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. House pricing remains as much art as science. The cost of a property depends not just upon its tangible assets such as the size of the property and its number of bedrooms, but also on its intangible assets such as how safe or busy a neighborhood feels, or how a house stands with relation to its neighbors. Real estate assessors face the challenging task of quantifying these effects and assigning to a property a realistic price that reflects what people are prepared to pay for these tangible and intangible assets. From an economic perspective, it is unsurprising that people are prepared to pay for intangible assets. The urban environment directly affects people's social, economic, and health outcomes. The design of a window placement can influence the amount of nature visible from within a home and also the perceived safety of a street [20] . The amount of greenery can influence both the pollutants at the street level and also its scenicness and ambiance [43] . These differences in the urban environment are reflected in the varying prices people are prepared to pay in a property market, holding other factors such as size and access to jobs constant [10, 23] . Some urban features are directly observable from photos, such as the activeness of a street frontage, the amount of greenery, or the width of the pavement. Others are less directly quantifiable such as the prestige of the neighborhood or the aesthetics of the street. Despite the strong link between urban design attributes and economic value, there is a clear lack of research, computational tools, and data that can be used to discover these attributes and inform planning policies. To date, the discussion regarding which urban design attributes lead to better cities or higher property values has largely been theoretical, supported quantitatively by only a handful of studies. To measure these urban quality metrics requires many street-level surveys and structured interviews with professionals. Collecting the data required to evaluate urban quality at the city scale is both costly and timeconsuming. One approach is to cast this as a problem of computer vision. This field has made great advances in image classification [22] , object detection [17] , image segmentation [8] , and edge detection [26] . However, these advances have hinged upon the ready availability of big data, or in this context, hundreds of thousands of diverse images annotated with these expensive quality metrics. Unfortunately, this is a chicken and egg scenario: to avoid the expensive and time-consuming hand annotation of images, we must first perform the expensive and time-consuming process of hand-labeling of thousands of such images. To avoid such issues, this research will not use machine vision methods to classify or to detect intermediate values, such as amount of greenery, that can be used in house price models but instead use deep learning machine vision techniques to extract visual latent response in images based on the property price in an end-to-end learning model. We extract visual urban features using convolutional neural networks on urban images at both the plan and street level, which can be used in conjunction with traditional housing features to estimate the price paid for a property in London. A fundamental tradeoff exists in econometrics between the use of tractable models [41] that are easy to analyze, and difficult to interpret black-box approaches such as [49] that often have significantly better accuracy. To handle this dichotomy, we consider two different approaches: (i) a full black-box model in which the neural network implicitly integrates the cues from standard ACM
Law et al. REF used both street-view images and satellite images for house price prediction.
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
TIST
Economics Computer Science