Property Value as a Spatial indicator of the Socioeconomic Status – Health Indicator Relationship — ASN Events

Property Value as a Spatial indicator of the Socioeconomic Status – Health Indicator Relationship (14706)

Neil T Coffee 1 , Tony Lockwood 2 , Catherine Paquet 1 , Natasha Howard 1 , Graeme Hugo 3 , Mark Daniel 1
  1. Spatial Epidemiology and Evaluation Research Group, University of South Australia, Adaliade, SA, Australia
  2. Centre for Regulation and Market Analysis, University of South Australia, Adelaide, South Australia, Australia
  3. Australian Population and Migration Research Centre, The University of Adelaide, Adelaide, South Australia, Australia

The association between socioeconomic status (SES), health and place has a long history.  The geography of SES is a marker of the health gradient with the less “well-off” reporting poorer chronic health outcomes than the “well-off”.  SES is typically represented using income, education and occupation and area-level census indices, such as the Australian Socio-Economic Indices for Areas. It has been suggested that SES measures that do not include wealth under report SES-health associations.  An emerging literature is examining real property as a wealth indicator.  

One property wealth measure designed to reflect spatial SES (SSES) is the Relative Location Factor (RLF) which uses a hedonic regression model and selected residential sales transaction data deliberately “blinded” to location. The difference between the predicted and actual sales price is taken to represent location, and the ratio of actual to predicted sale price is interpolated across the study area using ArcMap GIS. RLF is expressed at the individual property level rather than an arbitrary aggregated spatial unit resulting in a detailed view of the SSES, overcoming the problem of classifying neighbours with significantly different absolute property values into the same SES class. RLF highlights the within spatial unit SES variation that is lost when using area-level SES indices which can result in the modifiable areal unit problem (MAUP).

In a number of health analyses, lower RLF was significantly associated with higher relative risk of cardiometabolic disease. This presentation will focus on 1) the RLF methodology and 2) analyses investigating RLF and cardiometabolic risk.