Using machine learning algorithms for predicting real estate values in tourism centers

作     者：Alkan, Tansu Dokuz, Yesim Ecemis, Alper Bozdas, Asli Durduran, S. Savas 

作者机构：Necmettin Erbakan Univ Engn Fac Geomat Engn Konya Turkey Nigde Omer Halisdemir Univ Engn Fac Comp Engn Nigde Turkey Nigde Omer Halisdemir Univ Engn Fac Geomat Engn Nigde Turkey 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2023年第27卷第5期

页      面：2601-2613页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Real estate valuation Machine learning algorithm k-nearest neighbors Random forest Support vector machines Geographic information systems 

摘      要：Along with the development of technology in recent years, artificial intelligence (machine learning) techniques that perform operations, such as learning, classification, association, optimization, and prediction, have started to be used on data on real estate according to the criteria affecting the value. Using artificial intelligence (machine learning) techniques, valuation processes are performed objectively and scientifically. In this study, machine learning techniques were employed to balance the real estate market, affected by the tourism sector in Alanya district of Antalya province, Turkey, and examine changes in value objectively and scientifically. First, the criteria affecting the real estate value were determined as structural and spatial, and data on real estate were obtained from the online real estate website. Then, the values of the real estate in the selected application area were predicted using machine learning algorithms (k-nearest neighbors, random forest, and support vector machines). Unlike studies in the literature, algorithm-based valuation using machine learning algorithms was performed instead of mathematical modeling. When analyzed for performance metrics, the best result was achieved with the support vector machines algorithm (0.73). Objective methods should be used to balance the exorbitant differences between real estate values, to regulate market conditions and to carry out a real estate valuation process free from speculative effects in coastal areas where tourism factor is effective. This study indicated the applicability of algorithm-based machine learning techniques in real estate valuation.