QUALITY
2.3 Revealed Preference Methods
2.3.1 Hedonic Methods
2.3.1.1 Hedonic Property Method
The hedonic property method is based on the idea that the value of a house can be decomposed into the value of its individual characteristics – where it is located in relation to workplaces and amenities, how many bedrooms and baths it has, how large it is, and the nature of its surroundings, including environmental attributes. It has been extensively used to estimate the value households living in urban areas place on improving air quality and water quality. One of the most famous studies is the value of reducing sulfur dioxide and total suspended particulates in the Los Angeles area. This study by Brookshire, et al. (1982) compared house prices in areas of Los Angeles that had markedly different levels of air pollution. They found that a house in the best air quality (lower levels of total suspended particulates and nitric oxides) was worth significantly more than a house in an area with poor air quality, even adjusting for the other attributes of the house structure and neighborhood. Converting the house price differentials into monthly rental equivalents yielded estimates ranging from at least $48 per month to as much as $173 per month for an improvement from poor to good air quality (figures are in 1980 dollars; see Brookshire, et al., 1982:173). The same approach has been used for water quality, comparing house prices on shorelines with poor water quality to those with good water quality (d’Arge and Shogren, 1989; Leggett and Bockstael, 2000).
The basic form of a hedonic property price function is:
House Price = f(structural characteristics of the house, neighborhood characteristics such as schools and crime rate, environmental characteristics such as air quality)
For example, we might have the following:
By collecting data on houses from several areas within a city in which the air quality varies, a separate coefficient on Air Quality (AQ) can be estimated using multiple regression. This coefficient gives the present value of the household’s willingness to pay for a one unit change in air quality.
The value of a given increment can be approximated by calculating the change in house price with the change in air quality resulting from the program. That is, calculate the house price by inserting the new level of air quality, then again at the without-program level of air quality, and then take the difference in house price. We say approximate, as this estimate of the change in value is accurate only for small changes in air quality. As shown by d’Arge and Shogren, for large gains in environmental quality the regression coefficient will overstate the value of gains. Conversely, the regression coefficient will understate the value of losses.
To more accurately value larger or non-marginal changes in environmental quality, such as air quality, with the hedonic property model, the analyst can estimate a second stage or separate demand for environmental quality relationship from the first stage shown above. The second stage or demand for environmental attribute is:
where the prices are implicit prices calculated from the first stage hedonic price function, and AQ, subs and comp are the environmental quality attributes, substitute and complementary attributes for environmental quality in housing decisions. Substitutes for environmental quality might be square footage of housing space, while a complement in a lake water quality study might be lake frontage of the property (Boyle, et al., 1999).
Once this function is estimated, the net WTP for non-marginal changes in environmental quality can be calculated as the area under the demand for the characteristic between the original and new level of environmental quality.
The estimation of the second stage or characteristic demand function is more challenging in terms of assembling the data and in the statistical analysis. The data challenges involve the need to collect housing price data in multiple markets in order to econometrically identify the demand for the housing characteristics and deal with possible endogeneity of implicit prices of housing attributes. Specifically, the functional form of the first stage hedonic price function will determine the form of the implicit prices to be used in the second stage characteristic demand function. If the first stage hedonic price function is estimated in the linear form shown above, then the marginal implicit price does not depend on the level of the quantity of the environmental quality attributes. As such the implicit price is constant and does not vary within a single housing market. Thus, to obtain variation in the implicit prices of the environmental quality attribute one must have multiple housing markets which will each have their own prices.
Alternatively, if the first stage hedonic price function is non-linear, the implicit price will vary depending on the quantity of the environmental attribute, but this makes the price endogenous. This too creates problems when attempting to estimate the demand for environmental quality attribute, as classical regression techniques require the independent variables to be exogenous. Further, the implicit prices are simultaneously determined by the supply and demand for the characteristic, so the econometric problem of identification has to be solved. Again, the multiple market approach can contribute to solving this problem as does the use of an instrumental variable approach to estimating the demand equation. In the instrumental variable approach, the analyst use a separate equation to forecast the value of the price variable, conditioned on a set of independent variables. This forecasted level of the price variable is then used as the price variable in the second stage hedonic demand function. The use of the forecasted value of the price variable eliminates the endogeneity of the price variable in the hedonic demand function. For more information on the hedonic method see Freeman (1993) or Palmquist (1991).
