Homeownership and Wealth in Switzerland and Germany

Property wealth represents the most important wealth component in nearly all OECD countries. Homeownership is linked to wealth accumulation in several ways: Wealthier households are more likely to buy a house or apartment, home owners tend to save more and rising house values typically yield higher returns than money in a bank account. Moreover, owners can borrow on a mortgage to finance, e.g., the formation of an enterprise or other economic activities. At the aggregate level, these relations can explain why countries with low rates of homeownership tend to have a high wealth inequality. This paper looks at wealth and homeownership in Germany and Switzerland. These countries show the lowest proportion of owner-occupiers in Europe and a high wealth inequality. We analyse to what extent this high inequality can be explained by homeownership status. In the first part of this contribution, we review explanations for the low share of owner-occupiers in the two countries. In the second part, we analyse wealth and homeownership empirically using data of the SHP and the German Socio-Economic Panel (SOEP) from 2012. We make use of decomposition methods to analyse how renter and owner households differ in wealth levels and wealth inequality

Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short- or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneouscoefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years)