How Do Housing Prices Affect Residents' Health? New Evidence From China

Abstract

This paper aims to explore the effect and mechanism of rising housing prices on residents' physical and mental health. Using data from the China Family Panel Studies from 2014 to 2018, we investigate the impact and mechanism of rising housing prices on the mental and physical health of urban residents through multiple grouping regression and analysis of variance. The study finds that overall, rising housing prices have a positive effect on residents' mental health but a negative effect on physical health, and those who do not own a house show the greatest adverse effect. The impact of rising housing prices on health is mainly reflected in three aspects: the wealth effect, cost effect, and comprehensive environmental expectation effect. Of these, the wealth effect and comprehensive environmental expectation effect play a role in promoting residents' health, whereas the cost effect has a strong inhibitory effect. This paper also analyzes how house prices impact health and finds that having health insurance reduces residents' active health behavior, thus affecting their physical and mental health levels, which has a positive effect on uninsured residents.

The impact of housing prices on residents’ health: a systematic review

“…Nine studies analyzed the impact of housing prices on physical health—utilizing various measures of physical health, including objective assessments of physical health (e.g., body mass), self-rated physical health assessments, reports of specific health conditions (e.g., COVID-19), reported health behaviours (e.g., alcohol use, smoking), and mortality ( [ 24 , 28 , 29 , 37 , 44 , 30 , 36 , 38 , 39 ]). Seven studies included both physical and mental health measures as their outcome variable [ 19 , 20 , 23 , 26 , 27 , 34 , 45 ].…”

“…Zhang & Zhang [ 33 ] included self-reported housing price. Alternatively, many studies examined housing market prices using existing survey data [ 20 , 23 – 29 , 31 , 32 , 34 – 37 , 40 ].…”

“…(2020) [ 30 , 38 , 39 ] Atalay et al (2017); Wang & Liang (2021); Sung & Qiu. (2020); De & Segura-Escano (2021) [ 27 , 30 , 38 ] Chen et al (2021); Arcaya et al (2020); Yuan et al (2020); Wei et al (2021); Xu & Wang (2021) [ 20 , 21 , 26 , 32 , 36 ] Positive Effect Yue & Ponce (2021); Atalay et al (2017); Wang & Liang (2021) [ 19 , 27 , 31 ] Yue & Ponce (2021). Wang & Liang (2021) [ 19 , 31 ] Fichera & Gathergood (2016); Feng & Nie (2022); Hamoudi, & Dowd (2013); Ratcliffe (2015) [ 23 , 34 , 37 , 40 ] Yue & Ponce (2021); Ratcliffe (2015).…”

“…Wang & Liang (2021); Sung & Qiu. (2020) [ 19 , 31 , 38 , 40 ] Kim et al (2021); Daysal et al (2021); Bao et al (2022) [ 24 , 28 , 29 ] Null Effects Yue & Ponce (2021) [ 19 ] Yue & Ponce (2021) Atalay et al (2017) Wang & Liang (2021) [ 19 , 27 , 31 ] Hamoudi, & Dowd (2013 Sung & Qiu. (2020) Wong et al (2020) [ 37 – 39 ] De & Segura-Escano (2021) Wang & Liang (2021) Hamoudi, & Dowd (2013) Sung & Qiu.…”

“…National patient registry provides information for hospital visits. Housing data comes from the States Scales and Valuation Registry Analysis: Secondary analysis Outcomes: Birth weight; Pre-maturity – birth Registry; Number of days hospitalized; Number of emergency room visits – national Patient Registry Explanatory: Price changes for homeowners and non-homeowners from the States Scale and Valuation Registry in Denmark Confounding: Household income; Years of education; Number of children; Partner; Unemployed; Age Mediating/Moderating : Health investments; Income effect - 100,000 DKK (Danish Krone) increase in house prices leads to a 0.15% decrease in the likelihood of being premature ( p < 0.05), and a 0.05 percentage point reduction in the likelihood of low-birth weight (statistically insignificant) De & Segura-Escano (2021) [ 30 ] N: 3.1 million people Location: United States Timeframe : 2005–2012 Data: Cross sectional method using the Behavioral Risk Factor Surveillance System in relation to home values per square foot, assessed using the Zillow Home Value Index Analysis: Several variations of a linear probability model, reported as beta coefficients Outcome : Current drinker (assigned 1 if drank in the past 30 days); Binge drinker (assigned 1 if drank 5 drinks or more for female, four or more drinks for males on one occasion); Excessive drinker (1 = more than 30 drinks a month for women, 1 = more than 30 drinks per month for men); Alcohol intensity (number of drinks consumed in one day, and number of days consumed alcohol in the past 30 days Explanatory: Zillow home price index Confounding/control variables : Gender; Marital status; Race/ethnicity; Education; Employment status; Income; Country-level characteristics (taxes on beer/alcohol, smoking laws, population density, country median income, country college ratio, country diversity); Homeownership (owner vs. renter) Mediating/moderating: Stress/mental health 1% decrease in Zillow Home Value Index for homeowners - leads to an increase in being a current drinker (beta = 0.0013, P < 0.01) - leads to an increase in being a binge drinker (beta = 0.0003, P < 0.01) - leads to an increase in being an excessive drinker (beta = 0.0002, P < 0.01) - leads to an increase in drinks per day (beta = 0.0007, P < 0.01), and days of alcohol in past 30 days (beta = 0.0169, P < 0.01) 1% decrease in Zillow Home Value Index for renters - leads to an increase in being a current drinker (beta = 0.0003, P < 0.10) - leads to a decrease in binge drinker (beta = -0.0001, statistically insignificant) - leads to an increase in excessive drinking (beta = 0.0003, statistically insignificant) - leads to an increase in drinks per day (0.0004, statistically insignificant), and increase in days of alcohol in past 30 days (0.0056, P < 0.01) Wang & Liang (2021) [ …”