Mahdieh Yazdani, Maziar Raissi
arXiv 31 Jan 2023 · cs.CV · 1 citations (OpenAlex)
arXiv:2302.00117 · PDF · DOI · OpenAlex · Extracted main text
The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property. We collected and pre-processed a data set of real estate properties in the city of Boulder, Colorado and used it to train, validate and test our algorithm. Our data set consisted of qualitative images (including house interiors, exteriors, and street views) as well as quantitative features such as the number of bedrooms, bathrooms, square footage, lot square footage, property age, crime rates, and proximity to amenities. We evaluated the performance of our model using metrics such as Root Mean Squared Error (RMSE). Our findings indicate that these techniques are able to accurately predict the value of properties, with a low RMSE. The proposed algorithm outperforms traditional appraisal methods that do not leverage property images and has the potential to be used in real-world applications.
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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 | Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski… (2021) Emerging properties in self-supervised vision transformers | 0.874 | 5 | 2 | 100% |
| 2 | Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X… (2020) An image is worth 16x16 words: Transformers for image recognition at scale | 0.644 | 2 | 2 | 100% |
| 3 | He, K., Zhang, X., Ren, S., and Sun, J (2016) Deep residual learning for image recognition | 0.644 | 2 | 2 | 100% |
| 4 | Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gome… (2017) Attention is all you need | 0.585 | 3 | 1 | 100% |
| 5 | Del Giudice, V., Manganelli, B., and De Paola, P (2017) Hedonic analysis of housing sales prices with semiparametric methods | 0.511 | 2 | 1 | 100% |
| 6 | Park, B. and Bae, J. K (2015) Using machine learning algorithms for housing price prediction: The case of fairfax county, virginia housing data | 0.511 | 2 | 1 | 100% |
| 7 | Selim, S (2011) Determinants of house prices in turkey: A hedonic regression model | 0.511 | 2 | 1 | 100% |
| 8 | Ali, G., Bashir, M. K., Ali, H., et al (2015) Housing valuation of different towns using the hedonic model: A case of faisalabad city, pakistan | 0.405 | 1 | 1 | 100% |
| 9 | Arvanitidis, P. A (2014) The economics of urban property markets: an institutional economics analysis | 0.405 | 1 | 1 | 100% |
| 10 | Bigus, J. P (1996) Data mining with neural networks: solving business problems from application development to decision support | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 54 scored citations.