Amsterdam real estate startup Epum has raised $1.6 million Pre-Seed funding. Epum’s tech turns any commercial real estate developer, investment manager, or lender into a geospatial data science company.
Epum was founded by two former commercial real estate developers (Royden Cooper and Spencer Stieff), one ex-Adyen senior software engineer (Leonardo Costa), one ex-Globality senior data scientist (Tomasz Pietruszka), and one of the world’s preeminent researchers in the field of Geospatial Artificial Intelligence (Marvin Mc Cutchan), who has a PhD from the Vienna University of Technology.
Epum’s platform includes the largest real-time dataset of urban zoning and planning activity across the United States.
Based on this dataset and proprietary algorithms, the platform offers a powerful commercial real estate development site selection solution. It evaluates local demand drivers, zoning, ownership profiles, demographics, competitive supply, land area, topography, traffic data, and more to identify and support ideal acquisition opportunities.
Epum is already working with two nationwide commercial real estate developers and one real estate private equity fund.
“Raising $1.6 million for our pre-seed round is a significant milestone for Epum, and we are thrilled to have more resources to serve our customers,” said Royden Cooper, CEO of Epum.
“Commercial real estate developers are the main catalysts of value in the commercial real estate ecosystem since they are the ones who build the future of our cities.
Over the last 12 months of industry volatility due to rising interest rates, we are excited to support developers with new tools to derisk their projects and grow their assets under management.”
Curiosity VC led the oversubscribed round with co-investments from NP-Hard Ventures, Remote First Capital, and Heartfelt VC.
The investment will accelerate the development and distribution of Epum’s commercial real estate research platform, designed to allow developers, investment managers, and lenders to identify the optimal development sites based on inputted parameters, to safely organise their proprietary data, to forecast submarket trends with purpose-built machine learning models, and to prepare investment committee memos over 10x faster than with traditional human analysts.
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