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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (5): 19-26    DOI: 10.11925/infotech.2096-3467.2018.0881
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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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[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     
Received: 06 August 2018      Published: 03 July 2019

Cite this article:

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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