Grantee Research Project Results
1998 Progress 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 Period Covered by this Report: 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 is to critically examine the relative importance of data aggregation, attribute tradeoffs, and variation caused by space and time within a hedonic benefit study using a single, pooled cross-section, time-series data set. The primary focus is on the hedonic price of air quality. The analysis is being conducted in the South Coast Air Basin, which consists of the California counties Los Angeles, Orange, Riverside, and San Bernardino for the period 1980-1995. These counties contain over one hundred cities, which generates 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.Progress Summary:
There were five major accomplishments over the reporting period.
First, we conducted an extensive literature review of approximately sixty journal articles on the hedonic price method. 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.Second, we created a unique cross-section, time series data set consisting of approximately 1.6 million observations over the period 1980-95. 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 data set 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 pool, number of stories, roof type, number of fireplaces, etc. 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 data set 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 in order to assign a specific single family home the appropriate pollution measures: (1) the air pollution data, obtained from monitoring station or airport readings is aggregated into a summary statistic (e.g., annual average, median, etc.); (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; (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.
Third, we have estimated cross-sectional benchmark hedonic equations for each year in our sample. The results indicate that air pollution, as measured by ozone, total suspended particulates, and visibility, is a significant determinant of home sale price. We then examined the sensitivity of the benchmark equations by utilizing alternative pollution measures, using more detailed neighborhood variables, and estimating other functional forms. We have also employed a hierarchical linear model (sometimes called a mixed model or a multilevel model) to study the relationship between air quality and housing prices. These tests indicate that air pollution has a robust impact on home sale prices.
Fourth, we estimated inverse demand curves for visibility for the entire sample period using two different approaches: (1) ordinary least squares; and (2) two stage least squares. The dependent variable in the demand estimation is the individual marginal willingness to pay for a change in visibility, determined as follows. The hedonic equation is differentiated with respect to visibility using the characteristics corresponding to each individual data point. The hedonic prices are converted to constant 1995 dollars using the consumer price index and aggregated to the census tract level since data on individual homeowner attributes (e.g., income, education, and ethnicity) are not available. Thus, the dependent variable represents the average hedonic price or marginal willingness to pay in the census tract. This procedure produces approximately 2000 data points per year. Given these implicit prices, the inverse demand curve is estimated by regressing price against quantity (visibility) and other household shift variables, such as income and education. The independent variable set performs as expected and the estimated demand curves are generally robust to sensitivity analysis.
Fifth, we integrate the estimated demand curves to produce an estimate of the benefits that accrue to households given a change in visibility. Our preliminary analysis indicates that previous studies, based both on the hedonic price method and the contingent valuation method, have seriously underestimated the economic value of visibility improvements.
In conclusion, the research conducted to date contains several innovations relative to the existing literature. First, this report contains the most comprehensive review of the previous work attempting to determine benefits from housing data. Second, the data set 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, has also 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) procedures. Likewise, the demand curve estimation uses both OLS and instrumental variables. The result is a benefit assessment study that is state of the art.
Future Activities:
Our research has identified three specific areas that require more detailed investigation. First, we will continue to explore the use of the hierarchical linear model to determine the relative importance of aggregation levels. Second, our hedonic price function results indicate that neighborhood effects, measured by such variables as distance to beach, school quality, etc., have a significant impact on accurate determination of the individual effect of the pollution variables. These variables, in contrast to the house specific variables, are generally measured with error, which may interfere with the researcher's ability to ascertain the independent influence of the air pollutants. The solution to this problem may lie in the use of a spatial autoregressive framework. Third, our two stage least squares demand curve estimates are based on the use somewhat uncertain instrumental variables. We intend to conduct a thorough search of potential instrumental variables.Journal Articles:
No journal articles submitted with this report: View all 8 publications for this projectSupplemental Keywords:
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 assessmentProgress and Final Reports:
Original AbstractThe 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.