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Eviction research and data on one website

Conceptualising the right to housing

Determining and explaining the impact on national legal law

Using data science techniques to analyse legal big data

LOREM IPSUM DOLOR

The Impact of the International Right to Housing on National Legal Discourse: Using Data Science Techniques to Analyse Eviction LitigationThe Impact of the International Right to Housing on National Legal Discourse: Using Data Science Techniques to Analyse Eviction Litigation

Eviction – the involuntary loss of one’s home – has a devastating impact on people’s wellbeing and has severe consequences for society as a whole. During and after the financial crisis of 2007-2011, over 700,000 people in Europe either lost their homes or were at risk of losing them.

National courts use national laws to rule on whether an eviction is just. However, the right to housing, as laid down in international and European law, often demands more protection of the power- and propertyless than national laws prescribe. As a result, national courts are at the centre of the complex interaction between national and international law. In times of growing national resistance towards international law, the questions whether, how, and why international law impacts on national law are among the most topical that legal scholars face.

Evictions provide a timely opportunity to determine why international rights, such as the right to housing, may or may not have an impact on national law. The financial crisis has led to an enormous amount of case law (legal big data). The combination of the developed, but understudied, international right to housing and these vast amounts of national data offers a unique opportunity to examine the interaction between international law and national law.

It is impossible to analyse all judgments manually. Therefore, I will use a data-driven approach that is unique in the legal discipline. Using citation network analysis, I conceptualise the right to housing as a network of international rights and conduct the first empirical analysis of the impact of this right in case law from national supreme courts and lower level courts. With the use of machine learning, I will identify predictors for courts’ decisions, and explain how these predictors may mirror the right to housing. This approach has long been called for but, so far, rarely been executed. If successful, it could be used in future research projects in other areas of the law.Eviction – the involuntary loss of one’s home – has a devastating impact on people’s wellbeing and has severe consequences for society as a whole. During and after the financial crisis of 2007-2011, over 700,000 people in Europe either lost their homes or were at risk of losing them.

National courts use national laws to rule on whether an eviction is just. However, the right to housing, as laid down in international and European law, often demands more protection of the power- and propertyless than national laws prescribe. As a result, national courts are at the centre of the complex interaction between national and international law. In times of growing national resistance towards international law, the questions whether, how, and why international law impacts on national law are among the most topical that legal scholars face.

Evictions provide a timely opportunity to determine why international rights, such as the right to housing, may or may not have an impact on national law. The financial crisis has led to an enormous amount of case law (legal big data). The combination of the developed, but understudied, international right to housing and these vast amounts of national data offers a unique opportunity to examine the interaction between international law and national law.

It is impossible to analyse all judgments manually. Therefore, I will use a data-driven approach that is unique in the legal discipline. Using citation network analysis, I conceptualise the right to housing as a network of international rights and conduct the first empirical analysis of the impact of this right in case law from national supreme courts and lower level courts. With the use of machine learning, I will identify predictors for courts’ decisions, and explain how these predictors may mirror the right to housing. This approach has long been called for but, so far, rarely been executed. If successful, it could be used in future research projects in other areas of the law.

Prof. dr. Michel Vols and project team

Prof. dr. Michel Vols will be the Principal Investigator of the EVICT Project. Furthermore, two Postdocs, two PhD students and some research assistants will be part of the EVICT team

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Prof. dr. Michel Vols

University of Groningen

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