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.
Highlights
Academics suggest that housing market follows business cycles, and housing market activity affects the economy at both macroeconomic and microeconomic levels (Leamer 2007; Hassani et al, 2017).1 Since housing represents a large share of the total economy, from a macroeconomic perspective, movements in the housing sector spillover to the entire economy through new constructions, renovations of existing property, and the volume of home sales
The decision to use this broad housing sentiment index in forecasting home sales emanates from the favourable out-of-sample evidence provided by Bork et al (2017) who showed that the housing sentiment explains a large share of the time-variation in house prices during both boom and bust cycles and it strongly outperforms several macroeconomic variables typically used to forecast house prices.3. We rely on both classical and Bayesian VAR models for analysing the ability of housing sentiment in forecasting home sales of the U.S economy
We observe that when shrinkage is achieved by compressing the data instead of the parameters, growth of U.S home sales can be forecasted more accurately
Summary
Academics suggest that housing market follows business cycles, and housing market activity affects the economy at both macroeconomic and microeconomic levels (Leamer 2007; Hassani et al, 2017). Since housing represents a large share of the total economy, from a macroeconomic perspective, movements in the housing sector spillover to the entire economy through new constructions, renovations of existing property, and the volume of home sales. Dua et al, (1999) extended the model described in Dua and Smyth (1995) by adding six different leading indicators, namely housing permits authorized, housing starts, the US Department of Commerce’s composite index of eleven leading indicators, the short- and long-leading indices developed by the Center for International Business Cycle Research (CIBCR) at Columbia University, and the leading index constructed by CIBCR that focussed solely on employment related variables They found that the benchmark BVAR model (which included, as before, home sales, price of homes, mortgage rate, real personal disposable income, and unemployment rate) supplemented by the building permits authorized as the leading indicator consistently produced the most accurate forecasts. The general consensus is that the Bayesian type models are better equipped in forecasting home sales than their classical counterparts
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