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The purpose of this paper is to select a suitable procurement method for steel building construction in Sri Lanka following a systematic method which weigh, both procurement selection factors and existing procurement systems.

An abductive research stance is followed in this empirical study. Procurement selection factors were selected through a critical literature review which was followed by a quantitative questionnaire survey. The collected data were analysed using descriptive statistical analysis and relative important index.

The critical literature review outlined 46 procurement selection factors, out of which 26 factors were very important in steel building procurement selection. Short construction period and higher constructability of design are ranked at the top with the highest priority rating factors. Management-oriented procurement system was selected as the most appropriate procurement system for steel building constructions within the Sri Lankan context.

The study is limited to widely use three procurement systems in Sri Lankan construction industry. Yet, the process followed in selecting the most appropriate procurement system could be applied for other contexts. The implications of the study are mainly identifying management-oriented procurement as the most suitable procurement method for steel building construction in Sri Lanka.

The systematic procedure of procurement method selection for steel building construction may use in the Sri Lankan construction industry to limit the resource loss due to wrong selection of procurement.

A study which critically and comprehensively presenting a procurement selection process for steel building construction is not recorded in Sri Lanka prior to this study.

Examines some of the economic characteristics which arise when an intelligent building investment is made, by looking at the issues of change as businesses seek greater flexibility from their work space. Considers two recent studies in the field and summarizes by reviewing the benefits that are derived from efficiencies and effectiveness of intelligent buildings.

George Loewenstein, a prominent behavioral economist, recalls thatIn 1994, when Thaler, Camerer, Rabin, Prelec and I spent the year at the Center for Advanced Study in the Behavioral Sciences, we had a meeting to make a kind of final decision about what to call what we were doing. Remarkably, at that time, the name behavioral economics was not yet well established. I actually advocated “psychological economics,” and Thaler was strong on behavioral economics. I'm kind of glad that he prevailed; I think it's a better, catchier, label, although it creates confusion due to association with Behaviorism. (G. Loewenstein, personal email to author, June 16, 2008)

The literature on construction and project risk management published during the period from 1960 to 1997 is reviewed and analysed to identify trends and foci in research and practice. This analysis is used to identify gaps and inconsistencies in the knowledge and treatment of construction and project risk. The findings suggested that political, economic, financial and cultural categories of construction risk deserve greater research attention, as do those associated with quality assurance, and occupational health and safety. Temporal aspects of risk, and risk communication, are also important fields for investigation.

This study examines the relationship between environmental building certification Building Research Establishment Environmental Assessment Method (BREEAM-NL) and office rents in the Dutch office market.

A hedonic price model was used to assess the impact of BREEAM certification on office rents. The study is based on 4,355 rent transactions in the period 2015 to mid-2022, in which 331 transactions took place in certified office buildings and 4,024 transactions in non-certified office buildings.

The results provide empirical evidence on quantitative economic benefits of BREEAM-certified offices in the Netherlands. After controlling for all important office rent determinants, the results show a rental premium for certified office buildings of 10.3% on average. The green premiums highly differ across submarkets and vary between 5.1 and 12.6% in the five largest Dutch cities. Additionally, the results show significant positive correlation between BREEAM-NL label score and rents, whereby better performing buildings generally command higher rents.

The study contributes to the current literature on green building economics by providing, as one of the first, empirical evidence on the existence of financial benefits for BREEAM-certified office buildings in the Dutch office market.

Green Star is becoming a broadly accepted mark of design quality and environmental sustainability. Compared to other green tools, Green Star is considered as one of main streams green assessment tools, which cover almost sustainable criteria. Simultaneously, building information modelling (BIM) has also been introduced into the industry. BIM is expected to aid designers to shift the construction industry towards more environmentally and economically sustainable construction practice. Whilst the aspirations of Green Star rating and BIM implementation are broadly aligned, in the context of New Zealand this has led to some disconnects in design strategy and process. The purpose of this paper is to improve the practicality of BIM implementations for delivering Green Star certification in New Zealand.

The extensive literature review is conducted through a series of incremental steps. A conceptual framework focussing on the relationship between benefits and challenges of BIM and Green Star is then developed.

