Pinter Gergo, Mosavi Amir, Felde Imre
John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.
School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway.
Entropy (Basel). 2020 Dec 16;22(12):1421. doi: 10.3390/e22121421.
Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers' entropy, worker gyration, dwellers' work distance, and workers' home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott's index (WI). The proposed model showed promising results revealing that the workers' entropy and the dwellers' work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers' gyration, and the workers' home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.
开发准确的房地产价格预测模型对城市发展和若干关键经济功能至关重要。由于存在重大不确定性和动态变量，房地产建模一直被视为复杂系统进行研究。在本研究中，提出了一种新颖的机器学习方法来应对房地产建模的复杂性。通话记录（CDR）为深入研究出行特征提供了绝佳机会。本研究借助人工智能（AI）探索了CDR在预测房地产价格方面的潜力。几个重要的出行熵因素，包括居民熵、居民回转度、工作者熵、工作者回转度、居民工作距离和工作者家的距离，被用作输入变量。预测模型是使用多层感知器（MLP）的机器学习方法开发的，该方法由粒子群优化（PSO）进化算法训练。使用均方误差（MSE）、可持续性指数（SI）和威尔莫特指数（WI）评估模型性能。所提出的模型显示出有前景的结果，表明工作者熵和居民工作距离直接影响房地产价格。然而，居民回转度、居民熵、工作者回转度和工作者家的距离对价格的影响最小。此外，研究表明活动流量和出行熵通常与房地产价格较低的地区相关。
