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This paper aims to identify the main non-cost factors affecting accurate estimation of life cycle cost (LCC) in building projects.

Ten factors affecting LCC in building project cost estimates are identified through literature and interviews. A questionnaire survey is conducted to rank these factors in order of priority and provide the views of cost practitioners about the significance of these factors in the accurate estimation of LCC. The data from 138 construction building projects completed in UK were collected and analysed via multiple regression to discover the relationship between capital and LCCs and between non-cost factors and cost estimation at each stage of the life cycle (capital, operation, maintenance and LCC).

The results of analysis of existing LCC data of completing project and survey data from cost professionals are mostly consistent with many literature views and provide a reasonable description of the non-cost factors affecting the accuracy of estimates.

The value of this study is in the method used, which involves analysis of existing life data and survey data from cost professionals. The results provide a plausible description of the non-cost factors affecting the accuracy of estimates.

The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy.

A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs.

The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects.

A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample.

This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.

The purpose of this study is to investigate and compare the dynamic leverage policies of Islamic and conventional banks within selected Organization of Islamic Cooperation (OIC) countries. The study specifically focuses on the concepts of leverage procyclicality and prospect theory.

To achieve the research objectives, the study uses data from three distinct periods: Crisis I (2007–2009), Crisis II (2011–2012) and Crisis III (2020). The analysis uses dynamic panel-data regression, using the generalized method of moments (GMM) technique.

The research findings indicate that both Islamic and conventional banks demonstrate leverage procyclicality. Interestingly, Islamic banks exhibit weaker leverage procyclicality during normal conditions but display stronger procyclicality during crises compared to their conventional counterparts. The application of prospect theory reveals that both bank types exhibit risk-taking or risk-averse behavior through leverage under certain financial and market performance measures as the first-level domain of the gain-vs-loss condition. Furthermore, during crises (as the second-level domain of the normal-vs-crisis condition), both Islamic and conventional banks experience heightened leverage. Notably, Islamic banks, owing to their lower risk exposure and greater shock resilience, demonstrate lesser risk-taking behavior through leverage than conventional banks, both during periods of underperformance and worsening conditions amid crises. These findings validate the extension of prospect theory's applicability in a two-level domain perspective. The dynamic nature of leverage policy, being procyclical and adhering to prospect theory, also varies following different crises specifically.

The study's limitations include the unequal crisis periods (Crises I, II and III), leading to an imbalanced examination of their effects, certain financial and market performance metrics that fail to corroborate the expected hypotheses and the limited generalizability of findings beyond the selected OIC countries.

Understanding the intricate dynamics and behavioral aspects of leverage policy for both Islamic and conventional banks, particularly during crisis scenarios, proves crucial for reviewing banking regulations, making informed financial decisions and managing risks effectively.

This study enriches the current knowledge by presenting two key points. First, it highlights the dynamic nature of leverage procyclicality in Islamic banks, showing a change from weaker procyclicality in normal conditions to stronger procyclicality during crises compared to conventional banks. Second, it expands the application of prospect theory by introducing a dual-level domain context. Examining the comparative leverage policies of Islamic and conventional banks during different crises within OIC countries provides novel insights into leverage procyclicality and behavioral responses.