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Property Assessments and Information Asymmetry in Residential Real Estate

Author

Listed:
  • Fathali Firoozi

    (Department of Economics, University of Texas at San Antonio, 6900 North Loop 1604 West, San Antonio, Texas, 78249-0633)

  • Daniel R. Hollas

    (Department of Economics, University of Texas at San Antonio, 6900 North Loop 1604 West, San Antonio, Texas, 78249-0633)

  • Ronald C. Rutherford

    (Department of Economics, University of Texas at San Antonio, 6900 North Loop 1604 West, San Antonio, Texas, 78249-0633)

  • Thomas A. Thomson

    (Department of Economics, University of Texas at San Antonio, 6900 North Loop 1604 West, San Antonio, Texas, 78249-0633)

Abstract

This paper presents a game theoretic model of property tax assessment that allows a tax appraiser to either choose a high or a low assessment. The owner either accepts or challenges this assessment. A ‘‘fixed effects’’ regression model is used to evaluate the differences in the assessed values of a sample of houses from Bexar County, Texas during 2000 and 2001. Where the owner of the house is identified as a state licensed property tax consultant, the assessed value, after adjusting for size, age, and other economic characteristics, ranged from a statistically robust 2.5% to 6.2% lower than neighboring houses.

Suggested Citation

  • Fathali Firoozi & Daniel R. Hollas & Ronald C. Rutherford & Thomas A. Thomson, 2006. "Property Assessments and Information Asymmetry in Residential Real Estate," Journal of Real Estate Research, American Real Estate Society, vol. 28(3), pages 275-292.
    • Handle: RePEc:jre:issued:v:28:n:3:2006:p:275-292
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    References listed on IDEAS

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    Cited by:

    1. Elizabeth Plummer, 2014. "The Effects of Property Tax Protests on the Assessment Uniformity of Residential Properties," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(4), pages 900-937, December.
    2. Donner, Herman & Kopsch, Fredrik, 2016. "Housing Tenure and Informational Asymmetries," Working Paper Series 16/3, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    3. Chang, Chuang-Chang & Chao, Ching-Hsiang & Yeh, Jin-Huei, 2016. "The role of buy-side anchoring bias: Evidence from the real estate market," Pacific-Basin Finance Journal, Elsevier, vol. 38(C), pages 34-58.
    4. K. W. Chau & Lennon H. T. Choy, 2011. "Let the Buyer or Seller Beware: Measuring Lemons in the Housing Market under Different Doctrines of Law Governing Transactions and Information," Journal of Law and Economics, University of Chicago Press, vol. 54(S4), pages 347-365.
    5. Pedro M. M. L. Garcês & Cesaltina Pacheco Pires, 2011. "New housing supply: what do we know and how can we learn more?," CEFAGE-UE Working Papers 2011_18, University of Evora, CEFAGE-UE (Portugal).

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    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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