Spatial framework for real estate analysis: The case of the Czech Republic

It's been recognized for a very long time that the housing data are of spatial nature. Therefore, any analysis not utilizing the spatial framework suffers from a great informational loss. Our study utilizes the spatial frameworks in two separated analyses: the analysis of the "grandiose-clusters" and the analysis of the housing submarkets over the area of the entire Czech republic. We, firstly, confirm that spatial frameworks provide us with better and more robust results of the analyses, and then, utilizing the hedonic theory and the kriging interpolation techniques, we identify and evaluate the contribution of the location to the price of the estate - identifying the "grandiose clusters". In the second part of our empirical study, we demonstrate the methodology of identifying the housing submarkets - the clusters wherein considerably larger homogeneity of key price determinants is reached, and identify the housing submarkets of every single region of the Czech Republic individualy. Our results provide supports for the hypothesis that the effects of key price determinants vary in space and that the effect of location (on the price) also vary in space. Moreover, the methodology used allows not only to confirm these hypotheses, but also to accurately identify spatial positions of individual contributions.