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Presented By: Building Engines

Why Companies Need Standardized Technology Portfolio-Wide To Adequately Capture Data

By Building Engines September 25, 2023 4:27 pm
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Building Engines, a JLL company, brought its expertise to Commercial Observer’s readership during a custom event they presented on Sept. 20. Hosted by Commercial Observer Partner Insights, “Optimizing Data: Ensuring That Data Collection Leads to Optimizing Building Efficiency and NOI,” featured Daniel Russo, president of property management technology at JLL Technologies, and Josh Panknin, director of real estate AI research and innovation, Columbia Engineering — IEOR at Columbia University. Commercial Observer writer Larry Getlen moderated the panel.

Panknin noted that while many companies are clamoring for AI — and many have dipped their toes into the water with ChatGPT — few possess an understanding of how to use this technology in a way that provides real value.

SEE ALSO: Speaker Spotlight: Steven Song of Diald AI

“We see most real estate companies saying, ‘We want AI.’ But AI, machine learning and deep learning all run on vast amounts of data, and if that data doesn’t exist within the organization, then you’re not going to have AI,” said Panknin.

While data collection is an aspect of virtually every CRE enterprise, Panknin made the point that the way most companies collect data serves only the most basic methods of analysis. The ability to take advantage of AI, machine learning and more high-level methods of analysis will require a significant advancement in data collection methods.

“Real estate is extremely archaic in its use of data and technology,” said Panknin. “Most of the third-party providers that provide data to real estate do so in a way that allows humans to analyze it quickly, but humans and machines analyze data very differently. Most of the data we have is not conducive to using for higher-level analytics. There’s still tons of room for improvement in collecting good data, automating processes, creating efficiency, and then using that efficiency as a foundation to create strategic capabilities in the future.”

By way of example, Panknin mentioned the difficulties office owners have in automating valuation.

“In office properties, the financial aspect comes largely from leases,” said Panknin. “We have information available like the size of the tenant, the date they signed, maybe their lease rate per square foot. But we don’t have things like, what were the TIs. The amount of TIs paid upfront could significantly affect the price per square foot. We don’t have early termination clauses, reimbursements, the credit rating of the tenant, if they’re non-public. All that information is hugely relevant to understanding the value of a lease and, therefore, the value of a property.”

Russo added that because of considerations such as these, it’s essential that a company’s tech stack be assembled in a way that can successfully process and analyze portfolio-wide data.

“If you run a portfolio of a hundred buildings and you let each property team use a different system, and then you look for a sustainability system, now you’ve got this massive problem of disparate data and it just makes the entire process impossible,” said Russo.

Russo believes that for portfolio-wide analysis, having to compile and format data from hundreds of different sources can be a massive nightmare, taking up many months and requiring hundreds of people hours. Standardization, Russo said, is essential.

“The No. 1 thing we see operators failing at is that they don’t see the bigger picture,” said Russo. “They run each building as its own profit and loss, its own business, and they let individual buildings make their own technology choices. They don’t get the efficiencies in purchasing. They don’t get the efficiencies of running standard processes across their buildings, and comparing those buildings to each other. They won’t ever get the efficiencies of portfolio data if they do that.”

Panknin believes that for maximum efficiency, companies should develop their own systems in-house.

“Developing the ability to build your own tools internally is going to pay off in the long run,” said Panknin. “One of the problems is the heterogeneity of real estate. Every building is different, every tenant is different. Most technology that’s been successfully applied has been applied to industries or functions that are very standardized, like e-commerce or manufacturing, where everything is supposed to look the same. Real estate doesn’t have that.”

Panknin adds that a company using third-party providers will probably have the same technological capabilities as their competitors, which deprives them of a strong competitive advantage.

“If all you’re doing is investing in third-party providers the same as all your competitors, you’re never going to be able to build a competitive advantage in the core strategy of what you do,” Panknin said. “If you’re a multifamily investment company, then understanding what markets are going to achieve higher growth relative to other markets and being able to rank those will give you a competitive advantage. If you can identify those when your competitors can’t, then you can go into those markets, make those acquisitions, get higher yields, and be more attractive to investors and lenders.”

“There are four main operations in a building that you want to have technology for,” said Russo. “The accounting system, the leasing system, your operations platform, and then other things like tenant experience. Once you’ve got those standard things in place, then you can do all the things Josh is talking about.”

Panknin’s feelings on this point are so strong that he believes they could be existential for real estate companies.

“We’ve seen in industries like advertising and retail that were very similar to real estate, very heterogeneous and fractured, where lots of small companies have been destroyed because somebody else came along and centralized information,” Panknin said. “They took a much more systematic and holistic approach to understanding those industries, and they got rid of a lot of companies. So probably not today or tomorrow, but over the next 10 to 20 years this is going to become an existential part of your business. You either have the capabilities, or you’re probably going to go away.”

artificial intelligence, Machine learning, Building Engines, JLL
 
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