Foresite's data is collected from sources across the web, including governmental and municpal data, proprietary data, third-party datasets and ambient data. This data is then fed through our machine learning algorithm to identify time-series trends related to the input parameters. We test our algorithm on a holdout sample of historical data to determine accuracy and normalize the data to provide guidance on forecasted price trends.
Our intuitive map-based interface allows you to understand statistical and geospatial trends within a given market without any GIS experience. Powerful filters allow for granular control over the output while a property report can be created for a site with a single click of a button.
Foresite makes complexity simple.
Foresite's founding team is composed of three members with varied experience in data science, commercial real estate, machine learning and operations:
Bo is a former architect and recent graduate of Duke's Fuqua School of Business. He has 5 years of experience in architecture and development, including at two of the nation's largest firms--Hines and Perkins+Will--where he specialized in the application of data science techniques towards real estate.
Matt is a data scientist with deep expertise in machine and deep learning. Prior to his current position at Intel, Matt worked at Saffron, a deep learning startup based in San Francisco. He began his education in machine learning while developing tools to discover the Higgs Boson particle at the CERN lab in Switzerland.
Victor is a former Army Ranger and Captain, and recent graduate of Duke's Fuqua School of Business. A decorated combat veteran, Victor spent 8 years leading combat units of varying sizes. Last summer, he worked in product management at Magic Leap--one of the largest startups in the world.
Our data is aggregated from dozens of datasets across the web, automatically cleaned and aggregated to a simple, easy-to-use format. This data can be visualized using our user interface or printed as a property report for a specific site or area.
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