A method for describing landscape in the countryside and evaluating its impact on the real-estate market is suggested. Four databases with different resolutions (7, 30, 150, and 1,000 m) are used to simulate the visual properties of landscape in the depth of the field of view. The databases, comprising digital elevation models and land use images, are processed by a raster geographical information system. A model that simulates the relationship of visibility among all the points in a given space is devised and used to produce variables that are taken as explanatory variables in a hedonic regression. On this basis, the significant contribution of several landscape features to housing prices is estimated and then mapped. The study area is located in the urban fringe of Dijon (France). A total of 4,352 houses with known price, position, and landscape amenities provide the information for calibrating the hedonic model. The results confirm that landscape amenities influence house prices. Landscapes and visible features more than 100 to 200 m away all have insignificant hedonic prices. In this study area, forests and farmland in the immediate vicinity of houses have positive prices, whereas roads have negative ones.