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ABSTRACT
Transit-oriented development (TOD) housing aims to provide housing in locations with good public transportation network, and where residents can work, study and pursue leisure nearby. With this goal, TOD housing could create a more sustainable and time-saving living environment. However, a controversy then arises as these benefits may mean that TOD housing may be pushed to higher price brackets through demand and commercialisation. Although there is much research on TOD and non-transit-oriented development (non-TOD) housing the analysis of revealed and stated preference for TOD house demand and supply is rare. Using stated preference data, results derived from three different groups of residents in Adelaide – ‘Corridor Population’, ‘Working Population’ and ‘Mawson Lakes Population’ (a transit-oriented development [TOD] group) – are compared, revealing their different housing needs and demands. All three modelled populations show similar preference patterns regarding housing type, distance to the train station and housing affordability, but some differences are evident. The Corridor Population and Working Population seek houses closer to bus stops, while the Mawson Lakes Population desires housing with high-frequency train services and more activities nearby. The power of the modelling approach to identify factors pertinent for policy development is clearly demonstrated.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 In probability, and statistics, the vector is used for stating a multivariate random variable. It can be a column vector, (its transposition being a row vector) whose components are scalar-valued random variables on the same probability space as each other. See examples in Batten et al. (Citation2012) and Song, Zenou, and Ding (Citation2008).
2 We used Greene and Hensher’s (Citation2007) definition of heterogeneity, which identifies observable heterogeneity as related to the observed attributes of the individual and unobservable heterogeneity as that which cannot be related to the observed attributes of the individual. Observed heterogeneity can be identified by considering the observed attributes of the individual, while unobserved heterogeneity is estimated by considering random terms in the utility function.
3 The details of the questionnaire contents are explained in the section ‘Summary of Variables’.
4 Heterogeneity represents a systematic review of each individual, the distinguishment between different types of heterogeneity could indicate how factors with this effect may require attention in the development of policy.