Abstract

Accurate cost estimation is pivotal for the success of construction projects, yet the industry grapples with challenges related to cost overruns. Uncertainties and unforeseen variables often introduce discrepancies, particularly during the planning phase. Economic changes, considered uncontrollable risks, significantly influence these fluctuations. Various research endeavors have quantified the impact of economic conditions on construction costs, emphasizing the correlation between economic indicators and project expenses. Addressing these challenges, the Construction Cost Index (CCI), a tool introduced by Engineering News Record (ENR), accurately delineates the correlation between economic changes and construction costs. Despite its global efficacy, an Egyptian Construction Cost Index is yet to be established, presenting an opportune moment to gauge the country's economic performance precisely. Establishing an Egyptian Construction Cost Index not only augments project planning precision but also mitigates risks associated with estimating construction expenditures. This initiative aligns with global best practices, fostering a more informed and efficient construction industry in Egypt. This research endeavors to create a tailored Construction Cost Index (CCI) specifically for the Egyptian context. Simultaneously, an advanced machine learning model is developed with dual objectives: a) to uncover intricate relationships between CCI and various construction-related parameters, such as the Consumer Price Index (CPI), Producer Price Index (PPI), GDP, Money supply, domestic liquidity, and others; and b) to predict future CCI values based on these identified parameters. The methodology commences with meticulous data collection, focusing on both construction materials and macroeconomic leading indicators. Subsequently, the CCI is calculated from prevalent construction materials in Egypt, namely steel, cement, and bricks. Rigorous analyses, including correlation analysis and Granger causality tests, are then conducted to discern the impact of macroeconomic leading indicators on CCI predictions. In the final stage, the Vector AutoRegression (VAR) method is applied to

forecast CCI values for the next 8 months. All statistical analyses, encompassing correlation analysis, Granger causality tests, and the VAR model, have been executed with precision using the R software. This comprehensive methodological framework is intricately designed to elevate both the accuracy and applicability of CCI forecasts within the distinctive dynamics of the Egyptian construction landscape. The outcomes of the VAR analysis reveal a Mean Absolute Percentage Error (MAPE) of 14%, with a maximum observed percentage error of 18%. These values, indicative of superior predictive accuracy, position the model as a notable advancement in understanding CCI dynamics. Previous attempts lacked a comprehensive consideration of economic and political factors, emphasizing the significance of this research in developing a holistic CCI and strategically integrating dimensions to enhance forecasting accuracy. This research contributes invaluable insights to the field, addressing intricacies specific to the Egyptian construction sector.

School

School of Sciences and Engineering

Department

Construction Engineering Department

Degree Name

MS in Construction Engineering

Graduation Date

2-26-2024

Submission Date

1-23-2024

First Advisor

Ibrahim Abotaleb

Second Advisor

none

Third Advisor

none

Committee Member 1

Ibrahim Abotaleb

Committee Member 2

Khaled Nassar

Committee Member 3

Mohamed Saied

Extent

88 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Approval has been obtained for this item

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