[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.
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, 2019, 3(5): 19-26.
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