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澳大利亚住房市场的空间智能体预测模型

文章 arXiv papers · 作者: Benjamin Patrick Evans et al.
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总结

本研究为大规模城市住房市场基于智能体的模型提出一种基于图的空间组件。该模型表示社会和经济特征如何影响不同地点的买卖决策，包括错失恐惧、跟随趋势的倾向，以及智能体在子市场中活动的范围。该方法旨在捕捉侧重整体市场汇总数据的模型可能遗漏的空间关系。

模型使用大悉尼地区的住房数据进行校准和验证。模拟基于各智能体的决策，生成整体市场和地方政府区域的预测；同时揭示了模型中首次购房者、投资者以及本地和海外投资者对不同子市场的偏好差异。说明未提供预测误差指标、与其他预测方法的比较，也没有悉尼以外地区的证据。因此，报告的输出展示了模型的适用范围和行为细节，但其在所述范围之外的预测价值仍不明确。

核心观点

  • 基于图的结构将空间关系纳入城市住房市场的智能体模型。
  • 智能体决策考虑跟随趋势和错失恐惧等社会与经济因素。
  • 悉尼案例同时生成整体市场和地方政府区域预测。
  • 模拟区分了不同买家和投资者群体的模型偏好，但说明未提供预测准确度结果。

标签

多资产趋势跟踪市场情绪统计学
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# The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model


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









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。