Table 1 Data Extraction for Included Studies

Yue & Ponce (2021) [19]

N: 34,182 people

Location: United States

Timeframe: 1996–2016

Data: Longitudinal study with secondary analyses of Health and Retirement Study with linkage to zip-code level housing price data using Federal Housing Finance Agency

Analyses: Fixed-effects panel regression model with interaction term to test effect modification by housing status

Results reported in relative ratio

Outcome(s): Self-reported health (excellent/very good/good vs. fair/poor); Centre for Epidemiological Studies Depression Scale (CESD-8, 0–8 scores); Obesity (Body Mass Index ≥ 30 vs < 30); Current smoking (yes vs. no)

Explanatory Factor(s): Housing Price Index scores (i.e., zip-code level housing price index scores from the U.S. Federal Housing Finance Agency [FHFA])

Moderator(s)/Mediator(s): Housing status (outright owner, mortgaged owner, renter)

Confounder(s): Age, Gender, Birthplace, Race/Ethnicity, Educational attainment, Marital status, Employment status, County-level poverty rate, County-level median income, County-level unemployment, County-level number of hospital beds

- A 100% in HPI (Housing Price Index) scores is associated with 3.5% higher likelihood of reporting excellent/very good/good health status for renters, and 2.8% higher likelihood for mortgaged owners, both P < 0.01). For outright owners a 100% increase in HPI is associated with a 2.1% increase in reporting excellent/very good/good health though the association is not statistically significant (P < 0.1)

- For outright owners a 100% increase housing prices leads to a 2.7% decrease in CES-D scores, and a 3.96% decrease for mortgaged owners, though both are not statistically significant. For renters, a 100% increase in HPI leads to a 23.69% decrease in CES-D scores, (P < 0.01)

- A 100% increase in HPI leads to a 1.82% decrease in obesity for outright owners (P < 0.05) and 2.85% decrease for renters (P < 0.01). 100% increase in HPI leads to a 0.38% decrease in obesity for mortgaged owners, not statistically significant

- For outright owners, a 100% increase in HPI leads to a 0.95% increase in smoking, and for mortgaged owners a 0.03% decrease in smoking, both statistically insignificant. For renters, a 100% increase in HPI leads to a 3.03% decrease in smoking, P < 0.01)

Chen et al. (2021) [20]

N: 8318 people

Location: China

Timeframe: 2015

Data: Cross Sectional study design involving the Chinese General Social Survey and using Real Estate Statistics Database to obtain data on average selling price

Analysis: Multivariate ordered logistic regression models, results reported as coefficients

Outcome: Self rated physical health (what do you think of your current state of physical health – rate 1–5, very unhealthy – very healthy); Self rated mental health (how often have you felt depressed or depressed in the last four weeks – rate 1–5, always – never)

Explanatory: Average selling price from the Real Estate Statistics Database

Mediating/Moderator: Homeowner vs. renter; Related affordability; Reduction in purchasing power

Confounding: Age; Gender; Marriage; Political status; Income; Work status; Household residence type

- Housing prices were negatively related to self-rated health for east (-0,573, P < 0.01) and central residents (-0.707, p < 0.05), but not statistically significant for west residents (-0.637, P < 0.559)

-Housing prices are positively related to mental health among eastern respondents (0.332, p < 0.01), but a negative impact on central residents’ mental health (-0.040, P = 0.912), and negative impact on west respondents’ mental health (-0.457, P = .417)

Arcaya et al. (2020) [21]

N: 300 municipalities

Location: municipalities in Boston metro area

Timeframe: 2015–2020

Data: Cross Sectional study design. The qualitative component of this study includes 40–80 min in depth, semi-structured interviews. The quantitative data includes COVID19 case numbers from the Massachusetts Department of Public Health. Municipal level housing prices were created using Zillow data

Analysis: Semi-structured participant interviews with content analysis & ordinary least squares regression models using COVID-19 rate per 100,000

Outcome: Increased/decreased incidence of COVID19 cases (COVID19 data obtained from Massachusetts department of public health)

Explanatory: Five-year change in municipal level housing values (Zillow Home Value Index of 2015 and 2020) burden of housing cost for low-income households

