Vision-based real estate price estimation
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Vision-based real estate price estimation. / Poursaeed, Omid; Matera, Tomáš; Belongie, Serge.
In: Machine Vision and Applications, Vol. 29, No. 4, 01.05.2018, p. 667-676.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Vision-based real estate price estimation
AU - Poursaeed, Omid
AU - Matera, Tomáš
AU - Belongie, Serge
N1 - Publisher Copyright: © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2018/5/1
Y1 - 2018/5/1
N2 - 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.
AB - 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.
KW - Automated valuation method
KW - Computer vision
KW - Convolutional neural networks
KW - Crowdsourcing
KW - Real estate
UR - http://www.scopus.com/inward/record.url?scp=85044739259&partnerID=8YFLogxK
U2 - 10.1007/s00138-018-0922-2
DO - 10.1007/s00138-018-0922-2
M3 - Journal article
AN - SCOPUS:85044739259
VL - 29
SP - 667
EP - 676
JO - Machine Vision and Applications
JF - Machine Vision and Applications
SN - 0932-8092
IS - 4
ER -
ID: 301826358