Abstract
The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to identify the importance order of factors and HPM was adopted to reveal the value of the original non-market priced influential factors. The results showed that combining the two models can lead to good performance and increase understanding of the spatial variations in housing prices. Our work found that (1) the five most important variables for Shenzhen housing prices were distance to city centre, green view index, population density, property management fee and economic level; (2) space quality at the human scale had important effects on housing prices; and (3) some traditional factors, especially variables related to education, should be modified according to the development of the real estate market. The results showed that the demonstrated multisource geo-tagged data fusion framework, which integrated XGBoost and HPM, is practical and supports a comprehensive understanding of the relationships between housing prices and influential factors. The findings in this article provide essential implications for informing equitable housing policies and designing liveable neighbourhoods.
Highlights
In the last decade, housing prices have become one of the top issues in economic development and for determining whether urban residents can live a better life [1,2,3].The rapid growth of housing prices and spatial differentiation greatly concern managers, scholars, developers and residents [4,5,6]
The hedonic price model (HPM) has been widely applied to housing prices and can identify the economic value of influential factors well [5,13,14], the traditional HPM has been criticized for some limitations, including: (1) a poor ability to reduce the impact of collinearity; (2) the assumption of linear relationships between influential factors and housing prices; and (3) a lack of robustness in the results [15,16,17,18]
This is consistent with the XGBoost results, which suggests that these structural variables were less important for home prices in Shenzhen
Summary
In the last decade, housing prices have become one of the top issues in economic development and for determining whether urban residents can live a better life [1,2,3].The rapid growth of housing prices and spatial differentiation greatly concern managers, scholars, developers and residents [4,5,6]. In the last decade, housing prices have become one of the top issues in economic development and for determining whether urban residents can live a better life [1,2,3]. Understanding the mechanisms influencing spatial variations in housing prices is essential to formulate scientific housing policies, divide submarkets, optimize urban spatial layouts, allocate public infrastructure and equalize spatial resources [11,12]. Improvements to previous studies are required to deeply understand the complex relationships between housing prices and influential factors. The above limitations of the HPM might directly reduce the accuracy of housing price modelling and muddle our overall understanding of the influential factors of housing prices; housing prices modelling should be improved by applying new data sources, methods and technologies [5,19]
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