House Price Estimation from Visual and Textual Features

Eman H. Ahmed, Mohamed Moustafa


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 textual-only features housing dataset (Lichman, 2013), our proposed NN still outperformed the existing model published results (Khamis and Kamarudin, 2014).


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Paper Citation

in Harvard Style

H. Ahmed E. and Moustafa M. (2016). House Price Estimation from Visual and Textual Features . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 62-68. DOI: 10.5220/0006040700620068

in Bibtex Style

author={Eman H. Ahmed and Mohamed Moustafa},
title={House Price Estimation from Visual and Textual Features},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016)},

in EndNote Style

JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016)
TI - House Price Estimation from Visual and Textual Features
SN - 978-989-758-201-1
AU - H. Ahmed E.
AU - Moustafa M.
PY - 2016
SP - 62
EP - 68
DO - 10.5220/0006040700620068