Over the past several decades, the real estate market has surged in Beijing. Meanwhile, Beijing suffers severe air pollution now and then. Because of the dissatisfaction of air quality, clean air becomes a highly valued factor of the overall quality of life. When people choose to buy properties, they would be willing to pay more for a unit that is located in less polluted areas than for an otherwise identical unit that is located in more polluted areas. This study aims to establish a relationship between air pollution's spatial distribution and property price by using historical air quality record and property transaction data in Beijing. Although people cannot purchase clean air directly, variations in the air quality of different areas should be indirect reflected in housing prices. Employing the hedonic pricing model, the results suggest a strong positive relationship between a unit's resale price and the air quality of the area where the unit is located, meaning that properties that are located in an area with relatively better air quality are sold at a premium and vice versa. Besides, by using a matching regression, relationship between air quality's seasonal variation and property price is discovered, although it is not statistically significant. Another matching regression confirms that compared to property buyers, property renters care less about the air quality of the area where the unit is located, but care more about the unit's other specific locational attributes and physical attributes, such as job accessibility and interior decoration.

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.

This dissertation research focuses on understanding benefits or costs of some urban amenities and disamenities using the Hedonic Pricing (HP) method. It includes three Southern California case studies where different hedonic models (fixed effects, spatial Durbin model, and geographically weighted regression) are estimated to obtain unbiased and consistent parameter estimates. In the first case study, I analyze 20,660 transactions of single family detached houses sold in 2003 and 2004 in the city of Los Angeles, CA, to estimate the value of urban trees, irrigated grass, and non-irrigated grass areas. I rely on fine-grained hedonic models with many covariates to control for unobserved neighborhood characteristics. I find that Angelenos like lawns: 78 percent of the properties examined would gain value with additional irrigated grass in their neighborhood and even more (83 percent) on their parcel. However, additional parcel trees would decrease the value of almost half (46 percent) of the properties examined and they would have only a small positive impact on most of the others. By contrast, additional neighborhood trees would slightly increase the value of over 80 percent of the properties analyzed. This suggests that while Los Angeles residents may want additional trees, they are unwilling to pay for them. These results have implications for urban tree planting programs that rely primarily on private property owners. The second case study quantifies the impact of urban green spaces on the value of 1,197 multifamily buildings sold in 2003-2004 in the city of Los Angeles, California; these green spaces are either on their parcels or in their vicinity (an area 200 meters outward of each parcel boundary). It is necessary to examine multifamily houses separately because they belong to a different market. To assess the robustness of the results, I contrast a spatial Durbin model with a geographically weighted regression model and conduct an extensive sensitivity analysis. I find that increases in grassy areas either on the parcels of multifamily buildings or in their vicinity would typically not enhance their value, and neither would more parcel tree canopy cover (TCC); by contrast, most multifamily properties would benefit from an increase in vicinity TCC. These results suggest that most multifamily building owners have no incentives to increase the tree canopy cover or the grassy areas on their properties. In the third case study, I investigate the impact of freeway traffic on property values using hedonic pricing models, with a particular interest for truck traffic. I analyze 4,715 sales of single family houses that took place in 2003 and 2004 in part of the busy transportation corridor that links the Ports of Los Angeles and Long Beach to downtown Los Angeles. These houses are located at least 200 meters from the nearest arterial road to filter out the impact of traffic on arterial roads. In order to minimize the risk of omitted variable bias and spatial autocorrelation, I estimate a fine-grained fixed effects model. I find that a one percent increase in the proportion of truck traffic could decrease the value of a $420,000 house located between 100 and 400 meters from the nearest freeway by between $2,000 and $2,750. These results are important for policy makers and owners of single family houses located close to freeways as the ports of Los Angeles and Long Beach are forecasting sharp increases in drayage truck activity as the economy recovers.

America's air quality is better today than ever before in modern history and continues to steadily improve. How did this remarkable turnaround come about? Basing his conclusions on a painstaking compilation of long-term empirical data on air quality and emissions data extending from the pre- federalization era to the present (some dating back a century), Goklany challenges the orthodoxy that credits federal regulation for improving air quality. He shows that the air had been getting cleaner prior to—and probably would have continued to improve regardless of—federalization. States and localities, after all, have always been engaged in a race to improve the quality of life, which means different things at different stages of economic development. Goklany’s empirical data refute once and for all the race-to-the-bottom rationale for centralized federal regulation. Moreover, technological advances and consumer preferences continue to play important roles in improving air quality. Goklany accordingly offers a regulatory reform agenda that would improve upon the economic efficiency and environmental sensitivity of air quality regulation.

Abstract: This study contributes to the hedonic pricing literature in housing. Despite the large number of such studies and the broad range of variables examined, prior studies have not systematically considered the impact on housing prices of the physical condition of their immediate neighborhood. Most likely this failure is attributable to the fact that such data do not exist in published form. Most hedonic studies base their analysis on published data or data easily derived from satellite images. Few studies make use of data collected in the field. Yet both theory and casual observation suggest that the physical condition of neighboring houses should contribute to explaining housing prices. The size of this impact is important also as it plays a role in the theory of neighborhood change, which attributes the downward slide of neighborhoods to the initial deterioration of a few houses, which through their negative externalities start a broader trend. This study set out to show that physical disorder matters in explaining property prices and to derive estimates of the order of magnitude of the impact of disorder on sales prices. While physical disorder is a prominent part of the literature on fear of crime and neighborhood change, it so far has been neglected in hedonic pricing studies. This study has addressed this deficiency. The hedonic analysis confirms the importance of physical disorder in explaining housing values and derives the impact on price of individual disorder characteristics. This study also shows that the presence of neighborhood disorder significantly reduces the final sales price of a property, relative to the original listing price. The contribution of current finding is that this decline is not fully anticipated by the realtor's listing price. This analysis therefore shows the importance of any information on disorder when estimating the listing price. Finally, the study shows the cost that neighbors can inflict on each other, by not maintaining their houses and neighborhood. For the first time, it provides specific estimates of the cost associated with individual disorder attributes. The results also are indicative of the importance externalities play in explaining neighborhood change.

Spatial decisions and processes are fundamental to the understanding of spatial structure. In the earlier stages of spatial analysis proposed explanations were typically on the aggregate, phenomenological level. Now a new field has evolved and matured which calls for a deeper understanding of spatial structure with a particular emphasis on spatial decisions and processes. The present volume serves as a guide and as an anthology by discussing a wide variety of new modelling approaches, techniques and issues related to spatial decision and processes and drawing these seemingly disparate, but interlocking parts together.