  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.
