# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 3 Experiment and Results
### MGWR Regression Coefficient Analysis  
Similarly, we controlled for a series of locational and structural variables, adding visual factors to the MGWR model. The statistical description of the coefficients of the MGWR is shown in Table 7. To see variations in the spatial scales at which the different processes operate more clearly, the weighted average method is adopted to aggregate coefficients of each variable in the research units in Beijing and Chongqing (as shown in Figure 5). As shown in Table 7, six location variables significantly affect the house price in Beijing, namely, the D_Bus, D_Subway, D_Hospital, D_PrimarySchool, D_MiddleSchool, and D_Park. The coefficient of the D_Subway fluctuates between \(-0.061\) and \(-0.032\), indicating that as the distance to the subway decreases by \(1\) km, the house price of the northwest can rise by up to \(0.061\%\) in Beijing. The impact of traffic facilities on the house price in Chongqing is similar to that in Beijing. For every \(1\) km reduction in the distance to bus and subway in these areas, prices will rise by up to \(0.110\%\) and \(0.197\%\). For structural attributes, in Chongqing, the area of houses is positively correlated with the house price on a global scope. Specifically, the house price increases by \(0.717\%\) on average for every \(1\) square meter increase in the house area. However, the area of houses has an opposite effect on the house price in different areas of Beijing. Figure 5: Spatial distribution of MGWR visual factor coefficients in Beijing and Chongqing. The six visual indexes defined in this paper affect the house price to varying degrees. According to Figure 5, the GVI of the two cities both affect the house price on a global scope and are positively correlated with the house price. The average coefficients are 0.299 and 0.161, respectively. Specifically, when the GVI of Beijing and Chongqing increases by 1%, the house price increases by 0.299% and 0.161%, respectively. The SVI is positively correlated with the urban house price; that is, the larger the SVI, the higher the house price. In Beijing, the SVI affects the house price on a local scope, among which the most influential areas are mainly in the Xidan North Street of the second ring Road and Shijji Jinyuan Shopping Center of the fourth ring road. While the SVI affects Chongqing's house price on a global scope, the coefficient fluctuates between 0.136 and 0.153, the average coefficient is 0.147, indicating that with every 1% increase of the SVI, the house price increases by 0.147% on average. The MI affects the two cities' house prices on a local scope, and the MI has the opposite effect on the house price in different regions. For example, near the People's Square in Yuzhong District of Chongqing, the MI is negatively correlated with the house price. The D_PrimarySchool in this area also has a great impact on the house price, which may be the school district. Therefore, a large MI may not be friendly to the nearby schools. The influence of the MI on the house price in Beijing has a similar trend to that in Chongqing. Taking the western direction centered on Tian 'anmen Square and combining the distribution of the influence coefficient of the D_MiddleSchool, it is known that this area is mostly a middle school district, and the MI is negatively correlated with the house price. As can be seen from Figure 5, the impact of the BVI on the house price of the two cities is spatially nonstable. In particular, the BVI in the northwest of the third Ring in Beijing is negatively correlated with the house price, indicating that every 1% reduction of the BVI will lead to a maximum 0.186% increase in the house price. In the southwest of the third ring, the BVI is positively correlated with the house price. For Chongqing, the BVI has a significant negative correlation with the housing price; that is, the larger the BVI is, the lower the house price will be. High-rise and dense buildings may bring people a sense of psychological pressure, so the house price around them may be affected.