Housing market heterogeneity and cluster formation: evidence from Poland

Author:

Tomal Mateusz

Abstract

Purpose This study aims to identify clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level. In addition, this work is intended to detect the socio-economic factors driving the cluster formation. Design/methodology/approach To group the studied housing markets into homogeneous clusters, this analysis uses a proprietary algorithm based on taxonomic and k-means++ methods. In turn, the generalised ordered logit (gologit) model was used to explore factors influencing the cluster formation. Findings The results obtained revealed that Polish county housing markets can be classified into three or four homogeneous clusters in terms of the size and quality of the housing stock and price level. Furthermore, the results of the estimation of the gologit models indicated that population density, number of business entities and the level of crime mainly determine the membership of a given housing market in a given cluster. Originality/value In contrast to previous studies, this is the first to examine the existence of homogeneous clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level simultaneously. Moreover, this work is the first to identify the driving forces behind the formation of clusters amongst the surveyed housing markets.

Publisher

Emerald

Subject

General Economics, Econometrics and Finance

Reference47 articles.

1. Homogeneous groupings of metropolitan housing markets;Journal of Housing Economics,1994

2. Housing submarkets in Istanbul;International Real Estate Review,2008

3. Convergence in condominium prices of major US metropolitan areas;International Journal of Housing Markets and Analysis,2019

4. Florida metropolitan housing markets: examining club convergence and geographical market segmentation;Journal of Housing Research,2020

5. Arthur, D. and Vassilvitskii, S. (2006), “K-Means++: the advantages of careful seeding”, Technical Report No. 2006–13, Stanford InfoLab, Stanford, available at: http://ilpubs.stanford.edu:8090/778/ (accessed 2 November 2020).

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