Confounding: Share of population foreign born, living in poverty, proximity to Boston, crowding and density, racial/ethnic composition, work from home, and other suspected community-level differences

Mediator/Moderating: Crowding; Doubling up; Homelessness; Part-time work

- An increase of 1% in housing value, since 2015, is associated with an increase in 14 additional cases of COVID19 per 100,000. (P < 0.05)

Lee et al. (2021) [22]

N: 2556 quarterly observations of Taiwan housing index

Location:China

Timeframe: 2001–2011

Data: Longitudinal study with secondary analysis of National Health Insurance Research Database (NHIRD) & the Taiwan Housing Index (TH)

Analysis: Distributed lag nonlinear model (DLNM), results reported as relative risks

Outcome: Increase/decrease in antidepressant prescription incidence using ambulatory care data and inpatient expenditure data from the longitudinal health insurance database

Explanatory: Fluctuations in housing market using the Taiwan housing index

Moderating/Mediating: Socioeconomic status; Homeowner vs. buyer

Confounding: SARS, financial crises; Stock; Long-term trends

- A 13.3% increase in antidepressant prescriptions was observed when housing index peaked at 170.13 (p < 0.05)

Fichera & Gathergood (2016) [23]

N: 105,170 individual person-years of observation

Location: United Kingdom

Timeframe: 1993–2008

Data: A longitudinal study using the British Household Panel Survey

Analysis: Various fixed effect models, and results reported as regression coefficients

Outcome: Number of health conditions (13 asked); Self-assessed health (1–5, excellent – very poor); Depression; GHQ-12 for psychological health (0-poorest, 12-highest)

Explanatory: Housing price data

Confounding: Country level employment, annual income; Work-related behaviors

Mediating/Moderating: Labor market activity (Hours of work); Health care coverage

-A 100% increase in housing prices, leads to a 0.0819 decrease in multiple health conditions (p < 0.01)

-A 100% increase housing prices leads to a 0.0377-point decrease in self assessed health (P < 0.01)

-A 100% increase in housing prices leads to a 0.00491-point decrease in depression (statistically insignificant)

-A 100% increase in housing prices leads to a 0.0313-point decrease in general health questionnaire (statistically insignificant)

Kim et al. (2021) [24]

N: 423 "units of analysis" or neighborhoods

Location: South Korea

Timeframe: 2013–2018

Data: Cross Sectional study using data from the Ministry of Interior and Safety. Neighborhood housing price data obtained from the Ministry of Land, Infrastructure, and Transport. Supplementary data obtained from the Seoul Metropolitan Government’s information disclosure system

Analysis: Mapping distribution of all-cause mortality and housing prices using pooled OLS models

Outcome: All-cause mortality using Ministry of Interior and Safety data

Explanatory: Median houses price using the Ministry of Land, infrastructure, and transport data in Korea

Confounding: Poverty rate; Population density; Business workers; Number of nearby subway stations; Movement to other neighborhoods; Social and Public Health Policies

Mediating/Moderating: Education; Access to resources; Socioeconomic development of area

-A 1% increase in housing prices was related to a 0.05% decrease in all-cause mortality

Hamoudi & Dowd (2014) [25]

N: 4207 people

Location: United States

Timeframe: mid 1990s to mid 2000s

Data: Longitudinal study using data from the Health and Retirement Study (HRS), and housing data from DataQuick

Analysis: Regression modeling, results reported as regression coefficients

Outcome: Depression measured with CES-D; Beck Anxiety inventory; Mroczeck/Korarz Positive Affect Inventory; Mrocczek/Kolarz Negative Affect Inventory

Explanatory: Housing values from DataQuick

Confounding: Home value at baseline; Total non-housing wealth; Share of housing equity at baseline; Birth year; Sex; Area of residence; Self-rated health; Indicators for smoking and exercise at baseline; Share of housing equity at baseline; Study cohort

Mediating/Moderating: Homeowner vs. renter; Wealth; Local area improvements

- Movement from 10th-90th percentile for homeowners in terms of housing appreciation is associated with:

- decreased likelihood of anxiety in homeowner females (-19.6), and increased likelihood of anxiety in male homeowners (1.7)

- (b) decreased depression risk among female homeowners (-2.3), and decreased depression among male homeowners (-1.3)

