Modeling the fine-scale distribution of residential land prices (RLPs) is the basis for scientifically allocating land resources, managing the residential market and improving urban planning. The complex nonlinear relationship between RLPs and their determinants makes it challenging to model fine-scale RLPs. The continuous development and improvement of big data mining and machine learning methods provide new ideas for modeling RLP distribution. This paper attempts to couple the Hedonic model with machine learning algorithms to model block-level RLPs using the case of Wuhan in China. With the aid of data mining methods such as GIS spatial analysis and deep learning, urban residential land sales data and geographic big data such as urban points of interest (POI) and areas of interest (AOI), and Tencent street view images were used to map the location, neighborhood, and visual environment of urban residential land, to build a multi-level variable system for block-level RLP prediction. Then, nonlinear machine learning algorithms and ensemble learning methods were used to develop block-level RLP prediction models, and empirical study was carried out using Wuhan city as an example to test the validity and reliability of the model. Finally, based on the RLP data predicted by the machine learning regression model, the RLP at the block level in Wuhan was mapped, the contribution of several geographic variables to the RLP prediction was measured, and the nonlinear response of RLP to the change of prediction variables was analyzed. Our proposed framework provides a new approach for modeling fine-scale urban land price distribution, which is beneficial to intelligent land use planning and achieve smart city growth.

Peng Zhang
Chang’an University

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ID Abstract: 912