Applied Spatial Analysis and Policy

, Volume 4, Issue 1, pp 45–64 | Cite as

Measuring the Effects of a Land Value Tax on Land Development

  • Seong-Hoon Cho
  • Seung Gyu Kim
  • Roland K. Roberts


The objective of this research was to evaluate using land value tax as a potential policy tool to moderate sprawling development in Nashville, TN, the nation’s most sprawling metropolitan community with a population of one million or more. A land development model was used to evaluate the hypothesis that a land value tax encourages more development closer to areas of preexisting development than does the observed property tax scheme. For the median and lower and upper quartiles of empirical densities, results show that distances are shorter between areas of preexisting development and parcels predicted to be developed under a hypothetical land value tax policy than distances predicted under the observed tax scheme. This finding suggests that land value taxation could be used to design compact development strategies in Nashville, TN.


Compact development Land value tax Land development model Spatial-probit model Urban Sprawl 



Cho, Kim, and Roberts are, respectively, assistant professors, graduate research assistant, and professor, Department of Agricultural Economics, University of Tennessee, Knoxville, TN. The views expressed here do not necessarily represent those of the University of Tennessee.


  1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.CrossRefGoogle Scholar
  2. Alexander, M. (2004). The “Creative Class” and economic development: An analysis of workforce attraction and retention in the Atlanta Region.
  3. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27, 93–115.CrossRefGoogle Scholar
  4. Anselin, L. (2004). GeoDa0.9.5-i release notes. Urbana: Spatial Analysis Laboratory and Center for Spatially Integrated Social Science. Department of Agriculture and Consumer Economics, University of Illinois, Urbana-Champaign.Google Scholar
  5. Autant-Bernard, C. (2006). Where do firms choose to locate their R&D? A spatial conditional logit analysis on French data. European Planning Studies, 14(9), 1187–1208.CrossRefGoogle Scholar
  6. Beenstock, M., & Felsenstein, D. (2007). Spatial vector autoregressions. Spatial Economic Analysis, 2(2), 167–196.CrossRefGoogle Scholar
  7. Bell, K., & Irwin, E. G. (2002). Spatially-explicit micro-level modeling of land use change at the rural-urban interface. Agricultural Economics, 27, 217–232.CrossRefGoogle Scholar
  8. Beron, K. J., Murdoch, J. C., & Vijverberg, W. P. M. (2003). Why cooperate? Public goods, economic power, and the Montreal Protocol. Review of Economics and Statistics, 85(2), 286–297.CrossRefGoogle Scholar
  9. Bills, N. L. & Boisvert, R. N. (1987). New York's experience in farmland retention through agricultural districts and use-value assessment, Soil and Water Conservation Society, 231–250.Google Scholar
  10. Bockstael, N. (1996). Modeling economics and ecology: the importance of a spatial perspective. American Journal of Agricultural Economics, 78, 1168–1180.CrossRefGoogle Scholar
  11. Bockstael, N., & Bell, K. (1998). Land use patterns and water quality: the effect of differential land management controls. In R. Just & S. Netanyahu (Eds.), International water and resource economics consortium, conflict and cooperation on trans-boundary water resources. Dordrecht: Kluwater Academic Publishers.Google Scholar
  12. Bourassa, S. C. (1990). Land value taxation and housing development: effects of property tax three types of cities. American Journal of Economics and Sociology, 49, 101–111.CrossRefGoogle Scholar
  13. Brookings Institute. (2000). Moving beyond sprawl: The challenge for metropolitan Atlanta. Center on Urban and Metropolitan Policy.
  14. Brueckner, J. K. (1986). A modern analysis of the effects of site value taxation. National Tax Journal, 39, 49–58.Google Scholar
  15. Brueckner, J. K. (2000). Urban sprawl: diagnosis and remedies. International Regional Science Review, 23(2), 160–171.CrossRefGoogle Scholar
