Do US consumer survey data help beat the random walk in forecasting mortgage rates?

Abstract

In line with term structure theory, empirical studies suggest that it is difficult to beat the random walk in forecasting long-term interest rates. We ask whether consumer survey data on both mortgage interest rates and expected inflation help beat the random walk in forecasting the 30-year fixed rate mortgage. Using the vector autoregressive (VAR) modeling framework with the mortgage rate and consumer survey data as variables, we generate the mortgage rate forecasts for 1988–2016. Our forecast evaluation test results indicate that the VAR forecasts generally embody useful predictive information above and beyond that contained in the random walk forecasts for 2008–2016. The evidence is weaker for 1988–2007 in the sense that the VAR forecasts fail to outperform the random walk (but still contain distinct useful predictive information). In line with the notion that consumers are “economically” rational, our findings suggest that consumer survey data have become more informative due to the uncertainty created by the 2008 financial crisis.

Keywords:

• term structure
• predictive information content
• directional forecasting
• “economically” rational
• Michigan Surveys of Consumers

JEL classifications:

• E43
• G12

Public Interest Statement

Long-term interest rates, including the 30-year fixed rate mortgage, are difficult to forecast. Both theory and empirical evidence suggest that the best forecast of the future rate is today’s rate (a naïve forecast). Despite the inherent difficulty, market participants and policy-makers are regularly engaged in producing more accurate forecasts than the naïve benchmark. This study is another attempt at providing accurate forecasts of mortgage rates for 1988–2016, using consumer survey data on both mortgage interest rates and expected inflation, derived from the long-running Michigan Survey of Consumers. Our results indicate that the consumer survey data are more helpful in producing accurate forecasts of mortgage rates for 2008–2016 than for 1988–2007. One explanation for this difference may be that the uncertainty created by the 2008 economic crisis has induced consumers to become more attentive by closely following relevant information and producing more informed assessments.

Notes

1. One question on the survey asks: “No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months—will they go up, stay the same, or go down?” Using the individual responses, the MSC calculates and reports the index values (=down − up + 100) which represent the expected change in interest rates. Our results show that this index does not help produce accurate forecasts of mortgage rates. Dräger, Lamla, and Pfajfar (2016, p. 90) analyze these data and conclude that “consumers have more difficulties in giving consistent expectations when interest rates would be expected to decrease.” Similarly, Baghestani and Kherfi (2008, p. 731) conclude that, “for 1984–2005, consumers assigned much cost (loss) to incorrect predictions when interest rates were rising and almost no cost (loss) to incorrect predictions when interest rates were falling.”

2. The data on the 30-year fixed rate mortgage come from the Federal Reserve Bank of St. Louis website (https://fred.stlouisfed.org). These data are available on a monthly and a weekly basis. The weekly data end on Thursday. Also, the MSC data for month t are generally released before the end of month t to the fee-paying Thomson Reuters subscribers. The historical MSC data are available on the Michigan Surveys of Consumers website (http://www.sca.isr.umich.edu).

3. Generating the combined forecasts as a simple average of the VAR and the random walk forecasts is justifiable, since the estimates of γ₁ (ranging from 0.429 to 0.525) and the estimates of γ₂ (ranging from 0.392 to 0.436) in rows 1–4 of Table 1 are not significantly different from 0.50.

4. As noted by Pesando (1979), short-term interest rates do not necessarily display a random walk behavior. Existing evidence suggests that professional (consensus) forecasts of short-term interest rates can beat the random walk benchmark, although there is room for improving the accuracy of such forecasts as demonstrated by Baghestani (2005).

Additional information

Funding

Funding. The author received no direct funding for this research.

Notes on contributors

Hamid Baghestani

Hamid Baghestani has a PhD in Economics from the University of Colorado, Boulder, 1982. He is currently a professor of Economics at the American University of Sharjah, UAE. His research interests include time series analysis, macroeconometric 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.