TY - JOUR
T1 - A Market-Specific Methodology for a Commercial Building Energy Performance Index
AU - Kontokosta, Constantine E.
N1 - Funding Information:
Special thanks to the New York City Mayor’s Office of Long-Term Planning and Sustainability and the CoStar Group for data access. This work was funded, in part, by the Real Estate Research Institute and the New York University Provost’s Office. I would also like to thank Piet Eichholtz, David Geltner, Frank Nothaft, Asieh Mansour, Norm Miller, Thies Lindenthal, and anonymous reviewers for their helpful comments. All errors remain my own.
Publisher Copyright:
© 2014, The Author(s).
PY - 2015/8/25
Y1 - 2015/8/25
N2 - 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.
AB - 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.
KW - Benchmarking
KW - Energy disclosure
KW - Energy efficiency
KW - Energy performance
KW - Green buildings
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U2 - 10.1007/s11146-014-9481-0
DO - 10.1007/s11146-014-9481-0
M3 - Article
AN - SCOPUS:84931955884
SN - 0895-5638
VL - 51
SP - 288
EP - 316
JO - Journal of Real Estate Finance and Economics
JF - Journal of Real Estate Finance and Economics
IS - 2
ER -