# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 1 Introduction  
As a hot topic concerning the government, real estate investors, and residents, the house price is strongly associated with economic activities and people's daily life. Investigating the spatial distribution characteristics and influencing factors of the house price can provide suggestions for house buyers, as well as researchers in the real estate market. Exploring the influencing factors of house prices has always been a research hotspot, and many relevant references have made achievements in this regard. In these studies, the decisive factors of house price are diverse and complex, which are generally classified into three categories: structure attributes, location attributes, and physical environment attributes. First, structure attributes refer to the properties of the house, such as the number of bedrooms, area, and age. [PERSON] et al. [1] find that the number of bedrooms and private space is important influencing factors for first-time buyers. When exploring the relationship between house orientation and house price in Shanghai, [PERSON] et al. [2] proved that the house prices in residential areas facing south are higher because they can enjoy more sunshine and beautiful scenery facing south. The floor is also one of the important factors affecting the house price. [PERSON] et al. [3] believe that the comfort of the landscape, in the same building on different floors, has vertical heterogeneity. Because the landscape viewing of different floors is different, the floor will also affect the house price. In Saudi Arabia, the size of the kitchen and the number of bedrooms are the most important factors influencing the houseprice [4]. Second, location attributes mainly refer to the accessibility of the house to the surrounding infrastructure (such as transportation facilities, education facilities). People are also willing to pay extra for houses closer to schools and subway stations. Residential areas with transfer stations in the suburbs also have higher house prices than residential areas without transfer stations [5; 6]. Educational facilities are another influencing factor widely concerning everyone. Families with children are more willing to buy houses near the school at a higher price to ensure that children get better educational resources [7]. The quality of school education also significantly promotes the surrounding house prices. [PERSON] et al. [8] take Hangzhou as an example and find that the quality of secondary education has the most significant impact on house prices, followed by primary schools and high school education. Last, physical environment attributes mainly refer to the urban landscape, such as rivers, mountains, and parks. The urban landscape provides aesthetic, entertainment, and ecological functions, which are beneficial to the spiritual, emotional, and physical health of residents. In China, with the improvement of the quality of life, people have begun to pursue a higher quality of living environment. In Hangzhou, mountains, lakes, rivers, parks, and other internal urban landscapes have a positive impact on house prices [9; 10]. Through questionnaire surveys and field research, some studies have shown that street visual appearance not only directly affects residents' impression but also affects the house price of the neighborhoods [11; 12]. The emergence of big data and map services provides unprecedented opportunities to obtain a new kind of geographic data--street view images. As a kind of idiographic data, the street view images can be adopted to represent the local scenes of neighborhoods while emphasizing human perception [13; 14]. Although studies have shown that the sky view factor and building height affect the house price [15], the impact of other visual factors on the house price has not been confirmed. There are still few studies that explore the relationship between visual environment elements and house prices. Spatial heterogeneity is another common phenomenon in geographic distribution.