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Visual Estimation of Building Condition with Patch-level ConvNets

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Published:06 June 2018Publication History

ABSTRACT

The condition of a building is an important factor for real estate valuation. Currently, the estimation of condition is determined by real estate appraisers which makes it subjective to a certain degree. We propose a novel vision-based approach for the assessment of the building condition from exterior views of the building. To this end, we develop a multi-scale patch-based pattern extraction approach and combine it with convolutional neural networks to estimate building condition from visual clues. Our evaluation shows that visually estimated building condition can serve as a proxy for condition estimates by appraisers.

References

  1. Eman Ahmed and Mohamed Moustafa. 2016. House price estimation from visual and textual features. arXiv (2016). http://arxiv.org/abs/1609.08399Google ScholarGoogle Scholar
  2. Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. 2008. Speeded-up robust features (SURF). Computer vision and image understanding 110, 3 (2008), 346--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Anna Bosch, Andrew Zisserman, and Xavier Munoz. 2007. Image classification using random forests and ferns. In 11th International Conference on Computer Vision (ICCV). IEEE, 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  4. Charles Carter, Zhenguo Lin, Marcus Allen, and William Haloupek. 2011. Another Look at Effects of "Adults-Only" Age Restrictions on Housing Prices. The Journal of Real Estate Finance and Economics 46 (2011), 1--16.Google ScholarGoogle Scholar
  5. Xiaochen Chen, Lai Wei, and Jiaxin Xu. 2017. House Price Prediction Using LSTM. arXiv preprint arXiv:1709.08432 (2017).Google ScholarGoogle Scholar
  6. Vincenza Chiarazzoa, Leonardo Caggiania, Mario Marinellia, and Michele Ottomanellia. 2014. A Neural Network based model for real estate price estimation considering environmental quality of property location. Transportation Research Procedia 3 (2014), 810--817.Google ScholarGoogle ScholarCross RefCross Ref
  7. Miroslav Despotovic, Muntaha Sakeena, David Koch, Mario Döller, and Matthias Zeppelzauer. 2018. Poster abstract: predicting heating energy demand by computer vision. Computer Science - Research and Development 33, 1 (2018), 231--232.Google ScholarGoogle ScholarCross RefCross Ref
  8. Jia He and Jing Wu. 2016. Doing well by doing good? The case of housing construction quality in China. Regional Science and Urban Economics 57 (2016), 46--53.Google ScholarGoogle ScholarCross RefCross Ref
  9. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarGoogle Scholar
  10. Shanaka Herath, Johanna Choumert, and Gunther Maier. 2015. The value of the greenbelt in Vienna: a spatial hedonic analysis. The Annals of Regional Science 54, 2 (2015), 349--374.Google ScholarGoogle ScholarCross RefCross Ref
  11. Shinichiro Iwata and Hisaki Yamaga. 2008. Rental externality, tenure security, and housing quality. Journal of Housing Economics 17, 3 (2008), 201--211.Google ScholarGoogle ScholarCross RefCross Ref
  12. Michal Juszczyk. 2017. The Challenges of Nonparametric Cost Estimation of Construction Works With the Use of Artificial Intelligence Tools. Creative Construction Conference 2017. (2017).Google ScholarGoogle Scholar
  13. Michal Juszczyk, Theodore Simos, and Charalambos Tsitouras. 2016. Application of PCA-based data compression in the ANN-supported conceptual cost estimation of residential buildings. AIP Conference Proceedings. 1738, 1 (2016).Google ScholarGoogle ScholarCross RefCross Ref
  14. David Koch and Gunther Maier. 2015. The influence of estate agencies' location and time on Internet: An empirical application for flats in Vienna. Jahrbuch fur Regionalwissenschaft 35, 2 (2015), 147--171.Google ScholarGoogle ScholarCross RefCross Ref
  15. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. David Lowe. 1999. Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, Vol. 2. IEEE, 1150--1157. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Stephen Malpezzi. 2003. Hedonic Pricing Models: A Selective and Applied Review. In Housing economics and public policy: Essays in Honour of Duncan Maclennan (1 ed.), Tony O O'Sullivan and Kenneth Gibb (Eds.). Blackwell Science Ltd, Oxford, 67--89.Google ScholarGoogle Scholar
  18. Omid Poursaeed, Tomas Matera, and Serge J. Belongie. 2018. Vision-based Real Estate Price Estimation. Machine Vision and Applications 29, 4 (2018), 667--676. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. H. Selim. 2009. Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications 36 (2009), 2843--2852. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. G Stacy Sirmans, Lynn MacDonald, and David Macpherson. 2010. A Metaanalysis of Selling Price and Time-on-the-Market. Journal of Housing Research 19, 2 (2010), 139--152.Google ScholarGoogle ScholarCross RefCross Ref
  21. Stacy Sirmans, David Macpherson, and Emily Zietz. 2005. The Composition of Hedonic Pricing Models. Journal of Real Estate Literature 13, 1 (2005), 3--43.Google ScholarGoogle Scholar
  22. TEGoVA. 2016. European Valuation Standards 2016. The European Group of Valuers Associations 8 (2016), 1--378.Google ScholarGoogle Scholar
  23. Andrea Vedaldi and Karel Lenc. 2015. Matconvnet: Convolutional neural networks for MATLAB. In Proceedings of the 23rd ACM International Conference on Multimedia. ACM, 689--692. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Yung Yau. 2008. Building conditions in Yau Tsim Mong, Hong Kong: Appraisal, exploration and estimation. Journal of Building Appraisel 3 (2008), 319--329.Google ScholarGoogle ScholarCross RefCross Ref
  25. Velma Zahirovich-Herbert and Karen M. Gibler. 2014. The effect of new residential construction on housing prices. Journal of Housing Economics 26 (2014), 1--18.Google ScholarGoogle ScholarCross RefCross Ref
  26. M. Zeppelzauer, M. Despotovic, M. Sakeena, D. Koch, and M. Döller. 2018. Automatic Prediction of Building Age from Photographs. In ACM International Conference on Multimedia Retrieval (ICMR). ACM. https://arxiv.org/abs/1804.02205 Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016. Learning deep features for discriminative localization. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2921--2929.Google ScholarGoogle ScholarCross RefCross Ref

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