This paper compares the performance of different forecasting models of California house prices. Multivariate, theory-driven models are able to outperform atheoretical time series models across a battery of forecast comparison measures. Error correction models were best able to predict the turning point in the housing market, whereas univariate models were not. Similarly, even after the turning point occurred, error correction models were still able to outperform univariate models based on MSFE, bias, and forecast encompassing statistics and tests. These results highlight the importance of incorporating theoretical economic relationships into empirical forecasting models.

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 book contains a special issue of the Journal of Real Estate Finance and Economics, comprising thirteen articles on house price measurement. These articles address the various procedures used to compute cross-sectional or temporal house price indices. Specifically, these articles contain research that: (1) evaluates hedonic, repeat sales, or hybrid approaches to constructing house price indices; (2) evaluates alternative sources of data on house prices and corresponding housing characteristics; (3) identifies the most influential land, structural, neighborhood, and proximity determinants of house prices (and associated changes in house prices); (4) provides a methodology for identifying housing market segments; (5) incorporates spatial autocorrelation in house price indices; and (6) provides more accurate estimates of the variance in house prices.

Housing prices can be seen as an important tool affecting business cycles via its impact on investment and consumption spending. Also, local specifications allow for differences in regional business cycles.Recent studies have shown that there is a link between the housing market and economic activity. Also, they suggest that house-price fluctuations lead to real activity, inflation, or both. Therefore the existence of good model to forecast is very crucial for policy makers. Good policy requires that first identification of relationship for data (linear or non _linear) because it can affect not only housing prices rather all the economy.Research evidence shows that for any system with non-linear instability patterns such as the market for housing, the utilization of the ANN methodology serve properly. Therefore, in order to elucidate the non-linearity in the housing market, this book develops a new framework for the neural network and fuzzy methods, and pioneers their application to the recursive forecasting techniques

Market Analysis for Real Estate is a comprehensive introduction to how real estate markets work and the analytical tools and techniques that can be used to identify and interpret market signals. The markets for space and varied property assets, including residential, office, retail, and industrial, are presented, analyzed, and integrated into a complete understanding of the role of real estate markets within the workings of contemporary urban economies. Unlike other books on market analysis, the economic and financial theory in this book is rigorous and well integrated with the specifics of the real estate market. Furthermore, it is thoroughly explained as it assumes no previous coursework in economics or finance on the part of the reader. The theoretical discussion is backed up with numerous real estate case study examples and problems, which are presented throughout the text to assist both student and teacher. Including discussion questions, exercises, several web links, and online slides, this textbook is suitable for use on a variety of degree programs in real estate, finance, business, planning, and economics at undergraduate and MSc/MBA level. It is also a useful primer for professionals in these disciplines.

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.