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The Value of Housing Characteristics: A Meta Analysis

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Author Info

  • G. Sirmans

    ()

  • Lynn MacDonald

    ()

  • David Macpherson

    ()

  • Emily Zietz

    ()

Abstract

This paper provides a meta regression analysis of the nine housing characteristics that are appear most often in hedonic pricing models for single-family housing: square footage, lot size, age, bedrooms, bathrooms, garage, swimming pool, fireplace, and air conditioning. Meta regression analysis is useful for comparing the estimated regression coefficients from different studies. The goal in this study is to determine if the estimated coefficients vary by geographical location, time, type of data, and model specification. The results show that the estimated coefficients for some characteristics vary significantly by geographical location. These include square footage, lot size, age, bathrooms, swimming pool, and air conditioning. Controlling for time shows that the effects of these housing characteristics on house price have not changed over time. Controlling for type of data produces differences in coefficients for bathrooms. Controlling for wealth as measured by median household income has no significant impact on the coefficients for the housing characteristics. If the study controlled for square footage, the coefficients for lot size decrease. Controlling for the size of the hedonic model affects the coefficient for square footage. Copyright Springer Science + Business Media, LLC 2006

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File URL: http://hdl.handle.net/10.1007/s11146-006-9983-5
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Bibliographic Info

Article provided by Springer in its journal The Journal of Real Estate Finance and Economics.

Volume (Year): 33 (2006)
Issue (Month): 3 (November)
Pages: 215-240

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Handle: RePEc:kap:jrefec:v:33:y:2006:i:3:p:215-240

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Web page: http://www.springerlink.com/link.asp?id=102945

Related research

Keywords: Selling price; Time on the market; Meta regression; Moderator variables; Hedonic models;

References

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  1. T.D. Stanley & Stephen B. Jarrell, 1998. "Gender Wage Discrimination Bias? A Meta-Regression Analysis," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 947-973.
  2. Stanley, T D & Jarrell, Stephen B, 1989. " Meta-Regression Analysis: A Quantitative Method of Literature Survey s," Journal of Economic Surveys, Wiley Blackwell, vol. 3(2), pages 161-70.
  3. Stephen B. Jarrell & T. D. Stanley, 1990. "A meta-analysis of the union-nonunion wage gap," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 44(1), pages 54-67, October.
  4. Chris Doucouliagos, 1995. "Worker participation and productivity in labor-managed and participatory capitalist firms: A meta-analysis," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 49(1), pages 58-77, October.
  5. Chris Doucouliagos & Patrice Laroche, 2007. "Unions and Profitability: A Meta-Analysis," Economics Series 2007_01, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  6. Laroche, P., 2000. "What do Unions do to Productivity? A Meta-Analysis," Papers 2000-5, Groupe de recherche en économie financière et en gestion des entreprises, Universite Nancy 2.
  7. Ian J. Bateman & Andrew P. Jones, 2003. "Contrasting Conventional with Multi-Level Modeling Approaches to Meta-Analysis: Expectation Consistency in U.K. Woodland Recreation Values," Land Economics, University of Wisconsin Press, vol. 79(2), pages 235-258.
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Citations

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Cited by:
  1. Alicia Rambaldi & Ryan McAllister & Kerry Collins & Cameron Fletcher, 2011. "Housing shadow prices in an inundation prone suburb," Discussion Papers Series 429, School of Economics, University of Queensland, Australia.
  2. Kopits, Elizabeth & McConnell, Virginia & Miles, Daniel, 2009. "Lot Size, Zoning, and Household Preferences: Impediments to Smart Growth?," Discussion Papers dp-09-15, Resources For the Future.
  3. Stephen Conroy & Jennifer Milosch, 2011. "An Estimation of the Coastal Premium for Residential Housing Prices in San Diego County," The Journal of Real Estate Finance and Economics, Springer, vol. 42(2), pages 211-228, February.
  4. Yadavalli, Anita P. & Florax, Raymond J.G.M., 2013. "The Effect of School Quality on House Prices: A Meta-Regression Analysis," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 151291, Agricultural and Applied Economics Association.
  5. Matthew J Osborne & Nathan H. Miller, 2011. "Competition among Spatially Differentiated Firms: An Estimator with an Application to Cement," BEA Working Papers 0072, Bureau of Economic Analysis.
  6. Bettina Aten & Eric Figueroa & Troy Martin, 2011. "Notes on Estimating the Multi-Year Regional Price Parities by 16 Expenditure Categories: 2005-2009," BEA Working Papers 0071, Bureau of Economic Analysis.
  7. Coulson, N. Edward & McMillen, Daniel P., 2008. "Estimating time, age and vintage effects in housing prices," Journal of Housing Economics, Elsevier, vol. 17(2), pages 138-151, June.

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