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This paper addresses the question as to what extent the outsourcing degrees of property management influence the operating costs of owner‐operated real estate. For this purpose, the outsourcing degrees of technical, infrastructural and commercial property management of over 100 Swiss office buildings were reviewed. In terms of costs, the administrative costs as well as the costs of utilities, waste disposal, cleaning, upkeep and maintenance were included. As the analysis of the data revealed, commercial property management primarily impacts on the administrative costs. The office buildings of the four project partners that were examined incurred higher costs when commercial property management was outsourced. Similarly, the costs of utilities and waste disposal are higher for real estate with outsourced infrastructural property management. An inverse relationship was identified in respect of the cleaning costs, where the costs are lower when outsourcing infrastructural property management. The impact of technical property management becomes apparent with regard to the maintenance costs, which are lower for real estate with outsourced technical property management. On balance, the situation appears to be rather heterogeneous, as outsourcing results in higher costs for some cost groups and in lower costs for others. The reasons offered for these differences go far beyond the actual functions being outsourced. For instance, the project partners involved believe that it is, in particular, low service levels and reduced maintenance strategies that go hand in hand with high degrees of outsourcing. Therefore, the interviews with real estate owners, and also the data collected, give rise to the assumption that outsourcing is a measure for the implementation of cost reduction strategies. However, this assumption requires verification by way of further exploration.

Nowadays, the so‐called management by objectives (MBO) is used as a management instrument of corporate real estate management (CREM), using cost targets as the yardstick of CREM success. In Switzerland, CREM success is increasingly linked to cost reductions, with the cross‐company corporate strategy often requiring CREM to deliver a significant reduction in the level of cost. The cost concept used is material for the agreement or stipulation of cost targets. As the presented analysis shows, CREM has, for the most part, only very limited potential impact on costs. In particular, the use of the occupancy cost concept (sum of all imputed costs as well as costs recognised in the profit and loss account) poses a problem. This comprehensive cost type is determined by the following factors, which are in many cases outside the control of CREM: Book value as per balance sheet; Depreciation period of the basic shell structure; Main objective of the owner; Maintenance strategies; Degree of outsourcing of infrastructure management. Therefore, where the corporate strategy centres around cost reduction, CREM must be given the opportunity to control these drivers. This would require the inclusion of CREM in the development of the cross‐company corporate strategy, as otherwise the cost targets would have to be restricted to individual cost types (costs recognised in the profit and loss account). This is the only way to utilise a management instrument, such as MBO, within CREM.

Today, the occupancy cost of owner‐operated property is the second or third largest cost factor for many companies. Benchmarking projects that focus on occupancy cost comparisons are, therefore, becoming increasingly important. Such projects must contrast comparable properties since the comparison otherwise cannot produce meaningful results. The paper aims to focus on the issues involved.

An occupancy cost benchmarking concept is explained using the example of a Swiss portfolio of office buildings used for own operations. The chosen approach works primarily with known cost drivers that are used in the selection of suitable benchmarking properties.

The benchmarking concept enables the benchmarking of comparable properties. In addition to a pure benchmarking, it also allows the reasons for cost differences between the properties under consideration and the specific benchmarks to be identified. Recommended actions can be derived from the comparison.

In support of future benchmarking projects, it will be necessary to expand the existing database and to analyse it with the aim of identifying additional cost drivers.

The concept discussed in the paper can be translated to other areas of application (for instance area benchmarking).

The costs of cleaning owner‐operated office buildings amount to an approximate 20 per cent share of the operating costs. As a result of their cost relevance and the growing cost pressures on real estate divisions, cleaning costs are increasingly coming under focus. This interest manifests itself in the search for cost indicators and their drivers to be used as a basis for the management of real estate. This paper seeks to address this issue.

A study of the specialist literature revealed, from a theoretical perspective, the relevant drivers of cleaning costs. The empirical study is based on cost and property information collected, on a uniform basis, from over 100 owner‐operated office buildings in Switzerland, with the analysis of cost drivers being carried out using regression analysis.

The cleaning cost indicators of the examined properties revealed a median of 41 CHF/m² usable floor area and year. With regard to cost drivers, it was established that both building characteristics and usage determine the costs. However, the decisive drivers are, above all, the management strategies and the owners' main objectives.

The management strategies proved to be specific to the individual project partners. The causal relationships between strategies (e.g. outsourcing strategies) and costs may be considered to be well‐substantiated hypotheses, which require verification by means of future analyses.

The study identified both indicators and relevant drivers of cleaning costs. To this end, this study collected data from over 100 owner‐operated office buildings within Switzerland and examined these data using regression analysis. As a result, the cleaning costs of the examined properties were ascertained.

The purpose of this paper is to show that building stock is currently one of the largest energy consumers. It is thus imperative that buildings are optimally planned, constructed, and used from both the environmental and the economic point of view. Cost models are relevant tools for achieving this objective as they can be used to estimate the occupancy costs in early project phases including energy costs.

In the paper a regression model for predicting the energy consumption and energy costs of office buildings is developed based on the results of a survey conducted in 80 Swiss office properties.

The proposed energy cost model shows good agreement with the observed field data. The mean absolute percentage error resulted in 12 per cent. Validation tests using five properties not used for the model development revealed percentage errors ranging between −17 per cent and 7 per cent. The proposed concept and the presented cost model can be used as a basis for future studies.

The energy consumption and energy cost model can be improved as the database for developing them is further extended (including properties from different owners with different strategies, for example energy contracting, outsourcing, and maintenance strategies).

