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This paper presents a set of step‐wise regression models which can incorporate multiple factors in modelling the costs of office buildings. The models appeared to be more accurate than the traditional method.

The data were collected from historical office building projects, which were then, adjusted using the construction price index. The step‐wise regression was conducted to produce the linear cost models.

Seven RC office buildings and 11 steel office buildings in Hong Kong completed in different years were selected randomly to verify the accuracy of the regression models developed. The data of these buildings were not used in the development of the cost models. The result shows that the variability of percentage difference is ranging from −4.11 per cent (4.11 per cent underestimate) to +2.74 per cent (2.74 per cent overestimate) for RC office buildings. For steel office buildings, it ranges from −6.65 per cent (6.65 per cent underestimate) to +2.78 per cent (2.78 per cent overestimate).

This study presents a methodology that can be used in cost estimation of office buildings in Hong Kong at early stage of construction project. The regression cost models developed above are based on, in total, historical data of 30 completed office buildings in Hong Kong. The reliability of the cost models can be further improved by including more office buildings to develop the cost models. Furthermore, the application of cost modelling by regression analysis is not limited to office buildings. The same approach can be applied to residential and other non‐residential buildings as well. Regression cost modelling, with sufficient updating for new cost data available, can provide economic, quick and accurate cost estimation at early stage of construction projects. It will become rational guide supplementing judgmental forecast of cost advisors in near future.

Step‐wise regression procedure was applied to develop the cost models. Jackknife re‐sampling was carried out and both of the models show stability. Cross‐validation shows that the developed regression models performed satisfactorily. The paper considers that it can provide economic, quick and accurate cost estimation at the early stage of construction project. In addition, the approach of this study can be adopted to develop cost models of other types of buildings in other locations.

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.

The integration of additive manufacturing (AM) processes into a production environment requires a cost‐model that allows the precise estimation of the total cost per part, although the part might be produced in the same build job together with other parts of different sizes, complexities and quantities. Several cost‐models have been proposed in the past, but most of them are not able to calculate the costs for each single part in a mixed build job or are not suitable for Selective Laser Melting (SLM). The purpose of this paper is to develop a cost model, including all pre‐ and post‐processing steps linked to SLM.

Based on collected data and the generic cost model of Alexander et al., an adapted model was developed for the SLM process including all required pre‐ and post‐processes. Each process was analysed and modelled in detail, allowing an evaluation of the influences of the different geometries on the cost of each part.

By simultaneously building up multiple parts, the manufacturing as well as the set‐up time and therefore the total cost per part can be significantly reduced. In the presented case study a cost reduction of 41 per cent can be achieved in average.

Using different cost allocation algorithms, the developed cost model enables a precise determination of total cost per part avoiding that any geometry is preferred in simultaneous manufacture. This helps to optimize build jobs and to manufacture SLM parts more economically by pooling parts from different projects, whereas the cost per part can still be precisely determined.

A model designed to assist management in understanding the cost structure of oil distributors in the UK is developed. This model can be programmed on to a spreadsheet and only requires inputs which can be simply retrieved and updated.

Ratios were constructed using bidding data for highway construction projects in Texas to study whether there are useful patterns in project bids that are indicators of the project completion cost. The use of the ratios to improve predictions of completed project cost was studied.

Ratios were calculated relating the second lowest bid, mean bid, and maximum bid to the low bid for the highway construction projects. Regression and neural network models were developed to predict the completed cost of the highway projects using bidding data. Models including the bidding ratios, low bid, second lowest bid, mean bid and maximum bid were developed. Natural log transformations were applied to the data to improve model performance.

Analysis of the bidding ratios indicates some relationship between high values of the bidding ratios and final project costs that deviate significantly from the low bid amount. Addition of the ratios to neural network and regression models to predict the completed project cost were not found to enhance the predictions. The best performing regression model used only the low bid as input. The best performing neural network model used the low bid and second lowest bid as inputs.

The nature of bid ratios that can describe the pattern of bids submitted for a project and the relationship of the ratios to project outcomes were studied. The ratio values may be useful indicators of project outcome that can be used by construction managers.

Maintenance and running costs contribute significantly to the total cost of running facilities. Almost half of the energy consumed in the UK is used in buildings. Energy consumption can be attributed to many factors. Describes the fundamentals associated with modelling running costs in leisure centres and then investigates 19 sport centres in the city of Liverpool, using data elicited from the Liverpool Leisure Services Directorate. The energy operating costs were analysed using statistical techniques and artificial intelligence methods. Three types of modelling, linear/non‐linear regression, neural networks and neurofuzzy were developed to predict energy cost. Testing and validation of the results showed that neural network models outperformed both regression and neurofuzzy techniques. However, all the models showed a high level of accuracy. The models would be of use to professionals involved in feasibility studies.

Describes the introduction of a quality costing system, using the process model, within a division of GEC Alsthom Engineering Systems Ltd. The system was introduced to identify and prioritize improvement areas within the context of a continuous improvement programme. Includes a critical review of a former quality costing system within the division based on the prevention, appraisal and failure model. Initial work focused on the use of the IDEF methodology, recommended in BS 6143: Part 1: 1992, to model major business processes. However, this approach was found to be inappropriate as the models created were complex and failed to gain widespread acceptance within the division. A hybrid model, using integrated flowcharts, was developed; this proved successful in securing understanding and ownership of major processes. Indicates a cost of non‐conformance of approximately 10 per cent and identifies four main areas for improvement activities.

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.

Existing quality‐cost models have been criticized for their imprecision and inadequate theoretical justification. Attempts to remedy these deficiencies by using systems dynamics to build a generic model relating quality conformance levels to the quality‐cost categories of prevention, appraisal and failure (PAF). Outlines in depth the assumptions underlying the model’s structure and links model parameters to published empirical data. Explores the model’s sensitivities to changes in factors including different initial values of conformance quality. Determines the potential for improvements in quarterly and cumulative quality costs by changing the PAF cost levels.

This paper deals with a cost‐related evaluation and a comparison of the problems that arise on pure IP networks (i.e. big fat routers), IP over static optical networks (i.e. IP over synchronous digital hierarchy/optical transport network (SDH/OTN)) and IP over ASON/GMPLS‐based dynamic optical network architectures. The aim is to develop easy‐to‐use CAPEX models, which can be evaluated using statistical parameters to describe the actual network and the traffic characteristics. Since there are no experimental results on these statistical parameters that can be practically used in real networks, the CAPEX model should be not only general, but also scalable enough to evaluate the network cost in extreme situations.

In this paper, the comparative CAPEX models are developed (in the framework of the IST Project MUPBED – Multi‐Partner European Test Beds for Research Networking) and the practical parameters are defined. Then the models are applied for the cost evaluation of a pan‐European research network based on the GÉANT2 network topology to validate the simplified comparison technique that is proposed.

The simulation results prove, on one hand, the applicability of the simplified cost models and on the other hand, the cost efficiency of the “IP over ASON/GMPLS” architecture.

This paper deals with a cost‐related evaluation and a comparison of the problems that are arise on pure IP networks, IP over static optical networks and IP over ASON/GMPLS‐based dynamic optical network architectures.