• • 上一篇    

基于机器学习的土地估值方法

周烨1, 2, 3, 詹宝强4, 杨晓光1, 2   

  1. 1. 中国科学院数学与系统科学研究院, 北京 100190;
    2. 中国 科学院大学, 北京 100049;
    3. 香港城市大学经济金融学院, 香港 999077;
    4. 哈尔 滨工业大学经济与管理学院, 哈尔滨 150000
  • 收稿日期:2022-05-27 修回日期:2022-11-20 发布日期:2023-05-18
  • 通讯作者: 杨晓光, Email:xgyang@iss.ac.cn
  • 基金资助:
    国家自然科学基金(72192800)资助课题.

周烨, 詹宝强, 杨晓光. 基于机器学习的土地估值方法[J]. 系统科学与数学, 2023, 43(4): 841-857.

ZHOU Ye, ZHAN Baoqiang, YANG Xiaoguang. Land Value Appraisal Based on Machine Learning[J]. Journal of Systems Science and Mathematical Sciences, 2023, 43(4): 841-857.

Land Value Appraisal Based on Machine Learning

ZHOU Ye1, 2, 3, ZHAN Baoqiang4, YANG Xiaoguang1, 2   

  1. 1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190;
    2. University of Chinese Academy of Sciences, Beijing 100049;
    3. Department of Economics and Finance, City University of Hong Kong, Hong Kong 999077;
    4. School of Management, Harbin Institute of Technology, Harbin 150000
  • Received:2022-05-27 Revised:2022-11-20 Published:2023-05-18
文章将基于互联网的区位信息和区域经济因素纳入土地评估,选取土 地市场网“招拍挂”出让数据构建训练和测试样本,建立城市土地评估的回归、树、神经 网络和深度学习四套模型,并对模型的预测能力和稳健性进行评价.研究表明, XGBoost 估值效果最优,适用于不同类型用地.此外, 文章发现土地基本属性、区位和宏观经济这三类因素均对土地估值有不可或缺的作用,其中监测地价和容积率上限的贡献度 最大,说明宏观市场价格和可利用程度是最重要的影响因素.
With the rapid increase of transaction volume in the land market and frequent usage of land as financial assets, traditional methodologies for determining land value are gradually unable to meet the needs of the current market due to their high cost and strong subjectivity. Therefore, cheap and efficient intelligent approaches are urgently needed. Based on previous literature, this paper takes land’s basic characteristics, business information nearby, and local macroeconomic information as the main pricing factors, and applies Linear Regression, Decision Tree, Artificial Neural Networks, and Deep Learning model to land evaluation. The data for training and testing the models is composed of 58, 815 parcels of land traded through bidding, auction, and listing, collected from Chinese websites in the period of January 2016 to June 2019. The study demonstrates that among those models, XGBoost outperforms all the other models and is well suited to different types of land. Furthermore, we measure the influence of enriched attributes on model performance and find that all three types of factors are indispensable for determining the price of land, especially the max floor area ratio and cities land prices, which means the macro-market value of the land and the degree of land availability are the most important factors.

MR(2010)主题分类: 

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