TITLE: Spatial prediction of apartment rent using regression-based and machine learning-based approaches with a large dataset 

ABSTRACT: Employing a large dataset (at most, the order of n = 10^6), this study
attempts enhance the literature on the comparison between regression and
machine learning (ML)-based rent price prediction models by adding new
empirical evidence and considering the spatial dependence of the observations.
The regression-based approach incorporates the nearest neighbor Gaussian
processes (NNGP) model, enabling the application of kriging to large datasets.
In contrast, the ML-based approach utilizes typical models: extreme gradient
boosting (XGBoost), random forest (RF), and deep neural network (DNN). The
out-of-sample prediction accuracy of these models was compared using Japanese
apartment rent data, with a varying order of sample sizes (i.e., n = 10^4,
10^5, 10^6). The results showed that, as the sample size increased, XGBoost and
RF outperformed NNGP with higher out-of-sample prediction accuracy. XGBoost
achieved the highest prediction accuracy for all sample sizes and error
measures in both logarithmic and real scales and for all price bands (when n =
10^5 and 10^6). A comparison of several methods to account for the spatial
dependence in RF showed that simply adding spatial coordinates to the
explanatory variables may be sufficient.
Employing a large dataset (at most, the order of n = 10^6), this study
attempts enhance the literature on the comparison between regression and
machine learning (ML)-based rent price prediction models by adding new
empirical evidence and considering the spatial dependence of the observations.
The regression-based approach incorporates the nearest neighbor Gaussian
processes (NNGP) model, enabling the application of kriging to large datasets.
In contrast, the ML-based approach utilizes typical models: extreme gradient
boosting (XGBoost), random forest (RF), and deep neural network (DNN). The
out-of-sample prediction accuracy of these models was compared using Japanese
apartment rent data, with a varying order of sample sizes (i.e., n = 10^4,
10^5, 10^6). The results showed that, as the sample size increased, XGBoost and
RF outperformed NNGP with higher out-of-sample prediction accuracy. XGBoost
achieved the highest prediction accuracy for all sample sizes and error
measures in both logarithmic and real scales and for all price bands (when n =
10^5 and 10^6). A comparison of several methods to account for the spatial
dependence in RF showed that simply adding spatial coordinates to the
explanatory variables may be sufficient.
Spatial prediction of apartment rent using regression-based and machine learning-based approaches with a large dataset
http://arxiv.org/abs/2107.12539v1