Numerous studies have explored the correlations between house prices and spatial accessibility, but few have delved into the nonlinearities between both. This study uses Cardiff (UK) as a case study and applies interpretable machine learning algorithms, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to estimate the nonlinear effects of geometric locational accessibility and street network accessibility on house prices. The findings suggest (1) proximity to the CBD, typically the major determinant of land values in hedonic house price models, does not continuously yield higher prices; (2) street closeness centrality, a network-modelling approach to measuring accessibility, exhibits a more generalised pattern with house prices compared proximity to the CBD regardless of analytical spatial scales. The findings challenge the generalizability of Alonso’s bid-rent theory in accurately portraying the relationship between accessibility and house prices in specific urban contexts, highlighting the importance of re-evaluating classical urban theories in different city contexts using novel measures and modelling techniques.

中文翻译：

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交通便利是否意味着房价更高？

许多研究探讨了房价与空间可达性之间的相关性，但很少有人深入研究两者之间的非线性。本研究以卡迪夫（英国）为案例研究，并应用可解释的机器学习算法、eXtreme Gradient Boosting (XGBoost) 和 SHapley Additive exPlanations (SHAP)，来估计几何位置可达性和街道网络可达性对房价的非线性影响。研究结果表明（1）靠近中央商务区（通常是享乐房价模型中土地价值的主要决定因素）并不会持续产生更高的价格； (2) 街道紧密度中心性是一种衡量可达性的网络建模方法，与 CBD 的邻近程度相比，无论分析空间尺度如何，都表现出更普遍的房价模式。这些发现挑战了阿隆索的竞租理论在准确描述特定城市环境中可达性和房价之间关系方面的普遍性，强调了使用新颖的措施和建模技术在不同城市环境中重新评估经典城市理论的重要性。