  Most existing automatic house price estimation systems rely only on some
textual data like its neighborhood area and the number of rooms. The final
price is estimated by a human agent who visits the house and assesses it
visually. In this paper, we propose extracting visual features from house
photographs and combining them with the house's textual information. The
combined features are fed to a fully connected multilayer Neural Network (NN)
that estimates the house price as its single output. To train and evaluate our
network, we have collected the first houses dataset (to our knowledge) that
combines both images and textual attributes. The dataset is composed of 535
sample houses from the state of California, USA. Our experiments showed that
adding the visual features increased the R-value by a factor of 3 and decreased
the Mean Square Error (MSE) by one order of magnitude compared with
textual-only features. Additionally, when trained on the benchmark textual-only
features housing dataset, our proposed NN still outperformed the existing model
published results.
