Hedonic estimations of the effect of transport infrastructure on property prices vary widely. This high variability demonstrates a deficit in our understanding of these relationships, limits the utility of econometrics for the valuation of urban property markets, and limits the development and implementation of effective and fair market-based policy tools. Several avenues may lead to improved consistency: re-consideration of accessibility, inclusion of urban design characteristics, assessment of spatial dependence and spatial heterogeneity, and consideration of geographic scale. This paper outlines the rationale and opportunities for inclusion of, and presents empirical tests for, these assertions using a case study in western Sydney, Australia. Results show a number of urban design characteristics to be significant determinants of residential property price. Street connectivity and higher density in areas surrounding residences negatively impact price, higher density close to train stations positively impacted price in one model. Park-and-ride stations led to decreases in property values. Smaller study area results indicate a nonlinear relationship between distance to train station and property price and a disamenity impact for residences within 400 m of train stations. Relative accessibility measured as frequency of peak hour trains is a significant and positive determinant of price in the larger study area. Incorporation of a price trend surface and estimation using a spatial error model reduce the extent to which spatial autocorrelation overstates the effect of a train station on prices. These conceptual and empirical improvements further develop our understanding of the effect of transport infrastructure on property values.

In the digital era of big data, data analytics and smart cities, a new generation of planning support systems is emerging. The Rapid Analytics Interactive Scenario Explorer is a novel planning support system developed to help planners and policy-makers determine the likely land value uplift associated with the provision of new city infrastructure. The Rapid Analytics Interactive Scenario Explorer toolkit was developed following a user-centred research approach including iterative design, prototyping and evaluation. Tool development was informed by user inputs obtained through a series of co-design workshops with two end-user groups: land valuers and urban planners. The paper outlines the underlying technical architecture of the toolkit, which has the ability to perform rapid calculations and visualise the results, for the end-users, through an online mapping interface. The toolkit incorporates an ensemble of hedonic pricing models to calculate and visualise value uplift and so enable the user to explore what if? scenarios. The toolkit has been validated through an iterative case study approach. Use cases were related to two policy areas: property and land valuation processes (for land taxation purposes) and value uplift scenarios (for value capture purposes). The cases tested were in Western Sydney, Australia. The paper reports on the results of the ordinary least square linear regressions – used to explore the impacts of hedonic attributes on property value at the global level – and geographically weighted regressions – developed to provide local estimates and explore the varying spatial relationships between attributes and house price across the study area. Building upon the hedonic modelling, the paper also reports the value uplift functionality of the Rapid Analytics Interactive Scenario Explorer toolkit that enables users to drag and drop new train stations and rapidly calculate expected property prices under a range of future transport scenarios. The Rapid Analytics Interactive Scenario Explorer toolkit is believed to be the first of its kind to provide this specific functionality. As it is problem and policy specific, it can be considered an example of the next generation of data-driven planning support system.