| Publication |
2014.
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| Summary/Abstract |
Many different governments have begun to require disclosure of building energy performance, in order to allow owners and prospective buyers to incorporate this information into their investment decisions. These policies, known as disclosure or information policies, require owners to benchmark their buildings and sometimes conduct engineering audits. However, given substantial variation in the cost to disclose different types of information, it is natural to ask: how much and what kind of information about building energy performance should be disclosed, and for what purposes? To answer this question, this paper assembles and cleans a comprehensive panel dataset of New York City multifamily buildings, and analyzes its predictive power using a Bayesian multilevel regression model. Analysis of variance (ANOVA) reveals that building-level variation is the most important factor in explaining building energy use, and that there are few, if any, relationships of building systems to observed energy use. This indicates that disclosure laws requiring benchmarking data may be relatively more useful than engineering audits in explaining the observed energy performance of existing buildings. These results should inform the further development of information disclosure laws.
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