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As the effects of anthropogenic climate change become more pronounced, local and federal governments are turning towards more aggressive policies to reduce energy use in existing buildings, a major global contributor of carbon emissions. Recently, several cities have enacted laws mandating owners of large buildings to publicly display an energy efficiency rating for their properties. While such transparency is necessary for market-driven energy reduction policies, the reliance on public-facing energy efficiency grades raises non-trivial questions about the robustness and reliability of methods used to measure and benchmark the energy performance of existing buildings. In this paper, we develop a building energy performance grading methodology using machine learning and city-specific energy use and building data. Leveraging the growing availability of data from city energy disclosure ordinances, we develop the GREEN grading system: a framework to facilitate more accurate, fair, and contextualized building energy benchmarks that account for variations in the expected and actual performance of individual buildings. When applied to approximately 7500 residential properties in New York City, our approach accounts for the differential impact of design, occupancy, use, and systems on energy performance, out-performing existing state-of-the-art methods. Our model and findings reinforce the need for more robust, localized approaches to building energy performance grading that can serve as the basis for data-driven urban energy efficiency and carbon reeduction policies.