- decrease likelihood of negative effect among female homeowners (-3.5) and decrease likelihood in negative effect for male homeowners (-7.7)

- increase in likelihood of positive affect for female homeowners (5.9), and male homeowners (1.9)

- increase in likelihood of anxiety for female renters (2.9), and increase in likelihood for male renters (68.2)

- increase in likelihood of depression for female renters (15.7), and increase in likelihood for male renters (16.8)

- increase in likelihood of negative effect for female renters (27.7) and increase for male renters (35.8)

- decrease in likelihood for positive affect among female renters (-18.7) and increase for male renters (8.3)

Yuan et al. (2020) [26]

N: 34,000 people

Location: China (25 provinces)

Timeframe: 2010 and 2014

Data: Cross sectional study using data from the 2010 and 2014 China Family Panel Study

Analysis: Regression analysis, reported as coefficients

Outcome: Physical health—"What do you think of your health" (quite healthy, very healthy, healthy = 1, normal, unhealthy-0); Mental acuity—"Can you remember the main things that happened to you in a recent week"(remember all, remember most = 1, remember half, remember a few, remember a little = 0); Emotional well-being—"How often have you felt upset, depressed, and unable to do anything in the last month", and "How often have you felt nervous in the last month (1–5 – almost every day – never, the average of two answers taken for emotional well-being score)"

Explanatory: Average annual growth rate of residential housing price over past five years

Confounding: Gender; Age; Years of education; Smoking; Exercise; Income; Housing ownership; City-level factors

Mediating/Moderating: Social status seeking behavior

-Rising housing prices negatively impacts the physical health (-0.575, P < 0.01), mental acuity (-0.198, P < 0.01), and emotional well-being (-0.092, P < 0.01) of middle-aged and elderly people

Atalay et al. (2017) [27]

N: 19,000 people

Location: Australia

Timeframe: 2001–2015

Data: Serial cross-sectional using data from the RP Data Historical house price dataset, and the Household, Income, and Labor Dynamic in Australia Survey (HILDA)

Analysis: Fixed effects model w/multiple linear regression analysis

Outcomes: Mental and physical health measures from the 36-item short form health survey (average of four responses taken to calculate a single score for mental health and physical health scores)

Explanatory: Local area median house price series from RP Data Historical data set

Confounding: Demographic controls; Employment; Year fixed effects; Individual LGA fixed effects; LGA level employment; LGA level average income; Household income; Tenure status (mortgaged owner, outright owner, renter)

Mediating/Moderating; N/A

-A one standard deviation increase in house prices leads to an increase in physical health for outright owners (p < 0.05), but the impact is statistically insignificant on the mental health for outright owners

-For renters, a one standard deviation increase in house prices leads to a decrease in physical health (p < 0.01), and a 0.801 decrease in mental health (p < 0.01)

-For mortgaged owners, the impact of house price growth on the physical and mental health is statistically insignificant

Bao et al. (2022) [28]

N: 9 countries

Location: Canada, France, Japan, Netherlands, Spain, Switzerland, Sweden, United Kingdom, USA

Timeframe: 1996–2019

Data: Cross Sectional Study design, using data for housing rent from the OECD, and the World Bank for the remaining variables

Analysis: The study adopts the fixed effect model (FEM) and random effect model (REM) methods, reporting as regression coefficients

Outcomes: Infant mortality rate (per 1,000 live births); Life expectancy at birth in total years

Explanatory: Housing rent prices are taken at the base of 2015. Data for house rent is extracted from the OECD

Confounding: GDP per capita; Health expenditures; Unemployment

Mediating/Moderating: Real estate development (including health facilities); Consumption

-In Fixed Effect Model (FEM) 1% increase in house rent leads a reduction in infant mortality rate (-0.020, p < 0.05) and increases life expectancy (0.040, p < 0.1)

-In the Random Effect Model (REM), a 1% increase in house rents leads to decrease in infant mortality rate, (-0.027, P < 0.01) and an increase in life expectancy (0.089, P < 0.01)

Daysal et al. (2021) [29]

N: 204,507 people

Location: Denmark

Timeframe: 1992–2011

Data; Longitudinal study; Birth registry, including hospital or home births and infant health. 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) [31]

