The Housing Cost Predicting Model for Conventional Private sector Construction Projects in Iran
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Abstract
The housing market has a special place in Iran's economy, including the impact of building on fixed capital, the effect on other economic activities, household livelihood, and the association of housing boom and bust periods with macroeconomic indices. The housing market is considered an engine that drives economic activity. Recognizing market trends, entering and exiting markets at the correct times, and making the best use of investment opportunities are some of the goals of every economic activist. Conclusive predictions may not be attainable due to certain aspects of the housing market; however, it is possible to make probabilistic and acceptable predictions by evaluating prior behavioral tendencies. The research is functional regarding purpose, and it uses is analytical method. The purpose of this study is to present a model for predicting housing costs in Iranian traditional private sector construction projects using a multiple regression. This research was done with a firsthand data survey based on data type criteria and a questionnaire based on field research. Data analysis tools are SPSS and AMOS, and developed with Matlab. A questionnaire was used to acquire construction cost data from builders during 2015 to 2019. The results of the bootstrap test and the regression model demonstrated that the construction costs of 3–7 story buildings could be predicted based on a time factor (completion time).