Neural networks and non-linear statistical methods: an application to the modelling of price–quality relationships

This paper examines the potential of a neural network (NN) approach to the analysis of ‘hedonic’ regressions, in which price is dependent on quality characteristics. The aim of the regressions is to measure, using objective data, the valuation consumers place on these characteristics. A neural network approach is employed because of potential non-linearities in the hedonic functions, using the property of ‘universal approximation’. Our NN implementation goes beyond the now-orthodox approach in using the Polytope algorithm, which we compare with Backpropagation, and uses two hidden layers. The results obtained provide an improvement on linear formulations, but the improvement in this case is relatively marginal. We view NN modelling as a useful means of specification testing and hence our results imply some support for a linear formulation as an adequate approximation. From a managerial perspective, the linear model is more easily interpreted. NN modelling is potentially very time-consuming, especially with the Polytope algorithm, and requires a good deal of technical skill.