Mass appraisal is the standardized procedure of valuing a large number of properties at the same time and is commonly used to compute real estate tax. While a hedonic pricing model based on the ordinary least squares (OLS) linear regression has been employed as the traditional method in this process, the stability and accuracy of the model remain questionable. This paper investigates the features of a house price predictor based on the Random Forest (RF) method by comparing it with that of a conventional hedonic pricing model. We used apartment transaction data from the period of 2006 to 2017 in the district of Gangnam, one of the most developed areas in South Korea. Using a data set covering 40% of all transactions in the sample area, we demonstrate that the accuracy of a machine learning-based predictor can be surprisingly high. The average of percentage deviations between the predicted and the actual market price was found to be only around 5.5% in the RF predictor, whereas it was almost 20% in the OLS-based predictor. With the RF predictor, the probability of the predicted price being within 5% of its actual market price was 72%, while only about 17.5% of the regression-based predictions fell within the same range. These results show that, in the practice of mass appraisal, the RF method may be a useful complement to the hedonic models, as it more adequately captures the complexity or non-linearity of actual housing markets.

中文翻译：

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基于随机森林法的房屋价格评估：韩国住宅物业的大规模评估

批量评估是同时评估大量资产的标准化程序，通常用于计算房地产税。虽然基于普通最小二乘（OLS）线性回归的享乐定价模型已被用作此过程中的传统方法，但是该模型的稳定性和准确性仍然值得怀疑。通过与常规享乐定价模型进行比较，研究了基于随机森林（RF）方法的房价预测器的特征。我们使用了江南地区（2006年至2017年）的公寓交易数据，江南地区是韩国最发达的地区之一。使用覆盖样本区域中所有交易的40％的数据集，我们证明了基于机器学习的预测器的准确性可能令人惊讶地高。在RF预测器中，预测价格与实际市场价格之间的平均百分比偏差平均值仅为5.5％左右，而在基于OLS的预测器中则接近20％。使用RF预测器时，预测价格在其实际市场价格的5％之内的概率为72％，而基于回归的预测中只有约17.5％处于同一范围内。这些结果表明，在大规模评估的实践中，RF方法可以作为享乐主义模型的有用补充，因为它可以更充分地捕捉实际住房市场的复杂性或非线性。预测价格在其实际市场价格的5％之内的概率为72％，而基于回归的预测中只有约17.5％处于同一范围内。这些结果表明，在大规模评估的实践中，RF方法可以作为享乐主义模型的有用补充，因为它可以更充分地捕捉实际住房市场的复杂性或非线性。预测价格在其实际市场价格的5％之内的概率为72％，而基于回归的预测中只有约17.5％处于同一范围内。这些结果表明，在大规模评估的实践中，RF方法可以作为享乐主义模型的有用补充，因为它可以更充分地捕捉实际住房市场的复杂性或非线性。