Grantee Research Project Results
Final Report: Improving Air Quality Benefit Estimates from Hedonic Models
EPA Grant Number: R825826Title: Improving Air Quality Benefit Estimates from Hedonic Models
Investigators: Thayer, Mark , Murdoch, James C. , Beron, Kurt
Institution: San Diego State University , The University of Texas at Dallas
EPA Project Officer: Chung, Serena
Project Period: October 1, 1997 through September 30, 1998
Project Amount: $124,931
RFA: Decision-Making and Valuation for Environmental Policy (1997) RFA Text | Recipients Lists
Research Category: Environmental Justice
Objective:
The objective of the research was to critically examine the relative importance of data aggregation, attribute tradeoffs, and variation caused by space and time within an air quality hedonic benefit study using a single, pooled cross-section, time-series dataset. The analysis was conducted in the South Coast Air Basin, which consists of the California Counties of Los Angeles, Orange, Riverside, and San Bernardino, for the period 1980?1995. These counties contain over 100 cities; this is sufficient spatial variation to test the relative importance of community characteristics on hedonic price estimation. The extensive time series nature of the data provides the required temporal variation.Three specific estimation approaches were used. We used traditional methods to estimate benchmark hedonic models. These results then were compared to results derived from both hierarchical linear and spatial econometric models.
Summary/Accomplishments (Outputs/Outcomes):
Four specific research tasks were completed. In Task 1, we conducted a comprehensive literature review concerning the hedonic method, data sources, and the magnitudes of estimates from the hedonic method. Approximately 60 journal articles were reviewed. For each article, we provided a detailed review, a discussion of the article's relevance, and information regarding data used and conclusions drawn concerning air pollution.In Task 2, we assembled the multilevel data necessary for the estimation of the hedonic models. The data were assembled at the site, neighborhood, school district, and environmental levels. All data were geo-referenced and maintained in a geographic information system (GIS). The dataset consisted of approximately 1.6 million observations over the period 1980?1995. An observation relates to a specific sale of an owner-occupied single family home in our study area. The dependent variable in the empirical analysis is the home sale price of these dwellings. The independent dataset includes variables that correspond to four types of attributes: house quantity and quality, neighborhood, community, and environment. House size or quantity is described through such variables as square footage of living space, number of bathrooms and bedrooms, and lot size or land area. House quality is depicted by variables such as the presence of a pool, number of stories, roof type, number of fireplaces, and so on. Neighborhood quality is based primarily upon neighborhood characteristics contained in the data tapes for both the 1980 and 1990 census. Community variables such as school quality and the crime rate are measured at the city level. Air pollution is measured by both pollutant concentration readings taken at monitoring stations and visibility readings from local airports. The pollution data were obtained from two sources: the South Coast Air Quality Management District (SCAQMD) and the National Climatic Data Center (NCDC). Variables that depict neighborhood and community influences are matched to the housing data using common location indicators. For example, each subset of the dataset is coded with GIS coordinates, allowing accurate matching of attributes at the various levels of aggregation. However, the air pollution data require the following multi-step procedure to assign a specific single family home the appropriate pollution measures: (1) the air pollution data, obtained from monitoring station or airport readings, are aggregated into a summary statistic (e.g., annual average, median, and so on); (2) these summary data are entered into the Surfer computer program to generate isopleth contours; (3) the isopleths are utilized to create pollution levels at grid points that cover the entire study area; and (4) each census tract is assigned the pollution level of the grid point that is closest to its centroid. Each single family home in a specific census tract is assigned the same pollution value.
In Task 3, we used traditional methods to estimate benchmark hedonic models. Results indicate that air pollution, as measured by ozone, total suspended particulates, and visibility, is a significant determinant of home sale price. We also examined the sensitivity of the benchmark equations by utilizing alternative pollution measures, using more detailed neighborhood variables, and estimating other functional forms. The benchmark results are compared to results derived from a hierarchical linear hedonic model (HLM) that specifically recognizes the multilevel structure of the data. The HLM results indicate that air pollution has a robust impact on home sale prices. Task 3 also was extended to explicitly investigate spatial econometric models. There were two reasons for this extension to the research plan. First, the spatial econometric model provides an alternative methodology for analyzing neighborhood effects. Thus, we believed that our HLM analysis would benefit from a comparison with results from an explicitly spatial model. Second, generalized method of moments (GMM) estimators for spatial models were developed during the research period that enabled us to estimate spatial models with large numbers of observations. The spatial econometric model results also indicate that air pollution has a robust impact on home sale prices.
In Task 4, we provided numerical estimates of the monetary benefits of changes in air pollution in the South Coast Air Basin. These estimates are presented throughout our full final report and are summarized in Chapter VI for total suspended particulates (TSP).
Conclusions:
The research conducted in this project contains several innovations relative to the existing literature. First, this report contains a comprehensive review of the previous work attempting to determine benefits from housing data. Second, the dataset assembled for this project is the most comprehensive ever assembled. The number of observations exceeds that used in any previous hedonic price study. In addition, the quality of the data and the application of Surfer to provide detailed assignment of pollution to individual homes are unprecedented. The time-series element of the data, which allows the identification of time-dependent market segments, also has not been used before in the study of urban air pollution. Third, our estimation procedures represent the latest innovations in econometrics. In the estimation of the hedonic price function, we use both traditional (OLS, fixed effects) and more innovative (random effects, hierarchical linear model, spatial econometric) procedures. Likewise, the demand curve estimation uses innovative econometric methods. The result is a benefit assessment study that is state of the art.Journal Articles on this Report : 2 Displayed | Download in RIS Format
Other project views: | All 8 publications | 3 publications in selected types | All 2 journal articles |
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Beron KJ, Murdoch JC, Thayer MA. Hierarchical linear models with application to air pollution in the South Coast Air Basin. American Journal of Agricultural Economics 1999;81(5):1123-1127. |
R825826 (Final) |
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Beron K, Murdoch J, Thayer M. The benefits of visibility improvement: new evidence from the Los Angeles Metropolitan Area. Journal of Real Estate Finance and Economics, 2001;22:319-337. |
R825826 (Final) |
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Supplemental Keywords:
property values, environmental damage, stigma., RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, Geographic Area, State, Economics, decision-making, Ecology and Ecosystems, Economics & Decision Making, air pollution policy, ecosystem valuation, valuation, decision analysis, air quality benefit estimates, hierarchical linear model, standards of value, house prices, hedonic models, public values, California (CA), willingness to pay, benefits assessmentThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.