Appraising Home Prices with HEDONIC Model: Case Study of Seattle, U.S.

被引：0
|
作者：
Chen W. [1 ]
Dai H. [1 ]
Jin Y. [1 ]
机构：
[1] School of Earth Sciences, Zhejiang University, Hangzhou
来源：
Data Analysis and Knowledge Discovery | 2019年 / 3卷 / 05期
关键词：
HEDONIC Model; House Price Evaluation; KNN; Neural Network; Random Forest;
D O I：
10.11925/infotech.2096-3467.2018.0881
中图分类号：
F [经济]; C [社会科学总论];
学科分类号：
02 ; 03 ; 0303 ;
摘要：
[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. © 2019 Authors. All rights reserved.
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页码：19 / 26
页数：7
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