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The hedonic approach based on a regression model has been widely adopted forthe prediction of real estate property price and rent. In particular, a spatialregression technique called Kriging, a method of interpolation that wasadvanced in the field of spatial statistics, are known to enable high accuracyprediction in light of the spatial dependence of real estate property data.Meanwhile, there has been a rapid increase in machine learning-based predictionusing a large (big) dataset and its effectiveness has been demonstrated inprevious studies. However, no studies have ever shown the extent to whichpredictive accuracy differs for Kriging and machine learning techniques usingbig data. Thus, this study compares the predictive accuracy of apartment rentprice 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 onthe data sample size (n = 10^4, 10^5, 10^6) and differences in predictiveperformance. Our analysis showed that, with an increase in sample size, theout-of-sample predictive accuracy of DNN approached that of NNGP and they werenearly equal on the order of n = 10^6. Furthermore, it is suggested that, forboth higher and lower end properties whose rent price deviates from the median,DNN may have a higher predictive accuracy than that of NNGP.

A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN