The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

This article aims at testing the possibilities of applying hierarchical spatial autoregressive models to create land value maps in urbanized areas. The use of HSAR (Hierarchical Spatial Autoregressive) models for spatial differentiation of prices in the property market supports the multilevel diagnosis of the structure of this phenomenon, taking into account the effect of spatial interactions. The article applies a two-level hierarchical spatial autoregressive model, which will permit the evaluation of interactions and control spatial heterogeneity at two levels of spatial aggregation (general and detailed). The results of the research include both the evaluation of the impact of location on prices (taking into account non-spatial factors) and the creation of the average land price map, taking into consideration the spatial structure of the city. In empirical studies, the HSAR model was compared with classic LM (Linear Model), HLM (Hierarchical Linear Model), and SAR (Spatial Autoregressive) models to perform comparative analyses of the results.

The COVID-19 pandemic represents a combined supply and demand shock to the financial and housing market but also an unusual negative shock in terms of the health of society (households) and national economy. The fall in housing demand was initially assumed together with price decreases as a consequence of the uncertainty of the health of society, significant falls in stock markets and corporate solvency. However, the results of research in selected Polish cities do not indicate such a significant market recession. This article examines the housing price dynamics and forecasting in Polish cities during the COVID-19 pandemic. The TRAMO/SEATS and ARIMA models were used for the decomposition and forecasting of dwelling time series. The Polish housing market, represented by selected local housing markets, still shows a growing trend despite the COVID-19 pandemic throughout 2020. The housing market may slow down in 2021, but the strong forecasted growth trends in Warszawa and Poznań suggest that there will be no significant price decline in Poland in the near future.

This article analyses the adverse impact of Chopin Airport in Warsaw on the prices of single-family houses located within the aircraft noise impact zone. The specific feature of the largest airport in Poland is its location within the city limits and the resulting direct surroundings of both multi- and single-family housing developments. Not only is the nuisance due to the proximity of the airport resulting from the actual exposure to an excessive noise level but also from legal restrictions associated with the Limited Use Area (LUA). The study used statistical modeling by applying a classic multiple regression model, spatial autoregressive model and geographically weighted regression model. Moreover, Geographical Information System (GIS) tools and geostatic modeling were used to visualise the results. The modeling results clearly show the significant impact of the neighborhood nuisance and the related spatial distribution of real estate prices. In addition, the geographically weighted regression model indicates that the proximity to an airport adversely affects the rate of price changes over time.

An immanent feature of the housing market is a large spatial dispersion of real estate prices along with their simultaneous high stratification. Application of classic methods of data interpolation results in an excessive simplification of the outcome because of a conversion of the dispersed data sets into areas of spatial continuity by reducing the above-average real estate prices. The main aim of the article was to search for spatial discontinuities of real estate prices’ distribution with 3D modeling using Voronoi diagrams as a method of irregular division of this space. Used methods of geospatial analyses with GIS tools enabled to identify clusters of high housing market activity and to avoid an excessive generalization of data resulting from the reduction of the above-average real estate prices. The research was conducted for over 7000 real estate transactions in years 2010–2017 in Olsztyn, the capital city of Warmia and Mazury in Poland, resulting in a 3D visualization of real estate prices for the chosen market, including the discontinuity in their spatial distribution.