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

Take a Look Around: Using Street View and Satellite Images to Estimate House Prices