quick Google search may leave you more confused than when you started.

I, therefore, present you with a simple “cheat sheet” that gives you the basics in clear, nontechnical language:

Artificial intelligence (AI):

AI is the broad umbrella term for intelligent systems driven by algorithms — software that can perceive inputs, learn from interactions and optimize to specific outcomes.

What it is

The frontier of machines doing things we once thought only humans could.

Best poker player in the world? It’s a computer.

Diagnosing lung cancer? Computers are more accurate.

Recognizing faces? Yep, Facebook’s algorithms beat human performance now, too.

Driving? We’ll see soon enough!

What it isn’t

The terminator.

General purpose intelligence that will replace you (or even your inside sales agent) won’t be here anytime soon. The current state of chatbots is a good example of the limitations of these layers of “intelligence.”

Many AI companies have humans running in the background until they build up a large enough dataset to accurately handle more use cases.

It’s easy for AI to look sloppy.

Machine learning (ML)

Machine learning (ML) is one fundamental branch of AI : It involves the core algorithms and statistical methodologies that enable machines to learn on their own without being taught by humans.

ML is essentially statistics on steroids.

What it is

Complicated. Seriously.

The junior data scientists on our team have Ph.D.s in experimental particle physics and can code to boot.

Decades of statistical and computer science advancements plus massive compute power and reams of data equals enormous business opportunities for products that could not have previously existed.

It’s why data scientist was the no. 1 hottest job title in the U.S. in 2016.

What it isn’t


ML requires massive data and incredibly talented oversight. Designing data science experiments that train machines to interactively improve performance on key business problems requires a huge amount of human input and effort; however, the results can be pretty darn magical.

Predictive analytics

This is any solution that uses past data to predict or estimate a future phenomenon. It can be as simple as an if-then rule or regression analysis.

It can also leverage the most complex ML techniques.

What it is

A primary objective of business analytics across the globe.

Predictions empower businesses to optimize marketing efforts, forecast success rates of new products, set prices and more.

You know how Netflix suggests movies you might like, Amazon knows what you might want to purchase next and Zillow prices homes? Whatever the current accuracy, those are some examples of predictive analytics.

Predictive analytics can also be remarkably simple  — if you’ve ever built a forecast with the goal of projecting future sales, you were performing predictive analytics.

What it isn’t


In some cases, new access to broad data and powerful tools enables significant increases in predictive power. The challenge is that people can slap “predictive analytics” on their website for just about anything  without actually providing meaningful value.

What the hype means for real estate

Full disclosure: I run a venture-backed real estate company with a product powered by ML-driven AI, so I care deeply about helping agents cut through the hype of data science jargon.

My hope is that we can go beyond the techniques  —  AI, ML or what have you — and focus on the results.

I believe the most exciting opportunities for data science in real estate involve supercharging what agents are already doing   without ripping out the heart and soul out of what makes real estate great.

Forget RoboCop; think Iron Man.

Did you know pilots actually fly commercial planes for an average of seven minutes? They can thank AI for that.

Has the pilot’s job gotten a lot easier? Yes.

Are pilots still essential? Absolutely.

I’m excited to see data scientists and engineers flooding into real estate to pilot this business into the future.

Mike Schneider is co-founder and CEO of First. Connect with Mike on LinkedIn.

Email Mike Schneider.

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