N: 51, 258 people

Location: China

Timeframe: 2014, 2016, 2018

Data: Cross sectional including data the China Family Panel Studies in 2014, 2016, 2018, and the housing price data is from the China Statistical Yearbook in 2014 and 2018

Analysis: Econometric regression model

Outcome: CES-D scores to assess psychological health; Five questions assess physical health: Self-rated physical health (How do you think your health status is?); Recent changes in health status (scale 1–3, 1-Worse, 2-No change, 3-Better); Recent physical discomfort (Binary, Yes/No); Degree of physical illness and injury, and chronic disease"(1–3, 0-No disease or injury 2-Moderate, 3- Serious); Chronic disease (Binary, Yes/No)

Explanatory: House price data from the China Family Panel Study by the Chinese Social Science Survey

Confounding/control variables: Personal; Family; Regional characteristics; Sex; Age; Education; Employment status; Weekly exercise duration; Homeownership (Owner vs. without houses); Housing area size; Total family property; Net assets; Disposable income; Number of urban health technicians

Mediating/Moderating: Health behaviors; Area level improvements

For residents who own houses, with mortgage, 10% increase in housing prices

- leads to decrease in CES-D scores (-0.103, P < 0.01)

- leads to decrease in self-assessment of physical health status (-0.038, P < 0.05)

- leads to a greater likelihood of reporting positive changes in health status (0.019, statistically insignificant)

- leads to greater likelihood of decrease in degree of physical disease and injury (-0.042, statistically insignificant)

- decrease in physical discomfort (-0.046, P < 0.01)

- leads to increase in prevalence of chronic disease (0.006, statistically insignificant)

For residents who own homes without mortgages, a 10% increase in housing prices leads to

- increase in CES-D scores (0.072, P < 0.05)

- increase in reporting better physical health status (0.026, P < 0.01)

- decrease in reporting positive changes in health status (-0.009, P < 0.10)

- decrease in likelihood of reporting physical discomfort (-0.025, P < 0.01)

- decrease reporting physical disease or injury (-0.028, P < 0.01)

- decrease in probability of chronic disease (-0.001, P < 0.01)

For residents without houses, with mortgages (previous loan), a 10% increase in housing prices leads to

- decrease in CES-D scores (-0.091, statistically insignificant)

- decrease in self-assessment of physical health (-1.10, P < .10)

- increase in better changes in health (1.41, P < 0.05)

- decrease in recent physical discomfort (-1.71, P < 0.01)

- decrease in degree of physical disease and injury (-1.5, P < 0.01)

- decrease in reporting chronic disease (-0.025, statistically insignificant)

For residents without houses, without mortgages 10% increase in housing prices leads to change in probability of reporting:

- increase in CES-D scores (0.024, P < 0.10

- decrease in physical health status (-0.0005, statistically insignificant)

- increase in better changes in health (0.023, P < .10)

- decrease in physical discomfort (-0.065, P < 0.01)

- decrease in degree of physical injury and illness (-0.07, statistically insignificant)

- decrease in reporting chronic disease (-0.025, P < 0.01)

Wei et al. (2021) [32]

N: 1116 observations from 32 cities

Each observation contains information on mental health and the monthly house price

growth rate of each city

Location: China

Timeframe: 2013–2017

Data: Longitudinal study with secondary analysis of serial cross-sectional (monthly) Chinese data using the China Health Insurance Research Association claim database; quality adjusted house price index

Analysis: Fixed-effect models

Outcome: Mental health consultation was measured using the China Health Insurance Research Association claims database, which included the rate at which the people consulted physicians regarding mental health concerns

Explanatory: Quality adjusted house price index- growth rate was computed at 1, 3, 6, 12 months

Confounding/Control: Disposable income; City fixed effects; Year by month fixed effects

Mediating/Moderating: Housing affordability (percentage of income spent on housing); Marriage prospects within Chinese culture favors males or males’ family who can afford to buy a home

-Increase of one standard deviation in the past 3 months leads to an increase of 0.0443 standard deviation in consultation rate for mental disorders. (p < 0.05)

-House price growth rates in the past 1, 3, and 6 months has a statistically significant impact on the consultation rate, however, not for house price growth in the past 12 months, suggesting a short-term impact rather than a long-term impact