  16. Brueckner, J. K. (2001). Urban sprawl: Lessons from urban economics. In W. G. Gale & J. R. Pack (Eds.), Brookings-Wharton papers on urban affairs (pp. 65–89). Washington, D.C: Brookings Institution Press.Google Scholar
  17. Brueckner, J., & Kim, H. (2003). Urban sprawl and the property tax. International Tax and Public Finance, 10(1), 5–23.CrossRefGoogle Scholar
  18. Carrion-Flores, C., & Irwin, E. G. (2004). Determinants of residential land-use conversion and sprawl at the rural-urban fringe. American Journal of Agricultural Economics, 86(4), 889–904.CrossRefGoogle Scholar
  19. Case, K. E., & Grant, J. H. (1991). Property tax incidence in a multijurisdictional neoclassical model. Public Finance Quarterly, 19(4), 379–392.CrossRefGoogle Scholar
  20. Chib, S., & Greenberg, E. (1995). Understanding the Metropolis–Hastings algorithm. Journal of the American Statistical Association, 40, 327–335.Google Scholar
  21. Cho, S., Lambert, D. M., & Roberts, R. K. (2010). Forecasting open space with a two-rate property yax. Land Economics, 86.Google Scholar
  22. Cho, S., & Newman, D. H. (2005). Spatial analysis of rural land development. Forest Policy and Economics, 7, 732–744.CrossRefGoogle Scholar
  23. Cho, S., Poudyal, N. C., & Lambert, D. M. (2008). Estimating spatially varying effects of urban growth boundaries on land development and land value. Land Use Policy, 25, 320–329.CrossRefGoogle Scholar
  24. Cho, S., & Roberts, R. K. (2007). Cure for urban sprawl: measuring the ratio of marginal implicit prices of density-to-lot-size. Review of Agricultural Economics, 29, 572–579.CrossRefGoogle Scholar
  25. Coughlin, C. C., Garrett, T. A., & Hernández-Murillo, R. (2003). Spatial probit and the geographic patterns of State Lotteries. St. Louis Federal Reserve Bank Working Paper 2003-042B.
  26. Cumberland Commercial Partners. (2009). Nashville development.
  27. Daniels, T., & Bowers, D. (1997). Holding our ground. Washington, DC: Island.Google Scholar
  28. Dixler, A. O. (2006). Direct taxes under the constitution: a review of the precedents. Taxanalysts.Google Scholar
  29. ESRI. (2004). ESRI data & maps 2004.
  30. Fisher, G. W. (1997). The evolution of the American property tax. In R.T. Golembiewski & J. Rabin (Eds.), Public budgeting and finance. Marcel Dekker.Google Scholar
  31. Franzese Jr. R. J., & Hays, J. C. (2004). Modeling international diffusion: Inferential benefits and methodological challenges, with an application to international tax competition. WZB Papers, SP II 2004-12.Google Scholar
  32. Garrett, T. A., Wagner, G. A., & Wheelock, D. C. (2005). A spatial analysis of state banking regulation. Papers in Regional Science, 84(4), 575–595.CrossRefGoogle Scholar
  33. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.CrossRefGoogle Scholar
  34. GeoDa Center. (2009). GeoDa.
  35. Gordon, P., & Richardson, H. W. (1998). Prove it. Brookings Review, 16(4), 23–26.CrossRefGoogle Scholar
  36. Hanham, R., & Spiker, J. S. (2005). Urban sprawl detection using satellite imagery and geographically weighted regression. In R. Jensen, J. Gatrell & D. McLean (Eds.), Geo-spatial technologies in urban economics. Berlinthebe: Springer.Google Scholar
  37. Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society, Ser. B, 41, 190–195.Google Scholar
  38. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov Chains and their applications. Biometrika, 57, 97–109.CrossRefGoogle Scholar
  39. Henderson, J. V. (1991). Optimal regulation of land development through price and fiscal controls. Journal of Urban Economics, 30, 64–82.CrossRefGoogle Scholar
  40. Holloway, G., Shankar, B., & Rahman, S. (2002). Bayesian spatial probit estimation: a primer and an application to HYV rice adoption. Agricultural Economics, 27, 383–402.CrossRefGoogle Scholar
  41. Hylton, T. (2004). The walking life.
  42. Irwin, E. G., Bell, K. P., & Geoghegan, J. (2003). Modeling and managing urban growth at the rural–urban fringe: a parcel-level model of residential land use change. Agricultural and Resource Economics Review, 32, 83–102.Google Scholar
