Forecasting real‐time economic activity using house prices and credit conditions

Using real‐time data from 1985:Q1 to 2017:Q3 and simple vector autoregression (VAR) models, we show that there is a substantial payoff in combining credit supply indicators with house prices for forecasting real economic activity in the USA. Consistent with the findings in the literature, we show that the forecasts from a bivariate VAR model of real activity and credit conditions dominate the forecasts from a univariate model of real activity. The most interesting finding of the paper is that once real house price growth is added to the bivariate VAR model of real activity and credit conditions, the forecasting performance improves significantly. The forecasts from the model that contains credit supply indicator and real house price growth are also competitive with the forecasts of the Survey of Professional Forecasters. These results provide further evidence in support of the recent theoretical and empirical research on the dynamic relationship between housing market and credit conditions and its role in explaining real economic activity fluctuations in the USA.