Relative residential property value as a socio-economic status indicator for health research

Abstract

BackgroundResidential property is reported as the most valuable asset people will own and therefore provides the potential to be used as a socio-economic status (SES) measure. Location is generally recognised as the most important determinant of residential property value.Extending the well-established relationship between poor health and socio-economic disadvantage and the role of residential property in the overall wealth of individuals, this study tested the predictive value of the Relative Location Factor (RLF), a SES measure designed to reflect the relationship between location and residential property value, and six cardiometabolic disease risk factors, central obesity, hypertriglyceridemia, reduced high density lipoprotein (HDL), hypertension, impaired fasting glucose, and high low density lipoprotein (LDL). These risk factors were also summed and expressed as a cumulative cardiometabolic risk (CMR) score.MethodsRLF was calculated using a global hedonic regression model from residential property sales transaction data based upon several residential property characteristics, but deliberately blind to location, to predict the selling price of the property. The predicted selling price was divided by the actual selling price and the results interpolated across the study area and classified as tertiles. The measures used to calculate CMR were collected via clinic visits from a population-based cohort study. Models with individual risk factors and the cumulative cardiometabolic risk (CMR) score as dependent variables were respectively tested using log binomial and Poisson generalised linear models.ResultsA statistically significant relationship was found between RLF, the cumulative CMR score and all but one of the risk factors. In all cases, participants in the most advantaged and intermediate group had a lower risk for cardio-metabolic diseases. For the CMR score the RR for the most advantaged was 19% lower (RR = 0.81; CI 0.76-0.86; p <0.0001) and the middle group was 9% lower (RR = 0.91; CI 0.86-0.95; p <0.0001) than the least advantaged group.ConclusionsThis paper advances the understanding of the nexus between place, health and SES by providing an objective spatially informed SES measure for testing health outcomes and reported a robust association between RLF and several health measures.

Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation (Preprint)

“…While other studies have shown that staying in rental housing is associated with an increased risk of frequent admissions [78] and readmission [77], as far as the authors are aware, there has not been studies in the Singapore context that has demonstrated the use of housing type as a proxy for SES. This finding is useful given that most clinical and administrative databases do not collect direct SES information, and other commonly used proxies such as area-based income level from census data [36,37], insurance status [38] or property value [39,40]. Given that postal codes or addresses are routinely collected in most datasets, other studies could also use housing type/size as a proxy of SES in the absence of direct SES data.…”

“…While some studies have used the Charlson Comorbidity Index (CCI) [28,[31][32][33] and drug burden [34,35] to estimate patient complexity, validity of these measures as an estimate for patient complexity has rarely been established in Asia. In the absence of income data, SES is typically derived from area-based income level from census data [36,37], insurance status [38] or property value [39,40]. However, these proxies require additional data as well, which is often not readily available in healthcare administrative datasets or EMRs.…”

Property Values as a Measure of Neighborhoods

“…Average neighborhood appraisal values are not a “neighborhood wealth metric” 2 because (1) housing is a large, but not the only, component of household wealth 37 ; (2) some neighborhood residents may rent—appraisal values would be related to income rather than wealth for these residents; and (3) many home owners have small equity stakes, so in fact an inverse relationship between net-wealth and appraisal values may exist. 38–40 Average neighborhood appraisal values are also not “an objective spatially informed SES measure.” 1 Hedonic models indicate that neighborhood characteristics reflected in appraisal prices are those valued by local housing market participants; they are local residents’ mean subjective valuation of neighborhood conditions. Features of neighborhoods valued in some neighborhoods may not be valued elsewhere.…”

“…However, there is limited clarity about which health-relevant constructs are being measured by appraisal data. Recent studies have reported inverse associations between property values and cardiometabolic risk factors, 1 self-rated health, 2 and obesity. 3,4 However, the construct measured by appraisal data is underdeveloped and inconsistent with economists’ in-depth understanding of housing markets.…”

Geospatial and contextual approaches to energy balance and health

“…For example, a number of recent papers have proposed the use of property appraisal values as a measure of neighborhood characteristics (Moudon et al 2011, Coffee et al 2013). This is an interesting path because property appraisal values are available in many countries and updated frequently.…”