The hedonic price method is well adapted to the calculation of relative prices and the estimation of the quality price relationship for a complex pro-duct. The main weakness lies in the use of multiple regression for the evaluation of the coefficients when there is very little data and the varia-bles are correlated. In this article, various methods, statistical and neu-ronal, are compared from both the predictive capacity point of view as well as that of the facial validity of the expected results. The neuronal ap-proach is globally more successful than PLS regression but neither of the two methods leads to an acceptable solution to the problems of inter-pretation of the coefficients (signs and values) stemming from colinearity.
Keywords: Hedonic Prices, Neural Network, PLS Regression, Automobile