A Comparison of Residential Apartment Rent Price Predictions Using a Large Data Set: Kriging Versus Deep Neural Network. Issue 2 (7th March 2021)

• Record Type: Journal Article
• Authors: Seya, Hajime; Shiroi, Daiki
• Is Part Of: Geographical analysis. Volume 54:Issue 2(2022)
• Page Start: 239
• Page End: 260
• Publication Date: 2021-03-07
• Subjects: Geography -- Methodology -- Periodicals; Electronic journals; 910.00182
• DOI: 10.1111/gean.12283
• Languages: English
• ISSNs: 0016-7363

Abstract

Abstract : 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.