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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 4/2018

03.04.2018 | Original Paper

Vision-based real estate price estimation

verfasst von: Omid Poursaeed, Tomáš Matera, Serge Belongie

Erschienen in: Machine Vision and Applications | Ausgabe 4/2018

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Abstract

Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow’s estimates.
Vorheriger Artikel Retinal vessel extraction using dynamic multi-scale matched filtering and dynamic threshold processing based on histogram fitting
Nächster Artikel An experimental comparison of superpixels detection methods for contour detection

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Fußnoten
2
This value refers to the reported error rate at the time we started collecting data (June 2016). While the latest reported median error rate of Zestimate is 5.6% (https://​www.​zillow.​com/​zestimate/​#acc), the same approach as what we describe in the paper can be used to decrease the error rate.
 
3
About the Redfin Estimate: www.​redfin.​com/​redfin-estimate.
 
4
What is a Zestimate? Zillow’s Home Value Forecast (http://​www.​zillow.​com/​zestimate/​).
 
5
About the Redfin Estimate: www.​redfin.​com/​redfin-estimate.
 
6
While the Houzz dataset contains millions of images in each category, we download and use 20k images in each category.
 
Literatur
1.
Bell, S., Bala, K.: Learning visual similarity for product design with convolutional neural networks. ACM Trans. Gr. (TOG) 34(4), 98 (2015)
2.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
3.
4.
Pagourtzi, E., et al.: Real estate appraisal: a review of valuation methods. J. Prop. Invest. Financ. 21(4), 383–401 (2003)CrossRef
5.
Benjamin, J., Randall, G., Sirmans, C.F.: Mass appraisal: an introduction to multiple regression analysis for real estate valuation. J. Real Estate Pract. Educ. 7(1), 65–77 (2004)
6.
Van Der Maaten, L., Weinberger, K.: Stochastic triplet embedding. In: International Workshop on Machine Learning for Signal Processing (MLSP), 2012 IEEE, pp. 1–6. IEEE (2012)
7.
Hollas, D.R., Rutherford, R.C., Thomson, T.A.: Zillow’s estimates of single-family housing values. Apprais. J. 78(1), 26–32 (2010)
8.
McGreal, S., Adair, A., McBurney, D., Patterson, D.: Neural networks: the prediction of residential values. J. Prop. Val. Invest. 16(1), 57–70 (1998)CrossRef
9.
Bagnoli, C., Smith, H.: The theory of fuzz logic and its application to real estate valuation. J. Real Estate Res. 16(2), 169–200 (1998)
10.
Byrne, P.: Fuzzy analysis: a vague way of dealing with uncertainty in real estate analysis? J. Prop. Val. Invest. 13(3), 22–41 (1995)CrossRef
11.
Wilber, M.J., Kwak, I.S., Belongie, S.J.: Cost-effective hits for relative similarity comparisons. In: 2nd AAAI Conference on Human Computation and Crowdsourcing (2014)
12.
Ordonez, V., Jagadeesh, V., Di, W., Bhardwaj, A., Piramuthu, R.: Furniture-geek: understanding fine-grained furniture attributes from freely associated text and tags. In: IEEE Winter Conference on Applications of Computer Vision (WACV), 2014, pp. 317–324. IEEE (2014)
13.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
14.
Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. J. Mach. Learn. Res. 11, 1109–1135 (2010)MathSciNetMATH
15.
Kusan, H., Aytekin, O., Ozdemir, I.: The use of fuzzy logic in predicting house selling price. Expert Syst. Appl. 37(3), 1808–1813 (2010)CrossRef
16.
Bell, S., Upchurch, P., Snavely, N., Bala, K.: Material recognition in the wild with the materials in context database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3479–3487 (2015)
17.
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)CrossRefMATH
18.
Karayev, S., Trentacoste, M., Han, H., Agarwala, A., Darrell, T., Hertzmann, A., Winnemoeller, H.: Recognizing image style. arXiv preprint arXiv:​1311.​3715 (2013)
19.
Worzala, E., Lenk, M., Silva, A.: An exploration of neural networks and its application to real estate valuation. J. Real Estate Res. 10(2), 185–201 (1995)
20.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef
21.
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)CrossRef
22.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
23.
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248-255. IEEE (2009)
24.
LeCun, Y.A., Bottou, L., Orr, G.B., Muller, K.: Efficient backprop. In: Neural Networks: Tricks of the Trade, pp. 9–48. Springer, Berlin (2012)
25.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213–3223 (2016)
26.
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J.: Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:​1506.​03365 (2015)
27.
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. arXiv preprint arXiv:​1608.​06993 (2016)
28.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)
29.
Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: Advances in Neural Information Processing Systems, pp. 2377–2385 (2015)
30.
31.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556 (2014)
32.
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems, pp. 487–495 (2014)
33.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRef
34.
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer vision and pattern recognition, 2006, vol. 2, pp. 2169–2178. IEEE (2006)
35.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
36.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)CrossRef
37.
38.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881–2890 (2017)
39.
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
40.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
41.
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
42.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
43.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2015). arXiv preprint arXiv:​1511.​06434
44.
Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., Belongie, S.: Stacked generative adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, p. 4 (2017)
45.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation (2017). arXiv preprint arXiv:​1710.​10196
46.
Metadaten
Titel
Vision-based real estate price estimation
verfasst von
Omid Poursaeed
Tomáš Matera
Serge Belongie
Publikationsdatum
03.04.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 4/2018
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-018-0922-2

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