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.
The aggregate saving indicator does not directly reflect changes in individuals’ microeconomic behavior. From the official statistics’ point of view, households choose between spending, which generates additional income and consumption in the economy, and setting money aside, which does not. Formally, households may not (if the authors disregard housing investment) choose to save, because the aggregate saving statistical indicator is a residual concept defined as the ensuing difference between aggregate disposable income and consumption. It measures the change in net worth, which, in a closed economy, may only be generated by the production of capital goods and an increase in inventories. Using an agent-based model, the authors show that shocks unrelated to structural changes in households’ behavior may generate positively correlated fluctuations in the aggregate saving rate, productivity growth and lending. Meanwhile, a genuine increase in the average individual propensity to save is not necessarily associated with a higher aggregate saving rate.