# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 3 Experiment and Results
### Results of the Global Regression Models  
The HPM is used to model house prices by the visual factors selected in this paper and explore the global impact of visual factors on house prices. In this study, we controlled for a series of locational and structural variables, including D_Bus, D_Subway, D_Hospital, D_Kindergarten, D_PrimarySchool, D_MiddleSchool, D_Government, D_Park, Elevator, Year, and Area. Table 6 provides the OLS regression coefficient statistical description. First, this paper selects eight influencing factors of location attributes. As can be seen from Table 4, factors affecting house prices in the global scope include six factors, the distance to the bus station, subway station, hospital, primary school, middle school, and park. In terms of transportation facilities and services, the distance between the bus station and subway station has a reverse promoting effect on housing prices. Specifically, the closer the distance between the bus station and subway station is, the higher the house price will be. According to the absolute value of the factor coefficient, the impact of the subway station on the housing price is greater than that of the bus station. In terms of health \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variibles**} & \multicolumn{2}{c}{**Beijing**} & \multicolumn{2}{c}{**Chongqing**} \\ \cline{2-5}  & **Coefficients** & **SE** & **Coefficients** & **SE** \\ \hline
**Covariates** & & & & \\ D\_Bus & \(-0.112\)*** & \(0.019\) & \(-0.057\)** & \(0.024\) \\ D\_Subway & \(-0.120\)*** & \(0.020\) & \(-0.131\)*** & \(0.025\) \\ D\_Hospital & \(0.050\)*** & \(0.019\) & \(0.136\)*** & \(0.026\) \\ D\_Kindergarten & \(0.074\) & \(0.017\) & \(0.029\) & \(0.023\) \\ D\_PrimarySchool & \(-0.037\)** & \(0.018\) & \(0.027\) & \(0.024\) \\ D\_MiddleSchool & \(-0.064\)*** & \(0.018\) & \(-0.030\)* & \(0.023\) \\ D\_Government & \(0.038\) & \(0.018\) & \(0.051\) & \(0.024\) \\ D\_Park & \(-0.096\)*** & \(0.018\) & \(-0.036\) & \(0.022\) \\ Elevator & \(0.030\)* & \(0.020\) & \(0.009\)** & \(0.020\) \\ Year & \(-0.065\)*** & \(0.017\) & \(-0.382\)*** & \(0.025\) \\ Area & \(0.650\)*** & \(0.017\) & \(0.770\)*** & \(0.021\) \\ \hline \hline \multicolumn{5}{c}{**Visual factors**} \\ GVI & \(0.419\)*** & \(0.067\) & \(0.218\)*** & \(0.068\) \\ SVI & \(0.199\)*** & \(0.070\) & \(0.241\)** & \(0.062\) \\ WI & \(0.020\) & \(0.017\) & \(0.022\) & \(0.020\) \\ MI & \(0.072\)*** & \(0.018\) & \(-0.040\) & \(0.028\) \\ BVI & \(-0.325\)*** & \(0.035\) & \(-0.103\)** & \(0.044\) \\ NAI & \(0.040\)** & \(0.017\) & \(0.010\)* & \(0.020\) \\ \hline \hline \end{tabular}  
* significant at the 0.1 level, *** significant at the 0.05 level, *** significant at the 0.01 level.  
\end{table} 
Table 6: Results of the global regression models. care, Chongqing and Beijing performed similarly, with housing prices falling the closer a neighborhood is to a hospital. Combined with the social functions of hospitals and the geomatic theory that Chinese people believe in, it is speculated that the complex noise and chaotic social environment of hospitals may make home buyers take extra consideration when buying houses. Therefore, hospitals near communities may restrain housing prices to some extent. Second, from the perspective of structural attributes, the three structural characteristics of the two cities have a significant impact on the housing prices and have the same impact trend. Among them, the area and the presence of elevators have a positive impact on the housing price. That is to say, from the overall perspective, a house with an elevator has a larger area; therefore, the higher the price. Housing age refers to the difference between the current year and the year when the house was built. According to the regression coefficient, housing age is negatively correlated with housing price; that is, the house price of a new house is higher than that of an old house. Last, it can be seen from Table 4 that the GVI, SVI, and BVI have the same influence trend on the house prices of the two cities.