COMPARING THE HEDONIC MODEL VS. ARTIFICIAL NURAL NETWORK IN HOUSING PRICE PREDICTION IN ISTANBUL, TURKEY

Abstract

BACKGROUND: In forecasting housing-unit prices, the conventional hedonic model is the most common used method, and the newer artificial neural network (ANN) models also have been used in forecasting various, different economic and financial variables. More recently, combining forecasts models have been introduced to enhance forecasting accuracy. The current study aims in developing a more accurate forecasting model by combining the hedonic and neural network forecasts of housing-unit prices. MATERIAL AND METHODS: A total of 100 apartments in Istanbul, Turkey were included in the study. Housing-unit characteristics were taken including the price, the geographical location, the land size, the age of apartments, the number of bedrooms and bathrooms, and the floor within the building. First, the hedonic and ANN models are applied, and their forecasts are compared to detect the better model. Second, combining models are generated by combining the forecasts of hedonic and ANN model using different sets of forecast weights, generated by restricted and unrestricted, weighted least squares (WLS) regression technique, respectively. Average absolute forecast error (MAFE) of each model is calculated, and the average difference in MAFE among all pairs of models are compared and tested, and the superior model is the one with the lowest average absolute forecasting error (MAFE). RESULTS: The study finds that between the ANN- and hedonic models, the ANN model performs better. However, the ANN model was outperformed by the combination forecast formed with restricted WLS estimated regression coefficients as component forecast weights. In all, seven combining models were generated using different methods in calculating the forecast weights. The unrestricted combining iv models were outperformed by the ANN model, while the constrained combining models performed better than ANN model. The study finds that the restricted combining models have the lowest MAFEs and are considered as the superior models. CONCLUSION: The present study successfully generates combining forecasts models from the housing units‟ forecasts of the hedonic and neural network models. The study finds the combining forecasts model formed with weights generated by constrained WLS regressions generally perform the best out of all other forecasts‟ models. Our study demonstrates that combining forecasts can improve estimations of housing units‟ prices in Istanbul, Turkey