Forecasts of growth in US residential investment: accuracy gains from consumer home-buying attitudes and expectations

ABSTRACT

This study focuses on the Federal Reserve and private forecasts of growth in real residential investment. The aim is to improve predictive accuracy by first evaluating these forecasts. The results for 1984–2015 reveal that the Federal Reserve and private forecasts are generally free of systematic bias, superior to the naïve benchmark, and predict directional change with high accuracy rates. However, these forecasts do not contain detailed information in consumer home-buying attitudes and expectations. Using a subset of such information and real-time data on residential investment, a knowledge model (KM) is constructed to produce comparable forecasts. The test results indicate that the KM forecasts of growth in residential investment contain distinct and useful predictive information, and the combined Federal Reserve, private, and KM forecasts show reductions in forecast errors that are more significant at longer horizons. As such, we conclude that consumer survey responses help improve forecast accuracy. Given that accurate forecasts contribute to the success of policy, more transparency in Federal Reserve Open Market Committee (FOMC) decisions is encouraged. With more transparency and clear communication, consumers are able to provide more informative responses, which can then be employed to produce more accurate forecasts of growth in residential investment.

KEYWORDS:

• Real Estate
• investment
• attitudinal data
• knowledge model
• forecast combination

JEL CLASSIFICATION:

• codes: E22
• E27
• E32
• E58

Acknowledgments

This research has received funding from American University of Sharjah (Faculty Research Grant 2020, Proposal No.: FRG20-S-B42).

Disclosure statement

This is to acknowledge that NO financial interest or benefit has arisen from the direct applications of this research.

Notes

¹ The MSC Index of Current Economic Condition (ICC) and Index of Consumer Expectations (ICE) are the two components of the ICS. The ICC (ICE) is constructed using the responses to two current-looking (three-forward-looking) questions. Baghestani (2016) and Baghestani and Palmer (2017) rely on the relationships between ICC and ICE to investigate various research questions.

² Similar results emerge when we test orthogonality under flexible loss, proposed by Elliott, Komunjer, and Timmermann (2005). This may be due to our findings that the Federal Reserve and private forecasts are generally free of systematic bias. For a recent application of flexible loss, see Baghestani and AbuAl-Foul (2020b).

³ The annualized quarterly percentage rate of growth is g_t = 100×[(RI_t ÷ RI_t-1)^4–1], where RI_t is real-time residential investment in quarter t.

⁴ The use of real-time data on real residential investment yields forecasts that are comparable to the Federal Reserve and private forecasts. The real-time data are available on the Federal Reserve Bank of Philadelphia website.

⁵ Our conclusions remain unchanged when we use the forecast combination method proposed by Granger and Ramanathan (1984). This method uses the parameter estimates reported in Table 5 to generate the combined forecasts.

Additional information

Funding

This work was supported by the American University of Sharjah [Faculty Research Grant 2020, Proposal No.: FRG20-S].