Lucro – Smarter real estate investments with machine learning

Lucro – Smarter real estate investments with machine learning


Name: Lucro Global

Legal: Lucro, LLC

Location: Chicago, IL

Founded: 2015

Founder(s): Brian Axline


Social Media: 75 Followers on Twitter, 1,195 Followers on Facebook, 73 Followers on LinkedIn

Industry – Global Real Estate Market

Projection: $45.3T in 2020 (Source: PwC)


Lucro is a cloud-based real estate underwriting platform that enables real estate companies to build accurate and efficient financial models using machine learning. The platform also leverages cloud-based modern collaboration tools to crunch data by consolidating all deal functions into a standard, user-friendly platform. The startup is headquartered in Chicago, where its engineering team sits, with a small data team in Cambridge, MA.

The Product

Lucro processes and analyzes a property’s financials and operating history, condensing large data sets to fit it into a single interface that enables real estate professionals to take faster and more data-driven investment decisions. 

As the platform processes more data from past deals, it gets more sophisticated in its ability to predict the potential value of future deals. The algorithm also ensures that all financial information is devoid of inconsistencies and miscategorization, and regulary checks for anomalies that may skew the numbers. The frictionless processing of data, due diligence, and a constantly evolving understanding of deals help professionals free their time from building and analyzing vast spreadsheets, and instead spend their efforts on closing deals.

Origin and Founding Team

As a former consultant, Brian Axline helped his clients deploy over $2 billion in debt and equity across 10 different countries, closing nearly all deals using Microsoft Excel. He realized that a considerable amount of his time would be spent fixing human errors and defending the models they’d built. This slowed down the process, affecting the deals. When he began searching for more efficient tools to work with, he could find none. This prompted Brian to use his background in mathematics and finance and learn how to code so that he could build a prototype of a tool that could help him and other professionals such as him become more efficient. 

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