Zhang & Zhang (2019) [33]

N: 9414 people

Location: 28 provinces of Mainland China

Timeframe: 2011

Data: Cross sectional secondary analysis of China Household Finance Survey

Analysis: Ordered prohibit model, coefficients reported

Outcome: Subjective well-being (5-point Likert scale, 1- very unhappy, 2- unhappy, 3- neutral, 4- happy, 5-very happy)

Explanatory: 2011 China Household Finance Survey—house value appreciation; Home ownership; House value at the time of purchase; Current house value

Mediating/Moderating: Homeownership; Income; Region of residence

Confounding: not listed

-A 1% increase in home values leads to an increase in the subjective well-being of homeowners. (0.070, p < 0.01)

Feng & Nie. (2022) [34]

N: 44,495 observations physical health. 29,647 observations mental health

Location: China

Timeframe: 2012, 2014, 2016

Data: Cross- sectional secondary analysis of China Family Panel Study; House price data came from the CEInet statistics database

Analysis: Regression modelling, coefficients reported

Outcome: Physical health measured using four health indicators leading to a complex index (ranging from 0–1); Mental health measured by average value from 20 questions (ranging from 1 to 4). Higher the value, worse the mental and physical health

Explanatory: Housing price data from CEInet statistics database

Mediating/Moderating: Number of owner-occupied houses; Net housing value

Confounding: Year fixed effects; Regional year fixed effects; Country-individual fixed effects

-Housing prices have a significant positive impact of the physical healthof residents (-0.050, P < 0.05) and a negative impact on mental health (0.161, P < 0.01)

Chun (2020) [35]

N:191,121 people

Location: South Korea

Timeframe: 2009–2015

Data: Longitudinal design using Korea Health Panel data; Korea Appraisal Board data for the housing price index

Analysis: Empirical analysis of the impact of house price changes on depression, coefficients, and OR reported

Outcome: Depression (as a proxy of mental health)

Explanatory: Korea appraisal board housing price data

Confounding: Sex; Age; Marital status; Number of households members; Education level; Status of employment; Income; Smoking; Drinking

Mediating/Moderating; Homeownership vs. renters,

-The rise in housing prices, decreases likelihood of depression for homeowners (-0.01, P < 0.05), and renters (-0.01, statistically insignificant)

Xu & Wang (2021) [36]

N: 9,515 people

Location: 9 provinces of China

Timeframe: 2000–2011

Data: Longitudinal study design using China Health and Nutrition Survey (CHNS) including data on health status and behaviors. Housing price data from the China Real Estate Statistics Yearbook. The sample includes working age individuals between the age of 15–60

Analysis: Use an instrumental variable approach, coefficients reported

Outcome: Incidence of chronic diseases

Explanatory: Province level data from the China Real Estate Statistics Yearbook

Mediating/Moderating: Culture; Marriage prospects

Control variables: Number of medical personnel per 1,000 residents; Wastewater emissions and sulfur dioxide emissions; GDP per capita; Regional level fixed effects; Individual level fixed effects

-Result show that a 10% increase in housing prices leads to an increase the prevalence of chronic diseases (0.329, P < 0.01)

Hamoudi, & Dowd (2013) [37]

N: 4207 people

Location: United States

Timeframe: 1992–2006

Data: Quasi experimental design; Secondary analysis of Health and Retirement study and housing data from Data Quick

Analysis: Regression modelling, coefficients reported

Outcome: Capacity for daily living activities; Incidence of cardiovascular disease (Yes or No); Peak expiratory flow—measured with the Mini Wright Peak Flow Meter; Balance test; Timed walk task for participants aged 65 + ; Waist circumference; Diastolic and systolic blood pressure

Explanatory: Natural logarithm of home values in 2006

Confounding: Home value at baseline; Total non-housing wealth; Share of housing equity at baseline; Non-housing debt at baseline; Indicators for self-rated health at baseline; Area of residence; Birth year; Race; Gender

Mediating/Moderating: Homeownership vs. renters; Percent of wealth in home; Health investments

- An increase in housing prices was related to,

For all homeowners,

- a decrease in ADL difficulties (-6.3, P < 0.05)

- increase in full balance (10.4, P < 0.01)

