Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction
房地产价格预测中的机器学习、深度学习与特征价格方法AI 解读 ↓
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房地产价格预测中的机器学习、深度学习与特征价格方法
- 研究问题
- 能否通过学习住宅特征与售价的非线性关系，提高房地产估价的预测准确性，并减少对人工估价过程的依赖？
- 方法与思路
- 作者使用美国科罗拉多州博尔德市住宅数据，对比人工神经网络、随机森林、近邻算法与特征价格回归的预测表现。
- 材料陈述的贡献
- 摘要称住宅特征与价格存在非线性关系，随机森林和神经网络在该城市的价格预测中可成为优于特征价格回归的选择。
- 阅读关注点
- 应核对数据划分、预测指标和调参公平性，以及是否直接检验种族偏差降低和跨城市推广，而非仅报告预测改善。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要未提供性能数值，跨城市推广及偏差降低尚不能据此确认。
原文摘要
In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado.