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.

本研究使用深度卷积神经网络对一大量室内外照片进行分析，以评价房屋的外观对市场价值的影响，并基于照片和房屋特征开发出自动估价方法，结果表明本方法优于 Zillow 对房屋价格的估计。