This study involves a financial analysis of 43 publicly listed and large private companies in the building and construction supply chain from 2005 to 2010; straddling the period of the global financial crisis (GFC); and examines the impact of the GFC on the performance of these companies. The construction supply chain was divided into four sectors – material suppliers, construction companies, property developers and real estate investment trusts (REITs). The findings indicate that the impact was minimal for both material suppliers and construction companies, but especially severe for the more leveraged property developers and REITs. Building material suppliers and construction companies have benefitted substantially from the building economic stimulus package provided by the Australian government to mitigate the effects of the GFC. Decreases in the valuation of assets have, to a large extent, reduced the profitability of property developers and REITs during the GFC but these companies have recovered quickly from these adverse conditions to return to a sound financial position by the end of the 2010 financial year. The results will inform investors, managers and construction professionals in devising strategies for prudent financial management and for weathering future financial crises.
Purpose – The study reported in this paper proposed the use of artificial neural networks (ANN) as viable alternative to regression for predicting the cost of building services elements at the early stage of design. The purpose of this paper is to develop, test and validate ANN models for predicting the costs of electrical services components. Design/Methodology/Approach – The research is based on data mining of over 200 building projects in the office of a medium size electrical contractor. Of the over 200 projects examined, 71 usable data were found and used for the ANN modeling. Regression models were also explored using IBM Statistical Package for Social Sciences Statistics Software 21, for the purpose of comparison with the ANN models. Findings – The findings show that the cost forecasting models based on ANN algorithm are more viable alternative to regression models for predicting the costs of light wiring, power wiring and cable pathways. The ANN prediction errors achieved are 6.4, 4.5 and 4.5 per cent for the three models developed whereas the regression models were insignificant. They did not fit any of the known regression distributions. Practical implications – The validated ANN models were converted to a desktop application (user interface) package – “Intelligent Estimator.” The application is important because it can be used by construction professionals to reliably and quickly forecast the costs of power wiring, light wiring and cable pathways using building variables that are readily available or measurable during design stage, i.e. fully enclosed covered area, unenclosed covered area, internal perimeter length and number of floors. Originality/value – Previous studies have concluded that the methods of estimating the budget for building structure and fabric work are inappropriate for use with mechanical and electrical services. Thus, this study is unique because it applied the ANN modeling technique, for the first time, to cost modeling of electrical services components for building using real world data. The analysis shows that ANN is a better alternative to regression models for predicting cost of services elements because the relationship between cost and the cost drivers are non-linear and distribution types are unknown.
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