-decrease in timed walking (-0.07, P < 0.66)

-decrease in lung capacity L/min (-0.75, P < 0.94)

- decrease in waist circumference (-1.2, P < 0.06)

- decrease in incident CVD (-1.0, P < 0.73)

-increase in systolic blood pressure (1.8, P < 0.42)

-increase in diastolic blood pressure (0.55, P < 0.67)

For renters, an increase in housing prices was related to:

- increase in ADL difficulties (5.6, P < 0.38)

- decrease in full balance (-3.6, P < 0.73)

- increase in times walking (0.22, P < 0.71)

- decrease in lung capacity (-45.7, P < 0.08)

- increase in waist circumference (1.3, P < 0.36)

- increase in incident CVD (3.7, P < 0.58)

- decrease in systolic blood pressure (-7.4, P < 0.38)

- decrease in diastolic blood pressure (-3.3, P < 0.3

Sung & Qiu. (2020) [38]

N: 983, 277 people

Location: United States

Timeframe: 2002–2012

Data: Serial cross-sectional study and data is from Behavioral Risk Factor Surveillance System, multiple surveys comprised into a single constructed dataset. The house price changes data comes from the MSA Freddie Mac House Price Index and the MSA median rent levels

Analysis: Fixed effect and time-series regression modeling, coefficients reported

Outcome: Self-assessed health is reported as a five-level ordinal variable (excellent – poor); Physical and mental health reported as count variables (number of physical/mentally unhealthy days in past 30 days); Obesity, Exercise, Smoking, Binge drinking, Flu shot, Seatbelt usage– yes/no, dichotomous variables; BMI, Average drinks per day, Binge drinking—continuous variables

Explanatory: Monthly MSA Freddie Mac House Price Index

Confounding: Household income; Education level; Age; Race; Marital status

Mediating/Moderating: Homeowners vs. tenants

Confounding: Rent levels; Economic conditions; Unemployment rate; Health infrastructure

-An increase of one standard deviation in the Freddie Mac Housing Price Index, leads to

For homeowners:

- decrease in reporting excellent health (-0.0021, statistically insignificant)

- decrease in reporting very good health (-0.0007, statistically insignificant)

- increase in reporting fair health (0.0010, statistically insignificant)

- increase in reporting poor health (0.0004, statistically insignificant)

- decrease in physically bad days (-0.0449, statistically insignificant)

- increase in mentally bad days (0.1001, P < 0.05)

- increase in any exercise (0.0020, statistically insignificant)

- increase in moderate exercise (0.0008, statistically insignificant)

- increase in vigorous exercise (0.0070, statistically insignificant)

- increase in being a current smoker (0.0054, P < 0.01)

- decrease in smoking everyday (-0.0032, statistically insignificant)

- increase in average drinks (0.0424, statistically insignificant)

- increase in binge drinking (0.0312, P < 0.10)

- decrease in flu shot (-0.0042, statistically insignificant)

- decrease in wearing seatbelt (-0.0018, statistically insignificant)

- increase in drunken driving (0.0131, statistically insignificant)

For renters:

- decrease in reporting excellent health (-0.0112, P < 0.01)

- decrease in reporting very good health (-0.0069, P < 0.01)

- increase in reporting good health (0.0064, P < 0.01)

- increase in reporting fair health (0.0087, P < 0.01)

- increase in reporting poor health (0.0030, P < 0.01)

- increase in physically bad days (0.0998, statistically insignificant)

- increase in mentally bad days (0.378, P < 0.01)

- decrease in BMI (-0.1205, statistically insignificant)

- increase in obesity (0.0021, statistically insignificant)

- increase in any exercise (0.0020, statistically insignificant)

- increase in moderate exercise (0.0008, statistically insignificant)

- increase in vigorous exercise (0.0045, statistically insignificant)

- increase in being a current smoker (0.0084, statistically insignificant)

- decrease in smoking everyday (-0.0037, statistically insignificant)

- increase average drinks (0.252, P < 0.01)

- increase in binge drinking (0.0574, statistically insignificant)

- decrease in flu shot (-0.0057, statistically insignificant)

- decrease in wearing a seatbelt (-0.0070, statistically insignificant)

- decrease in drunken driving (-0.0099, statistically insignificant)

