将圣地亚哥大都会地区房地产评估的自动化估值模型进行比较:关于机器学习算法和带有空间调整的享乐价格的研究
Jocelyn Tapia1, Nicolas Chavez-Garzon1, Raúl Pezoa2
1Department of Business Engineering, Universidad Técnica Federico Santa María, Santiago, Chile.
PloS one
|March 25, 2025
概括
这项研究表明,从图像中添加视觉数据可以显著改善房地产价格预测. 机器学习和空间模型从图像分析中受益,揭示了房地产价值的关键驱动因素,如大小和邻里质量.
科学领域:
- 房地产经济学 房地产经济学
- 计算机科学 计算机科学
- 地理空间分析的研究.
背景情况:
- 自动估值模型 (AVM) 对于房地产市场分析至关重要.
- 传统的AVM通常仅依赖于财产属性和位置.
- 整合各种数据源,如视觉信息,可以提高AVM的准确性.
研究的目的:
- 为了比较LightGBM (机器学习) 和空间自回归 (SAR) 模型用于房地产估值的精度和可解释性.
- 评估将基于图像的特征 (通过卷积神经网络) 纳入AVM的影响.
- 使用夏普利增值解释 (SHAP) 识别房地产估值的关键驱动因素.
主要方法:
- 利用LightGBM和SAR模型用于智利圣地亚哥大都会地区的房地产价格预测.
- 使用卷积神经网络 (CNN) 从财产图像中提取视觉特征.
- 应用了SHapley添加式解释 (SHAP) 来确定属性重要性和模型可解释性.
主要成果:
- 整合基于图像的变量大大降低了房屋价格估计中的错误率.
- SHAP分析揭示了房地产价格与尺寸和社区质量等属性之间的复杂,非线性关系.
- 确定的主要价值驱动因素包括总面积,市政质量指数,附近学校的学术水平和浴室数量.
结论:
- 来自物业图像的视觉信息大大提高了自动化估值模型的性能.
- 像LightGBM和SHAP这样的先进方法在捕获复杂的财产价值决定因素方面是有效的.
- 该研究强调了多模式数据集成对于准确的房地产市场评估的重要性.
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