Any attempt to explicitly define the property market involves an interdisciplinary approach. Commonly applied notions referring to the genesis of risk in the area of medicine, humanities and, in particular, social and other sciences, have a significant impact on the strictly technical definition. The juxtaposition of the established definitions concerning the phenomenon of risk is an attempt to refer their types and risk factors to the extensive subject area of risk concerning the property market itself. In the future, this may contribute to the development of new risk analysis methods or methods of determining its probability and, therefore, levelling the effect of uncertainty in developing real estate management processes. The aim of the paper is to carry out a review of literature for a deeper analysis of this issue.
Hedonic models, commonly applied for analyzing prices in the property market, do not always fulfil their role, mainly due to the application of simplified assumptions concerning the distribution of variables, the nature of relations or spatial heterogeneity. Classical regression models assumed that the variation of the explained variable (price) is explained by the effect of market features (fixed effects) and the residual component. The hierarchical structure of market data, both as regards market segments and the spatial division, suggests that statistical models of prices should also include random effects for selected subgroups of properties and interactions between variables. The mixed model provides an alternative for constructing various regression models for individual groups or for using binary variables within one model. With its appropriate structure, it makes it possible to take into account both the spatial heterogeneity and to examine the effects of individual features on prices within various property groups. It can also identify synergy effects. The article presents the issue of mixed modelling in the property market and an example of its application in a market of dwellings in Olsztyn. The research used transaction data from the price and value register, supplemented with spatial data. The obtained model was compared with classical regression models and geographically weighted regression. The study also covered the usefulness of mixed models in the mass evaluation of properties, and the possibility of using them in spatial analyses and for the development of property value maps.
The real estate market, as an open, complex and dynamic system, responds to changes in the environment of economic, legal or social conditions, although the pace and direction of these changes depends on the level of inertia of this system. At the same time, this market stimulates the market environment through prices. This study attempts to identify cause-and-effect relationships in the scope of the impact of selected economic and social indicators on prices of residential premises, as well as to identify the effects of price changes on these indicators. The time horizon of the study covered the years from 2008 to 2018. In the studies, to assess the stationarity of time series, an extended Dickey-Fuller test was used for the model with a free expression and linear trend, a vector autoregression model (VAR) was then constructed and Granger tests and impulse response analysis were performed using the Impulse Response Function (IRF). As a result, it was demonstrated that the response of real estate prices to the impulse from explanatory variables appears between the first and the fourth quarters, and expires after about three years.