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Estimation of perceived risk and its effect on property values

  • McCluskey, Jill Jennifer
  • Rausser, Gordon C

    ()

    (University of California, Berkeley. Dept of agricultural and resource economics and policy)

A dynamic discrete time model is estimated in order to analyze the evolution of perceived risk around a hazardous waste site and its effect on property values. Residential property values are modeled as a function of housing attributes and perceived risk to health from a nearby hazardous waste site using an hedonic price framework. Perceived risk enters the model as a state equation, which includes a media coverage variable. An aggregate measure of perceived risk is estimated and weighted by the distance to the hazardous waste site in order to individualize risk to each location. Using a data set that spans seventeen years of property values around a hazardous waste site, the results indicate that media coverage and high prior risk perception increase current perceived risk. Increased perceived risk surrounding the hazardous waste site, in tum, lowers property values.

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Paper provided by University of California at Berkeley, Department of Agricultural and Resource Economics and Policy in its series CUDARE Working Paper Series with number 879R.

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Length: 33 pages
Date of creation: 2000
Date of revision:
Handle: RePEc:are:cudare:879r
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  1. Loewenstein, George & Mather, Jane, 1990. " Dynamic Processes in Risk Perception," Journal of Risk and Uncertainty, Springer, vol. 3(2), pages 155-75, June.
  2. Mark Thayer & Heidi Albers & Morteza Rahmatian, 1992. "The Benefits of Reducing Exposure to Waste Disposal Sites: A Hedonic Housing Value Approach," Journal of Real Estate Research, American Real Estate Society, vol. 7(3), pages 265-282.
  3. Viscusi, W. Kip, 1985. "A Bayesian perspective on biases in risk perception," Economics Letters, Elsevier, vol. 17(1-2), pages 59-62.
  4. Viscusi, W Kip & O'Connor, Charles J, 1984. "Adaptive Responses to Chemical Labeling: Are Workers Bayesian Decision Makers?," American Economic Review, American Economic Association, vol. 74(5), pages 942-56, December.
  5. Adamowicz W. & Louviere J. & Williams M., 1994. "Combining Revealed and Stated Preference Methods for Valuing Environmental Amenities," Journal of Environmental Economics and Management, Elsevier, vol. 26(3), pages 271-292, May.
  6. Golan, Amos & Judge, George & Karp, Larry, 1996. "A maximum entropy approach to estimation and inference in dynamic models or Counting fish in the sea using maximum entropy," Journal of Economic Dynamics and Control, Elsevier, vol. 20(4), pages 559-582, April.
  7. Viscusi, W Kip, 1991. "Age Variations in Risk Perceptions and Smoking Decisions," The Review of Economics and Statistics, MIT Press, vol. 73(4), pages 577-88, November.
  8. Goldberger, Arthur S, 1972. "Maximum-Likelihood Estimation of Regressions Containing Unobservable Independent Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(1), pages 1-15, February.
  9. Smith, V Kerry & Desvousges, William H, 1986. "The Value of Avoiding a Lulu: Hazardous Waste Disposal Sites," The Review of Economics and Statistics, MIT Press, vol. 68(2), pages 293-99, May.
  10. Burmeister, Edwin & Wall, Kent D & Hamilton, James D, 1986. "Estimation of Unobserved Expected Monthly Inflation Using Kalman Filtering," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(2), pages 147-60, April.
  11. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
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