Statistical Analysis of Housing Situation in EU Member States

Statistical Analysis of Housing Situation in EU Member States

Artur Zimny (State University of Applied Sciences in Konin, Poland) and Karina Zawieja-Żurowska (State University of Applied Sciences in Konin, Poland)
DOI: 10.4018/978-1-5225-2458-8.ch021
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Abstract

This chapter attempts to analyze the housing market. In particular, it attempts to modelling through a statistical analysis the housing market in member states of the European Union.
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The Scope Of The Analysis, Data Sources And Statistical Tools

In the research, which embraced 28 EU Member States, the used data comes from the website of Eurostat, the office providing statistics at European level (Population and social conditions – Income, Social Inclusion and Living conditions and social protection)5.

The severity (degree of intensity) of the direct, namely interior housing problems in particular EU Member States, was determined through calculating the mean value of four selected variables:

  • X1: Share of total population whose dwelling is in poor condition (leaking roof, damp walls, floors or foundations, damaged window frames and crumbling floors),

  • X2: Share of total population whose dwelling is not equipped with a bathtub or a shower,

  • X3: Share of total population whose dwelling is not equipped with an indoor flushing toilet for the sole use of their household,

  • X4: Share of total population considering their dwelling as too dark, making use of the following formula:

    (1)

for i = 1, 2,..., nwhere:

Xij – the value of the j-variable in the i-country,m – number of the variables,n – number of countries.

The severity (degree of intensity) of the indirect housing problems, namely connected with the residential area in particular EU Member States, was determined through calculating the mean value of three selected variables:

  • Y1: Share of total population whose dwelling is situated in a noisy neighborhood (the noise is made by neighbors or caused by traffic - comes from the street),

  • Y2: Share of total population whose dwelling is located in a polluted area (messy and filthy surrounding, environmental problems),

  • Y3: Share of total population whose dwelling is situated in a dangerous area (crime, violence, vandalism), making use of the following formula:

    (2)

for i = 1, 2,..., nwhere:

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