Forecasting House Prices: The Role of Fundamentals, Credit Conditions, and Supply Indicators

Abstract

This paper evaluates the ability of various indicators related to macroeconomic fundamentals, credit conditions, and housing supply to predict house price growth in the United States during the post-financial crisis period. We find that the inclusion of different measures of housing supply indicators significantly improves the forecasting performance for the period of 2010-2022. Specifically, incorporating the monthly supply of new homes into a VAR model with house price growth reduces the RMSE by over 30 percent compared to a univariate benchmark. Moreover, forecasting accuracy improves further at a longer forecast horizon (greater than three months) when the mortgage rate spread is also used as a predictor. Further improvements are made if "Direct" forecasts are used instead of iterative forecasts. The shrinkage method like LASSO shows that the monthly supply of new homes is an important predictor at all forecasting horizons, while the mortgage spread is most relevant for longer forecast horizons.