We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data-mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction. Boosting enables capturing of complex nonlinear relationships and interaction effects between input variables.We report mean relative errors and mean absolute error for all regions and compare our models with a standard linear regression approach. Our model improves prediction performance by up to 39% compared with linear regression and by up to 20% compared with a log-linear regression model. Next, we interpret the boosted models: we determine the most influential characteristics and graphically depict the relationship between the most important input variables and the house price. We find the size of the house to be the most important input for all but one region, and find some interesting nonlinear relationships between inputs and price.Finally, we construct hedonic price indices and compare these with the mean and median index and find that these indices differ notably in the urban regions of Amsterdam and Rotterdam. and Rubinfeld (1978), for example, use a hedonic model to find a relationship between air pollution and house prices.The third way in which the hedonic model can be useful, is when it is used to create a hedonic price index. A hedonic price index uses a hedonic model to correct for quality differences over time. Ordinary indices may give a deceptive view, because the average or median product in year t may be a better (or worse) product than in year t − 1. An average house in the 1930s, for instance, can in no way be compared with an average house sold in the year 2004 (since these have different characteristics); nonetheless, this is what a regular price index does. Hedonic indices for housing are, for instance, constructed by Wallace (1996) and Clapp (2004). Also, two leading price indices in the UK, namely the Halifax house price index and the Nationwide house price index, use a hedonic technique developed by Nellis (1984, 1985).Traditional hedonic models have the advantage they are easy to interpret and estimate, but they often suffer from misspecification: The assumptions made on functional form do not allow a good representation of reality, i.e. the hedonic model does not fit the data well. Several researchers (e.g. have used semi-and non-parametric methods to estimate a hedonic price model and compared these models with the traditional parametric hedonic models. Usually, these new models outperformed the parametric models in terms of prediction performance. Also, artificial neural nets, a popular machine-learning technique, are frequently used for the estimation of the hedonic function (e.g. Daniels and Kamp, 1999;Kershaw and Rossini, 1999;Lomsombunchai et al., 2004). An artificial neural net is a very flexible model, which in theory is a universal function approximator.A recent successful machine-lear...