Suburbanization is one of the processes of urban expansion which is inseparably linked with the real estate market. It takes place when residents of cities gradually leave their administrative limits in order to live in satellite communities. The article presents studies dealing with the demographic and market-related aspects of this phenomenon. The authors analyzed the dynamics of prices of undeveloped plots of land chosen due to their location in municipalities (gminas) located in the proximity of Szczecin. The analysis was supplemented with a survey of average plot sizes and the number of transactions concluded in subsequent years. The dynamics of population flow from the West Pomeranian capital to the urban-rural fringe were also investigated. The analyses resulted in the proposal of a synthetic measure illustrating the suburbanization dynamics which combines the market dimension (prices and the number of transactions) with the demographic dimension (population flow). The study covered the period of 2006-2011.

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The paper proposes a means of determining the impact of real estate characteristics based on the residuals of an accordingly specified econometric model. The econometric model contains explanatory variables whose values are easily measurable. Then, the hypothesis that the residuals of the econometric model encompass the impact of specific factors indicating that the real estate is atypical is verified, thus supporting real estate market analysis. The work describes various types of residuals (predictive and studentized residuals).Key words: identification of specific (unobserved) factors, predictive and studentized residuals, econometric analysis of real estate market. JEL Classification: C01, R30.

The main bases for land taxation are its area or value. In many countries, especially in Eastern Europe, reforms of property taxation, including land taxation, are being carried out or planned, introducing property value as a tax base. Practice and research in this area indicate that such a change in the tax system leads to large changes in land use and reallocation. The taxation of land value requires construction of mass valuation system. Different methodological solutions can serve this purpose. However, mass land valuation requires a large amount of information on property transactions. Such data are not available in every case. The main objective of the paper is to evaluate the possibility of applying selected algorithms of machine learning and a multiple regression model in property mass valuation on small, underdeveloped markets, where a scarce number of transactions takes place or those transactions demonstrate little volatility in terms of real property attributes. A hypothesis is verified according to which machine learning methods result in more accurate appraisals than multiple regression models do, considering the size of training datasets. Three types of models were employed in the study: a multiple regression model, k nearest neighbor regression algorithm and XGBoost regression algorithm. Training sets were drawn from a larger dataset 1000 times in order to draw conclusions for averaged results. Thanks to the application of KNN and XGBoost algorithms, it was possible to obtain models much more resistant to a low number of observations, a substantial number of explanatory variables in relation to the number of observations, a low property attributes variability in the training datasets as well as collinearity of explanatory variables. This study showed that algorithms designed for large datasets can provide accurate results in the presence of a limited amount of data. This is a significant observation given that small or underdeveloped real estate markets are not uncommon.

Research background: The value of the property can be determined on an individual or mass basis. There are a number of situations in which uniform and relatively fast results obtained by means of mass valuation undoubtedly outweigh the advantages of the individual approach. In literature and practice there are a number of different types of models of mass valuation of real estate. For some of them it is postulated or required to group the valued properties into homogeneous subset due to various criteria. One such model is Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA). When using this algorithm, the area to be valued should be divided into the so-called location attractiveness areas (LAZ). Such division can be made in many ways. Regardless of the method of clustering, its result should be assessed, depending on the degree of implementation of the adopted criterion of division quality. A better division of real estate will translate into more accurate valuation results. Purpose of the article: The aim of the article is to present an application of hierarchical clustering with a spatial constraints algorithm for the creation of LAZ. This method requires the specification of spatial weight matrix to carry out the clustering process. Due to the fact that such a matrix can be specified in a number of ways, the impact of the proposed types of matrices on the clustering process will be described. A modified measure of information entropy will be used to assess the clustering results. Methods: The article utilises the algorithm of agglomerative clustering, which takes into account spatial constraints, which is particularly important in the context of real estate valuation. Homogeneity of clusters will be determined with the means of information entropy. Findings & Value added: The main achievements of the study will be to assess whether the type of the distance matrix has a significant impact on the clustering of properties under valuation.