Housing price prediction using deep learning - Song, Yutong (Nanyang Technological University, 2022)
Housing price forecasting is critical in assisting both developers and consumers in maximizing their advantages. In this research, the performance of deep learning approaches will be compared to that of other machine learning algorithms in predicting the housing resale price index in Singapore and the United States. Data inputs contain both historical housing resale prices and potential macroeconomic indicators. Fundamental and technical analysis will be conducted to evaluate different machine learning models. Throughout the study, we will compare and contrast multiple models, the Long Short-Term Memory, Recurrent Neural Network, Gated Recurrent Unit, Multi-Layer Perceptron, Support Vector Regressor, and Gradient Boosting Regressor.