Assessing the forecast performance of machine learning algorithms and econometric models in real estate

In this paper we study the performance of a range of methodologies to forecast real estate prices. We compare the forecast accuracy of econometric and time series models to machine learning algorithms. The target series is the yield impact a metric which is based on changes in yields (cap rates) and a prime determinant of capital value or price changes (appreciation returns). We focus on the main sectors – offices, retail and industrials – in the UK and we perform the analysis with monthly data taken from MSCI. Using monthly MSCI data results in a sample that begins in 1987. The econometric and time series models include ARMA, ARMAX, stepwise and Lasso regressions. Machine learning methods include random forest, XGBoost, and support vector machines. We use a large set of economic, financial and survey data to predict movements in yield impact. We assess the forecast performance of the selected methodologies over different time horizons, one, three, six, and twelve months. The forecast evaluation follows conventional forecast evaluation metrics. This includes basic measures such as the mean error, mean absolute error and RMSE and more sophisticated measures such Diebold-Mariano tests. We are particularly interested in forecasting gains arising from the combination of forecasts from different methods.The results have significant practical value. The forecast assessment can pick up directional changes and be used for price discovery. Real estate data in the private market are produced with a lag (even monthly data) and early information about changes in prices are valuable to real estate investors and lenders. The study aims to identify the methods or the combination of methods with the best predictive ability and focus investor attention to these methods.