The role of housing sentiment in forecasting U.S. home sales growth :evidence from a Bayesian compressed vector autoregressive model

Rangan Gupta, Chi Keung Marco Lau, Vasilios Plakandaras, Wing Keung Wong

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2554-2567
Number of pages14
JournalEconomic Research-Ekonomska Istrazivanja
Volume32
Issue number1
DOIs
Publication statusPublished - 18 Aug 2019

Bibliographical note

Funding Information:
We would like to thank three anonymous referees for many helpful comments. However, any remaining errors are solely ours.

Publisher Copyright:
© 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

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