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Cherre and Near Space Labs Announce Partnership to Combine High Frequency Stratospheric Imaging into Real Estate Data Platform

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This partnership will enable mutual customers to add high-resolution real estate imagery into Cherre’s real estate data platform for analysis and decision making

Cherre, the NYC-based real estate data and analytics platform, today announced a new partnership with Near Space Labs, the leading provider of high frequency geospatial imagery. In this partnership, Cherre will integrate Near Space Labs’ technology within their data platform, enabling mutual customers to leverage real estate geospatial imaging capabilities when analyzing opportunities or making investment decisions.

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Near Space Labs provides a scalable platform to deliver unprecedented high resolution, high frequency imagery of large economic hubs and high valued assets nationwide. Real estate customers are able to subscribe to a continuously updated imagery feed of their properties without needing to task an airplane or send a drone. Near Space Labs obtains, processes, and delivers the data at a fraction of the cost of other providers.

“Our partnership with Cherre will give our mutual customers a competitive advantage by providing them with the most up-to-date visual information and relevant market intelligence,” said Rema Matevosyan, CEO of Near Space Labs.

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Cherre’s award-winning real estate data platform seamlessly connects disparate datasets, enabling customers to automate workflows and build predictive analytics. With Cherre, customers are able to evaluate opportunities and trends faster and more accurately, while saving millions of dollars in manual data collection and analytics costs.

“Geospatial imaging is a critical tool for insurance and real estate companies to gain insight into properties and neighborhoods under consideration,” said L.D. Salmanson, CEO and Co-Founder of Cherre. “Adding Near Space Labs’ API to Cherre’s platform will enable our customers to utilize high-resolution aerial photography with their connected real estate data for decision making.”

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