Vision-based real estate price estimation
Vision-based real estate price estimation
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DOI：
10.1007/s00138-018-0922-2
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发表时间：
2018-05-01
影响因子：
3.3
通讯作者：
Belongie, Serge
中科院分区：
文献类型：
--
作者：
Poursaeed, Omid;Matera, Tomas;Belongie, Serge
自从Zillow、Trulia和Redfin等在线真实的房地产数据库公司出现以来，自动估计房屋市场价值的问题受到了相当大的关注。几个真实的房地产网站使用专有公式提供了这样的估计。虽然这些估计往往接近实际销售价格，但在某些情况下，它们非常不准确。影响房屋价值的关键因素之一是其内部和外部外观，这在计算自动价值估计时不被考虑。在本文中，我们评估的影响，视觉特征的房子，其市场价值。我们在家庭内部和外部照片的大型数据集上使用深度卷积神经网络，开发了一种估计真实的房地产照片的豪华水平的方法。我们还开发了一个新的框架，用于自动化价值评估，使用上述照片以及房屋特征，包括大小，提供的价格和卧室数量。最后，通过将我们提出的价格估计方法应用于真实的房地产照片和元数据的新数据集，我们表明它优于Zillow的估计。
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.