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Housing markets are inherently spatial, yet many existing models fail tocapture this spatial dimension. Here we introduce a new graph-based approachfor incorporating a spatial component in a large-scale urban housingagent-based model (ABM). The model explicitly captures several social andeconomic factors that influence the agents' decision-making behaviour (such asfear of missing out, their trend following aptitude, and the strength of theirsubmarket outreach), and interprets these factors in spatial terms. Theproposed model is calibrated and validated with the housing market data for theGreater Sydney region. The ABM simulation results not only include predictionsfor the overall market, but also produce area-specific forecasting at the levelof local government areas within Sydney as arising from individual buy and selldecisions. In addition, the simulation results elucidate agent preferences insubmarkets, highlighting differences in agent behaviour, for example, betweenfirst-time home buyers and investors, and between both local and overseasinvestors.

The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model