Notice of Violation of IEEE Publication Principles: Research on Urban Land Price Assessment Based on Artificial Neural Network Model

Abstract

At present, there are many problems to be solved in the study of residential land price evaluation, such as the imbalance of residential land price data categories, the small sample of residential land price data sets, the possibility of the evaluation model falling into the local optimal value, and the large error of feature quantification. This paper introduces the problems in the process of residential land price evaluation from three aspects: land price data, evaluation model and feature quantification. In this paper, an extraction method for land price features based on transfer learning is proposed in connection with defects such as the small total amount of residential land price data and unbalanced class distribution, and different land price assessment models are used to carry out a precision evaluation and explore the models and methods suitable for land price assessment. In this paper, the extraction method for residential land price features is improved. The experimental results show that the extraction algorithm for land price features based on transfer learning has better classification accuracy than the commonly used principal component analysis and extraction method and linear normalization method, with a mean average precision 10.9% and 4.73% higher, respectively. The optimal feature set classification accuracy reached 90.28%; therefore, the algorithm for residential land price assessment proposed in this paper is able to satisfy the precision requirement of actual land price assessment.

Land Price Classification Map in Jakarta Using Random Forest and Ordinary Kriging

“…The classification of land prices carried out with Random Forest and spatial interpolation using Ordinary Kriging with the Spherical Semivariogram model is good enough with a classification accuracy of 82% and an Interpolation RMSE of 1.014896e7. Compared to a similar study that discusses land price classification [4] , which has the highest Random Forest accuracy of 90.28%, the accuracy we get is indeed smaller, but the study did not predict prices using Ordinary Kriging and no map of the distribution of land prices was found. Compared to the study [24][25] [26] , where the study predicted land prices using Ordinary Kriging but the land prices used were not the result of price classification from Random Forest but with existing price data.…”

“…Several methods were used to classify land prices, namely Random Forest, BP neural network, and Support Vector Machine in research [4] by Chai Shousong et al in 2019. From the comparison of the three methods, a good classification accuracy was obtained where the average accuracy was 90.28%. The author of the paper concludes that all three methods including random forest can perform classification and valuation appropriately.…”