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**18. Hedonic Price Analysis**

Kolstad, Ch. 8 (IV) Hanley and Barbier, Ch 5

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1. Intuition and Stories Revealed preference studies using housing (or land markets) and labor markets Simple hedonics story w/ 2 houses Assume houses are identical in every respect except that B is located in a polluted area If they sell for the same price, everyone buys A Air pollution A B

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**Say B is offered at some discount (to entice buyers)**

Now, individuals implicitly face the decision: Is the clean air worth $5,000? If yes, buy A If no, buy B Individuals reveal something about the value (wtp) of clean air in the market decision.

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**How would markets clear in a simple case like this?**

Imagine many A’s and B’s Imagine all houses start at $100,000 Excess demand for A Prices rise Excess supply for B Prices fall Gap opens up and continues to widen until individuals are sufficiently compensated by price drop to live in polluted area. Settle into an equilibrium in the housing market Implicit value of clean air is $30,000/house. We call this a “hedonic” or “implicit” price. A B $105,000 $75,000

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Comments open mkt and governing the price identical people and perfect compensation rent vs. asset value Value for environmental quality “revealed” in housing or property values and based on individual preferences (and supply). More important air quality is the high the hedonic price will be.

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**Each has an implicit value **

Real housing (land) markets are not so simple. Housing choice and price depends on many characteristics: structural, neighborhood, and locational. Each has an implicit value Hedonic price technique is a method for sorting out these values Think about the $30,000 being due to more than just pollution. Works for any differentiated product: cars, food, comic books

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**Environmental Goods in Land Markets**

Air pollution (37 studies) Landfill/Hazardous waste sites Proximity to open space Crop damage/Agricultural Airport noise “Capitalized” values Crime Frat House

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**2. The Mechanics of the Hedonic Price Method (Property Values) **

Postulate a functional relationship between housing prices and characteristics of houses: Hedonic price function Vector of Environmental Attributes: Air quality Distance to waste site Noise levels and so on Vector of Structural Attributes: # Rooms Lot size Age Basement (yes/no) Design and so on Vector of Neighborhood Attributes: School quality Open space Distance from downtown Distance form shopping and so on

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**You may set this up in a linear or nonlinear way**

You may set this up in a linear or nonlinear way. Here is a typical linear form: Focus on the parameter . It tells us the relationship between Price and Air Quality. Take the first derivative of f(.) with respect to AirQ and you get Coefficients tell us the relationship between each characteristic and housing prices. Note expected signs above the coefficients. Implicit Price of a Unit of Air Quality in the Housing Market

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So, we set out to estimate the coefficients in the Hedonic Price Function. To do this we need data on housing sales: price and characteristics of houses sold in the market. Data set looks something like this House Price Age Lot Size Air Quality and so on ………………… . (Dollars) (Years) (Acres) (Visibility Days) 1 $85, 2 65, 3 211, 4 92, 5 300, . , More characteristics Number of observations

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**Focus on AirQ for an estimate of the implicit value of air quality. **

Regress House Price on characteristics and this gives estimates of the parameters in Focus on AirQ for an estimate of the implicit value of air quality. Theory This is the implicit price function for characteristic #1, holding all other characteristics fixed. Note: need not be flat!

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**3. Some Theory House Mkt Equilibrium gives P(z).**

Individuals take P(z) as fixed. Individuals choose z and x (composite good) to maximize utility Note that the individual is choosing attributes

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**Shopping chart analogy, “implicit” decision Graphically**

This is the implicit price function for characteristic #1, holding all other characteristics fixed. Note: need not be flat! There is a similar graph for each characteristic. It is as though the person is choosing n separate goods.

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**What if the population is homogeneous?**

Different people w/ different income and taste will consume different levels of each characteristic You end-up with different people at different points on the implicit price line. What if the population is homogeneous? Person #2 Person #1

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**5. Issues Functional form**

Probably not linear, so often estimated as a nonlinear function One nonlinear case

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**Measurement of quality variables**

Flexible forms (Box-Cox) Measurement of quality variables Air pollution TSP,SO2 Visibility indexes Clear days Landfill Distance Views Crime Levels Noise Colinearity Disamenities and amenities “run” together

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**Discreteness, lumpy bundles**

Perception Health vs. Visibility Market segmentation 2 or more housing mkts

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**Market disequilibrium Incomplete benefit assessment**

nonuse other non-resident benefits

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**The identification problem**

We estimate the blue line (the implicit price function) when we estimate the hendonic price function. But, we can not in a single housing mkt estimate the individual marginal value (MV) functions. These are the demand curves for the attributes.

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**Everyone the same, again. Implications**

Because we only observe each individual at one point on his or her demand curve, we can not determine the shape of the entire demand curve. Everyone the same, again. Implications really only have marginal values for each person forced to use hedonic to approximate non-marginal changes need multi-market data to identify marginal value’s (at least this is the major way to fix the problem) See next slide.

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**Now we can begin to get at the shapes (“identify”) of the mv functions,**

and begin to think about consumer surplus w/ hedonics.

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Repeat sale analysis Having data on the same houses before and after an episode of some sort. New landfill, airport …. Controlling for other factors difficult

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**Wages and Land interacting**

C.A.Kolstad, Environmental Economics, Oxford U. Press, 2000. C.A.Kolstad, Environmental Economics, Oxford U. Press, 2000. Pollution is productive Pollution is not productive

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**5. Valuation/Consumer Surplus**

Marginal changes Use marginal values from the hedonic Non-marginal changes Short-run A B Consider an increase in quality (as a housing attribute) Area A is the short run benefits of the improvement. Discuss. Since mv is difficult to measure, often use Area A+B as an upperbound. What if z declines?

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**Long run Gets complicated because hedonic shifts and people move**

Consider this shift from a clean-up

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Special cases Small mkt Identical individuals

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**6. Wage Hedonics and the Value of Life**

The Basics Same basic analysis w/ job mkt data Interested in valuing small reductions in risk of death Many regs are concerned w/ health and associated risk reduction air pollution hazardous waste removal transportation safety health care initatives Hedonic wages studies are one way to infer value for risk of death

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**Intuition and Story Scaffolding story Wage compensating differential**

Dynamics Risk of Injury Risk of Death Vector of Job Characteristics: Union (yes/no) Skilled Management Location and so on Vector of worker characteristics: Age Experience Education and so on Wage Hedonic

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**Specify functional form and focus on parameter on the rd variable**

Many of the same issues here as in property value studies collinearity identification Statistical Lives Common Parlance Definition (by example) Population 100,000 Risk of Death per Person .003 In this case we say 300 statistical lives will be lost. 100,000 * .003 = 300 We say that these are not “identified” lives

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**$500,000 is the Value of a Statistical Life**

Assume we can reduce risks by introducing some regulations. Say risks drop from .003 to .002. After the regulation, 200 statistical lives are lost in the population We say that the regulation saved 100 statistical lives Assume in a top notch wage hedonic study that we discover individuals are willing to pay $500 (revealed in the job market) for a reduction of .001. If so, in a population of 1,000 people, one statistical life will be saved. Each of the 1,000 persons has an ex ante value for risk reduction .001 of $500. Ex ante really value risk reduction $500,000 is the Value of a Statistical Life

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**Cost per life saved studies and use of value of life estimates Issues**

EPA, OSHA, and so on Cost per life saved studies and use of value of life estimates Issues Voluntary Illness and quality of life

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