Natural Hazards

, Volume 47, Issue 3, pp 317–330 | Cite as

Spatial dependencies in wind-related housing damage

  • Dakshina G. De Silva
  • Jamie B. Kruse
  • Yongsheng WangEmail author
Original Paper


This article examines the spatial dependence among housing losses due to tornadoes using data from the May 1999 Oklahoma City tornado. In order to examine the existence of spatial dependence and its impacts on the damage analysis, we compare an estimation based on a traditional ordinary least square model with the general spatial model. The results show that housing damage in this disaster area is highly correlated. Monetary losses not only depend on the tornado that struck residences, but are related to the damage magnitudes of neighboring houses. Average losses as well as the loss ratio increase with the Fujita Scale damage rating. We conclude that the general spatial model provides unbiased estimates compared to the ordinary least square model. In order to construct appropriate home insurance policies for tornado disasters or to improve the damage resistance capabilities of houses, it is necessary for insurance underwriters and builders to consider spatial correlation of tornado damage.


Housing damage Spatial dependence Tornado Fujita Scale 



This work was performed under the Department of Commerce NIST/TTU Cooperative Agreement Award 70NANB8H0059. We would like to thank Dr. Douglas A. Smith, and the participants at both the 2005 Western Economic Association International meeting (San Francisco, CA) and the 2004 Southern Economics Association meeting (New Orleans, LA) for useful comments and suggestions.


  1. Anselin L (1988) Spatial econometrics: methods and models. Kluwer Academic Publishers, DordrechtGoogle Scholar
  2. Anselin L, Bera AK (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah A, Giles DEA (eds) Handbook of applied economic statistics. Marcel Dekker, New YorkGoogle Scholar
  3. ASCE Standard—Minimum Design Loads for Buildings and Other Structures (2002) American Society of Civil EngineersGoogle Scholar
  4. Cho S, Gordon P, Richardson HR, Moore JE II, Shinozuka M (2000) Analyzing transportation reconstruction network strategies: a full cost approach. Rev Urban Region Dev Stud 12(3):212–227Google Scholar
  5. De Silva DG, Kruse JB, Wang Y (2006) Catastrophe-induced destruction and reconstruction. Nat Hazards Rev 7(1):19–25CrossRefGoogle Scholar
  6. Ewing BT, Kruse JB, Thompson MA (2003) Labor market responses to tornadoes. In: Proceedings of the 11th international conference on wind engineering, Lubbock, TXGoogle Scholar
  7. Fronstin P, Holtmann AG (1994) The determinants of residential property damage caused by hurricane Andrew. South Econ J 61(2):387–397CrossRefGoogle Scholar
  8. Fujita TT (1971) A proposed characterization of tornadoes and hurricanes by area and intensity. SMRP Paper 91(42), University of ChicagoGoogle Scholar
  9. Golden JH, Snow JT (1991) Mitigation against extreme windstorms. Rev Geophys 29(4):477–504CrossRefGoogle Scholar
  10. Greene WH (1997) Econometric analysis, 3rd edn. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  11. Kawawaki Y, Ota M (1996) The influence of the great Hanshin-Awaji earthquake on the local housing market. Rev Urban Region Dev Stud 8:220–233CrossRefGoogle Scholar
  12. Kruse JB, Simmons K, Tiglioglu T (1999) Nashville tornado-economic damage estimation using tax assessor and insurance data. In: Larsen A, Larose GL, Livesey FM (eds) Wind engineering into the 21st century. Brookfield Publication, A. A. Balkema/RotterdamGoogle Scholar
  13. LeSage JP (1999) Spatial econometrics. (Data retrieved in May 2003)
  14. Marshall TP (2002) Damage survey at Moore, Oklahoma Tornado. Weather Forecast 17(3):582–598CrossRefGoogle Scholar
  15. Marshall TP, Foster M (2002) Damage survey and radar analysis of the Forth Worth and Arlington, TX tornadoes on 28 March 2000. In: Proceedings 21st conference on severe local storms, San Antonio, TXGoogle Scholar
  16. McDonald JR, Selvam P (1990) The West Memphis, Arkansas tornado of December 14, 1987. J Wind Eng Ind Aerodynam 6:279–287CrossRefGoogle Scholar
  17. Meade C, Abbott M (2003) Assessing federal research and development for hazard loss reduction. Prepared for the White House Office of Science Technology and Policy, RAND, Santa Monica, CAGoogle Scholar
  18. Merrell D, Simmons KM, Sutter D (2002a) The market for tornado safety: analysis of applications to the Oklahoma saferoom initiative. J Econ 28(1):35–50Google Scholar
  19. Merrell D, Simmons KM, Sutter D (2002b) Taking shelter: estimating the safety benefits of tornado safe rooms. Weather Forecast 17:619–625CrossRefGoogle Scholar
  20. O’Sullivan A (2003) Urban economics. McGraw-Hill Higher Education, BostonGoogle Scholar
  21. Phan LT, Simiu E (1999) The Fujita Intensity Scale: a reassessment from structural engineering perspective. Proceedings of 31st joint meetings wind and seismic effects. US/Japan Natural Resources Development Program, Tsukuba, Japan, pp 469–474Google Scholar
  22. Simmons KM, Kruse JB (2000) Market value of mitigation and perceived risk: empirical results. J Econ 26(1):41–51Google Scholar
  23. Simmons KM, Kruse JB, Smith D (2002) Valuing mitigation: real estate market response to Hurricane loss reduction measures. South Econ J 68(3):660–671CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Dakshina G. De Silva
    • 1
  • Jamie B. Kruse
    • 2
  • Yongsheng Wang
    • 3
    Email author
  1. 1.Department of EconomicsTexas Tech UniversityLubbockUSA
  2. 2.Center for Natural Hazard Mitigation Research, Department of EconomicsEast Carolina UniversityGreenvilleUSA
  3. 3.Department of EconomicsWashington and Jefferson CollegeWashingtonUSA

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