Abstract
This paper presents the principles of studying global spatial autocorrelation in the land property market, as well as the possibilities of using these regularities for the construction of spatial regression models. Research work consisted primarily of testing the structure of the spatial weights matrix using different criteria and conducting diagnostic tests of two types of models: the spatial error model and the spatial lag model.
The paper formulates the hypothesis that the application of spatial regression models greatly increases the accuracy of transaction price prediction while forming the basis for the creation of cartographic documents including, among others, maps of land value.