How much is your apartment building worth? Lucro uses AI to find out

by Andreas Rekdal
March 20, 2018
lucro chicago tech company
image via lucro

In real estate, every deal is a creature of its own.

The value of two seemingly similar buildings can fluctuate based on occupancy, maintenance upkeep and financial history, as well as the cost of funding the deal. To protect themselves against making less-than-savvy investments, real estate companies hire consultants to crunch the numbers for each deal by hand.

Lucro, a Chicago real estate technology company founded by one of those consultants, wants them to rely on artificial intelligence instead.

“I helped my clients deploy over $2 billion in debt and equity across 10 different countries, all working in Excel,” said founder and CEO Brian Axline. “The technologist in me felt like that was a crazy system. I spent half my time fixing errors caused by others while collaborating. The other half, I spent defending the models we had built. It slowed down every single deal.”

After researching more efficient tools to use for his job and realizing there were none, Axline, who has a background in mathematics and finance, decided to teach himself to code and build a prototype on his own.

Lucro uses machine learning to process and analyze a building’s financials and operating history, in turn using that information to generate financial models. Its algorithms also double check financials for inconsistencies, miscategorizations and anything else out of the ordinary — for instance, if an accounting error or major one-time expense might have skewed the numbers.

From there, users can share models with potential partners, who can make tweaks based on factors like financing, deal structure and improvements that can make a building more attractive to renters.

This analysis, said Axline, is usually done by hand, line by line, placing a limit on the number of deals a company can consider in any given time period.

The upshot is that Lucro lets real estate developers close deals faster without skimping on due diligence. As the platform is exposed to more data about past and prospective deals, it can also become more sophisticated in its predictions.

One of Lucro’s biggest differentiators, said Axline, is its ability to adjust financial models based on nuances in deal structures. Doing so without overwhelming less sophisticated users, he said, has been one of his team’s biggest challenges.

“There’s a wide variety of financial sophistication in the real estate world,” said Axline. “Some people just want to do back-of-the envelope calculations, and you have to get the complexity out of their way. But some users have Wall Street experience and want every single option available.”

Founded in 2015, Lucro currently has a team of eight full-time employees, primarily engineers. The startup is headquartered in Chicago, where its engineering team sits, with a small data team in Cambridge, MA.

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