A variant of the standard hedonic property method described above is to use the repeat sales or resales method. If an important discrete environmental change has occurred in a geographic area, it may be possible to uncover the economic value of that change by comparing house prices before and after the environmental change, controlling for other factors unrelated to the environmental change. For example, the economic value to a family from avoiding a flood might be measured by comparing the sale price of a house after a recent flood in the area to what that same house sold for prior to the flood event. This requires a large enough real estate market that one can observe the same house selling at the two different points in time. Besides flooding, this technique can be used with earthquake hazards,
discovery of new hazardous waste sites, fire risk, etc. Essentially, the analyst needs to find a natural experiement, where the risk was not apparent to home buyers before the event as compared to after the event. The economic value of avoiding this new environmental degradation will be capitalized into the housing prices in the repeat sale.
In either case, the aggregate value of the improvement in environmental quality would be calculated by multiplying the change in house price by the number of residences affected by the change. If the value of environmental quality changes with the household’s distance from a particular point source of pollution or natural resource, then this needs to be explicitly accounted for in the estimation of the first stage by including a distance variable (e.g., Leggett and Bockstael, 2000). Then a spatial valuation gradient would be used to more accurately arrive at the total value to residences in the area.
The hedonic property method is quite powerful and can be quite convincing to decision makers, as it is based on actual behavior of affected households living in the area. However, besides the traditional concerns of any method relying heavily on statistical estimation (e.g., is the right functional form used, are there any omitted variables that are correlated with environmental quality; see Chapter 6), there is the potential for underestimation due to measuring only residential household values. Not everyone who benefits from an improvement in urban air quality lives in the city, for instance. Some simplifying assumption would be needed to determine how to generalize this value to renters in apartment buildings and non-resident visitors to an area. That is, improvements in water quality at a lake in town is likely to have benefits to more than just the residents living around the lake, but also to those who might bike or drive to the lake for fishing or a picnic lunch.
2.3.1.2 Hedonic Wage Method
Another form of hedonic analysis -- the hedonic wage approach – should also be mentioned here. The underlying idea of hedonic wage analysis is similar to hedonic property analysis: here, wage is decomposed into the attributes of the job, including environmentally-related attributes. In particular, the analyst observes how much additional wages must be paid to an employee to have the employee accept higher risk of illness from air pollution, noise pollution, or other occupational dangers in the workplace.
This method is particularly suited for valuation of workplace hazards, a common occupational/environmental policy question. The estimates of how much additional workers must be paid to accept higher illness or death rates is also used to calculate a value of statistical life. For instance, if an additional $1000 per year in salary is required to induce workers to accept a 0.0001 increase in death (one in ten thousand) per year, the value of a
statistical life is found by dividing the change in annual income for a higher risk by the change in the risk: in this case, $1000/0.0001 = $10,000,000 as the value of a statistical life. This value is often used, not only for workplace safety, but also for valuing roadway safety, health risks associated with pesticides on food, airline safety regulation, and other programs affecting the probability of someone dying. Using a workplace value of a human life for other programs is not a perfect match: workplace hazards are voluntarily accepted by the worker for higher wages, whereas the increased risk of death from lung cancer due to breathing air in the city where one lives is an involuntary risk. Nonetheless, this is a commonly used tool for valuing regulations that save human lives. As with any method, there are numerous simplifying assumptions that are required for wage differentials to accurately reflect what workers are willing to accept for increased risk of injury or death. One of these is perfect information about the relative riskiness of alternative jobs, so that a fully informed trade-off of risk for money can be made. However, in some cases dealing with chemicals or substances with long latency periods (e.g., asbestos), the job risks are not known to the worker (or even to the industry for that matter), at the time the wage-risk trade-off is made. Thus the method is better suited for chemicals or risks which are rather obvious or are well known to workers. For more detail on the theory and mechanics of this method see Palmquist, 1991, Freeman, 1993 or Viscusi, 1993.
The hedonic techniques are very useful and accepted methods of estimating the benefits of many facets of environmental quality. The hedonic property method can be used to value changes in air quality, water quality, stream restoration, wetlands, parks, beaches, open space, noise, and even access to public transportation. If, however, the change in environmental quality being evaluated occurs in a public land or rural setting, the hedonic property method might not accurately reflect the full user benefits. To measure these benefits, we turn to a demand estimating technique called the Travel Cost Method.
2.3.2 Travel Cost Method For Estimating Recreation Demand And