In this paper, the authors construct a unique data set of Internet offer prices for flats in 48 large European cities across 24 countries. The data collected between January and May 2012 from 33 websites, are drawn from Internet advertisements of dwellings. Using the resulting sample of more than 1,000,000 announcements, the authors compute the quality-adjusted city-specific house prices. Based on this information, they investigate the determinants of the apartment prices. Four factors are found to be relevant for the dwelling price level using Bayesian Model Averaging: Population density, mortgage per capita, income inequality, and unemployment rate. The results are robust to applying two alternative estimation techniques: OLS and quantile regression. Based on the auhors´ estimation results they are able to identify cities where the prices are overvalued. This is a useful indication of a build-up of house price bubbles.