This study examines the relationship between residential property values and accessibility indicators derived from transit smart card data. The use of smart card data to estimate accessibility indicators for explaining the housing market has not yet been explored in the literature. Hence, this paper employs information from Brisbane, Australia’s “go card” and corresponding property data to develop residential property hedonic pricing models using an ordinary least square (OLS) model, a spatial lagged model (SL), a spatial error model (SE), and a geographically weighted regression (GWR). Due to the systematic coincidence between location and price similarities, these spatial econometric models yield superior goodness-of-fit over the OLS model. Using the proposed definition of public transit accessibility in this study, it was found that properties located in well-connected, well-serviced, and accessible locations generally experience premiums in their values. The results indicate that there is value added to the property market from the public investment in public transport services and infrastructure, which supports the adoption of transit funding mechanisms, such as value-capture taxes. Furthermore, the analysis of spatial interactions between transport accessibility and the housing market could be of use to policy makers to ensure a just distribution of capital investment in future infrastructure projects.
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