Abstract
The Home Sales Index (HSI) is considered one of the most crucial factors for predicting economic trends in the real estate and construction industries. Accordingly, numerous studies have investigated the precise estimation and prediction of the HSI. However, previous studies have shown limitations in collecting valuable data that can be used for such estimations and predictions. Several studies have shown that web search data have a significant relationship with various social trends, including the HSI. The goal of this study is to analyze the relationship between the HSI and feasible web search data and suggest an HSI prediction model based on the results. Our analysis includes a method for enhancing the prediction accuracy using principal component analysis. The varimax rotation method is used to find significant factors, and a genetic algorithm is used for optimization. Our results demonstrate that the prediction accuracy of the proposed model is enhanced compared to previous studies, and its capability is increased for practical field applications.
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Web Search Queries data available from Internet: http://datalab.naver.com/keyword/trendSearch.naver.
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Han, S., Ko, Y., Kim, JY. et al. Enhancement of Prediction Accuracy for Home Sales Index Prediction Model based on Integration of Multiple Regression Analysis and Genetic Algorithm. KSCE J Civ Eng 22, 2159–2166 (2018). https://doi.org/10.1007/s12205-017-1648-9
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DOI: https://doi.org/10.1007/s12205-017-1648-9