Traditional linear models often struggle to capture regional housing markets' complex, non-linear dynamics. This study addresses this gap by developing and applying advanced machine learning algorithms to unlock unique insights into South Australian housing price behavior. Leveraging a comprehensive dataset of over 10,000 regional house sales from 2010 to 2021, we explore the non-linear relationships between housing prices and microeconomic factors (e.g., house size, land area, building quality) and socioeconomic characteristics (e.g., proximity to amenities and income levels). Our analysis employs a multi-step approach, including feature engineering, spatial data integration, correlation tests, multilevel modeling, and non-linear machine learning algorithms including Decision Tree, Random Forest, Gradient-Boosted Tree, and Artificial Neural Network. The key finding is that machine learning models outperform traditional econometric models in predicting regional housing prices, with higher accuracy and greater goodness of fit. Furthermore, we identify specific non-linear relationships, such as the increasing marginal impact of proximity to the sea on house prices as distance decreases. These findings offer valuable insights for policymakers, real estate professionals, and stakeholders, informing regional planning, infrastructure provision, and economic development strategies. This study sheds light on the complex dynamics of South Australian housing markets and lays the foundation for further research.

中文翻译：

---

南澳大利亚区域住房市场的非线性动态：机器学习方法

传统的线性模型常常难以捕捉区域住房市场复杂的非线性动态。这项研究通过开发和应用先进的机器学习算法来解决这一差距，以解锁对南澳大利亚房价行为的独特见解。利用 2010 年至 2021 年超过 10,000 个地区房屋销售的综合数据集，我们探讨了房价与微观经济因素（例如房屋面积、土地面积、建筑质量）和社会经济特征（例如靠近便利设施和设施）之间的非线性关系。收入水平）。我们的分析采用多步骤方法，包括特征工程、空间数据集成、相关性测试、多级建模和非线性机器学习算法（包括决策树、随机森林、梯度提升树和人工神经网络）。主要发现是，机器学习模型在预测区域房价方面优于传统计量经济模型，具有更高的准确性和更好的拟合优度。此外，我们还确定了特定的非线性关系，例如随着距离的缩短，靠近大海对房价的边际影响不断增加。这些发现为政策制定者、房地产专业人士和利益相关者提供了宝贵的见解，为区域规划、基础设施供应和经济发展战略提供信息。这项研究揭示了南澳大利亚房地产市场的复杂动态，并为进一步研究奠定了基础。