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Transforming Housing Supply: Leveraging City Learning Cohorts to Develop AI Solutions Through a Home Genome Project

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A home genome project: How a city learning cohort can create AI systems for optimizing housing supply

The U.S. faces a critical housing shortage intensified by fragmented data practices. To optimize housing supply, a proposed “Home Genome Project” (Home GP) aims to unify cities under shared data standards and AI integration. By gathering initial cohorts of four to six cities, Home GP seeks to develop open datasets and institutional frameworks that utilize AI for better decision-making. Cities like Rockford have demonstrated that integrated, real-time data can significantly reduce homelessness, highlighting the need for a collaborative approach to housing data management. AI innovations can streamline processes like land identification, but success relies on standardized infrastructures and trained personnel to navigate complex data landscapes. Home GP envisions a structured sharing of methodologies, enhancing cities’ capabilities to increase housing stock effectively by 2030. By fostering collaboration, public accountability, and transparency, this initiative could transform housing dynamics and address chronic shortages across the nation.

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