Makine Öğrenmesi Yöntemleri ile Konut Talep Tahmini

Abstract

Housing is a place where sustainable urban spaces are produced and where people's physical, cultural, environmental, economic, social and psychological needs are evaluated together with their surroundings, rather than just a building where the need for shelter is met. With the acceleration of urbanization, new needs arise, and the first of these is the need for housing. The housing sector has become one of the most dynamic and continuous sectors associated with the increase in the need for housing. The need for adequate and accessible housing comes to the forefront in our country as well as in the world. Understanding and predicting the key features determining housing prices and value is an important consideration for urban planners and housing policymakers. In this study, machine learning and artificial neural network models were used to predict the housing demand of Konya, and their forecasting performances were compared. As a result, it was concluded that ANN is a better alternative for housing demand forecasting in Konya.