Every hedonic price index is an estimate of an unknown economic parameter. It depends, in practice, on one or more random samples of prices and characteristics of acertain good. Bootstrap resampling methods provide atool for quantifying sampling errors. Following some general reflections on hedonic elementary price indices, this paper proposes acase-based, amodel-based, and awild bootstrap approach for estimating confidence intervals for hedonic price indices. Empirical results are obtained for adata set on used cars in Switzerland. Asimple and an enhanced adaptive semi-logarithmic model are fit to monthly samples, and bootstrap confidence intervals are estimated for Jevons-type hedonic elementary price indice
