  Housing markets are inherently spatial, yet many existing models fail to
capture this spatial dimension. Here we introduce a new graph-based approach
for incorporating a spatial component in a large-scale urban housing
agent-based model (ABM). The model explicitly captures several social and
economic factors that influence the agents' decision-making behaviour (such as
fear of missing out, their trend following aptitude, and the strength of their
submarket outreach), and interprets these factors in spatial terms. The
proposed model is calibrated and validated with the housing market data for the
Greater Sydney region. The ABM simulation results not only include predictions
for the overall market, but also produce area-specific forecasting at the level
of local government areas within Sydney as arising from individual buy and sell
decisions. In addition, the simulation results elucidate agent preferences in
submarkets, highlighting differences in agent behaviour, for example, between
first-time home buyers and investors, and between both local and overseas
investors.
