  The hedonic approach based on a regression model has been widely adopted for
the prediction of real estate property price and rent. In particular, a spatial
regression technique called Kriging, a method of interpolation that was
advanced in the field of spatial statistics, are known to enable high accuracy
prediction in light of the spatial dependence of real estate property data.
Meanwhile, there has been a rapid increase in machine learning-based prediction
using a large (big) dataset and its effectiveness has been demonstrated in
previous studies. However, no studies have ever shown the extent to which
predictive accuracy differs for Kriging and machine learning techniques using
big data. Thus, this study compares the predictive accuracy of apartment rent
price in Japan between the nearest neighbor Gaussian processes (NNGP) model,
which enables application of Kriging to big data, and the deep neural network
(DNN), a representative machine learning technique, with a particular focus on
the data sample size (n = 10^4, 10^5, 10^6) and differences in predictive
performance. Our analysis showed that, with an increase in sample size, the
out-of-sample predictive accuracy of DNN approached that of NNGP and they were
nearly equal on the order of n = 10^6. Furthermore, it is suggested that, for
both higher and lower end properties whose rent price deviates from the median,
DNN may have a higher predictive accuracy than that of NNGP.
