Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
Stephen Law, Brooks Paige, Chris Russell

TL;DR
This paper demonstrates that deep learning models using street view and satellite images can effectively quantify neighborhood qualities and improve house price estimation in London, providing interpretable visual desirability metrics.
Contribution
It introduces a pipeline combining visual features from images with traditional housing data to enhance house price prediction and interpret neighborhood appeal.
Findings
Visual features improve house price prediction accuracy.
Linear models allow extraction of interpretable neighborhood desirability metrics.
The approach generalizes to unseen London boroughs.
Abstract
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…
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Taxonomy
TopicsHousing Market and Economics · Impact of Light on Environment and Health · Land Use and Ecosystem Services
MethodsInterpretability
