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张金娟,郭海燕. 数据驱动下的房地产批量评估研究综述与展望[J]. 西华大学学报(哲学社会科学版),2024,43(3):13 − 27. doi: 10.12189/j.issn.1672-8505.2024.03.002
引用本文: 张金娟,郭海燕. 数据驱动下的房地产批量评估研究综述与展望[J]. 西华大学学报(哲学社会科学版),2024,43(3):13 − 27. doi: 10.12189/j.issn.1672-8505.2024.03.002
ZHANG Jin-juan, GUO Hai-yan. Review and Outlook on Data-driven Real Estate Mass Appraisal[J]. Journal of Xihua University (Philosophy & Social Sciences) , 2024, 43(3): 13-27. DOI: 10.12189/j.issn.1672-8505.2024.03.002
Citation: ZHANG Jin-juan, GUO Hai-yan. Review and Outlook on Data-driven Real Estate Mass Appraisal[J]. Journal of Xihua University (Philosophy & Social Sciences) , 2024, 43(3): 13-27. DOI: 10.12189/j.issn.1672-8505.2024.03.002

数据驱动下的房地产批量评估研究综述与展望

Review and Outlook on Data-driven Real Estate Mass Appraisal

  • 摘要: 为全面呈现房地产批量评估研究的体系与脉络,文章采用文献归纳法对该领域研究文献进行系统梳理和总结。研究结果发现:(1)房地产批量评估模型与改进方面,新型的现代模型尤其是机器学习模型得到广泛的应用和讨论;(2)模型绩效评价方面,文献中采用的主要指标有R2、 RMSE、 MAPE、COD 等;(3)批量评估中的数据方面,数据规模和数据质量成为批量评估研究的重点问题。通过明确当前房地产批量评估研究的热点及进展,文章认为,未来研究将集中于多种模型的融合发展、多源数据的挖掘与使用以及批量评估模型的实践应用等领域。

     

    Abstract: With the aim to effectively present the system and context of real estate mass appraisal, the article employs a literature induction method to systematically organize and summarize the research literature in this field. The findings indicate that: (1) In terms of mass appraisal models and improvements, new modern models, especially machine learning models, are widely applied and discussed; (2) In terms of model performance evaluation, the evaluation measures used in literatures include R2, RMSE, MAPE, COD and the like; (3) In terms of data in mass appraisal, data scale and quality have become key issues in mass appraisal. By clarifying the current hotspots and trend, the article proposes that future research will focus on the integration of various models, the mining and use of multi-source data, and the practical application of mass appraisal models.

     

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