Regression analysis and use of artificial neural networks in housing valuation forecasting: case example of Güvenevler neighbourhood in Mersin
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Real estate valuation is the process of determining the value of the real estate made to evaluate the factors related to the properties, use and benefits of the real estate. Along with the developing technologies, real estate valuation processes move to modern evaluation methods. In this study, forecasting models were developed for sale price using Regression and Artificial Neural Networks (ANN) methods by using residential data for sale located Güvenevler neighbourhood in Mersin. In this study, the effects of some physical properties such as age, location, number of rooms, balcony condition, number of bathrooms, gross area, facade of the houses in Güvenevler neighbourhood on sales pricing were investigated. In this study in order to make the data more meaningful, Min –Max normalization was performed and regression and ANN modelling was done. SPSS program was used for regression analysis and Matlab program was used for ANN method. With the developed forecasting model, it is aimed to control the price tracking of the old residential district Güvenevler neighbourhood. According to the results of the study, while the rate of explaining the total variation of the independent variables in the housing prices is 92% in the regression analysis, in the network created with ANN, the ratio of independent variables to explain dependent variables is 93%. Both methods are successful because they give results within certain limits.
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