On accuracy of survey forecasts of US mortgage spread

Abstract

The cyclical variation behavior of the mortgage spread has motivated some studies to investigate its relationship to economic activity. Indeed, recent empirical findings indicate that the mortgage spread is a determinant/predictor of economic activity. We define the mortgage spread as the difference between the 30-year mortgage and 10-year Treasury rates and ask whether the Blue Chip (consensus) forecasts of these series are accurate for 1988–2015. Our findings indicate that the Blue Chip forecasts of both the 30-year mortgage and 10-year Treasury rates are biased, fail to outperform the random walk benchmark, and are directionally inaccurate. However, the Blue Chip forecasts of the mortgage spread are generally unbiased, outperform the random walk benchmark, and are directionally accurate—thus of value to a user. Given such evidence, a natural extension for future research is to explore whether the predictive information content of Blue Chip forecasts of the mortgage spread is a better predictor of output growth and whether it helps improve Blue Chip forecasts of output growth.

Keywords:

• mortgage spread
• unbiasedness
• predictive information content
• directional accuracy
• asymmetric loss

JEL classification number:

• E37
• E43
• E44

Public Interest Statement

The literature indicates that the US mortgage spread is a determinant/predictor of economic activity. Motivated by such findings, we define mortgage spread as the difference between 30-year mortgage and 10-year Treasury rates and ask whether experts’ forecasts of these series are accurate for 1988–2015. Long-term interest rates are difficult to forecast. Both theory and empirical evidence suggest that the best forecast of future rate is today’s rate (a naïve forecast). In line with this, we show that experts’ forecasts of 30-year mortgage and 10-year Treasury rates fail to outperform naïve forecasts and are directionally inaccurate. However, experts’ forecasts of mortgage spread (calculated as the difference between experts’ forecasts of 30-year mortgage and 10-year Treasury rates) outperform naïve forecasts and are directionally accurate. Such evidence is important since accurate forecasts of mortgage spread have the potential to be a better predictor of economic activity than the actual spread.

Notes

1. Term spreads have also been found useful in predicting directional change in home sales (Baghestani & Kaya, 2016). In another study, Baghestani and Toledo (2017) show that the Blue Chip forecasts of the Australian–US (UK–US) term spread differential helps predict directional change in the Australian–US (UK–US) exchange rate.

2. There are studies which have produced more accurate forecasts than the random walk. For example, Baghestani (2010b) utilizes the information in expected inflation to generate the 10-year Treasury rate forecasts that are superior to the random walk benchmark. Also, Baghestani (2017) shows that consumer survey data on both mortgage interest rates and expected inflation can help produce more accurate 30-year mortgage rate forecasts than the random walk benchmark.

3. The historical Blue Chip Financial Forecasts were purchased from Aspen Publishers, Inc.

4. The actual quarterly rates of the 30-year mortgage and the 10-year Treasury are obtained from the Federal Reserve Bank of St. Louis database.

Additional information

Notes on contributors

Hamid Baghestani

Hamid Baghestani has a 1982 PhD in Economics from the University of Colorado, Boulder. He is currently professor of Economics at the American University of Sharjah, UAE. His research interests include time-series analysis, macro-econometric modeling and forecasting, energy economics, monetary economics, and financial markets. He has published widely on these topics in internationally respected peer-reviewed journals such as Applied Economics, Energy Economics, Energy Policy, Journal of Business, Journal of Forecasting, Journal of Industrial Economics, Journal of Macroeconomics, and Oxford Bulletin of Economics and Statistics.