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<p>The study aims to analyse the diversity of residential vacancy rates in Poland and identify local factors influencing their formation. Census data from 2021 and a set of economic, demographic, political, and technical-location variables for 378 counties were used. A two-stage research procedure was applied, based on the methods of multiscale geographically weighted regression (MGWR) and k-means + + clustering. MGWR enabled the determination of the spatial variability of the impact of individual predictors, thereby capturing local mechanisms of vacancy formation. Clustering, on the other hand, enables grouping counties according to similar vacancy rates and sets of determinants, providing a basis for creating territorially differentiated interventions. The results indicate that the most critical predictor of vacancy rates is the average year of construction, and the younger the properties in the county, the lower the vacancy rate. Price expectations regarding an increase in property prices also reduce vacancy rates. In contrast, positive net migration has the opposite effect, increasing vacancies in areas with population inflows. Factors such as living costs, local taxes, and security were statistically insignificant. The five clusters of counties identified show different characteristics. The study enables the creation of a more precise housing policy and has international implications.</p>

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Identification of County-Specific Policies on Vacant Housing in Poland

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Mateusz Tomal,
  • Klaudia Tomasik

摘要

The study aims to analyse the diversity of residential vacancy rates in Poland and identify local factors influencing their formation. Census data from 2021 and a set of economic, demographic, political, and technical-location variables for 378 counties were used. A two-stage research procedure was applied, based on the methods of multiscale geographically weighted regression (MGWR) and k-means + + clustering. MGWR enabled the determination of the spatial variability of the impact of individual predictors, thereby capturing local mechanisms of vacancy formation. Clustering, on the other hand, enables grouping counties according to similar vacancy rates and sets of determinants, providing a basis for creating territorially differentiated interventions. The results indicate that the most critical predictor of vacancy rates is the average year of construction, and the younger the properties in the county, the lower the vacancy rate. Price expectations regarding an increase in property prices also reduce vacancy rates. In contrast, positive net migration has the opposite effect, increasing vacancies in areas with population inflows. Factors such as living costs, local taxes, and security were statistically insignificant. The five clusters of counties identified show different characteristics. The study enables the creation of a more precise housing policy and has international implications.