| Popis: |
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 = 10(4), 10(5), 10(6)), 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 = 10(6). 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. |
