<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Despite the wide application of deep neural networks (DNN) models, their application over small-sized real-estate price prediction is limited due to the reduced prediction accuracy and the high-dimensionality of the dataset. This study motivates small-sized real-estate agencies to take DNN-driven decisions using the available local dataset. To improve the high-dimensionality of real-estate price datasets and thus enhance the price-prediction accuracy of a DNN model, this paper adopts principal component analysis (PCA). The PCA benefits in improving the prediction accuracy of a DNN model are threefold: dimensionality reduction, dataset transformation and localisation of influential price features. The results indicate that, through the PCA-DNN model, the transformed dataset achieves higher accuracy (90%–95%) and better generalisation ability compared with other benchmark price predictors. The spatial and building age proved to have the most impact in determining the overall real-estate price. The application of PCA not only reduces the high-dimensionality of the dataset but also enhances the quality of the encoded feature attributes. The model is beneficial in real-estate and construction applications, where the absence of medium and big datasets decreases the price-prediction accuracy.</p>
</abstract>

Despite the wide application of deep neural networks (DNN) models, their application over small-sized real-estate price prediction is limited due to the reduced prediction accuracy and the high-dimensionality of the dataset. This study motivates small-sized real-estate agencies to take DNN-driven decisions using the available local dataset. To improve the high-dimensionality of real-estate price datasets and thus enhance the price-prediction accuracy of a DNN model, this paper adopts principal component analysis (PCA). The PCA benefits in improving the prediction accuracy of a DNN model are threefold: dimensionality reduction, dataset transformation and localisation of influential price features. The results indicate that, through the PCA-DNN model, the transformed dataset achieves higher accuracy (90%–95%) and better generalisation ability compared with other benchmark price predictors. The spatial and building age proved to have the most impact in determining the overall real-estate price. The application of PCA not only reduces the high-dimensionality of the dataset but also enhances the quality of the encoded feature attributes. The model is beneficial in real-estate and construction applications, where the absence of medium and big datasets decreases the price-prediction accuracy.

Real-estate price prediction with deep neural network and principal component analysis

Organization, Technology and Management in Construction: an International Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Despite the wide application of deep neural networks (DNN) models, their application over small-sized real-estate price prediction is limited due to the...

Real-estate price prediction with deep neural network and principal component analysis

Article Category: Research Paper

Published Online: Dec 31, 2022

Page range: 2741 - 2759

Received: Apr 18, 2022

Accepted: Nov 10, 2022

DOI: https://doi.org/10.2478/otmcj-2022-0016

Keywords
principal component analysis, deep neural network, high-dimensional dataset, real-estate price prediction, stepwise regression

© 2022 Fatemeh Mostofi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Real-estate price prediction with deep neural network and principal component analysis

Article Category: Research Paper

Published Online: Dec 31, 2022

Page range: 2741 - 2759

Received: Apr 18, 2022

Accepted: Nov 10, 2022

DOI: https://doi.org/10.2478/otmcj-2022-0016

Keywordsprincipal component analysis, deep neural network, high-dimensional dataset, real-estate price prediction, stepwise regression

© 2022 Fatemeh Mostofi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
principal component analysis, deep neural network, high-dimensional dataset, real-estate price prediction, stepwise regression