  43. Irwin, E. G., Bell, K. P., & Geoghegan, J. (2006). Forecasting residential land use change. In R. J. Johnston & S. K. Swallow (Eds.), Economics and contemporary land use policy: development and conservation at the urban-rural fringe. Washington, DC: Resource for the Future Press.Google Scholar
  44. Irwin, E. G., & Bockstael, N. E. (2001). The problem of identifying land use spillovers: Measuring the effects of open space on residential property values. American Journal of Agricultural Economics, 83(3), 698–704.CrossRefGoogle Scholar
  45. Irwin, E. G., & Bockstael, N. E. (2002). Interacting agents, spatial externalities and the evolution of residential land use patterns. Journal of Economic Geography, 2, 31–54.CrossRefGoogle Scholar
  46. Irwin, E. G., & Bockstael, N. E. (2004). Land use externalities, open space preservation, and urban sprawl. Regional Science and Urban Economics, 34, 705–725.CrossRefGoogle Scholar
  47. Katz, B. (2000). The federal role in curbing sprawl. Annals of the American Academy, 572, 66–77.CrossRefGoogle Scholar
  48. Katz, B. (2002). Smart growth: The future of the American metropolis? Centre for Analysis of Social Exclusion London School of Economics. CASEpaper 58.Google Scholar
  49. Lacombe, D. J., & Shaughnessy, T. M. (2005). An examination of a congressional vote using Bayesian spatial probit techniques. Paper presented at the 2005 Meetings of the Public Choice Society.Google Scholar
  50. LeSage, J. P. (2000). Bayesian estimation of limited dependent variable spatial autoregressive models. Geographical Analysis, 32, 19–35.CrossRefGoogle Scholar
  51. Levine, N. (1999). The effects of local growth controls on regional housing production and population redistribution in California. Urban Studies, 36(12), 2047–2068.CrossRefGoogle Scholar
  52. McMillen, D. P. (1992). Probit with spatial autocorrelation. Journal of Regional Science, 32, 335–348.CrossRefGoogle Scholar
  53. McMillen, D. P., & McDonald, J. F. (1993). Could zoning have increased land values in Chicago? Journal of Urban Economics, 33(2), 167–188.CrossRefGoogle Scholar
  54. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical physics, 21, 1087–1091.CrossRefGoogle Scholar
  55. Mills, E. S. (1998). The economic consequences of a land tax. In D. Netzer (Ed.), Land value taxation: Could it work today? Cambridge: Lincoln Institute of Land Policy.Google Scholar
  56. MPD (2009). Metro Planning Department, Davidson County.
  57. Mukherjee, B., & Singer, D. A. (2007). Monetary institutions, partisanship, and inflation targeting. International Organization, 62(2), 323–358.Google Scholar
  58. Murdoch, J. C., Sandler, T., & Vijverberg, W. P. M. (2003). The participation decision versus the level of participation in an environmental treaty: A spatial probit analysis. Journal of Public Economics, 87(2), 337–362.CrossRefGoogle Scholar
  59. Nasser, H. E., & Overberg, P. (2001). A comprehensive look at sprawl in America. USA Today.
  60. Nechyba, T. J. (1998). Replacing capital taxes with land taxes: efficiency and distributional implications with an application to the United States economy. In D. Netzer (Ed.), Land value taxation: Could it work today? Cambridge: Lincoln Institute of Land Policy.Google Scholar
  61. Nechyba, T. J., & Walsh, R. P. (2004). Urban sprawl. Journal of Economic Perspectives, 18(4), 177–200.CrossRefGoogle Scholar
  62. Nelson, A. C., & Sanchez, T. W. (2005). The effectiveness of urban containment regimes in reducing exurban sprawl. DISP, 160, 42–47.Google Scholar
  63. Netusil, N. R. (2005). The effect of environmental zoning and amenities on property values: Portland. Oregon Land Economics, 81(2), 227–246.Google Scholar
  64. Novo, A. (2003). Contagious currency crises: A spatial probit approach. Working Paper:
  65. Oates, W. E., & Schwab, R. M. (1997). The impact of urban land taxation: the Pittsburgh experience. National Tax Journal, 50(1), 1–21.Google Scholar
