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In recent years, a lot of empirical effort has been made to search for potential nonlinear responses of house prices to various demand and supply factors. This paper examines Poland's heterogeneous regional housing market reactions to key economic variables from 2000 to 2022. The study raises two research questions related to the asymmetric adjustment of housing markets to selected demand and supply shocks.

There is a broad consensus in international statistical organizations such as Eurostat and the International Monetary Fund that house price indices (HPIs) should be constructed using transaction data. We show here how transaction data for new‐built properties can undermine the timeliness of hedonic HPIs when new‐built properties are pre‐sold during the planning or building stage, but entered into deed books only once the projects are completed. As a consequence, HPIs for new‐builds will include stale transaction prices. We investigate this issue for two Polish cities (Warsaw and Poznan) and find that HPIs for existing properties lead indices for new‐builds by up to 2 years. This lag can dramatically distort National HPIs. The lag also has implications for the flagship measure of inflation in Europe—the Harmonized Index of Consumer Prices (HICP)—since it is planned to include owner‐occupied housing in the HICP using a transactions HPI specifically for new‐builds. We show that the timeliness issue disappears when preliminary agreements on new‐builds are used instead of transactions in the compilation of an HPI. [ABSTRACT FROM AUTHOR]

Objective: The article aims to investigate the synchronisation of metropolitan house prices from 2000 to 2020 based on novel empirical data. We applied several econometric procedures to address the cyclicity of housing markets. Research Design & Methods: Using a unique dataset of over four million offers, we investigated the synchronisation of 18 regional housing markets in Poland from 2000 to 2020. The study used spectral analysis. Findings: The study proved the strong synchronisation of housing cycles in Polish regional housing markets. Moreover, when the cycle amplitude was considered, the econometric analysis showed regional disparities in the cyclical behaviour of housing prices. According to the research, Lublin and Opole had the lowest housing cycle magnitude, and Gdańsk, Katowice, and Łódź had the largest magnitude. Implications & Recommendations: The information about housing cycles has important policy implications. Housing wealth effects generated by cyclical house prices may be more persistent than financial wealth effects. Consequently, they may substantially affect consumption dynamics. Therefore, precisely identifying market cycles and differences in cycle synchronisation is vital when constructing an effective, selective, and well-timed monetary policy. Contribution & Value Added: The research narrows the knowledge gap on the synchronisation of housing markets in an emerging economic environment. With their distinct institutional structures, Eastern European countries have little knowledge of this issue. [ABSTRACT FROM AUTHOR]

This article attempts to use spatial maps as a way of presenting additional information about the phenomena occurring in the housing market. In our opinion, spatial maps may facilitate understanding and provide more detailed information, which undoubtedly should increase the transparency of the housing market. The study used 12,219 transactions of apartments in Poznań in the years 2013–2017. General principles of price visualization activity and housing market dynamics were established in this study. The map of prices may reflect the location values determined by the quality of the urban infrastructure, distance from specific locations, and environmental factors. Market activity maps reveal areas where the market is dynamically developing, while information on trends in the number of transactions and price changes may demonstrate the growing or declining attractiveness of areas. The research is based on a model of hedonic regression in the form of ordinary least squares (OLS), quantile regression (QR), and geographically weighted regression (GWR). The maps presented should increase the transparency of the residential market (e.g., by providing more detailed information). However, one should bear in mind the limitations in the use of these methods resulting from a small number of transactions in a thin market. [ABSTRACT FROM AUTHOR]