  When an individual purchases a home, they simultaneously purchase its
structural features, its accessibility to work, and the neighborhood amenities.
Some amenities, such as air quality, are measurable while others, such as the
prestige or the visual impression of a neighborhood, are difficult to quantify.
Despite the well-known impacts intangible housing features have on house
prices, limited attention has been given to systematically quantifying these
difficult to measure amenities. Two issues have led to this neglect. Not only
do few quantitative methods exist that can measure the urban environment, but
that the collection of such data is both costly and subjective.
  We show that street image and satellite image data can capture these urban
qualities and improve the estimation of house prices. We propose a pipeline
that uses a deep neural network model to automatically extract visual features
from images to estimate house prices in London, UK. We make use of traditional
housing features such as age, size, and accessibility as well as visual
features from Google Street View images and Bing aerial images in estimating
the house price model. We find encouraging results where learning to
characterize the urban quality of a neighborhood improves house price
prediction, even when generalizing to previously unseen London boroughs.
  We explore the use of non-linear vs. linear methods to fuse these cues with
conventional models of house pricing, and show how the interpretability of
linear models allows us to directly extract proxy variables for visual
desirability of neighborhoods that are both of interest in their own right, and
could be used as inputs to other econometric methods. This is particularly
valuable as once the network has been trained with the training data, it can be
applied elsewhere, allowing us to generate vivid dense maps of the visual
appeal of London streets.
