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This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.

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This study serves as a practical model for optimizing production planning, allocation of precast component storage, and transportation sites as well as for making timely adjustments for contracted projects. To ensure that the structure of the research model is reasonable and matches actual applications, the study uses a field survey to directly observe the largest precast concrete plants in Taiwan for a period of 6 months, followed by in-depth interviews with experts involved with the planning, design, installation, and manufacturing for precast projects. The mathematical model is then established and evaluated using the data containing over 90% of national production in Taiwan. The results show that the tested corporate profits increase by an impressive 38.4% and performance is significantly increased by 97.75%. The proposed model can not only make up for oversights in human decision-making but improve the decision-making process boosting corporate competitiveness.