This study uses content analysis and a controlled experiment as data generation methods to investigate the precise nature of the largely unexplored relationship between the content of real estate news and activities in the real estate market. The theoretical base of the research is Kahneman’s two system view (2003) of cognitive processing, which is applied to an individual’s decision-making about the residential real estate market. The affect heuristic provides the theoretical basis for studying the relationship between the emotional content of local media information and decision making in the residential real estate market. The research question seeks to measure the “framing effect” of news on real estate market activity. It is posited that the way local real estate news is framed will influence transaction prices and the number of pending sales. A behavioral approach is utilized to understand the underlying relationship between a residential real estate market and a news article to audiences; an effect called frame setting. It is conjectured that when media coverage about the real estate market is negative there is more downward pressure on the market compared to when media coverage is more objective and includes descriptive statistics on the current real estate market.

A nation’s housing sector has been the cornerstone of economic activity over the past several years. Right now, the economy of the United States is in a recession. To recover the economy, activity in the U.S. housing market deserves more attention, especially the new home market. Economists in the United States believe that if new home sales could keep increasing in the future, recovery of the whole housing market, even the whole economy in the U.S., would be hopeful. To bring the hope closer to the reality, forecasting changes in the new home market is important. An accurate forecast can provide useful information for the future, so that proper planning can take peace. The purpose of this thesis is to look for an appropriate method which can accurately forecast changes in new home sales and prices in the U.S. housing market, so that policy makers base decisions on reliable information.

Consumer sentiment has the ability to provide researchers with many avenues to test existing Finance and Economic theories. Chapter 1 introduces the issues that I seek to explore within the area of Behavioral Finance. Chapter 2 utilizes thirty years of consumer sentiment data to explore extant economic theories and hypotheses. In particular, I study the Prospect Theory and the Life Cycle Investment Hypothesis. In addition, I also study how changes in consumer sentiment can foretell future stock returns for firms in different industries and of different sizes. By studying how individuals of different ages display optimism and pessimism through consumer sentiment surveys, I am able to contribute to the literature by shedding additional light on just how the important age is with respect to a person's economic outlook. One particular phenomenon that I discuss in this chapter is downside risk. I will provide further support to the existing literature which shows that gains and losses are not viewed equally by individuals. To account for this discrepancy, this paper models the time series relationship between consumer sentiment and stock returns using asymmetric response models. Chapter 3 builds upon the previous chapter's findings by using consumer sentiment to explore if this index can forecast housing market variables such as changes in home sales and home prices. Given the recent financial market turmoil that stemmed from the U.S. housing market debacle, this chapter is timely. Using widely cited housing indices, I explore regional differences in the U.S. housing market and how the sentiment of local consumers can possibly affect their housing markets. I also include analyses in which the age of the consumer is accounted for to see if evidence of the Life Cycle Investment Hypothesis emerges. This theory postulates that younger individuals are more likely to demand housing as a financial asset and if this were true, I hypothesize that changes in younger individuals' sentiment would have more forecasting power with respect to future housing sales and price changes. Lastly, I conclude this dissertation with Chapter 4 which includes additional discussions of the issues studied.

This study analyzes the market share of the top 10 home builders in nine Metropolitan Statistical Areas, along with fourteen other independent variables, to find a statistical relationship with median home sales price. Through a stepwise regression of the independent variables it is determined that there is no correlation between median home sale price and market share of the top 10 home builders. In the stepwise regression two variables are found to be correlated to median homes sales price: Owner Occupancy Percentage and Residential Construction Wages, a data point compiled for this study. A linear regression is run between market share of the top 10 and median home sale price and no correlation is found. / Master of Science / This study observes changes in median home sale price and changes in the market concentration of the top 10 home builders. Data from nine cities was analyzed. Using linear regression with the two previously mentioned variables along with fourteen other variables the study finds no relationship between the two variables. Among the variables the two that had the highest statistical relationship are Owner Occupancy Percentage and Residential Construction Wages. This study is relevant to the on-going discussion about concentration in the housing market.