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

Mostofi Fatemeh (), Toğan Vedat and Başağa Hasan Basri
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Mostofi Fatemeh: Department of Civil Engineering, Karadeniz Technical University, P.O. Box 61080, Trabzon, Türkiye, 393989@ogr.ktu.edu.tr
Toğan Vedat: Department of Civil Engineering, Karadeniz Technical University, P.O. Box 61080, Trabzon, Türkiye
Başağa Hasan Basri: Department of Civil Engineering, Karadeniz Technical University, P.O. Box 61080, Trabzon, Türkiye

Organization, Technology and Management in Construction, 2022, vol. 14, issue 1, 2741-2759

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.

Keywords: principal component analysis; deep neural network; high-dimensional dataset; real-estate price prediction; stepwise regression (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:otamic:v:14:y:2022:i:1:p:2741-2759:n:3

DOI: 10.2478/otmcj-2022-0016

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