Cities are growing worldwide and their sprawl is increasingly challenged for its pressure on open spaces and environmental quality. Economic arguments can help to decide about the trade-off between preserving environmental quality and developing housing and business surfaces, provided the benefits of environmental quality are adequately quantified. To this end, this book focuses on the use and advancement of the “hedonic approach”, an economic valuation technique that analyses and quantifies the sources of rent and property price differentials. Starting from theoretical foundations, the hedonic approach is applied to the valuation of natural land use preservation and noise abatement measures, as well as to residential segregation and discrimination, extending the analysis to the role of the buyers and sellers' identity on housing market prices and to the issue of environmental justice.

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.

This dissertation aims to demonstrate applications of regional science methodologies to analyze issues in real estate and urban economics in different scales: city, region, and country. The methodologies used in this dissertation include geographic information systems (GIS), spatial econometrics, and computable general equilibrium (CGE) modeling. There are three chapters in this dissertation. The first chapter studies the impact of the new mass transit systems on the land values of residential development in Bangkok, Thailand. GIS and spatial econometrics are used to examine the impacts. The study has found that the proximity to mass transit stations spatially correlates with an increase in the prices of residential land. The benefit of new mass transit stations, however, may not be equally distributed to the residents of Bangkok due to the lack of value capture mechanisms such as a capital gain tax or a property tax. Policy implications on property taxation are also discussed in this study. Chapter two discusses the economic impact of Cornell University on Tompkins County, New York, focusing on the impact of the investment on the new mixed-used development in Collegetown. This study is one of the first attempts to study the economic impact of a university using a CGE model. In addition, the assumption of increasing-returns-to-scale is incorporated into the framework of a small-area CGE model. This extension of the model allows for a more realistic representation of the imperfect competition in the economic simulation. In the last chapter, a financial CGE model is used to investigate the role of real estate investment in the economy of Thailand. This study discusses how the overinvested real estate market can cause the country to be vulnerable to a financial crisis. In addition, the relationship of real estate asset and property markets is incorporated into the model to captures interconnections between production sectors and financial sectors. The macroeconomic and socioeconomic indicators from the model simulation show that moderate investment in real estate sectors can lead to steady economic growth with small impact on income disparity.

This dissertation attempts to provide a comprehensive examination on the non-market valuation of the effect of open space amenities and local public infrastructure on the value of urban land and housing with both spatial heterogeneity and project heterogeneity. The demand for raw land is a derived demand for housing built on it. Therefore, we need to examine the land market and the housing market together. On the one hand, we estimate the value of urban land in a market that does not satisfy the usual assumptions of a competitive market structure as well as incentive incompatibility issues for transaction participants, with an application to a Chinese regional wholesale land market. These two violations to the traditional hedonic theory also generate two separate valuations on land with differentiated characteristics. On the other hand, we utilize the relative plane coordinates system, the three-dimensional distances, as well as the aggregate weight matrix, to implement the spatial hedonic estimation on the high-rise residential buildings in the same regional housing retail market in China. After these two steps, this dissertation, therefore, focuses on the profit maximization behavior of the property developer, which is the key role to link the factor market (i.e., the land market) and the commodity market (i.e., the housing market) together. Two methods are then employed to implement the hypothesis test on the hedonic price estimation including both inputs and outputs. First, a set of partial derivatives of the profit function with respect to various characteristics gives us the relationship between the marginal valuations in the land market and in the housing market. Second, we introduce a joint estimation approach that we call the spatial full information maximum likelihood (SFIML), which considers the land market, the housing market, and the property developer's profit maximization behavior all together in the estimation. Finally, we conduct a hypothesis test in both of these two scenarios to examine the validity of our linked markets assumption on the hedonic price estimation.