Real Estate Market Prediction Using Deep Learning Models
Document Type
Article
Publication Title
Annals of Data Science
Publication Date
1-1-2024
Abstract
Real estate significantly contributes to the broader stock market and garners substantial attention from individual households to the overall country’s economy. Predicting real estate trends holds great importance for investors, policymakers, and stakeholders to make informed decisions. However, accurate forecasting remains challenging due to it’s complex, volatile, and nonlinear behavior. This study develops a unified computational framework for implementing state-of-the-art deep learning model architectures the long short-term memory (LSTM), the gated recurrent unit (GRU), the convolutional neural network (CNN), their variants, and hybridizations, to predict the next day’s closing price of the real estate index S &P500-60. We incorporate diverse data sources by integrating real estate-specific indicators on top of fundamental data, macroeconomic factors, and technical indicators, capturing multifaceted features. Several models with varying degrees of complexity are constructed using different architectures and configurations. Model performance is evaluated using standard regression metrics, and statistical analysis is employed for model selection and validation to ensure robustness. The experimental results illustrate that the base GRU model, followed by the bidirectional GRU model, offers a superior fit with high accuracy in predicting the closing price of the index. We additionally tested the constructed models on the Vanguard Real Estate Index Fund ETF and the Dow Jones U.S. Real Estate Index for robustness and obtained consistent outcomes. The proposed framework can easily be generalized to model sequential data in various other domains.
DOI
10.1007/s40745-024-00543-2
Recommended Citation
Rimal, R., Rimal, B., Bhandari, H., Pokhrel, N., & Dahal, K. (2024). Real Estate Market Prediction Using Deep Learning Models. Annals of Data Science https://doi.org/10.1007/s40745-024-00543-2
ISSN
21985804
E-ISSN
21985812
