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Over-budgeting due to inflation is a common phenomenon in the construction industry of both developed and developing countries. Inflation, with time changes, leaves an adverse effect on the project budget. Hence, this study aims to focus on the construction price index (CPI) behavior and inspect its correlation with inflation in Thailand's construction industry as there has not been much work performed. The prediction of CPI was made from 2024 to 2028, relying on the data set from 2000 to 2023. The relationship between inflation and CPI categories helps in prediction by considering inflation as the independent variable and CPI (All Commodities, Lumber and Wood Products, Cement, and Iron Products) as the dependent variable that was incorporated in EViews to perform automated ARIMA forecasting. The correlation results show that out of four CPI, only Iron Products showed a significant relationship with inflation. For All Commodities, Lumber, and Wood Products, the predicted values were fluctuating, while for Cement and Iron Products, a clear seasonal pattern was observed. This prediction gives a direction to construction industry practitioners to make necessary adjustments to their budget estimation before signing the contract to overcome cost overrun obstruction. [ABSTRACT FROM AUTHOR]

Over-budgeting due to inflation is a common phenomenon in the construction industry of both developed and developing countries. Inflation, with time changes, leaves an adverse effect on the project budget. Hence, this study aims to focus on the construction price index (CPI) behavior and inspect its correlation with inflation in Thailand’s construction industry as there has not been much work performed. The prediction of CPI was made from 2024 to 2028, relying on the data set from 2000 to 2023. The relationship between inflation and CPI categories helps in prediction by considering inflation as the independent variable and CPI (All Commodities, Lumber and Wood Products, Cement, and Iron Products) as the dependent variable that was incorporated in EViews to perform automated ARIMA forecasting. The correlation results show that out of four CPI, only Iron Products showed a significant relationship with inflation. For All Commodities, Lumber, and Wood Products, the predicted values were fluctuating, while for Cement and Iron Products, a clear seasonal pattern was observed. This prediction gives a direction to construction industry practitioners to make necessary adjustments to their budget estimation before signing the contract to overcome cost overrun obstruction.

Machine Learning is a subset and technology developed in the field of Artificial Intelligence (AI). One of the most widely used machine learning algorithms is the K-Nearest Neighbors (KNN) approach because it is a supervised learning algorithm. This paper applied the K-Nearest Neighbors (KNN) algorithm to predict the construction price index based on Vietnam's socio-economic variables. The data to build the prediction model was from the period 2016 to 2019 based on seven socio-economic variables that impact the construction price index (i.e., industrial production, construction investment capital, Vietnam's stock price index, consumer price index, foreign exchange rate, total exports, and imports). The research results showed that the construction price index prediction model based on the K-Nearest Neighbors (KNN) regression method has fewer errors than the traditional method. [ABSTRACT FROM AUTHOR]

The purpose of this study is to suggest policies to improve the price escalation system in a construction contract through, a Po licy Delphi technique. The Delphi, including two times questionnaires and a group discussion, was conducted by joining 14 experts. We also examined the escalation provisions of various countries. Results of the Delphi showed that the minimum fluctuation rate fo r price escalation was desirable at a level of 3%. To compute, the fluctuatio rate, calculating the price fluctuation of overalhrticles, was more desirable than using price indices. A bidding date was more reasonable as the initial date of change in price. Losses caus ed by price change should be shared between contractor and owner; therefore a deduction rate should be introduced in contract price escalation. Meanwhile, overhead and profit should be adjusted in proportion to the fluctuation rate; but advance payment or the delayed construction amount should be deducted from the adjusted amount. [ABSTRACT FROM AUTHOR]