  66. Phillips, J., & Goodstein, E. (2000). Growth management and housing prices: the case of Portland, Oregon. Contemporary Economic Policy, 18(3), 334–344.CrossRefGoogle Scholar
  67. Plantinga, A. J., & Bernell, S. (2005). A spatial economic analysis of urban land use and obesity. Journal of Regional Science, 45, 473–492.CrossRefGoogle Scholar
  68. Princeton Survey Research Associates. (2000). Research—straight talk from Americans—2000. National Survey for the Pew Center for Civic Journalism.
  69. Rathbun, S. L., & Fei, S. (2006). A spatial zero-inflated Poisson regression model for oak regeneration. Environmental and Ecological Statistics, 13(4), 409–426.CrossRefGoogle Scholar
  70. Richardson, J. J., Jr., Gough, Z. M., & Puentes, R. (2003). Is home rule the answer?: Clarifing the influence of Dillon’s rule on growth management. Washington, D. C.: Brookings Institution Center on Urban and Metropolitan Policy.Google Scholar
  71. Rybeck, R. (2004). Using value capture to finance infrastructure and encourage compact development. Public Works Management & Policy, 8(4), 249–260.CrossRefGoogle Scholar
  72. Schoettle, F. P. (2003). What public finance do State Constitutions allow? In S. B. White, R. D. Bingham & E. W. Hill (Eds.), Financing economic development in the 21st century. ME: Sharpe.Google Scholar
  73. Schofield, N., Miller, G., & Martin, A. (2003). Critical elections and political realignments in the USA: 1860–2000. Political Studies, 51(2), 217–240.Google Scholar
  74. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.CrossRefGoogle Scholar
  75. Shao, J. (1997). An asymptotic theory for linear model selection. Statistica Sinica, 7, 221–264.
  76. Shinkai, K., Kanagawa, S., Takizawa, T., & Yamashita, H. (2008). Decision analysis of fuzzy partition tree applying AIC and fuzzy decision. In I. Lovrek, R. J. Howlett & L. C. Jain (Eds.), KES 2008, Part III, LNAI 5179 (pp. 572–579). Berlin: Springer.Google Scholar
  77. Siegel, S. (1956). Kolmogorov-Smirnov two-sample test. In S. Siegel (Ed.), Nonparameteric statistics for the behavioral sciences (pp. 127–136). Tokyo: McGraw-Hill Kogakusha Ltd.Google Scholar
  78. Sierra Club. (2009). Stop sprawl. A complex relationship: population growth and suburban sprawl.
  79. Skaburskis, A. (1995). The consequences of taxing land value. Journal of Planning Literature, 10(1), 3–21.CrossRefGoogle Scholar
  80. Smart Growth America. (2000). Americans want growth and green; demand solutions to traffic, haphazard development.
  81. Smart Growth America. (2003). Measuring sprawl and its impact: The character & consequences of metropolitan expansion.
  82. Smith, T. E., & LeSage, J. P. (2000). A Bayesian probit model with spatial dependencies. Working paper, Department of Systems Engineering, University of Pennsylvania.Google Scholar
  83. Snyder, K., & Bird, L. (1998). Paying the costs of sprawl: Using fair-share costing to control sprawl. Washington DC: U.S. Department of Energy.Google Scholar
  84. Southeast Watershed Forum. (2001). Growing smarter: Linking land use & water quality. Newsletter 4.
  85. Turnbull, G. K., & Geon, G. (2004). Local Government internal structure, external constraints, and the median voter hypothesis. Georgia State University. Working paper.Google Scholar
  86. U.S. Department of Housing and Urban Development. (2000). The state of the cities 2000.Google Scholar
  87. Walls, M., & McConnell, V. (2004). Incentive-based land use policies and water quality in the Chesapeake Bay. Discussion paper 04-20. Washington, DC: Resources for the Future.
  88. Wooldridge, J. M. (2002). Econometric analysis of cross-section and panel data. Cambridge: The MIT.Google Scholar
  89. Wu, J., & Cho, S. (2007). The effect of local regulations on urban development in the Western United States. Regional Science and Urban Economics, 37, 69–86.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Seong-Hoon Cho
    • 1
  • Seung Gyu Kim
    • 1
  • Roland K. Roberts
    • 1
  1. 1.University of TennesseeKnoxvilleUSA

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