使用机器学习对集体拥有的商业化建筑用地进行批量评估
Wenzhu Zhang1, Licheng Huang2, Shengquan Lu3
1College of natural resources and surveying and mapping, Nanning Normal University, Nanning, 530100, China.
Scientific reports
|August 7, 2025
概括
机器学习模型改善了中国集体拥有的商业化建筑用地 (CCCL) 价格评估. 随机森林模型实现了94.77%的准确性,为市场进入估值提供了更高效,更精确的方法.
科学领域:
- 农业和经济科学 农业和经济科学
- 土地经济 土地经济学
- 机器学习应用 机器学习应用
背景情况:
- 中国农村土地制度改革包括集体拥有的商业化建筑用地 (CCCL) 进入市场.
- 传统的估价方法缺乏CCCL市场准入价格批量估价的效率和准确性.
研究的目的:
- 开发基于机器学习的批量评估模型,用于CCCL的市场入口价格.
- 为了提高CCCL价格预测的效率和准确性.
- 确定影响CCCL价格的关键因素.
主要方法:
- 实施了三种机器学习模型:随机森林 (RF),反向传播神经网络 (BPNN) 和支持矢量机器 (SVM).
- 开发一个定制的指标系统来预测贝柳市的价格,这是一个改革试点地区.
- 基于预测准确度和平均绝对误差对模型性能进行比较分析.
主要成果:
- 随机森林 (RF) 模型表现出卓越的性能,预测准确率为94.77%,平均绝对误差为17.50元.
- 射频的表现优于BPNN (91.21%的准确率) 和SVM (91.94%的准确率).
- CCCL价格受到乡镇经济水平和进入市场的方法等因素的影响,具有独特的特点.
结论:
- 机器学习模型,特别是RF,对于批量评估CCCL进入市场价格是有效的.
- 该研究为标准化土地市场提供了科学基础,并指导了农村土地改革的政策制定.
- 准确的CCCL价格预测对于高效的土地市场运营和改革至关重要.
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