A statistical framework for analyzing housing quality: a case study of New York City

Damien Chambon () and Jacob Gerszten ()
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Damien Chambon: University of Virginia
Jacob Gerszten: University of Virginia

Computational Statistics, 2023, vol. 38, issue 4, No 5, 1669-1685

Abstract: Abstract The physical condition of an occupant’s home represents a significant factor in determining the dweller’s overall quality of life. This paper provides a statistical framework for measuring housing quality in an urban area through a standardized index. This index is constructed using principal component analysis, incorporating demographic, geographic, and economic factors from the New York City Housing and Vacancy Survey. This metric allows for investigating differences in housing quality based upon ownership status. Analysis of the index demonstrates that renters face more housing quality issues than owners. Several of the index’s input variables driving these differences were found to exhibit varying effects on housing quality over time, possibly due to events such as the 2008 financial crisis. Implementing this novel statistical framework, housing quality indices can be constructed for other cities to examine housing disparities and inform policies aimed at improving quality of life for urban residents.

Keywords: Housing quality index; Ownership status; Principal component analysis; Linear regression; Mann–Whitney test (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-023-01394-w

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