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.

The fast growing ubiquitous infrastructure technologies are capable of improving current urban management and infrastructure planning and development capabilities. These technological advancement urban infrastructure developments in the Republic of Korea have recently shifted from an old paradigm of conventional infrastructure to a new paradigm of intelligent infrastructure provision. This new paradigm, so called ubiquitous infrastructure, is a combination of urban infrastructures, information and communication technologies and digital networks. Ubiquitous infrastructure refers to an urban infrastructure system where any citizen can access any infrastructure and services via any electronic devices regardless of time and location. This paper introduces this new paradigm and new schemes for urban infrastructure planning and development in the Republic of Korea and discusses the potential positive effects of ubiquitous infrastructure on Korean cities to achieve sustainable urban development.No Full Tex

Geographic location and neighbourhood attributes are major contributors to residential property values. Automated valuation models (AVM) often use hedonic pricing with location and neighbourhood attributes in the form of numeric and categorical variables. This paper proposed a novel approach to automated property valuation using a machine learning model with a convolutional neural network (CNN), fully connected neural network layers with numeric and categorical variables. In this study we compare the results of a fused model, which treat geographical data as an input with the performance of the baseline neural network model with only numerically or categorically represented data. Furthermore, the residential valuation by the proposed fused model was tested with actual sold price data in Greater Sydney, Australia. The study found that the fused model produced valuations with a significantly lower mean absolute percentage error (MAPE) (8.71%) than the MAPE of the baseline model (11.59%). The results show that the fused model with CNN significantly improves the accuracy for residential valuation, reducing spatial information loss by data manipulation and distance calibration.