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with real estate projects as study cases, this study explored the influencing factors (contract term, project visa, project changes, etc.) for dynamic cost control and performed numerical analysis on these factors. The SIMCA-P software was used to perform partial least squares (PLS) regression on the actual cost and a regression prediction model was built. The difference between the checked cost and that forecasted by the model was compared, the reliability of the mathematical models for the residence projects was verified. The research results show that the explanation capacity of the explanatory variables for the dependent variables reached 0.937, which means that all these before-mentioned factors had significant impacts on dynamic cost control. The average error of the predicted residence cost obtained by the model based on actual data was 0.006582346, indicating the high prediction accuracy and that the PLSs regression method performed well in solving the multiple correlations between independent variables. Therefore, the PLS regression method could provide a good solution for prediction of dynamic cost of real estate projects.

Dynamic real estate project cost prediction based on SIMCA-P