Although building operating charges have turned out to be a major determinant of profitability for real estate investments, there is a noticeable lack of reports or studies that analyze these costs with state-of-the-art statistical techniques. Specifically, past studies usually assume linear relationships between costs and building attributes, they do not control for cluster-specific or longitudinal effects and do not account for the simultaneous structure of cost categories. Therefore, in this study we provide a novel approach to real estate cost benchmarking: we analyze the effects of building attributes on electricity, heating and maintenance costs for office buildings in Germany in a multivariate structured additive regression (STAR) model simultaneously, modeling potentially nonlinear effects as P(enalized)-splines and controlling for cluster-specific and individual heterogeneity in a three-way random effects structure. This way, we gain insights into how building attributes influence costs, and how cost levels vary across cities, companies and buildings. We furthermore construct quality-adjusted time indexes for the two major German submarkets. The results obtained can be used to derive portfolio allocation strategies and for planning, constructing, operating and redeveloping real estate. (C) 2010 Elsevier B.V. All rights reserved.