Determinants of House Prices in Sweden

Abstract

The thesis analyses the main determinants of the Swedish house prices. We use panel data of 290 Swedish municipalities across 2003 - 2016 to estimate Spatial Durbin Model, which allows us to capture spatial dependencies in the data, thus obtaining unbiased estimates of the main drivers. Further, we run the cross-sectional regressions for every year to discover the dynamics in the determinants. This proves to be especially valuable having the period of the financial crisis of 2008 in our data set. We obtain a comprehensive picture of the spatial dependencies and spillovers from one municipality to the others. Proper analysis of the dynamics in the housing sector is vital for assessing policy implications made by important players such as the central banks. Since the price development has not been even among the individual municipalities, analysis on the regional level may give us better insight into the sector than country-level studies. We estimate the total effects as well as the direct and indirect effects of the spatially autocorrelated variables. We find that the main determinants for the Swedish house prices are construction costs and real income. We find that developers are not fully able to transfer the cost onto the final buyers. Real income is a stable driver of the house prices, dropping in importance only in 2009; we attribute it to the higher uncertainty in the markets, thus people withholding their house purchases. Comparing to other countries, availability of credit for households plays more significant role; possibly due to the fact that Swedish households rely more on credit and have more often floating-rate mortgages. Housing supply and unemployment have effect on prices only when there is a same shift also in the neighbouring municipalities. There is no visible difference for the spillover patterns of different municipalities. The spatial model proves to be a better suit for the estimation, since it outperforms the non-spatial model in out-of-sample forecast.