Natural landscape views have positive sides, such as providing restorative effects to urban residents, and negative sides, such as deepening wealth inequality. Previous studies have mainly focused on the positives and rarely on the negatives. From this perspective, this study aimed to analyze the unequal impact of natural landscape views on housing prices for apartments in Seoul. We proposed a visual perception model to analyze natural landscape views and, based on a hedonic price model, we used ordinary least squares and quantile regression to estimate the marginal impacts on housing prices. The results show that: (1) natural landscape views had positive impacts on housing prices, but their impacts did not reach the level of structural and locational characteristics such as apartment area and the distance to subway stations; (2) natural landscape views had different marginal impacts by housing price range and, in particular, had much higher value-added effects on higher-priced apartments, meaning that if old apartment complexes are redeveloped into high-rise ones, the improvement in natural landscape views generates great profit for apartment owners and intensifies wealth inequality; (3) the geographic information system-based visual perception model effectively quantified the natural landscape views of wide areas and is thus applicable for the rigorous analysis of various landscape views.

This research has the purpose to develop a method to evaluate whether station's area of influence has been formed, and verify formation of the area of influence through empirical analysis of all subway stations in Seoul. First, we created buffers of 100m intervals from 100m to 1000m, based on subway station exits, calculated the average land price of each buffer, and divided station areas of influence into 10 clusters using K-means clustering with the average land prices as values of observation. Subsequently, we have assumed a decreasing price curve from increasing distance from a nearby subway station, estimated a price curve and evaluated whether the area of influence actually exists using regression analysis of each cluster. The 10 area of influence clusters were largely divided into strong, weak, and no area of influence of subway station. The stations where the strong areas of influence are formed are mainly located in center, sub-centers, and local centers; stations where weak and no areas of influence are formed are mostly located in the adjacent areas of center or sub-centers or suburbs. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.