The scaling of energy efficiency initiatives in the commercial building sector has been hampered by data limitations, information asymmetries, and benchmarking methodologies that do not adequately model patterns of energy consumption, nor provide accurate measures of relative energy performance. The reliance on simple metrics, such as Energy Use Intensity (EUI), fails to account for significant variation across occupancy, construction characteristics and other elements of a building – both its design and its users – that influence building energy consumption. Using a unique dataset of actual building energy use, physical, spatial, and occupancy characteristics – collected from New York City’s Local Law 84 energy disclosure database, the Primary Land Use Tax Lot Output (PLUTO) database, and the CoStar Group – this paper analyzes energy consumption across commercial office buildings and presents a new methodology for a market-specific benchmarking model to measure relative energy performance across peer buildings. A robust predictive model is developed to normalize across multiple building characteristics and to provide the basis for a multivariate energy performance index. The paper concludes with recommendations for data collection standards, computational approaches for building energy disclosure data, and targeted policies using k-means clustering and market segmentation.
The Journal of Real Estate Finance and Economics – Springer Journals
Published: Aug 12, 2014
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