Directional Forecasts for Yields Using Econometric Models and Machine Learning Methods

In this paper, we evaluate the performance of various methodologies for forecasting real estate yields. Expected yield changes are a crucial input for valuations and investment strategies. We conduct a comparative study to assess the forecast accuracy of econometric and time series models relative to machine learning algorithms. Our target series include net initial and equivalent yields across key real estate sectors: office, industrial, and retail. The analysis is based on monthly UK data, though the framework can be applied to different contexts, including quarterly data. The econometric and time series models considered include ARMA, ARMAX, stepwise regression, and VAR family models, while the machine learning methods encompass Random Forest, XGBoost, Decision Tree, Gradient Boosting and Support Vector Machines. We utilise a comprehensive set of economic, financial, and survey data to predict yield movements and evaluate forecast performance over three-, six-, and twelve-month horizons. While conventional forecast metrics are calculated, our primary focus is on directional forecasting. The findings have significant practical implications. By capturing directional changes, our assessment aids price discovery in real estate markets. Given that private-market real estate data are reported with a lag - even for monthly data - early signals of price movements are valuable for investors and lenders. This study aims to identify the most successful methods to gauge forthcoming yield movements.