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博士生沈航,李霖的论文在ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION刊出
发布时间:2022-03-15 15:13:08     发布者:易真     浏览次数:

标题: A Pricing Model for Urban Rental Housing Based on Convolutional Neural Networks and Spatial Density: A Case Study of Wuhan, China

作者: Shen, H (Shen, Hang); Li, L (Li, Lin); Zhu, HH (Zhu, Haihong); Li, F (Li, Feng)

来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION : 11 : 1 文献号: 53 DOI: 10.3390/ijgi11010053 出版年: JAN 2022

摘要: With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighborhood variables, based on the synthetic spatial density, effectively reflect the aggregated effects of the urban facilities on rental housing prices, thereby improving the accuracy of the model. Taking Wuhan, China, as the study area, the proposed method achieves satisfactory and accurate rental price estimations (coefficient of determination (R-2) = 0.9097, root mean square error (RMSE) = 3.5126) in comparison with other commonly used pricing models.

入藏号: WOS:000758407400001

语言: English

文献类型: Article

作者关键词: rental housing price; POI; geographic information systems; deep learning

地址: [Shen, Hang; Li, Lin; Zhu, Haihong; Li, Feng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

通讯作者地址: Li, L (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: shenhang@whu.edu.cn; lilin@whu.edu.cn; hhzhu@whu.edu.cn; liwind@whu.edu.cn

影响因子:2.899


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