Price data is sparse, often from a small number of sales, and censored when number of sales is smaller than six. So the local price levels, and especially trends are not at all clear from the raw data.

The model has three-level geographical hierarchy from zip-code prefixes, and the local population density as a (hierarchical) covariate. There are also covariances for price level, trend and trend change, and residual model takes number of sales into account and adapts to outliers.

All this helps in getting a better idea of what really happens behind the more or less random individual sales.

http://louhos.github.io/figs/2015-05-07-asuntohintojen-muuto...