CRE Innovator Interview: Ilan Zachar, Carr Properties
We sat down with Carr Properties‘s SVP and Chief Technology Officer for a candid conversation about commercial real estate, shifting priorities, and – yes – COVID.
How do you see the role of big data in the CRE world?
The role of big data is critical today in order to manage and operate correctly. But one thing we really need to understand very well is that it’s not just how the data is structured, and the accessibility, but also the maturity of the actual company. In a leasing company like ours, we have a single source of truth. Companies who don’t have that – have a problem reporting, when they look at big data they look at a specific problem, not strategy. Which means they are looking at data as a means to clear a specific mess rather than a way of life.
Is it the Big Data project? Is it an innovation driver? Or is it a problem solver? Is it a solution to a problem that you have in the company? Or is it part of the strategy of the company to look forward? These are crucial questions.
How ready is CRE for AI – has it changed since COVID-19 and how?
Let’s break this into a couple of sections. So first of all, Real Estate is one of the most complicated industries to generate and create reports and visualize data. The reason being that real estate data is not sliced and diced very easily, like any other industry because of the nature of deals and renewals. So when you try to normalize the data together in the platform, it’s very, very difficult. Attaching AI on top of that can be challenging. Most of the people who work within the Real Estate market at a high level are from a different generation, they’re not the younger generation that relies on technology. Real estate data has a history of being allowed to be very flexibleThere’s a lot of complexity and decision making around leases renewals and it can be challenging for a machine to learn that. That’s why AI is so difficult to adapt to the CRM market.
But the analytics side of AI is very different. When you look at your customer risk, for example, you take the blender of the block of information, and then you assess the risk. Your AI engine almost does a human interpretation at this point. Because you cannot interpret it; Even if you looked at the numbers, and you added them together, that wouldn’t add up correctly. So that’s why I think it really stands out. And I think that our first project, which was the sentiment analysis, was looking at data in a very, very, very different way, in a way that people haven’t looked at it before. Because people wouldn’t connect the dots. Data was in silos – whether it’s the sustainability or wellness package etc. I used to be a Medic in the Israeli Navy, and I relate to our projects almost like blood pressure: you have a systolic and you have a diastolic, the lower number and the higher number can both be green. The difference between them can be bad, and now it’s red. So system A can be green, and B can be green. But when you put them both together, you look at the data from both of them. Now you realize that there is a problem. So the same thing, I think that only through an AI engine, can we bring all of this together and back to the big data, analyze a vast amount of information, and be able to come back with a solution like this. The company has to do a mind shift, and has to agree to understand and to agree to this information. Because they’re not used to looking at the information this way.
How ready are people in CRE to trust in AI when it comes to different use cases? Do you think there are specific areas in commercial real estate where people are more open to that, or applications that are like that?
I think people are ready for it, because they’re realizing that they have sheets and sheets and sheets of Excel, that they try to bring them together, and they cannot bring them together. But we need to allow them to also have their opinion in there. Because it’s not a summing of all of the numbers. But I like how [Okapi’s CTO] Maya once once explained is that the problem is that they get too stuck to their original ways of doing things. So I think that they have to go through the mind shift . “Let’s look at that, because maybe there’s something that we haven’t seen.” Having some way of intervention in teaching the machine, what is okay, and how to adjust it and tweak it is important. I think that human intervention at this point would become more of a tweak, so we’re able to point out and adjust anomalies – whether it’s an extra risky customer, or someone we know pays above market rent etc.
I see these cases of using AI more as a utility or even an amenity, that add value within the company. I heard a very interesting statement the other day that said that technology is the remedy and not a vitamin. We are not going to implement technology just for the sake of doing it, but as a response to a need or a cure to a problem.
In one way, we really leveraged Big Data during the pandemic. And I think that the people that sat on the sidelines are now believers. And when you get into big data projects, it’s a black hole, you can just go deeper and deeper and deeper. But in this scenario, what is happening is that it actually opened the eyes for people because it shows value that we can actually use. It’s not, oh, it’s great to know that there’s 1000 people walking into the building. Oh, and tomorrow, there are only 900 people that walked into the building. That’s wonderful. But now when you take the data and you say, look, only 30 people walked into the building, and now I can actually not send cleaners to specific locations, and I negotiated with the cleaners. Now my data is becoming valuable. And people couldn’t understand it at the beginning until we brought that to the table.
As a tech visionary in the real estate world, what should CIOs, CTOs and startup startups know about the collaboration between the innovation and the rest of the company? And about innovation readiness?
The operations and leasing sides often operate in silos. But it helps to have have somebody from the technology side that understands, that is not afraid of the technology, that says: Look, I will take all the complexity of the technology away, you need to look at this as a utility that you can then use to make your life easier and eliminate much of the grunt work. Again, that comes back to maturity – understanding that implementing AI isn’t an IT project, it’s not an operations project – It’s a business project. That’s why it’s very important to have somebody behind the different stakeholders who can say, “Let me simplify it for you, this is what we’re going to do, this is how we’re going to do it, we’re gonna have to set it up for you. And this is how it works, and this is what you’re going to be able to do with it afterwards.”
Has the CTO role changed since and throughout the pandemic?
As a CTO or CIO, if all you did is ERP disaster recovery, IT governance etc, then you couldn’t help during the pandemic, you couldn’t get into the touchless systems, you couldn’t learn how to provide better and better and wellness. What happens is that operations people went out into the shadow IT world to get that somewhere else because it was not there to support them, their IT department was not there to support them.So the pandemic either leveraged the CIO and now the company understands how important this role is, and the person internally that was willing to step into that world if they weren’t there before. And if they didn’t, I think companies are going to make the change that they need. They’re going to either use the existing CIO or CTO where they are, and then add another body to address the operational side of it, or get rid of the existing and bring somebody that can do both.
What is exciting to you in the world of CRE and technology right now, or looking a little bit in the future?
What’s exciting to me right now is all new, and specifically because of COVID, all of the sensory data, and information that I’m going to be able to collect and know from the technology side and delivering it to the people side. I’m looking forward to being able to distinguish ourselves based on knowing how to combine the two of these together. Having AI that is learning what is normal, that is learning how the building should operate, and so long as you give it its variables, it can actually come back and tell me how to better operate – that’s going to be an exciting added value.
Thank you so much, Ilan, for this conversation. We highly recommend following Ilan’s speaking engagements for a first-row view of the most interesting CRE innovation insights.