Housing Policy in the Eurozone Countries: a Multidimensional Contribute to the Analysis of Socio-economic Factors (2010-2014)

• Autor(a) principal: Santos, António Duarte
• Data de Publicação: 2016
• Outros Autores: Castela, Guilherme, Silva, Nelson
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/11144/2793
• topic: Housing policy; Housing affordability; BIPLOT

The latest global economic and financial crisis translated in adverse social consequences on many aspects, including the income and social situation of households and their living conditions, especially when the housing phenomenon is addressed. This reality of uncertainty as made even more relevant the study of the housing phenomenon, in particular from a perspective of analysis of its evolution. In this context, we revisit EUROSTAT’s databases which contain general economic indicators, housing stock indicators, housing affordability indicators, population and social conditions indicators and housing quality indicators, for twelve Euro Area countries, during 5 years, with a multidimensional perspective, using the HJ -BIPLOT method developed by Galindo (1986).This methodological approach identified and categorized twelve Euro Zone sample countries in latent constructs of reduced dimensionality related to the housing policy problematic. The simultaneous factorial representation identified the most relevant variables to characterize these countries, their trajectories during the period in analysis, and the relations between variables, between countries, and between variables and countries. This approach also made it possible to identify the most significant factors contributing to the countries performances as population at risk of poverty, unemployment rate, overburden rate, share of housing cost in disposable income, tenant and owner with mortgage or loan and also to cluster these countries according to their (dis)similitude. This analysis can be useful for housing research, when studying multivariate data and also, by its visual nature, a potential tool for producing richer information not only for the academia but also for policy makers.