# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 4 Conclusions  
Among various factors that affect the house price, structure and location attributes have been intensively investigated. In this paper, we propose a new factor, the visual environment factor, for consideration. Our main contribution lies in that we extract ten visual elements and define six visual indexes to investigate their influences on house prices. To validate that our proposed indexes make sense, we introduce them into different methods, such as MGWR, GWR, and HPM. Then we implement our experiments on real-world data, which is extracted from Baidu Street View of Beijing and Chongqing. Some interesting results are as follows: (1) By visualizing the regression coefficient distribution, we can get the local or global change rules of different visual indexes affecting the house price. (2) The green view index is the most critical visual index affecting the house price and is positively correlated with the house price in the global scope. It indicates that the proportion of green area and vegetation in the community is positively promoting the rise of community house prices. Therefore, reasonable planning of community greening is not only conducive to the physical and mental health of residents but also conducive to the evaluation of house prices by real estate developers. (3) The sky view index, building view index, walkability index, motorization index, and nature artificial index also have a significant impact on the house price. Moreover, spatial heterogeneity exists in the influence of the five visual indexes on the house price, and the intensity of the visual indexes' influence on the house price in different regions of the same city can be identified through the regression coefficient distribution. The influence intensity and trend of different visual indexes on house prices in different regions are significantly different. For example, near the school district, the motorization index has a significant negative correlation with house prices, while in the suburbs, the motorization index has a significant positive correlation with house prices. (4) In the MGWR, the influencing factors with small bandwidth have strong spatial heterogeneity and do not significantly affect the house price in the global scope. For example, the walkability index affects the house price only at small spatial scales. Our research integrates computer science and social science research by utilizing advanced techniques with emerging data sources and could provide new insights for buyers and urban planners.  
In the future, we plan to improve this work in the following directions. First, we intend to incorporate the time attribute of BSV images into the relationship modeling of the house price and visual index and explore the influence of the physical environment on the house price in different stages with the change of time. Second, the influencing factors of the house price come from all aspects and are diverse; we plan to combine multi-source data (such as satellite remote sensing images) to enrich and quantify the factors influencing the house price.  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data and contributed to the analysis tools; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The work was supported by the National Natural Science Foundation of China (No. 62172066 and 61872050).  
**Conflicts of Interest:** The authors declare no conflict of interest.