On the use of spline smoothing in estimating hedonic housing price models: Empirical evidence using Hong Kong data

Traditionally in estimating hedonic housing price functions, investigators use parametric models involving specific functional forms and a finite number of unknown parameters. Some investigators have questioned whether the underlying theory is capable of conveying sufficient information to enable a correct and successful specification of parametric models and have instead proposed the less restrictive semiparametric approach to the problem. In this paper, we illustrate how the technique of smoothing splines can be used to estimate hedonic housing price models. Smoothing splines are a powerful approach to the analysis of housing data as they are exceptionally flexible in their functional forms and provide a computationally tractable method even with a large number of explanatory variables. Our illustration takes the form of a rather limited, but very promising, application with Hong Kong data. In the forecasting comparison, the spline smoothing procedure outperforms the traditional parametric Box-Cox model in mean square error terms for out-of-sample predictions. Our results also suggest that the distortion caused by underfitting the model is smaller for spline smoothing than for the kernel and k-nearest-neighbor semiparametric procedures.