Real Factors provides commercial real estate investors with software to accelerates and inform the deal-making process, enhances analysis, and optimize risk-adjusted returns.

Our platform unifies all the internal, commercial, and publicly available data resources used by our clients within a federated environment that enhances data accessibility and usability throughout the entire deal process.

We extend the analytical power of front-office teams through our geovisual analytics workspace and user friendly spatial data science toolkit.

With these applications we enable the worlds largest financial institutions to precisely measure CRE market risks and rapidly identify investment opportunities.

Our Story

Institutional investment in CRE has grown tremendously over the past twenty years, and yet the methodologies used to value the asset class have remained the same for decades.

Not to mention, the due diligence process in institutional commercial real estate requires processing a vast amount of data of different types and formats.

Our company was founded to transform how CRE professionals leverage data. We want to empower our customers to ask interesting new questions and close more deals, faster.

We’ve set out to change the way institutional commercial real estate professionals operate and to make the qualitative, quantitative.

 

FOUNDING TEAM

Colin George
Founder and CEO

Tom Hunter
Co-Founder, Product/Data

Andrew Ermogenous
Co-Founder, Sales/Marketing

Zach Wade
Head of R&D and Data Science

TECHNICAL ADVISORS

Dr. Luc Anselin - Head of Spatial Data Science Lab, University of Chicago

Dr. Andy Krause - Sr. Data Scientist, Greenfield Advisors and former Data Scientist, Zillow

Tim LaMarca - VP Product & Software Development, Semantic Research, Inc.

SENIOR ADVISORS

Jeff Epstein - Former CFO of Oracle, Nielsen, and DoubleClick

Harjot Chopra - Former Head of S&P CapitalIQ

Michael Mandel - CEO, Compstak

Chip Harrison - CEO, Semantic Research

Open Positions


Full Stack Developer

Drive design, implementation, testing, release in an Agile environment, using modern methodologies, and open source tools. You will lead your work and deliver the most elegant and scalable solution.


Data Engineer

Responsible for improving the core platform by developing a range of algorithms incorporating Machine Learning, Data Science, AI, and/or Big Data


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