NREL ResStock U.S. Residential Building Stock Metadata 2021 is a comprehensive Parquet dataset for energy simulation and modeling. This dataset provides granular building-characteristic metadata for a representative sample of residential dwellings across the United States. Structured as a Parquet file, it contains extensive variables detailing construction types, mechanical systems, thermal envelopes, and occupancy patterns essential for accurate building stock simulation. The data is designed to be joined with county-level hourly load profiles, enabling researchers to perform detailed end-use energy analysis. Every record is indexed to facilitate high-performance queries, ensuring that large-scale housing models remain computationally efficient while maintaining statistical accuracy across diverse climate zones and housing archetypes.

The download is a 46.2 MB file containing .PARQUET files.

Professionals typically integrate this Parquet dataset into data science environments such as Python or R to conduct energy modeling and stock analysis. Users can join these metadata tables with corresponding end-use load profiles to map specific building characteristics to energy consumption patterns. By leveraging standard data processing libraries, analysts filter the dataset to isolate specific housing archetypes, allowing for rapid simulation of energy efficiency interventions or grid impact studies at a regional or national scale.

This dataset belongs in the toolkit of energy consultants, urban planners, and policy researchers tasked with decarbonisation strategies and housing stock assessments. It is essential for projects involving large-scale building performance simulations, utility grid planning, and the evaluation of residential retrofit programs. When paired with hourly load profile data, it supports evidence-based decision-making during the master planning or policy development stages. Practitioners often search for these files when conducting longitudinal housing studies or developing scalable energy efficiency models for municipal or federal housing authorities.