On the Predictive Content of Leading Indicators: The Case of US Real Estate Markets
This paper employs a probit model and a Markov switching model using information from the Conference Board Leading Indicator series to detect the turning points in four key US commercial rents series. We find that both the approaches based on the leading indicator have considerable power to predict changes in the direction of commercial rents up to two years ahead, exhibiting strong improvements over a naïve model, especially for the warehouse and apartment sectors. The empirical support for the adequacy of these prediction methodologies, from both in-sample and real time forecasting assessments, makes them a valuable tool to real estate professionals forecasting the US real estate markets. We find that while the Markov switching model nominally appears to be more successful in predicting periods of negative growth, it lags behind actual turnarounds in market outcomes whereas the probit is able to detect turning points several quarters ahead.