Simulating the Spatial Heterogeneity of Housing Prices in Wuhan, China, by Regionally Geographically Weighted Regression

Abstract

Geographically weighted regression (GWR) is an effective method for detecting spatial non-stationary features based on the hypothesis of proximity correlation. In reality, especially in the social and economic fields, research objects not only have spatial non-stationary characteristics, but also spatial discrete heterogeneity characteristics. Therefore, how to improve the accuracy of GWR estimation in this case is worth studying. In this paper, a regionally geographically weighted regression (RGWR) is proposed. Using incoming dummy variables, the zoning discrimination is added to the spatial kernel function of GWR, the spatial kernel function is modified, the spatial weight is optimized, and the influence of “near heterogeneous” observation points is reduced. In this paper, the residential sale price in Wuhan City is taken as an example in the analysis of three aspects: model performance, fitting effect and influencing factors. The results show that the introduction of a zoning dummy variables can significantly improve the model accuracy of a fixed bandwidth and adaptive bandwidth. Under a fixed bandwidth, compared with the GWR model, RGWR increases R2 and R2adj from 0.6776 and 0.6732 to 0.777 and 0.7746, respectively, and the Akaike information criterion, corrected (AICc) standard decreases by 37.4006 compared with GWR, which proves the effectiveness of the method.

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

“…RGWR is an extension of GWR used to explore spatial non-stationary and spatially discrete heterogeneity by adding regional dummy variables to GWR [35]. The model can be expressed as:…”

“…The specification of spatial heterogeneity can be classified into continuous heterogeneity and discrete heterogeneity [33]. Existing GWR models and extended models can detect spatially continuous heterogeneity, to a certain extent, in practical applications by means of bandwidth optimization [34]; however, they cannot reveal the discrete heterogeneity [35]. In fact, housing prices not only have the characteristics of spatial non-stationary, but also spatial discrete heterogeneity, which is particularly obvious in China, where real estate prices are directly affected by the macro-economy and policies.…”

Statistical modeling to analyze factors affecting the middle-income trap in Indonesia using panel data regression

“…Because the slope and intercept coefficients are not the same, there are three ways to estimate panel data regression models: the Common Effect Model (CEM), the Fixed Effect Model (FEM), and the Random Effect Model (REM) [ 10 , 15 ]. Several studies have examined and developed panel data regression models, including [ 4 , [16] , [17] , [18] , [19] , [20] , [21] , [22] ]. This research focuses on analyzing what variables significantly affect MIT in Indonesia, which provinces have the most critical influence on MIT conditions in Indonesia, and their characteristics from year to year so that this Province can be maximized.…”

The Impacts of Public Schools on Housing Prices of Residential Properties: A Case Study of Greater Sydney, Australia

“…Using data from 380 counties in Poland in 2018, it is shown by the modelling results of Cellmer, et al [23] that the impact of the analysed price determinants is spatially differentiated. Furthermore, Wang, et al [24] proposed a regionally geographically weighted regression (RGWR) method that incorporates zoning discrimination and optimised spatial weights to improve the accuracy of geographically weighted regression (GWR) estimation and conducted an analysis of residential sale prices in Wuhan City. These studies have demonstrated that GWR can effectively model both global and local spatial relationships [25].…”