The representative heuristic and catastrophe-related risk behaviors


Building on the work by Volkman-Wise Journal of Risk and Uncertainty, 51, 267–290 (2015) and Dumm et al. (The Geneva Risk and Insurance Review, 42, 117–139 (2017), we examine behavioral aspects of risk through the representative heuristic’s impact on catastrophe-related probability assessment and insurance demand of Florida homeowners. The representative heuristic models individuals as underweighting prior probabilities and overweighting posterior probabilities, thereby overweighting the probability of a loss from a disaster after a disaster occurs. Using data for homeowners’ insurance purchases through Florida’s residual market over a time period (2003-2008) that includes a sub-period of many losses (2004-2005) and sub-period of few catastrophic losses (2003, 2006-2008), we find increases in demand at the individual policyholder level for coverage following losses. Also consistent with the representative heuristic, we find that the effect attenuates as the losses fade from memory. That is, the effect of losses on demand is much higher for more recent losses. We also are able to parameterize the representative heuristic model showing that individual policy holders overweight the probability of another catastrophic event occurring by nearly 50%, after one has occurred.

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Fig. 1
Fig. 2


  1. 1.

    Note that if no events have occurred recently, individuals still overweight the current state (no events), leading to a belief that would underweight the true probability of an event.

  2. 2.

    For example, a consumer that chooses not to insure an automobile worth $5,000 because s∖he has not had a recent loss faces a different potential financial impact when deciding to not insure (or underinsure) a house.

  3. 3.

    Hurricane Katrina also occurred during this window, but its impact in Florida was minimal.

  4. 4.

    In fact, the average tenure for a flood insurance policy is only three years (Michel-Kerjan 2010).

  5. 5.

    NFIP premiums do not change much from year to year though and the premiums charged do not accurately reflect the true probability of a loss. Therefore, using this data to estimate probability updating for natural disasters is somewhat limited.

  6. 6.

    An indirect loss would be a loss by a neighbor, acquaintance, etc.

  7. 7.

    AIR Worldwide. (2016) reports that the exposure now exceeds $4 trillion.

  8. 8.

    According to the Insurance Information Institute. (2004), approximately one-fifth of homes in Florida were damaged during the 2004 hurricane season.

  9. 9.

    Loss data is from the Florida Office of Insurance Regulation.

  10. 10.

    Citizens was the largest insurer during the period examined in this paper.

  11. 11.

    HRA accounts also have a separate funding mechanism.

  12. 12.

    To control for size effects, we use the natural log of these variables.

  13. 13.

    Since the demand models will have an estimated regressor, we bootstrap the standard error (Pagan 1984).

  14. 14.

    Virtually all deductible changes were from 2% to $500 or 2% to 5%, representing an increase or decrease in demand.

  15. 15.

    The claims data and underwriting data were matched by policy number.

  16. 16.

    Citizens codes each catastrophe loss by the catastrophe name.

  17. 17.

    If property changes ownership a new policy is issued with a new policy number. Similarly, if an insured leaves Citizens and returns at a later date, they are issued a new policy number.

  18. 18.

    We do not, however, know the demand (either increasing or decreasing) of individuals that do not purchase insurance from Citizens or those that changed carriers. Since we control for price in the models, we are not concerned that individuals are dropping out of the sample because of prices changes.

  19. 19.

    The standard deductible for wind damage is 2%. One problem with measuring the changes in demand is that prior to 2007 Citizens standard deductibles in the PLA for wind damage were not percentage deductibles but recorded as dollar deductibles ($250, $500, rather than 2% or 5%). These dollar deductibles were not always “round” numbers, but included values such as $1,267, or $5,345 which were percentage deductibles recorded as dollar values. That changed in 2007 where the standard deductible for wind damage in the PLA became 2%. Thus for 2007 only, if the deductible in the PLA went from a dollar amount to 2%, we considered that no change in demand.

  20. 20.

    The coefficients on the first and second lags (Log(CatLosst− 1) andLog(CatLosst− 2)) are significantly different from the third lag at the 1% level for all models. The coefficients between the first and second lags are never significantly different from each other.

  21. 21.

    Here, the coefficients on the first lag (Log(CatLosst− 1)) are (statistically) significantly different from the second and third lags at the 1% level for all models. The coefficients between the second and third lags are still never (statistically) significantly different from each other.

  22. 22.

    The Wald test of differences in the coefficients is significant at the 1% level in all models.

  23. 23.

    The coefficient on Age is insignificant in models analyzing only PLA insureds.

  24. 24.

    State-level data is available from 1913, onward; county-level data is available beginning in 1960.

  25. 25.

    If this frequency parameter estimate understates (overstates) the true probability, it will overestimate (underestimate) the effect of the representative heuristic.

  26. 26.

    Hazards and Vulnerability Research Institute, Department of Geography, University of South Carolina, Columbia, South Carolina 29208.

  27. 27.

    Both the Poisson and Lognormal distributions were chosen based on the Akaike Information Criteria goodness of fit test.

  28. 28.

    These levels are chosen to be representative of the average home in our sample.

  29. 29.

    We focus on the 2% and $500 deductible as these are the two most frequently used deductibles in our sample.

  30. 30.

    The results here are, of course, sensitive to the assumptions made in our analysis. However, a 50% difference between the “true” probability and the estimated probability from a policyholder exhibiting behavior consistent with the representative heuristic is significant. With this distortion, the policyholder anticipates a catastrophic loss once every three years instead of once every five years.


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Correspondence to David L. Eckles.

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Dumm, R.E., Eckles, D.L., Nyce, C. et al. The representative heuristic and catastrophe-related risk behaviors. J Risk Uncertain 60, 157–185 (2020).

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  • Natural disasters
  • Insurance demand
  • Catastrophic Risk
  • Risk beliefs
  • Heuristics

JEL Classification

  • D03
  • D81
  • D83
  • G02
  • G22