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18. Hedonic Price Analysis Kolstad, Ch. 8 (IV) Hanley and Barbier, Ch 5.

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Presentation on theme: "18. Hedonic Price Analysis Kolstad, Ch. 8 (IV) Hanley and Barbier, Ch 5."— Presentation transcript:

1 18. Hedonic Price Analysis Kolstad, Ch. 8 (IV) Hanley and Barbier, Ch 5

2 1. Intuition and Stories ÝRevealed preference studies using housing (or land markets) and labor markets ÝSimple 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 AB

3 Say B is offered at some discount (to entice buyers) åA $100,000 åB $ 95,000 Now, individuals implicitly face the decision: Is the clean air worth $5,000? åIf yes, buy A åIf no, buy B Individuals reveal something about the value (wtp) of clean air in the market decision.

4 ÝHow would markets clear in a simple case like this? Imagine many As and Bs Imagine all houses start at $100,000 åExcess demand for A Prices rise åExcess 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 AB $105,000$75,000 Implicit value of clean air is $30,000/house. We call this a hedonic or implicit price.

5 Comments åopen mkt and governing the price åidentical people and perfect compensation årent vs. asset value ÝValue 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.

6 ÝReal 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 åThink about the $30,000 being due to more than just pollution. åWorks for any differentiated product: cars, food, comic books

7 ÝEnvironmental Goods in Land Markets Air pollution (37 studies) Landfill/Hazardous waste sites Proximity to open space Crop damage/Agricultural Airport noise ÝCapitalized values Crime Frat House

8 2. The Mechanics of the Hedonic Price Method (Property Values) Postulate a functional relationship between housing prices and characteristics of houses: 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 Vector of Environmental Attributes: Air quality Distance to waste site Noise levels and so on Hedonic price function

9 You may set this up in a linear or nonlinear way. Here is a typical linear form: Focus on the parameter. åIt tells us the relationship between Price and Air Quality. åTake 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

10 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 SizeAir Quality and so on …………………. (Dollars) (Years) (Acres)(Visibility Days) 1$85,000 170.5101 265,000 20.25 23 3211,000 51 4 492,000 220.33 5 5300,000 11.5 1. 100202,50020.6614 More characteristics Number of observations

11 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!

12 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

13 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.

14 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 #1 Person #2

15 5. Issues ÝFunctional form Probably not linear, so often estimated as a nonlinear function One nonlinear case

16 Flexible forms (Box-Cox) ÝMeasurement of quality variables Air pollution åTSP,SO2 åVisibility indexes åClear days Landfill åDistance Views Crime Levels Noise ÝColinearity Disamenities and amenities run together

17 ÝDiscreteness, lumpy bundles ÝPerception Health vs. Visibility ÝMarket segmentation 2 or more housing mkts

18 ÝMarket disequilibrium ÝIncomplete benefit assessment nonuse other non-resident benefits

19 ÝThe 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.

20 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 åreally only have marginal values for each person åforced to use hedonic to approximate non-marginal changes åneed multi-market data to identify marginal values (at least this is the major way to fix the problem) See next slide.

21 Now we can begin to get at the shapes (identify) of the mv functions, and begin to think about consumer surplus w/ hedonics.

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

23 ÝWages 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

24 5. Valuation/Consumer Surplus ÝMarginal changes Use marginal values from the hedonic ÝNon-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?

25 Long run åGets complicated because hedonic shifts and people move åConsider this shift from a clean-up

26 ÝSpecial cases Small mkt Identical individuals

27 6. Wage Hedonics and the Value of Life ÝThe 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 åair pollution åhazardous waste removal åtransportation safety åhealth care initatives Hedonic wages studies are one way to infer value for risk of death

28 Intuition and Story åScaffolding story åWage compensating differential åDynamics Vector of Job Characteristics : Union (yes/no) Skilled Management Location and so on Vector of worker characteristics : Age Experience Education and so on Risk of Death Risk of Injury Wage Hedonic

29 Specify functional form and focus on parameter on the rd variable Many of the same issues here as in property value studies åcollinearity åidentification ÝStatistical Lives Common Parlance Definition (by example) åPopulation 100,000 åRisk of Death per Person.003 åIn this case we say 300 statistical lives will be lost. » 100,000 *.003 = 300 åWe say that these are not identified lives

30 Assume we can reduce risks by introducing some regulations. Say risks drop from.003 to.002. åAfter the regulation, 200 statistical lives are lost in the population åWe 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. åEx ante åreally value risk reduction $500,000 is the Value of a Statistical Life

31 EPA, OSHA, and so on Cost per life saved studies and use of value of life estimates Issues åVoluntary åIllness and quality of life

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