Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model

Abstract

A spatial and temporal heterogeneity analysis of residential land prices, in general, is crucial for maintaining high-quality economic development. Previous studies have attempted to explain the geographical evolution rule by studying spatial-temporal heterogeneity, but they have neglected the contextual information, such as school district, industrial zone, population density, and job density, associated with residential land prices. Therefore, in this study, we consider contextual factors and propose a revised local regression algorithm called the contextualized geographically and temporally weighted regression (CGTWR), to effectively address spatiotemporal heterogeneity, and to creatively extend the feasibility of importing the contextualization into the GTWR model. The quantitative impact of contextual information on residential land prices was identified in Shijiazhuang (SJZ) city from 1974 to 2021. Empirical analyses demonstrated that school district and industrial zone factors played important roles in residential land prices. Notably, the distance from a residential area to an industrial zone was significantly positively correlated with residential land prices. In addition, a positive relationship between school districts and residential land prices was also observed. Finally, the R2 value of the CGTWR model was 92%, which was superior to those of ordinary least squares (OLS, 76%), geographically weighted regression (GWR, 85%), contextualized geographically weighted regression (CGWR, 86%), and GTWR (90%) models. These evaluation results indicate that the CGTWR algorithm, which incorporates contextual information and spatiotemporal variation, could provide policy makers with evidence for understanding the nature of varying relationships within a land price dataset in China.

Exploring the Spatial Discrete Heterogeneity of Housing Prices in Beijing, China, Based on Regionally Geographically Weighted Regression Affected by Education

“…In recent years, in addition to the obvious spatial non-stationary features, the housing price changes in China's major cities are also directly affected by the macro-economy and policies [1][2][3]. China's macro-policy of "housing is for living in, not for speculation" and new housing policies for school districts in Beijing, Shanghai, and other places have led to a slowdown in the growth rate of housing prices in large cities, which had risen rapidly in recent years.…”

“…Since GWR has been used for real-estate research, the non-stationary nature and spatial heterogeneity of housing prices have been widely revealed. Subsequently, an increasing number of studies have explored the non-stationary nature and spatial heterogeneity of housing prices from different perspectives [1][2][3][25][26][27][28][29][30][31][32]. For example, Yu et al have employed GWR to examine the spatial dependence and heterogeneity of housing-market dynamics in the city of Milwaukee [29].…”

Spatial‐temporal evolution characteristics of residential land prices based on the 2010–2019 land transfer data in China

“…Land prices play a crucial role in land use and land management, so many studies have focused on land prices to provide references for land market development and urban development (Kheir & Portnov, 2016; Murray, 2021; Zhang et al, 2020; Zou et al, 2015). Since residential land is an important land‐use type related to basic housing needs, researchers have extensively studied RLP in terms of influencing factors (Chai et al, 2021; Lee, 2015, Mostafa, 2018; Yang, Hu et al, 2017), spatial‐temporal variation (Davis et al, 2017; Hu et al, 2012, 2013; Huang et al, 2018; Yang et al, 2020), and change effect (Ding & Zhao, 2014; Mou et al, 2017; Wen & Goodman, 2013). These studies generally adopted methods including multiple linear regression (MLR) (Mulley & Tsai, 2016; Yen et al, 2018; Zheng et al, 2021), hedonic models (Bourassa & Hoesli, 2022; Glaesener & Caruso, 2015; Kanasugi & Ushijima, 2018), exploratory spatial data analysis (ESDA) (An et al, 2021; Florence et al, 2011; Wei & Zhao, 2022), spatial econometric models (Glumac et al, 2019; Nichols et al, 2013; Qu et al, 2020), geographically weighted regression (GWR) (Hu et al, 2016; Mulley, 2014; Nakamura, 2019; Yuan et al, 2022), and machine learning algorithms (MLAs) (Ma et al, 2020).…”

“…Land prices play a crucial role in land use and land management, so many studies have focused on land prices to provide references for land market development and urban development (Kheir & Portnov, 2016;Murray, 2021;Zhang et al, 2020;Zou et al, 2015). Since residential land is an important land-use type related to basic housing needs, researchers have extensively studied RLP in terms of influencing factors (Chai et al, 2021;Lee, 2015, Mostafa, 2018Yang, Hu et al, 2017), spatial-temporal variation (Davis et al, 2017;Hu et al, 2012Hu et al, , 2013Huang et al, 2018;Yang et al, 2020), and change effect (Ding & Zhao, 2014;Mou et al, 2017;Wen & Goodman, 2013).…”

The Effect of Flood Risk on Residential Land Prices

“…There is an extensive body of research examining the factors that influence residential land prices. With respect to research methods, in addition to the basic hedonic price model (HPM) [12][13][14] and geographically weighted regression model (GWR) [15][16][17], scholars have begun to extend the methodological system of land price research with the help of the Structural Equation Model [18,19], Spatial Lag Model (SLM) [20][21][22], Spatial Error Model (SEM) [23,24], and Spatial Durbin Model (SDM) [25][26][27]. As for the factors, many scholars have explored the mechanism of population size, urban planning, land transfer Land 2022, 11, 1612 2 of 18 policy, and socioeconomic conditions on land prices from a macro perspective [28][29][30][31][32][33].…”