Are Satellite Images Effective for Estimating Land Prices on Deep Neural Network Models?

Estimating land prices is useful for assessing values of sites. Several works study estimating land prices from land features that are extracted from geodetic data. However, the estimation accuracy is not high enough yet because it is difficult to thoroughly collect geodetic data that affects land prices. In this paper, we study the effectiveness of the satellite images to estimate land prices for the first time. To verify effectiveness of satellite images, we estimate land prices by using three deep neural network models: multilayer perceptrons (MLP) model only with geodetic data, convolution neural network (CNN) model only with satellite images, and concatenation model that concatenates the MLP with the CNN models. We demonstrate through experiments using real land prices, geodetic data, and satellite images in Japan that the simultaneous use of satellite images and geodetic data improves the estimation accuracy of land prices.