BIM supports practitioners to achieve the majority of Green Star criteria (75 per cent). Energy efficiency criterion is the key factor affecting the assessment process of Green Star and National Australian Built Environment Rating System in New Zealand. Research questions about lessening the challenges which can be encountered during the BIM and Green Star implementation are developed.

This paper is limited to a conceptual research. Further empirical research should be conducted to validate and modify the conceptual framework and the propositions presented in this paper to provide an initial insight into BIM and Green Star connectivity within the context of New Zealand.

This paper provided a clear picture for investors, developers, practitioners about benefits and challenges of BIM and Green Star implementation. The outcomes are anticipated to deliver visions for shifting the country further towards development of sustainable future cities.

The purpose of this paper is to empirically examine whether public knowledge of poor conditions of multi‐storey residential buildings affected the sale prices of these properties based on an analysis of panel data in Hong Kong.

Previous studies suggested that physical conditions of residential properties, particularly those concerning communal areas and services, might not be fully priced by the market because of the problem of information asymmetry. Therefore, it was envisaged that additional information pumped into the housing market could alter the price gradient between high‐quality and low‐quality properties. In November 2000, the Hong Kong Government publicly announced a list of poorly‐performing buildings, and this study aims to examine whether the blacklisting exercise brought about negative impacts on the sale prices of the affected properties. Hedonic price analysis was conducted on a set of panel data which consists of property transactions in both blacklisted and non‐blacklisted buildings.

The analysis results showed that properties in blacklisted buildings were not transacted at a discount, compared with those in non‐blacklisted buildings before the blacklisting exercise. Also, the release of public information on the blacklist did not create a relative diminution of the property prices of the blacklisted buildings.

Thin property transactions in derelict buildings limited the number of observations for running the regression analysis.

The piecemeal blacklisting exercise could not create price differential in the housing market, and thus it was not possible for the Hong Kong Government to lure homeowners to invest in their properties by market forces. The government should consider the implementation of a territory‐wide building classification scheme or other alternatives to solve the problems of building disrepair.

This study is the first attempt to empirically investigate the value diminution effects of the government's blacklisting against dilapidation buildings.

This research aims to develop conceptual phase building project cost forecasting models by exploring the relationship of existing plan shape complexity indices and general design morphology parameters with total construction cost.

Plan shape indices proposed to date by the literature for measuring building design complexity are critically reviewed. Building morphology is also dictated by town planning restrictions such as plot coverage ratio or number of storeys. This study analyses historical data collected from 49 residential building projects to develop multiple linear regression (MLR) and artificial neural network (ANN) models for forecasting construction cost. Existing plan shape coefficients are calculated to evaluate the geometrical complexity of sampled projects. Ten regression-based cost estimating equations are totally derived from stepwise backward and forward methods, and their predictive accuracy is contrasted: to performance levels reported in past studies and to ANN models developed in this research with multilayer perceptron architecture.

Analysis of plan shape indices revealed that 85.7% of examined past projects possess a high degree of design complexity, hence resulting in expensive initial decisions. This highlights the need for more effective early design stage decision-making by developing new building economic tools. The most accurate regression model, with a mean absolute percentage error (MAPE) of 19.2%, predicts the log of total cost from wall to floor index and total building envelope surface. Other explanatory variables resulting in MAPE values in the order of 20%–22% are total volume, volume above ground level, gross floor area below ground level, gross floor area per storey and total number of storeys. The overall MAPE of regression-based equations is 24.3% whilst ANN models are slightly more accurate with MAPE scores of 21.8% and 21.6% for one hidden and two hidden layers, respectively. The most accurate forecasting model in the research is the ANN with two hidden layers and the sigmoid activation function which predicts total building cost from total building volume (19.1%).

This paper introduces MLR-based and ANN-based conceptual construction cost forecasting models which are founded solely on building morphology design parameters and compare favourably with previous studies with an average predictive accuracy less than 25%. This paper is expected to be beneficial to both practitioners and academics in the built environment towards more effective cost planning of building projects. The methodology suggested can further be implemented in other countries provided that accurate and relevant data from historical projects are used.