Despite several attempts to compare and examine the predictive accuracy of real estate sales and rent prices between the regression-based and neural-network (NN)-based approaches, the results are largely mixed. Prior study limitations include a small sample size and a disregard for spatial dependence, which is an essential characteristic of real estate properties. Hence, this study aims to add new empirical evidence to the literature on comparing regression-based with NN-based rent price prediction models through sophistications by (1) examining different and relatively large-scale sample sizes (n = 10⁴, 10⁵, 10⁶), and (2) considering the spatial dependence of either the application of nearest neighbor Gaussian processes (NNGP) or the latitude-longitude coordinate function (in the case of a deep neural network [DNN]). A case study of apartment rent prices in Japan shows that, given an increase in sample size, the out-of-sample predictive accuracies of the DNN approaches and that of NNGP are nearly equal in the order of n = 10⁶. However, the DNN may have higher predictive accuracy than the NNGP for both higher- and lower-end properties whose rent prices deviate from the median.

中文翻译：

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使用大型数据集比较住宅公寓租金价格预测：克里金法与深度神经网络

尽管多次尝试比较和检查基于回归和基于神经网络 (NN) 的方法之间的房地产销售和租金价格的预测准确性，但结果大相径庭。先前的研究限制包括样本量小和无视空间依赖性，这是房地产的基本特征。因此，本研究旨在通过 (1) 检查不同且相对大规模的样本量 ( n  = 10 ⁴ , 10 ⁵ , 10 ⁶) 和 (2) 考虑最近邻高斯过程 (NNGP) 或纬度-经度坐标函数（在深度神经网络 [DNN] 的情况下）的应用的空间依赖性。日本的公寓租金案例研究表明，随着样本量的增加，DNN 方法和 NNGP 方法的样本外预测准确度几乎相等，大约为n  = 10 ⁶。然而，对于租金价格偏离中位数的高端和低端房产，DNN 可能比 NNGP 具有更高的预测准确性。