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

Rangan Gupta, Chi Keung Lau, Vasilios Plakandara, Wing Keung Wong

Research output: Contribution to journalArticle

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.

LanguageEnglish
Pages2554-2567
Number of pages14
JournalEconomic Research-Ekonomska Istrazivanja
Volume32
Issue number1
Early online date18 Aug 2019
DOIs
Publication statusPublished - Sep 2019

Fingerprint

Economic variables
Sentiment
Sales growth
Vector autoregressive model
Forecasting accuracy
Bayesian VAR
Real estate
Market sentiment
Bayesian vector autoregression
Housing market
Financial institutions
Shrinkage
Politicians

Cite this

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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.",
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The role of housing sentiment in forecasting U.S. home sales growth : evidence from a Bayesian compressed vector autoregressive model. / Gupta, Rangan ; Lau, Chi Keung; Plakandara, Vasilios; Wong, Wing Keung.

In: Economic Research-Ekonomska Istrazivanja, Vol. 32, No. 1, 09.2019, p. 2554-2567.

Research output: Contribution to journalArticle

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