Wong et al. (2020) [39]

N: 163,651 people

Location: United States

Timeframe: 2011–2015

Data: Cross-sectional observational study using data from the Behavioral risk factor surveillance system and the metropolitan statistical area home and rental value prices from Zillow

Analysis: Regression modelling, average marginal effects reported

Outcome: 6-item brief dietary assessment—Dark green vegetables; Orange vegetables; Other vegetables; Legumes; Whole fruit; 100% fruit juice. Respondents asked number of times food eaten and if eaten at least twice per week

Explanatory: Housing value measured using the Zillow Home Value Index

Mediating/Moderating: Homeowner vs. renters; Educational attainment; Race/ethnicity

Confounding/Controlling- Race/ethnicity; Education; MMSA level aggregate food price; Age; Educational attainment; Marital status; Income categories

-When MMSA-level home rental prices increase by $100:

- decrease in the frequency of eating vegetables per week (-0.028, statistically insignificant)

- decrease in the frequency of eating fruits per week (-0.108, statistically insignificant)

- decrease in the frequency of eating legumes per week (-0.011, statistically insignificant)

- decrease in the frequency of consuming juice per week (-0.055, statistically insignificant)

- decrease in the probability of eating all types of vegetables at least twice per week (-0.12, statistically insignificant)

- increase in the probability of eating fruits at least twice per week (0.01, statistically insignificant)

- increase in the probability of eating legumes at least twice per week (0.03, statistically insignificant)

- increase in the probability of consuming juice at least twice per week (0.01, statistically insignificant)

When MMSA-level home prices increase by 10,000 for homeowners:

- increase in the frequency of eating all types of vegetables per week (0.001,

- decrease in the frequency of eating fruit per week (-0.005, statistically insignificant)

- decrease in the frequency of eating legumes per week (-0.002, statistically insignificant)

- decrease in the frequency of consuming juice per week ( -0.001, statistically insignificant)

- decrease in the probability of eating all types of vegetables at least twice per week (-0.01, statistically insignificant)

- decrease in the probability of eating fruit at least twice per week (-0.03, P < 0.05)

- decrease in the probability of eating legumes at least twice per week (-0.02, P < 0.01)

- decrease in the probability of consuming juice at least twice per week (-0.01, statistically insignificant)

Ratcliffe (2015) [40]

N: 115 postcodes

Location: United Kingdom

Timeframe: 1991–2007

Data: Cross Section study using the BHPA between 1991–2007, contains General Health Questionnaire (12 questions aggregated to produce a 0–36 Likert scale value), house prices are matched using postal code data

Analysis: Correlations and proxies of area quality

Outcome: Mental well-being assessed using the GHQ 12 question survey

Explanatory: House price fluctuations

Mediating/Moderating: Homeowners (outright owners); Homeowners (mortgaged); Renters in private renting; Renters in social renting

Confounding/Controlling: Age; Marital status; Household consumption; Income; Macroeconomic shocks; New or old homeowners with mortgages

-The results for this study show that a 1% increase in local house prices, leads to an increase in the General Health Questionnaire by 0.005–0.006 units for both homeowners and renters (p < 0.05)

Joshi (2016) [41]

N: individuals less than age 65 in BRFSS between 2005–2011 (exact number not given)

Location: United States

Timeframe: 2005–2011

Data: Longitudinal study on individual level data from the BRFSS, monthly county-level Zillow Home Value Index (ZHVI) as a proxy for local house prices

Analysis: Exploiting a fixed-effects mode, regression coefficients

Outcome: Dependent variable, poor mental health days, from the response to the following question in the BRFSS: “Now thinking about your mental health, which includes stress, depression, and problems with emotions, how many days during the past 30 days was your mental health not good?”

Explanatory: Housing prices using Zillow

Mediating/Moderating: Predicted home ownership distribution

Confounding/Controlling: Age; Gender; Education; Race/Ethnicity; Marital status; Unemployment rates; Country fixed effects

-For those with a predicted home ownership level at 25th percentile, a 1% decrease in house prices leads to a 0.0050 increase in poor mental health days

- For those with a predicted home ownership level at 75th percentile, a 1% decrease in house prices leads to a 0.0029 increase in poor mental health days