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.

住房市场本质上是空间的，但是许多现有模型都无法捕捉到这一空间维度。在这里，我们介绍了一种新的基于图的方法，用于将空间成分合并到大型的基于城市住房代理的模型（ABM）中。该模型明确捕获了影响代理商决策行为的若干社会和经济因素（例如，害怕错过代理商，他们的趋势追随能力以及他们的子市场拓展实力），并从空间角度解释了这些因素。拟议的模型已通过大悉尼地区的房地产市场数据进行了校准和验证。ABM模拟结果不仅包括对整个市场的预测，而且还根据个人的购买和出售决策在悉尼当地政府区域一级产生针对特定区域的预测。