These are the high holy days of the commercial real estate brokerage community. So said a speaker at a CRE event two years ago that was attended by Blaine Strickland, principal of HBS Resources. The comments struck a chord because the speaker went on to say that it is unclear how long this dominance will last.

Ever since then Strickland has been thinking about what could disrupt the CRE brokerage model and has come to a few answers. Pricing transparency is one. The growing use of artificial intelligence is another.

He points to travel agents and their heyday more than two decades ago. One reason for their demise was that pricing information became more readily available to consumers and technology introduced new ways for consumers to buy tickets.

“There is a good case for comparison between yesterday’s travel agents and today’s CRE brokers,” Strickland tells GlobeSt.com. Both involve the client gaining access to valuable price information and overall transparency into the system.”

But Strickland is not referring to the Zillows or CoStars of the world, at least not entirely. Valuable price information also entails an overlay of AI, necessary to accommodate the complexity of CRE. There are, for example, algorithms that can price a residential home once the square footage is plugged in, but that doesn’t work for CRE as there are too many differences among the buildings, Strickland says.

That said, there are firms that are trying to create algorithms for some types of CRE, such as single-tenant net lease properties. “A Wells Fargo building in Orlando, Fl., can compare to a similar building in Phoenix. There is a lot of commonality and an algorithm can predict what the sales price will be,” Stickland says.

There are four to five vendors that are working on this, some of which have received investment from the big brokers. These firms include Bowery Valuation, Skyline AI, GeoPhy and GroceryAnchored.com.

Skyline AI, as one example, recently helped an investor select an apartment building in Atlanta to buy, by processing data from review sites with natural language processing. Online reviews of the asset were flagged by the system as indicating an opportunity for optimization.

Groceryanchored.com, as another example, has mapped out grocery anchor center sales and attributed the seasonality—or time to get the best price—to that sale. For instance, Strickland says, the system could tell you when a center’s financing expired and that the lenders have to get the last $40 million out if they want their bonuses. Thus, the property’s seasonality is at the end of the year.

Not that long ago, Strickland adds, the investor would ask the broker about any grocery-anchored properties that were coming to market.

Or possibly a broker would contact a building owner to offer a broker opinion of value about, say, an apartment complex. The building owner agrees, giving the broker the necessary information to complete the analysis. The broker returns with a BOV that values the building at $2.7 million and the owner agrees to sell the building if the broker can get that price. Thus, a sale is born.

“The broker used that information as a way to create a relationship,” Strickland says. “But think about what happens if a seller and buyer already have that information.” It’s happening in the residential world already, he says, and will soon be a part of CRE.

The post How AI Could Compete With CRE Brokerages One Day appeared first on Artificial Intelligence.

The evolution took over the world so fast that now when we look back and analyze we observe that a lot has changed over a decade when it comes to computers and technology. Who would have thought that one day AI could diagnose medical conditions, translate languages and transcribe speeches? Artificial Intelligence or AI can surely outshine humans at complicated strategy games, create photo-realistic images and suggest useful replies for ones emails. AI can surpass humans in almost all aspect yet it cannot replace humans entirely.

Machine-learning systems can be duped or confounded by situations they haven’t seen before. A self-driving car gets flummoxed by a scenario that a human driver could handle easily. An Artificial Intelligence system laboriously trained to carry out one task (identifying cats, say) has to be taught all over again to do something else (identifying dogs). In the process, it’s liable to lose some of the expertise it had in the original task. Computer scientists call this problem “catastrophic forgetting.”

Such shortcomings are the reason why AI cannot replace humans yet. Let’s understand further what Artificial Intelligence lacks when compared to human intelligence.

One of the major issues is this space is Narrow AI which doesn’t fit for all. Businesses currently need to rely on individual solutions to perform certain AI-centric tasks. Trying to use a range of different tools to carry out a range of artificially intelligent tasks can become expensive, time-consuming and messy. Artificial Intelligence in any form is artificial and unnatural which requires regular supervision. Businesses are in still very much need to plan, design and run the marketing campaign.

So technically humans are the ones feeding the AI system with all the new information required for them to learn in the first place. This form of ‘supervised learning’ does not mimic the way a human learns naturally and experts believe this is one of the biggest obstacles when it comes to creating a more human-like AI. Artificial Intelligence, after all, is merely a program and it is can only do what it is programmed to do. However, it normally does this extremely well, but unlike a real person, it is unable to make split-second judgments. For example, any good marketer will change or cancel any scheduled messaging when a tragic event occurs. This is because they are capable of showing empathy and compassion to the victims and their families as humans. As a machine, however, Artificial Intelligence does not have any ability to show emotion. Even if the market is buzzing with Emotional AI, yet the technology is far from human emotion.

As noted by a report, like any form of new technology, there can be a significant cost of purchase and a need for on-going maintenance and repair. The AI software will also require regular upgrades in order to adapt to the continually changing business environment. The return on investment needs to be carefully considered by the company before it goes ahead and implements any AI system.

Lastly and more importantly, AI lacks creativity. Machines simply lack the ability to be creative. Unlike machines, humans can think and feel, which often guides their decision making when it comes to being creative. Yes, AI can definitely assist in terms of helping to determine what sort of imagery, for example, a consumer is likely to click on – from color preferences to style and price. But when it comes to originality and creative thinking, a machine simply cannot compete with the human brain. We still need the collaboration of both humans and machines.

The post AFTER YEARS OF ADVANCEMENT, WHAT ARE THE THINGS AI CANNOT DO YET? appeared first on Artificial Intelligence.