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数据分析与知识发现  2019, Vol. 3 Issue (5): 19-26    DOI: 10.11925/infotech.2096-3467.2018.0881
  专题 本期目录 | 过刊浏览 | 高级检索 |
基于数据挖掘方法的HEDONIC房屋价格评估模型——以美国城市西雅图为例
陈万成(),戴浩然,金映含
浙江大学地球科学学院 杭州 310027
Appraising Home Prices with HEDONIC Model: Case Study of Seattle, U.S.
Wancheng Chen(),Haoran Dai,Yinghan Jin
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
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摘要 

【目的】依据HEDONIC理论, 利用不同类型特征构建商品房价格评估模型, 为房屋价格评估工作提供一个效率更高、成本更低、准确性更高的解决方案。【方法】利用空间分析方法, 对预处理后的数据构造并选取重要特征, 基于随机森林、神经网络以及KNN建立融合模型。【结果】分析西雅图2014年至2015年商品房价格评估结果可以得出: 该模型明显优于线性HEDONIC模型, 准确度提升11.20%, 较为可靠。【局限】选取样本数据时, 时间截面并不完全一致, 导致模型存在潜在缺陷; 由于市场环境不同等多种因素, 将该模型运用于中国房屋价格的评估可能会存在偏差。【结论】本文提出的融合模型是一种较为可靠的房屋价格评估模型。

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陈万成
戴浩然
金映含
关键词 房价评估HEDONIC模型随机森林神经网络KNN    
Abstract

[Objective] This paper proposes a model based on the HEDONIC theory, aiming to assess home prices more efficiently, cost-effectively and accurately. [Methods] We adopted the spatial analysis method to extract important features from pre-processed data. Then, we built the model with Random Forest, KNN and Neural Networks. [Results] We examined our model with property price data of Seattle (USA) from 2014 to 2015 and found its precision was 11.20% higher than the linear model. [Limitations] The sample data was not retrieved from the same time slice, which might affect the performance of our model. Using this model to assess home prices in China might be biased due to different market environment and other factors. [Conclusions] The proposed model is a reliable method to appraise property prices.

Key wordsHouse Price Evaluation    HEDONIC Model    Random Forest    Neural Network    KNN
收稿日期: 2018-08-06     
引用本文:   
陈万成,戴浩然,金映含. 基于数据挖掘方法的HEDONIC房屋价格评估模型——以美国城市西雅图为例[J]. 数据分析与知识发现, 2019, 3(5): 19-26.
Wancheng Chen,Haoran Dai,Yinghan Jin. Appraising Home Prices with HEDONIC Model: Case Study of Seattle, U.S.. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0881.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0881
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