The relationship between the built environment and subjective wellbeing – Analysis of cross-sectional data from the English Housing Survey. (April 2022)

• Record Type: Journal Article
• Authors: Huebner, Gesche M.; Oreszczyn, Tadj; Direk, Kenan; Hamilton, Ian
• Is Part Of: Journal of environmental psychology. Volume 80(2022)
• Publication Date: 2022-04
• Subjects: Subjective wellbeing -- Built environment -- Health -- Housing -- Neighbourhood -- English Housing Survey (EHS); Environmental psychology -- Periodicals; Environment -- Periodicals; Psychology -- Periodicals; 155.905
• DOI: 10.1016/j.jenvp.2022.101763
• Languages: English
• ISSNs: 0272-4944

Abstract

Abstract: This paper assesses how subjective wellbeing is related to housing and neighbourhood characteristics, controlling for personal variables. The secondary data analysis was based on the English Housing Survey, 2017: Housing Stock Data and the English Housing Survey: Fuel Poverty Dataset, 2017, collected in the period April 2016 to March 2018 (N = 9205). Subjective wellbeing was measured with four variables - life satisfaction, the perception of things being worthwhile in life, feeling happy and feeling anxious - that were dichotomized into low and high wellbeing. Logistic regression analysis showed that personal variables are most strongly related to wellbeing but that both housing and neighbourhood variables are also significantly related to it. Finding it difficult to keep the living room warm, being in fuel poverty, and finding it difficult to meet heating costs were associated with lower wellbeing. Low area satisfaction and not feeling safe were also significantly associated with lower wellbeing. The effects of variables are not constant across all four wellbeing measures used which raises the question 'which wellbeing' should be addressed. Results also showed that targeting householders with lowest wellbeing and hence in greatest need of wellbeing interventions based on publicly available data would be challenging. Finally, the research community needs to address methodological challenges around identifying the most appropriate covariates, defining wellbeing and considering the measurement of key variables. Highlights: Personal variables explain most variance in subjective wellbeing. Housing and neighbourhood variables also related to wellbeing. Different facets of wellbeing differentially related to predictors. Cold and difficultto keep warm homes associated with lower wellbeing. Lower area satisfaction and feeling unsafe associated with lower wellbeing.