We employ 47 different algorithms to forecast Australian log real house prices and growth rates, and compare their ability to produce accurate out‐of‐sample predictions. The algorithms, which are specified in both single‐ and multi‐equation frameworks, consist of traditional time series models, machine learning (ML) procedures, and deep learning neural networks. A method is adopted to compute iterated multistep forecasts from nonlinear ML specifications. While the rankings of forecast accuracy depend on the length of the forecast horizon, as well as on the choice of the dependent variable (log price or growth rate), a few generalizations can be made. For one‐ and two‐quarter‐ahead forecasts we find a large number of algorithms that outperform the random walk with drift benchmark. We also report several such outperformances at longer horizons of four and eight quarters, although these are not statistically significant at any conventional level. Six of the eight top forecasts (4 horizons × 2 dependent variables) are generated by the same algorithm, namely a linear support vector regressor (SVR). The other two highest ranked forecasts are produced as simple mean forecast combinations. Linear autoregressive moving average and vector autoregression models produce accurate olne‐quarter‐ahead predictions, while forecasts generated by deep learning nets rank well across medium and long forecast horizons.

中文翻译：

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预测澳大利亚的实际房价指数：时间序列和机器学习方法的比较

我们采用 47 种不同的算法来预测澳大利亚对数真实房价和增长率，并比较它们产生准确的样本外预测的能力。在单方程和多方程框架中指定的算法由传统的时间序列模型、机器学习 (ML) 程序和深度学习神经网络组成。采用一种方法从非线性 ML 规范计算迭代多步预测。虽然预测准确性的排名取决于预测范围的长度以及因变量（对数价格或增长率）的选择，但可以进行一些概括。对于提前一个季度和两个季度的预测，我们发现大量算法的性能优于带有漂移基准的随机游走。我们还报告了四个和八个季度的长期表现，尽管这些在任何常规水平上都没有统计意义。八个顶级预测中的六个（4 个范围 × 2 个因变量）由相同的算法生成，即线性支持向量回归器 (SVR)。其他两个排名最高的预测是作为简单的平均预测组合产生的。线性自回归移动平均和向量自回归模型产生准确的 olne-四分之一提前预测，而深度学习网络生成的预测在中长期预测范围内排名良好。即线性支持向量回归器（SVR）。其他两个排名最高的预测是作为简单的平均预测组合产生的。线性自回归移动平均和向量自回归模型产生准确的 1/4 前预测，而深度学习网络生成的预测在中长期预测范围内排名良好。即线性支持向量回归器（SVR）。其他两个排名最高的预测是作为简单的平均预测组合产生的。线性自回归移动平均和向量自回归模型产生准确的 1/4 前预测，而深度学习网络生成的预测在中长期预测范围内排名良好。