The Role of Housing Sentiment in Forecasting US Home Sales Growth: Evidence from a Bayesian Compressed Vector Autoregressive Model

Rangan Gupta, Chi Keung Lau, Vasilios Plakandaras and Wing-Keung Wong

No 201842, Working Papers from University of Pretoria, Department of Economics

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 paper is to analyze the role of consumers’ home buying attitudes in forecasting quarterly US home sales growth. Our results show that the home sentiment index in standard classical and Minnesota prior-based Bayesian VARs 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 VAR (instead of the parameters as in the BVAR), growth of US 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.

Keywords: Home Sales; Housing Sentiment; Classical and Bayesian Vector Autoregressive Models (search for similar items in EconPapers)
JEL-codes: C32 R31 (search for similar items in EconPapers)
Pages: 10 pages
Date: 2018-07
New Economics Papers: this item is included in nep-for and nep-ure
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Journal Article: The role of housing sentiment in forecasting U.S. home sales growth: evidence from a Bayesian compressed vector autoregressive model (2019)
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