Forecasting House Prices in Germany

In the academic debate there is a broad consensus that house price fluctuations have a substantial impact on financial stability and real economic activity. Therefore, it is important to have timely information on actual and expected house price developments. The aim of this paper is to measure the latest price movements in different real estate markets in Germany and forecast near-term price developments. Therefore we construct hedonic house price indices based on real estate advertisements on the internet platform ImmobilienScout24. Then, starting with a naive AR(p) model as a benchmark, we investigate whether VAR and ARDL models using additional macroeconomic information can improve the forecasting performance as measured by the mean squared forecast error (MSFE). While these models reduce the forecast error only slightly, forecast combination approaches enhance the predictive power considerably.Die Finanz- und Wirtschaftskrise hat erneut gezeigt, dass Preisfluktuationen am Immobilienmarkt nicht nur einen substanziellen Einfluss auf die Finanzmarktstabilität sondern auch auf die gesamtwirtschaftliche Aktivität ausüben. Demnach ist es aus makroökonomischer Sicht wichtig, Immobilienpreisentwicklungen frühzeitig erkennen und akkurat prognostizieren zu können. Ziel dieser Arbeit ist es, Preisentwicklungen für Deutschland in verschieden Segmenten des Immobilienmarktes am aktuellen Rand zu erfassen und für die kurze Frist von bis zu sechs Monaten zu prognostizieren. Basierend auf monatlich verfügbaren Immobilieninseraten auf der Internetplattform von ImmobilienScout24 werden dazu in einem ersten Schritt hedonische Preisindizes gebildet. In einem zweiten Schritt wird die Entwicklung dieser Preisindizes prognostiziert. Beginnend mit einem einfachen AR(p)-Prozess wird der Informationsgehalt verschiedener zusätzlicher Indikatoren mit Hilfe von VAR und ARDL-Modellen untersucht. Während sich der durchschnittliche quadratische Schätzfehler in den einzelnen Modellen im Vergleich zum AR-Modell nur geringfügig verringert, zeigt sich, dass mit Hilfe von geeigneten Prognosekombinationen die Prognosegüte signifikant verbessert werden kann

Predicting the Equity Market with Option Implied Variables

We comprehensively analyze the predictive power of several option implied variables for monthly S & P 500 excess returns and realized variance. The correlation risk premium (CRP) emerges as a strong predictor of both excess returns and realized variance. This is true both in- and out-of-sample. A timing strategy based on the CRP leads to utility gains of more than 4.63% per annum. In contrast, the variance risk premium (VRP), which strongly predicts excess returns, does not lead to economic gains

In-Sample and Out-of-Sample Prediction of Stock Market Bubbles: Cross-Sectional Evidence

We evaluate the informational content of ex post and ex ante predictors of periods of excess stock (market) valuation. For a cross section comprising 10 OECD economies and a time span of at most 40 years alternative binary chronologies of price bubble periods are determined. Using these chronologies as dependent processes and a set of macroeconomic and financial variables as explanatory variables, logit regressions are carried out. With model estimates at hand, both in-sample and out-of-sample forecasts are made. Overall, the degree of ex ante predictability is limited if an analyst targets the detection of particular turning points of market valuation. The set of 13 potential predictors is classified in measures of macroeconomic or monetary performance, stock market characteristics, and descriptors of capital valuation. The latter turn out to have strongest in-sample and out-of-sample explanatory content for the emergence of price bubbles. In particular, the price to book ratio is fruitful to improve the ex-ante signalling of stock price bubbles

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases