Time Series Forecasting of U.S. Housing Price Index Using Machine and Deep Learning Techniques

The Housing price index (HPI) plays a key role in real estate dynamics, which majorly contributes to the United Sstates economy. Forecasting accurate housing price indices will be very beneficial for investors, policymakers, banks, and homeowners. There was a wide range of approaches implemented for forecasting HPI using microeconomic features with very little concentration on macroeconomic features such as economic indicators at a national level. HPI directly correlates with these macroeconomic features, which have a direct influence on overall real estate dynamics and the economy. In this paper, we applied various time series, machine learning [ML], and deep learning [DL] models to forecast HPI using macroeconomic features. Our goal is to get accurate predictions and get the relationships that directly impact the housing market. Macroeconomic features of the dataset were collected from economic and federal agencies with the aim of forecasting HPI values. We implemented the autoregressive model integrated moving average model [ARIMA] and Facebook Prophet [FB Prophet] to know the temporal dependencies. Next, we applied machine learning and deep learning models Random Forest, Extreme Gradient Boosting (XGBoost), Long Shorth term model (LSTM), Recurrent neural networks (RNN), and a hybrid approach, which is an ensemble of Extreme Gradient Boosting and LSTM. We studied the efficacy of implemented models and evaluated the performance of the models using the performance metrics Mean Absolute Error (MAE), and Root Mean square Error (RMSE), and R-squared.