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The Journal of Real Estate Finance and Economics

Erschienen in:

18.11.2020

Housing Wealth, Health and Deaths of Despair

verfasst von: Ariadna Jou, Nuria Mas, Carles Vergara-Alert

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 3/2023

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Abstract

We use household-level data to study the causal effects of exogenous changes in housing wealth on health and the drug crisis in the US attributed to “deaths of despair”. We find that a one standard deviation positive shock in housing wealth increases the probability of an improvement in self-reported health (mental health) by 1.0 (1.10) percentage points, decreases the change in drug-related mortality rate by 4.3%, and has no effect on alcohol- or suicide-related deaths. The opposite effect also holds, such that a negative shock on wealth increases the probability of a decline in health. We also find that the impact of housing wealth on health varies across socioeconomic groups and is more pronounced in MSAs in which housing supply is more inelastic, which explains the differential effect of economic cycles across geographical areas. Our results suggest that housing-related policies could have important implications for general health outcomes as well as for the opioid crisis.

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Hedegaard et al. (2017) document that the age-adjusted rate of drug-overdose has increased from 6.1 per 100,000 in 1999 to 19.8 per 100,000 in 2016.

Case and Deaton (2015, 2017) named this crisis “deaths of despair”. They suggest that this increase has been due to difficult social and economic environments that have led to cumulative disadvantage over time.

See Kish and Lansing (1954); Follain and Malpezzi (1981); Goodman Jr and Ittner (1992); Agarwal (2007); Benítez-Silva et al. (2015); Kuzmenko and Timmins (2011); Corradin et al. (2017).

Housing wealth misestimation is large, even with the proliferation of online real estate appraisals such as Zillow, as well as the existence of real estate municipal tax assessments and appraisals for extracting home equity value. Zillow documents that 45.6% (25.5%) of Zillows estimates are off by 5% (10%) or more (see https://www.zillow.com/zestimate). Moreover, the geographical variation is sizable. For example, 32.7% (14.7%) of Zillows estimates are off by 5% (10%) or more in Phoenix, while 62.1% (44.9%) of Zillows estimates are off by 5% (10%) or more in New York.

We define change in health outcome as the difference in health from two years after the unexpected wealth shock to the year of the wealth shock (i.e., when the household moves). This definition addresses a potential concern related to the fact that health shocks might trigger moving houses.

Adler et al. (1994); Backlund et al. (1999); Chandola (2000); Contoyannis et al. (2004); Cutler et al. (2010); Cutler et al. (2016); Feinstein (1993); Golberstein et al. (2016); Humphries and Van Doorslaer (2000); Lewis et al. (1998); Lleras-Muney (2005); Meara (2001); Meer et al. (2003); World Health Organization (2003)

Panel Study of Income Dynamics, restricted use dataset. Produced and distributed by the Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI (2017). The collection of data used in this study was partly supported by the National Institutes of Health under grant number R01 HD069609 and R01 AG040213, and the National Science Foundation under award numbers SES 1157698 and 1,623,684.

As we focus on the SRH of the head of the household, we drop observations that indicate a change in age of more than five years from one period to the next. We also remove observations with a negative change in age.

If a household sells its house and buys a new one between years t − 1 and t, we can only obtain its declared value of the previous house at time t − 1 (before selling it) and the transaction price of the new house at time t. This

In the PSID data there are many variables related to health outcomes. For instance, there is information about specific health conditions such as strokes, cancer, high blood pressure, and diabetes in PSID. Instead, we use the most common composite measures of health status in the health economics literature: (1) SRH, (2) total ADLs, and (3) mental ADLs. Moreover, we have long time series of the variables that we need to calculate SRH and ADLs in the PSID data.

The list of activities asked at the PSID are: bathing or showering, dressing, eating, getting in or out of bed or a chair, walking, getting outside, using the toilet, preparing own meals, shopping for personal toilet items or medicines, managing own money, using the telephone, doing heavywork, doing lightwork.

“Has a doctor ever told you that you have... Any emotional, nervous, or psychiatric problems?”; “...loss of memory or loss of mental ability?”; “...a learning disorder?”

See https://wonder.cdc.gov/mcd.html.

See https://seer.cancer.gov/popdata.

We also show that our results are robust to the control for portfolio choice characteristics at the household level such as the ratio of housing to net wealth and stock holdings over total net wealth. Table 7 in the Appendix reports these results.

See http://www.arf.hrsa.gov.

See http://www.ddorn.net/data.htm.

See https://www.ers.usda.gov/data-products/county-level-data-sets/county-level-data-sets-download-data. 21 See http://www.pdaps.org.

An alternative approach could be to use interval regressions. Both methodologies produce coefficients of the same significance and order of magnitude, and have a similar fit in terms of log-likelihood. Although our empirical analysis is based on an ordered probit approach, we present results for both methodologies in the next section.

Our results are robust to the use of interval regressions.

The p-values for the t-tests on employment status, number of family members, and marital status are 0.28, 0.78, and 0.61.

Even if they do not sell, they would report a lower value of their house if they found that it was worth less because the question in PSID states “Could you tell me what the present value of this house (farm) is? I mean about what would it bring if you sold it today?”

Changes in the elasticity of supply at the MSA level are large in the cross-section but small in the time-series since we consider time lags of 2 years for changes in health outcomes in our panel. Recent studies that consider changes in the house price elasticity do not find relevant changes over short periods of time (e.g., Kirchhain et al. (2019)). Furthermore, there are no available time-varying measures of land elasticity at the MSA or city level that cover our period of study (1986–2015). For instance, Kirchhain et al. (2019) cover the time period 2014–2016.

See http://www.federalreserve.gov/.

When limiting the specification to only those who move, results are consistent since RHWM will only be different from zero for those households that move when they move.

Davidoff (2016) criticizes the use of housing-supply constraints as IVs for house prices in studies in which the dependent variable has an economic component, such as consumption growth, leverage, or investments, because some demand factors that could affect both house prices and the dependent variable of interest might have been omitted. This is not the case in our study, as the dependent variable is change in health status.

We estimate this model using maximum likelihood. The estimation is performed using the CMP user-provided package in STATA. See https://ideas.repec.org/c/boc/bocode/s456882.html and Roodman (2009). This approach has been used extensively in the literature (e.g., Einav et al. (2012); Cullinan and Gillespie (2016)).

This choice of 33% divides our sample in about half, that is, 50% of the households in our sample live in the top 33% inelastic MSAs. Our results are robust to the choice of 33% as the threshold between elastic and inelastic cities. In the Appendix, we also report these results using a continuous measure of elasticity. These results are also robust, but less significant.

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Titel: Housing Wealth, Health and Deaths of Despair
verfasst von: Ariadna Jou
Nuria Mas
Carles Vergara-Alert
Publikationsdatum: 18.11.2020
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 3/2023
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-020-09801-5

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