Forecasting US home sales using BVAR models and survey data on households’ buying attitudes for homes

This study uses Bayesian vector autoregressive models to examine the usefulness of survey data on households’ buying attitudes for homes in predicting sales of homes. It finds a negligible deterioration in the accuracy of forecasts of home sales when buying attitudes are dropped from a model that includes the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. This suggests that buying attitudes do not add much of the information contained in these variables. It also finds that forecasts from the model that includes both buying attitudes and the aforementioned variables are similar to those generated from a model that excludes the survey data but contains the other variables. Additionally, the variance decompositions suggest that the gain from including the survey data in the model that already contains other economic variables is small.