The objective of the study was to develop and validate a predictive model to facilitate the estimation of occupancy costs in early project phases. A procedure is presented on how quantitative energy consumption and cost models can be developed. Provided that sufficient empirical data are available, this proecdure can be used in further studies as a suitable and practicable concept to advance occupancy cost models.

The purpose of this paper is to examine the utility costs in Swiss office buildings. Owing to their high amount, as well as from an ecological perspective, the utility costs of a building are a relevant aspect of facility management. Against this backdrop the provision of utilities and waste disposal of buildings must be optimized. Benchmarking is an important instrument in this effort. On the one hand, it requires indictors and on the other, it requires knowledge of how they can be influenced (relevant cost drivers).

A study of the specialist literature revealed, from a theoretical perspective, the relevant drivers of utility costs. Using regression analyses, these drivers are examined on the basis of a set of primary data collected within Switzerland (105 owner‐operated office buildings). In addition, this data set also provided indicators for utility costs.

For the properties surveyed, a utility cost median of CHF 39/m² usable floor area and year was ascertained (with a lower and upper quartile of CHF 32 and CHF 47/m² usable floor area and year, respectively). Furthermore, it should be noted that in principle it is the building characteristics (extent and standard of technical installations) that determine the utility costs, while aspects of usage (e.g. share of residential and recreational area) are of secondary importance.

The majority of the projects studied are office buildings of banks and insurance companies. For this reason, it can be assumed that these properties have a comparatively high standard. Whether the interrelations found also apply to office buildings with other standards must be clarified by additional studies.

This study identifies cost indicators for utilities and waste disposal of Swiss office buildings.

Construction duration is referred to as one of the most crucial success elements for a construction project along with quality and cost. Modelling construction duration supports decision making at the early stages of a project. Exploring the complex structure of construction duration is necessary; hence this forms a basis to develop a predictive model. Therefore the main aim of this study is to determine the subset of variables for a descriptive model that explains the most substantial part of the variation in construction duration.

Secondary data set which includes 1,695 observations with 30 quantitative and four qualitative variables was utilized. Multiple linear regression was employed to define the relationship between explanatory variables and construction duration. General procedure for variable selection was presented in detail for data sets that suffer multicollinearity and singularity.

Analysis indicated that gross external floor area and cost of construction works are the major variables for describing the construction duration. In addition, categorisation with respect to type of facility, project location, availability of construction area, and market conditions was justified to be statistically significant.

The study was limited to the population: German building industry. Besides, the affect of various qualitative variables, identified in the literature, could not be assessed due to lack of data availability.

To the authors' knowledge, the study is the first to assess construction duration for German building projects. Moreover, it provides a method for describing variable selection routine transparently.

Today the costs of real estate are the second or third largest cost factor in most companies. The planning of so‐called occupancy costs, therefore, plays a particularly important role. Cost models that permit a forecast of these costs serve to assist in such planning. The objective of this study is to support occupancy cost planning and benchmarking.

Two regression models are presented. They permit a forecast of total occupancy costs as well as the subset of these costs that is recognized in the profit and loss account. Both models are based on 70 Swiss owner‐operated office buildings.

The forecast accuracy with mean absolute percentage errors (MAPEs) of 10 and 11 percent can be classified as good. The quality of the cost models is further tested on the basis of ten additional properties that were not used for building the models. The forecast accuracies again prove to be comparatively high (absolute percentage errors from 2 to 18 percent and from 0.2 to 25 percent).

In order to be able to improve the quality of forecasting occupancy costs, future studies should focus especially on the strategic dimension of real estate management (e.g. maintenance and outsourcing strategies).

The proposed concept and the cost model forms the starting point for further studies.

Planning the construction duration is an important consideration in any construction project. Indicators that permit an early forecast of the duration provide the basis for such planning. To date, such a basis has been lacking in the German‐speaking area. This paper aims to discuss this matter.

Indicators are identified that enable a forecast of the construction duration. In addition, a simple regression model is provided to assist in selecting construction speed indicators. This empirical analysis relies on the data, collected on a uniform basis, from 115 German residential buildings.

Project size (measured in m² gross external floor area) and project standard (measured in € building construction cost/m² gross external floor area) are found to be significant drivers of construction speed. It appears that project size, in contrast to the project standard, is positively correlated with construction speed.

An expansion of the data pool is required for more extensive study. On the one hand, this means including relevant drivers that have only been insufficiently considered to date, such as project complexity, project environment, management‐related attributes. On the other hand, the data pool must also be expanded to include other types of use.

As corporate real estate is increasingly being recognised as an important cost and production factor, senior management now pays considerable attention to this asset group. These assets are managed, inter alia, by using benchmarks. In addition to monetary benchmarks, building efficiency (e.g. m² of usable floor area per m² of gross external floor area) and capacity benchmarks in particular (e.g. m² of usable floor area per existing workplace) must be highlighted. Previously, specific values and their drivers for the assessment of buildings or portfolios were not available.

This study is based on a survey carried out in Switzerland, involving the collection of floor data (in accordance with DIN 277) of 116 owner‐operated office buildings. In addition, their drivers were ascertained using regression analyses.

The building efficiency may be assessed on the basis of the share of usable floor area or the share of office space in the gross external floor area. The main drivers are the shares of vehicle parking space, areas for residential and recreational purposes and areas for storage, distribution and retail. These drivers must be taken into consideration when assessing the building efficiency. It became apparent that capacity benchmarks are determined primarily by factors such as the space use management strategies, and only to a lesser degree by the building itself.

The study provides space benchmarks and their drivers. The results therefore permit an objective evaluation of office buildings. However, further work transcending the influence of the building itself will be required with regard to the capacity benchmarks.