Anna Mathieu clarifies if real estate decisions are affected by investor and consumer sentiment and how severely the sentiment should be considered. With regard to international capital markets Mathieu conducts an analysis of the impact of investor sentiment on the return of the real estate-specific investment vehicle “Real Estate Investment Trust (REIT)” by applying a GARCH-Model. She investigates the effects of investor sentiment on the return and the underlying volatilities of REITs and Non-REITs during the financial crisis. The hypotheses are tested for validity in a GARCH-Model. Parallel to capital markets and thereby in changing from an indirect Real Estate investment perspective to a direct perspective the author conducts an analysis if consumer sentiment impacts the household decision to buy a new home in the US. Therefore a dataset with 385 monthly observations from 1978 to 2010 is tested by a component model.

Fiduciary responsibilities and related court-imposed liabilities have forced investors to assess market conditions beyond gut level, resulting in the development of sophisticated decision-making tools. Roger Brown's use of historical real estate data enables him to develop tools for gauging the impact of circumstances on relative risk. His application of higher level statistical modeling to various aspects of real estate makes this book an essential partner in real estate research. Offering tools to enhance decision-making for consumers and researchers in market economies of any country interested in land use and real estate investment, his book will improve real estate market efficiency. With property the world's biggest asset class, timely data on housing prices just got easier to find and use. - Excellent mixture of theory and application - Data and database analysis techniques are the first of their kind

Nicolai C. Striewe analyzes potential opportunistic behavior of REIT managers and provides empirical evidence on the effectiveness of institutional monitoring as a corporate governance mechanism. The author also suggests ways to promote sustainable management by means of institutional participation. The results of his study provide valuable insights to enhance corporate governance, transparency and efficiency in the REIT market. They encourage (a) academics to include a behavioral component into studies of the REIT market, (b) REIT managers to incorporate effective monitoring and control mechanisms, (c) investors to become more aware of agency conflicts in REITs and (d) policy makers to facilitate a legal framework conducive to a sustainable REIT market.

Amidst the dramatic real estate fluctuations in the first decade of the twenty-first century, this study recognized that there is a necessity to create a real estate prediction model for future real estate ventures and prevention of losses such as the mortgage meltdown and housing bust. This real estate prediction model study sought to reinstall the integrity into the American building and development industry, which was tarnished by the sudden emergence of various publications offering get-rich-quick schemes. In the fast-paced and competitive world of lending and real estate development, it is becoming more complex to combine current and evolving factors into a profitable business model. This prediction model correlated past real estate cycle pinpoints to economical driving forces in order to create an ongoing formula. The study used a descriptive, secondary interpretation of raw data already available. Quarterly data was taken from the study's seven independent variables over a 24-year span from 1985 to 2009 to examine the correlation over two real estate cycles. Public information from 97 quarters (1985-2009) was also gathered on seven topics: consumer confidence, loan origination volume, construction employment statistics, migration, GDP, inflation, and interest rates. The Null hypothesis underwent a test of variance at a .05 level of significance. Multiple regression analysis uncovered that four of seven variables have correlated and could predict movement in real estate cycle evidence from previous data, based in the Inland Empire. GDP, interest rates, loan origination volume, and inflation were the four economical driving variables that completed the Inland Empire's real estate prediction model and global test. Findings from this study certify that there is correlation between economical driving factors and the real estate cycle. These correlations illustrate patterns and trends, which can become a prediction model using statistics. By interpreting and examining the data, this study believes that the prediction model is best utilized through pinpointing an exact numerical location by running calculations through the established global equation, and recommends further research and regular update of quarterly trends and movements in the real estate cycle and specific variables in the formula.