A Multidimensional Analysis of Socioeconomic Factors in Housing Policy in the Eurozone Countries (2010–2014)

António Duarte Santos, N. Silva, G. Castela
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引用次数: 1

Abstract

Summary The latest global economic and financial crisis has had adverse social consequences in many areas, including income and the social situation of households and their living conditions, especially when the housing phenomenon is addressed. The reality of this uncertainty has made the study of the housing phenomenon even more relevant, in particular from the perspective of an analysis of its evolution. In this context, we revisit EUROSTAT’s databases. This analysis was done for twelve Euro Area countries over five years, using the HJ-BIPLOT method developed by Galindo (1986). This multidimensional approach identified and represented twelve Eurozone sample countries in latent constructs of reduced dimensionality related to the housing policy problem. The simultaneous factorial representation identified (a) the most relevant variables to characterize these countries, (b) their trajectories during the period in analysis, and (c) the relations between variables, between countries, and between variables and countries. This approach also identified the most significant factors contributing to the countries' performance. This methodological approach can be useful in housing research, when studying data of a multivariate nature, and is also, by its visual interpretation, a potential tool for producing richer information not only for academia but also for policy makers.
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2010-2014年欧元区国家住房政策社会经济因素的多维分析
最近的全球经济和金融危机在许多领域产生了不利的社会后果,包括家庭的收入和社会状况及其生活条件,特别是在解决住房现象时。这种不确定性的现实使得对住房现象的研究更加相关,特别是从分析其演变的角度来看。在此背景下,我们重新审视欧盟统计局的数据库。使用Galindo(1986)开发的HJ-BIPLOT方法,对12个欧元区国家进行了为期5年的分析。这种多维方法确定并代表了12个欧元区样本国家与住房政策问题相关的降维潜在结构。同时因子表示确定了(a)表征这些国家特征的最相关变量,(b)分析期间的轨迹,以及(c)变量之间、国家之间以及变量与国家之间的关系。这一方法还确定了影响这些国家业绩的最重要因素。当研究多变量性质的数据时,这种方法方法在住房研究中是有用的,而且,通过其视觉解释,不仅为学术界而且为决策者提供更丰富的信息,也是一种潜在的工具。
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