Document Type

Article

Publication Date

2022

Publication Title

Sustainability

Keywords

construction investment management, multivariate analysis, construction cost index, prediction model

Abstract

Construction costs and investment planning are the decisions made by construction managers and financial managers. Investment in construction materials, labor, and other miscellaneous should consider their huge costs. For these reasons, this research focused on analyzing construction costs from the point of adopting multivariate cost prediction models in predicting construction cost index (CCI) and other independent variables from September 2021 to December 2022. The United States was selected as the focal country for the study because of its size and influence. Specifically, we used the Statistical Package for Social Sciences (SPSS) software and R-programming applications to forecast the elected variables based on the literature review. These forecasted values were compared to the CCI using Pearson correlations to assess influencing factors. The results indicated that the ARIMA model is the best forecasting model since it has the highest model-fit correlation. Additionally, the number of building permits issued, the consumer price index, the amount of money supply in the country, the producer price index, and the import price index are the influencing factors of investments decisions in short to medium ranges. This result provides insights to managers and cost planners in determining the best model to adopt. The improved accuracies of the influencing factors will help to enhance the control, competitiveness, and capability of futuristic decision-making of the cost of materials and labor in the construction industry.

DOI

doi.org/10.3390/su14031703

Comments

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Jiang, F.; Awaitey, J.; Xie, H. Analysis of Construction Cost and Investment Planning Using Time Series Data. Sustainability 2022, 14, 1703. https://doi.org/10.3390/su14031703

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