The paper uses hedonic regression techniques in order to decompose the price of a house into land and structure components using readily available real estate sales data for a small Dutch city. To get sensible results, it was useful to estimate a nonlinear model on data that cover multiple time periods. It also proved necessary to incorporate exogenous information on the rate of growth of construction costs in the Netherlands in order to obtain meaningful constant quality indexes for the price of land and structures separately.

This paper compares two model-based multilateral price indexes: the time-product dummy (TPD) index and the time dummy hedonic (TDH) index, both estimated by expenditure-share weighted least squares regression. The TPD model can be viewed as the saturated version of the underlying TDH model, and we argue that the regression residuals are "distorted toward zero" due to overfitting. We decompose the ratio of the two indexes in terms of average regression residuals of the new and disappearing items. The decomposition aims to explain the conditions under which the TPD index suffers from qualitychange bias or, more generally, lack-of-matching bias. An example using scanner data on packaged men's T-shirts illustrates our framework.