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

Mostofi F., TOĞAN V., BAŞAĞA H. B.

ORGANIZATION TECHNOLOGY AND MANAGEMENT IN CONSTRUCTION, cilt.14, sa.1, ss.2741-2759, 2022 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.2478/otmcj-2022-0016
  • Dergi Adı: ORGANIZATION TECHNOLOGY AND MANAGEMENT IN CONSTRUCTION
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI)
  • Sayfa Sayıları: ss.2741-2759
  • Anahtar Kelimeler: principal component analysis, deep neural network, high-dimensional dataset, real-estate price prediction, stepwise regression, DIMENSIONALITY REDUCTION TECHNIQUES, MACHINE-LEARNING ALGORITHMS, SUPPORT VECTOR MACHINE
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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.