Cost drivers of operating charges and variation over time: An analysis based on semiparametric SUR models

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.