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Enhancing Real Estate Market Prediction: A Comparative Analysis of Modeling Techniques

Conference: Symposium on Data Science and Statistics (SDSS) 2024
06/06/2024: 2:00 PM - 2:05 PM EDT
Lightning 

Description

The real estate market holds significant interest for researchers in industry and academia due to its impact on every household. Despite numerous studies leveraging available data and emerging technologies like artificial intelligence, there remains a need for an efficient and robust approach to predict market trends. Our study conducts a comparative analysis of various deep learning and hybrid models for predicting the future price of real estate market indices. To build our models, we select several predictors, including fundamental market indicators, macroeconomic factors, and technical indicators. We then assess model performance using standard regression metrics and employ statistical analysis for model selection and validation to ensure robustness.

Keywords

Real Estate Market

Deep Learning

Hybrid Models

Regression Modeling

BiLSTM

BiGRU 

Presenting Author

Ramchandra Rimal, Middle Tennessee State University

First Author

Ramchandra Rimal, Middle Tennessee State University

CoAuthor(s)

Binod Rimal, The University of Tampa
Hum Nath Bhandari, Rogers William University
Keshab Dahal, State University of New York Cortland
Nawa Raj Pokhrel, Xavier University of Louisiana

Tracks

Software & Data Science Technologies
Symposium on Data Science and Statistics (SDSS) 2024