Abstract: Location of urban housing directly affects housing price. Choice of housing involves considerations of various public service facilities such as schools, job accessibilities, among many others, which have been widely discussed in existing literature. In this paper, we explore the spatial correlation of landscape accessibility with housing price in Beijing. Based on ArcGIS spatial analysis method and geographically weighted regression model, this paper examines the spatial heterogeneity and the main determinants of the second-hand housing prices in the urban area of Beijing. Through major real estate dealer websites, we collected the second-hand housing data on prices in January 2012 for downtown Beijing, with a total number of 3174 samples. After establishing the housing spatial database, spatial interpolation and kernel density estimation are applied to explore the spatial distribution and heterogeneity of housing price. The kernel density map shows that the residential space in downtown Beijing has evident agglomeration characteristics in general, that is, density decreases gradually from Tian'anmen Square to the periphery. High density also occurs at sub-centers formed near the subway transfer stations, and the sub-centers in Shijingshan and Tongzhou have begun to take shape. With the help of spatial interpolation analysis in ArcGIS, we mapped the spatial pattern of housing price in Beijing. It can be clearly seen from the result that housing price also decreases from city center to the periphery, which is similar to the spatial pattern of housing density. Housing price reaches the peak within the Second Ring Road, with some high price sub-centers emerge between the 3rd and the 4th Ring Road or at the outer suburban districts along the subway lines. Finally, by using geographically weighted regression model, we analyzed the influencing factors of housing price, including traffic factors, locational features, maintenance cost and landscape accessibility (green space coverage, distance to the nearest lake or river, distance to the nearest mountain) and so on. The results show that the distance to sub-centers has the most significant impact on housing price, and there is a certain degree of correlation between landscape accessibility and housing price. Specifically, houses with high greening rate and those located near a mountain is much more expensive; due to the poor water quality, waterscape has a negative impact on housing price; sewage treatment plants, burial grounds and other sources of pollution also exert negative impact on housing price. People prefer houses far from sources of pollution and near pleasant landscape features; low plot ratio and high green space coverage are also favored. The spatial correlation analysis of landscape accessibility and residential housing prices provides a foundation for the planning of urban residential space and references for the planning and management departments of the city government.