Combining Housing Price Forecasts Generated Separately by Hedonic and Artificial Neural Network Models

Abstract

Aims: A) To enhance accuracy in forecasting housing unit prices by forming combinations of component forecasts generated separately by hedonic and artificial neural network models; B) To help ascertain whether a constrained or unconstrained linear combining model achieves superior forecasting performance. Place and Duration of the Study: Department of Business Administration, Istanbul Aydin University, Istanbul 34295, Turkey; from 2019 to 2020. Study Design: A cross sectional data set of housing unit prices and corresponding housing unit attributes and characteristics is formed and then randomly divided into two segments: in sample (80%) and out of sample (20%). Three different methods (hedonic, artificial neural network and combining) are then employed to process the same in sample data set, and generate out of sample forecasts. The three forecasting methods are then tested and compared. Methodology: Out of sample combination forecasts are formed with component forecast weights generated by in sample weighted least squares (WLS) regression of realized price against in sample component forecasts. Four types of regressions are run: unconstrained, with and without a constant; constrained, with and without a constant. Then the mean absolute forecast error of each forecasting method is calculated and the mean difference in absolute forecast error between all pairs of models are compared and tested with a nonparametric Wilcoxon sign rank test. Results: The combining model formed with component forecast weights generated by weighted least squares (WLS) regression with the constant term suppressed and the sum-of-the-coefficients constrained to equal one, generally performs the best, in comparison with all other forecasting models (component and combination) examined in the study. Conclusion: The findings represent further evidence regarding the benefits of applying constraints on the linear combining forecast model; and demonstrate that a constrained linear combining model can be a successful technique for enhancing the forecast accuracy of housing unit prices.