Stephen Law, Brooks Paige, Chris Russell
arXiv 18 Jul 2018 · Econometrics · 22 citations (OpenAlex)
arXiv:1807.07155 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Steven Peterson and Albert Flanagan (2009) Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal | 1.000 | 8 | 3 | 100% |
| 2 | Quanzeng You, Ran Pang, Liangliang Cao, and Jiebo Luo (2016) Image Based Appraisal of Real Estate Properties | 1.000 | 5 | 3 | 100% |
| 3 | Eman Ahmed and Mohamed Moustafa (2016) House price estimation from visual and textual features | 0.874 | 5 | 2 | 100% |
| 4 | Stephen Law, Yao Shen, and Chanuki Seresinhe (2017) An Application of Convolutional Neural Network in Street Image Classification: The Case Study of London. In Proceedings of the 1… self | 0.874 | 5 | 2 | 100% |
| 5 | William McCluskey, Michael McCord, Peadar Davis, Martin Haran, and D… (2013) Prediction accuracy in mass appraisal: a comparison of modern approaches | 0.874 | 5 | 2 | 100% |
| 6 | Simon N. Wood (2006) Generalized additive models: an introduction with R | 0.874 | 5 | 2 | 100% |
| 7 | Omid Poursaeed, Tomas Matera, and Serge Belongie (2018) Vision-based Real Estate Price Estimation | 0.737 | 3 | 2 | 100% |
| 8 | Sherwin Rosen (1974) Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition | 0.737 | 3 | 2 | 100% |
| 9 | S. M. Arietta, A. A. Efros, R. Ramamoorthi, and M. Agrawala (2014) City Forensics: Using Visual Elements to Predict Non-Visual City Attributes | 0.644 | 2 | 2 | 100% |
| 10 | Paul Cheshire and Stephen Sheppard (1995) On the Price of Land and the Value of Amenities | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 50 scored citations.