Machine learning methods for prediction real estate sales prices in Turkey - Çılgın, Cihan; Gökçen, Hadi (Revista de la Construcción. Journal of Construction, 2023)
Owning a house is one of the most important decisions that low and middle income people make in their lives. The real estate market is a significant factor of the national economy as much as it is important for individuals. Therefore, predicting real estate values or real estate valuation is beneficial and necessary not only for buyers, but also for real estate agents, economists and policy makers. This issue represents an active area of research, as individuals, companies and governments hold considerable assets in real estate. In this context, the aim of the study is to predict real estate prices with Machine Learning methods using the real estate sales data set in June and July 2021 belonging to the province of Ankara. In particular, it is to perform a comprehensive comparison on Machine Learning regression types methods that give successful prediction results in various but similar tasks, which are not included in the real estate literature. Real estate data obtained over the Internet was first included in a detailed data preprocessing process, and then Linear, Lasso and Ridge Regression, XGBoost and Artificial Neural Networks (ANN) methods were used on this dataset. According to empirical findings, XGBoost and ANNs appear as very important alternatives in predicting real estate sales prices.