# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 3 Experiment and Results
### MGWR Regression Coefficient Analysis
The NAI mainly refers to the proportion between the sum of sky and green plants and other artificial environments. The NAI of the two cities is positively correlated with the house price. The \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{4}{c}{**Beijing**} & \multicolumn{4}{c}{**Chongqing**} \\ \cline{2-9}  & **Mean** & **S.D.** & **Min** & **Max** & **Mean** & **S.D.** & **Min** & **Max** \\ \hline
**Covariates** & & & & & & & & \\ D\_Bus & \(-\)0.101 & 0.117 & \(-\)0.556 & \(-\)0.046 & \(-\)0.065 & 0.022 & \(-\)0.110 & \(-\)0.037 \\ D\_Subway & \(-\)0.039 & 0.010 & \(-\)0.061 & \(-\)0.023 & \(-\)0.091 & 0.042 & \(-\)0.197 & \(-\)0.048 \\ D\_Hospital & 0.037 & 0.004 & 0.031 & 0.046 & 0.080 & 0.003 & 0.072 & 0.086 \\ D\_Kindergarten & 0.026 & 0.003 & 0.020 & 0.033 & 0.013 & 0.028 & \(-\)0.038 & 0.087 \\ D\_PrimarySchool & \(-\)0.022 & 0.010 & \(-\)0.035 & \(-\)0.006 & \(-\)0.156 & 0.045 & \(-\)0.263 & \(-\)0.082 \\ D\_MiddleSchool & \(-\)0.024 & 0.073 & \(-\)0.175 & \(-\)0.045 & \(-\)0.015 & 0.009 & \(-\)0.028 & \(-\)0.001 \\ D\_Government & 0.041 & 0.015 & 0.021 & 0.063 & 0.035 & 0.008 & 0.024 & 0.047 \\ D\_Park & \(-\)0.053 & 0.091 & \(-\)0.053 & \(-\)0.019 & \(-\)0.009 & 0.006 & \(-\)0.020 & \(-\)0.001 \\ Elevator & 0.008 & 0.002 & 0.005 & 0.014 & 0.208 & 0.453 & 0.113 & 1.571 \\ Year & \(-\)0.035 & 0.054 & \(-\)0.277 & 0.080 & \(-\)0.406 & 0.162 & \(-\)0.945 & \(-\)0.103 \\ Area & 0.664 & 0.211 & \(-\)0.042 & 1.305 & 0.717 & 0.147 & 0.331 & 1.308 \\ \hline \multicolumn{9}{c}{**Visual factors**} & \multicolumn{4}{c}{} \\ GVI & 0.299 & 0.003 & 0.295 & 0.304 & 0.161 & 0.004 & 0.168 & 0.156 \\ SVI & 0.185 & 0.204 & \(-\)0.603 & 0.956 & 0.147 & 0.005 & 0.136 & 0.153 \\ WI & \(-\)0.012 & 0.726 & \(-\)1.775 & 2.245 & 0.004 & 0.004 & \(-\)0.004 & 0.018 \\ MI & 0.033 & 0.109 & \(-\)0.395 & 0.318 & \(-\)0.068 & 0.084 & \(-\)0.319 & 0.082 \\ BVI & 0.186 & 0.203 & \(-\)0.450 & 0.924 & \(-\)0.097 & 0.122 & \(-\)0.608 & 0.222 \\ NAI & 0.196 & 1.473 & 0.038 & 3.910 & 0.599 & 1.016 & 0.390 & 2.918 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: MGWR regression coefficient statistical description. more natural environmental elements near the residential area, the higher the house price of the residential area. Based on the above analysis, the following conclusions can be obtained: (1) The GVI of the two cities has a significant impact on the house price. From the average coefficient, the GVI has a higher impact on the house price than other visual indexes. From the perspective of orientation, both cities show that the east, north, and south directions of the region's house prices are the most affected. (2) According to Table 7 and Figure 5, the WI has the lowest impact on the house price. (3) There are spatial differences in the impact of all visual indexes on the house price; that is, the same street views visual index may have different effects on the house price in different regions to different degrees and trends. (4) The impact of the street visual environment index on the house price may be affected by other surrounding infrastructure. For example, near school districts, the MI has a negative correlation with the house price; that is, the smaller the MI, the higher the house price.