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In many countries, achieving social mixing through a mix of housing tenures became a central strategy in socially inclusive urban renaissance initiatives. While several studies have explored the relationship between tenure and social mix, they often treat social and affordable housing as a homogeneous segment, neglecting the diversity of housing providers and their individualized housing allocation strategies. There is limited understanding of the role of diverse housing providers in creating social mix. This paper aims to address this gap by examining how the presence of different types of social and affordable housing providers impacts the level of social mix and the changes in social mix resulting from tenure conversions in the housing stock owned by different providers. Using population census data in 1988 and 2002 from Warsaw, Poland, from two periods characterized by different housing policies and tenures, the paper argues that social mix was primarily associated with a combination of housing tenures and property ownership, but only in 2002. The findings suggest that limitations on tenure conversions are necessary to achieve long-term social mix. In addition, there is a need for affordable housing providers to diversify and specialize in addressing changing housing needs and dynamic housing aspirations. • A 'socialist' social mix was prevalent in areas dominated by a single ownership form. • 40% of both public rentals and privately-owned units maintained social mix. • The presence of state-owned housing with a single tenure ensured social mixing after the conversion of tenures. • Privatization in cooperative housing with two tenures resulted in social upgrading. • Affordable housing providers need to specialize in order to address changing housing needs and dynamic housing aspirations. [ABSTRACT FROM AUTHOR]

• Novel universal methodology of spatio-temporal comparison of stability of spatial data-driven clusters. • Integrates spatial econometrics, spatial statistics and unsupervised learning. • K-means clusters of GWR model coefficients are rasterised and the mode cluster id labels given raster cell, Rand index and Jaccard Similarity test if pairs of pairs of raster cells belong to the same clusters in both periods. • Clusters of GWR coefficients indicate spatio-temoral stability of submarkets, while clusters of standard errors of GWR coefficients detect stability of local spatial heteroscedasticity. • Methodology can be applied to any spatial phenomena, including housing submarkets. This paper fills the gap in rich housing literature by testing the spatio-temporal stability of real estate submarkets. We start with standard Geographically Weighted Regression (GWR) estimation of the hedonic model on point data, and we cluster model coefficients to detect housing submarkets. We check spatio-temporal stability - we add novelty by comparing if clusters move over space or stay in the same place. We rasterise surface and apply the Rand Index and Jaccard Similarity to check if clusters assigned to raster cells yield stable spatial structure. This approach allows for quantitative assessments of how much determinants of price are stable over time and space. The same mechanism applied to standard errors of GWR coefficients is a good test of the spatio-temporal stability of local heteroscedasticity. A Case study of apartments' transactions in Warsaw-Poland for the 2006–2015 period, evidences relatively high spatio-temporal stability. [ABSTRACT FROM AUTHOR]