The role of housing sentiment in forecasting U.S. home sales growth: evidence from a Bayesian compressed vector autoregressive model. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Gupta, Rangan; Lau, Chi Keung Marco; Plakandaras, Vasilios; Wong, Wing-Keung
• Is Part Of: Ekonomska istraživanja. Volume 32:Issue 1(2019)
• Page Start: 2554
• Page End: 2567
• Publication Date: 2019-01-01
• Subjects: home sales -- housing sentiment -- classical and Bayesian vector autoregressive models; C32 -- R31; Economics -- Research -- Periodicals; Economics -- Research; Electronic journals; 330.072
• DOI: 10.1080/1331677X.2019.1650657
• Languages: English
• ISSNs: 1331-677X

Abstract

Abstract: Accurate forecasts of home sales can provide valuable information for not only policymakers, but also financial institutions and real estate professionals. Against this backdrop, the objective of our article is to analyse the role of consumers' home buying attitudes in forecasting quarterly U.S. home sales growth. Our results show that the home sentiment index in standard classical and Minnesota prior-based Bayesian V.A.R.s fail to add to the forecasting accuracy of the growth of home sales derived from standard economic variables already included in the models. However, when shrinkage is achieved by compressing the data using a Bayesian compressed V.A.R. (instead of the parameters as in the B.V.A.R.), growth of U.S. home sales can be forecasted more accurately, with the housing market sentiment improving the accuracy of the forecasts relative to the information contained in economic variables only.