The tremendous rise in house prices over the last decade has been both a national and a global phenomenon. The growth of secondary mortgage holdings and the increased impact of house prices on consumption and other components of economic activity imply ever-greater importance for accurate forecasts of home price changes. Given the boom-bust nature of housing markets, nonlinear techniques seem intuitively very well suited to forecasting prices, and better, for volatile markets, than linear models which impose symmetry of adjustment in both rising and falling price periods. Accordingly, Crawford and Fratantoni (2003) apply a Markov-Switching model to U.S. home prices, and compare the performance with ARMA and GARCH models. While the switching model shows great promise with excellent in-sample fit, its out-of-sample forecasts are generally inferior to more standard forecasting techniques. Since these results were published, some researchers have discovered that the Markov-Switching model is particularly ill-suited for forecasting. We thus consider other non-linear models besides the Markov Switching, and after evaluating alternatives, employ the Generalized Autoregressive (GAR) model. We find the GAR does a better job at out-of-sample forecasting than ARMA and GARCH models in many cases, especially in those markets traditionally associated with high home-price volatility.

This booklet discusses some major methodological issues relating to the construction of house price models on a macro level. There is no single method that always produces the optimal results; the choice of a particular approach, method, theory, model and technique is context-dependent. This is especially true in housing markets, where a multitude of different submarkets exist. The methodology chosen should be based on sound theory, from which the basic concepts of analysis can be derived. This booklet discusses the use of potential models, which can be constructed using a general field theory, and which act as a theoretical foundation for further analysis. If we use potential models for house price analysis we can discover additional features from the data set that other approaches would simply miss. This e-book presents a pragmatic overview of key methodological concerns with the emphasis on the use of potential models. Theoretical methodological questions are left unanswered, and are not even presented in this text, since they have little relevancy to real-world modelling questions.

This dynamic Research Handbook explores key perspectives, topics and methodologies used to understand housing, the home and society. Pairing social theory with a broad range of case studies from the Global North and South, it offers a unique insight into the field.

The applicability and performance of ARIMA, GARCH and multivariate regression models are analyzed and city as well as forecasting horizon-specific patterns are determined and interpreted by Alexander Bönner. Univariate rent forecasting models generally outperform multivariate rent forecasting regression models in the short run. In the long run, multivariate regression models dominate.

The five-volume set LNCS 9155-9159 constitutes the refereed proceedings of the 15th International Conference on Computational Science and Its Applications, ICCSA 2015, held in Banff, AB, Canada, in June 2015. The 232 revised full papers presented in 22 workshops and a general track were carefully reviewed and selected from 780 initial submissions for inclusion in this volume. They cover various areas in computational science ranging from computational science technologies to specific areas of computational science such as computational geometry and security.

The financial deregulation of the last quarter century has meantlarge flows of funds around the world seeking the highestrisk-adjusted return for investors. Real estate is now establishedas an important asset class and advances in information technologyprovide the necessary tools to complement global developments inreal estate finance and investment. A variety of investment vehicles have emerged, andReal EstateFinance in the New Economy examines these along withfinancing and risk in the context of globalization, deregulationand an increasingly integrated international world economy byexploring questions like: How have real estate financial structures evolved as economiesgrow and become internationalised? What role do economic change and financial systems play in thedevelopment of real estate investment? Are the risks associated with the ‘new economy’really new? What is the future direction for real estate financing? The authors develop an economic framework for discussions onindividual financial products to examine how real estate financialstructures change with economic growth and internationalisation andalso to show how developments in real estate finance impacteconomic growth.