Skip to main content

Advertisement

Log in

A Dual-rate Property Tax: Exploring the Potential for Moderating the Effects of Sprawl on Development

  • Published:
Applied Spatial Analysis and Policy Aims and scope Submit manuscript

Abstract

This research evaluates the effectiveness of a dual-rate property tax on moderating sprawl, focusing particularly on how land-development decisions accumulate over space and affect changes in spatial patterns of development. A spatial process model of landowners’ conversion decisions links the effects of a dual-rate property tax on parcel-level land conversion through ex ante simulations. We conclude that the dual-property tax helps reduce the mean nearest neighbor of residential parcels and thus promotes the development of land around existing infrastructure more than land distant from existing infrastructure; however, the dual-property tax alone does not completely mitigate sprawl.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Notes

  1. First proposed by the American social economist Henry George in the nineteenth century, land value taxation is an ad valorem tax where only the value of land itself is taxed (George 1896).

  2. The details of how these matrices are constructed are found in Cho et al. (2011a, b).

  3. Illinois first adopted the state constitution requiring real estate property to be taxed according to its value (ad valorem tax) in 1818. Missouri followed in 1820 and Tennessee in 1834. All real estate property was taxed equally by value for 33 states by the end of the 18th Century (U.S. Census 2015).

  4. The neighbor parcels are defined by spatial weight matrix composed of Thiessen polygon (queen contiguity, order 3) specification.

  5. Inverse distance-based Moran’s indexes for per unit land and structure values for the 2460 parcels are 0.22 and 0.01, respectively.

References

  • Blais, P. (2000). Inching toward sustainability: The evolving urban structure of the greater Toronto area. Toronto: University of Toronto and Metropole Consultants.

    Google Scholar 

  • Bockstael, N. (1996). Modeling economics and ecology: the importance of a spatial perspective. American Journal of Agricultural Economics, 78, 1168–1180.

    Article  Google Scholar 

  • Brewer, C. K., Monty, J., Johnson, A., Evans, D., & Fisk, H. (2011). Forest carbon monitoring: A review of selected remote sensing and carbon measurement tools for REDD+. RSAC 10018-RPT1. Salt Lake City, UT: U.S.: Department of Agriculture, Forest Service, Remote Sensing Applications Center.

    Google Scholar 

  • Brueckner, J. K. (1986). A modern analysis of the effects of site value taxation. National Tax Journal, 39, 49–58.

    Google Scholar 

  • Brueckner, J. K., & Kim, H. A. (2003). Urban sprawl and the property tax. International Tax and Public Finance, 10(1), 5–23.

    Article  Google Scholar 

  • Carrión-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.

    Article  Google Scholar 

  • Case, K. E., & Grant, J. H. (1991). Property tax incidence in a multijurisdictional neoclassical model. Public Finance Review, 19(4), 379–392.

    Article  Google Scholar 

  • Census, U. S. (2010). American fact finder. Washington, DC: United States Census Bureau. Available at http://factfinder.census.gov/faces/nav/jsf/pages/community_facts.xhtml.

  • Cho, S., & Newman, D. H. (2005). Spatial analysis of rural land development. Forest Policy and Economics, 7(5), 732–744.

    Article  Google Scholar 

  • Cho, S., Lambert, D. M., & Roberts, R. K. (2010). Forecasting open space with a two-rate property tax. Land Economics, 86(2), 263–280.

    Article  Google Scholar 

  • Cho, S., Kim, S. G., & Roberts, R. K. (2011a). Measuring the effects of a land value tax on land development. Applied Spatial Analysis and Policy, 4(1), 45–64.

    Article  Google Scholar 

  • Cho, S., Roberts, R. K., & Kim, S. G. (2011b). Negative externalities on property values resulting from water impairment: the case of the Pigeon River watershed. Ecological Economics, 70, 2390–2399.

    Article  Google Scholar 

  • Cunningham, C. R. (2006). House price uncertainty, timing of development, and vacant land prices: evidence for real options in Seattle. Journal of Urban Economics, 59(1), 1–31.

    Article  Google Scholar 

  • Daniels, T. L., & Bowers, D. (1997). Holding our ground: Protecting America’s farms and farmland. Washington DC: Island Press.

    Google Scholar 

  • ESRI (2008). Data and maps 2008.

  • Ewing, R., Pendall, R., & Chen, D. (2002). Measuring sprawl and its impact. Washington: Smart Growth America.

    Google Scholar 

  • Fleming, M. (2004). Techniques for estimating spatially dependent discrete choice models. In L. Anselin, R. J. G. M. Florax, & S. J. Rey (Eds.), Advances in spatial econometrics (pp. 145–168). Heidelberg: Springer.

    Chapter  Google Scholar 

  • George, H. (1896). Progress and poverty, reprinted in 1970. New York: Robert Schalkenbach Foundation.

    Google Scholar 

  • Hargis, C. D., Bissonette, J. A., & David, J. A. (1998). The behavior of landscape metrics commonly used in the study of habitat fragmentation. Landscape Ecology, 13(3), 167–186.

    Article  Google Scholar 

  • 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, 698–704.

    Article  Google Scholar 

  • Irwin, E. G., & Geoghegan, J. (2001). Theory, data, methods: developing spatially explicit economic models of land use change. Agriculture, Ecosystems & Environment, 85, 7–24.

    Article  Google Scholar 

  • 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(1), 83–102.

    Google Scholar 

  • Irwin, E. G., Bell, K. P., & Geoghegan, J. (2006). Forecasting residential land use change. In R. J. Johnston & R. J. Swallow (Eds.), Economics and contemporary land use policy: Development and conservation at the urban-rural fringe. Washington, DC: Resources for the Future.

    Google Scholar 

  • Kahn, M. (2000). The environmental impact of suburbanization. Journal of Policy Analysis and Management, 19, 569–586.

    Article  Google Scholar 

  • Klier, T., & McMillen, D. P. (2008). Clustering of auto supplier plants in the United States: generalized method of moments spatial logit for large samples. Journal of Business and Economic Statistics, 26, 460–471.

    Article  Google Scholar 

  • Knox County (2011). Property Tax Search (http://www.knoxcounty.org/apps/tax_search/).

  • Landis, J. (1995). Improving land use futures: applying the California urban future model. Journal of the American Planning Association, 61, 438–457.

    Article  Google Scholar 

  • Landis, J., & Zhang, M. (1998). The second generation of the California urban futures model. Part 1: model logic and theory. Environment and Planning B, 25, 657–666.

    Article  Google Scholar 

  • Lei, Q., & Bin, L. (2008). Urban sprawl: A case study of Shenzhen, China. 44th ISOCARP Congress (www.isocarp.net/data/case_studies/1159.pdf).

  • LeSage, J.P., & Pace, R.K. (2009). Introduction to spatial econometrics. Chapman & Hall/CRC.

  • Lopez, R. (2004). Urban sprawl and risk for being overweight or obese. American Journal of Public Health, 94, 1574–1579.

    Article  Google Scholar 

  • Maddala, G. S. (1992). Introduction to econometrics. New York: Macmillan.

    Google Scholar 

  • 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 

  • MPC, Metropolitan Planning Commission (2008). Directory of Neighborhood Organizations.

  • Mueller, J. M., & Loomis, J. B. (2008). Spatial dependence in hedonic property models: do different corrections for spatial dependence result in economically significant differences in estimated implicit prices? Journal of Agricultural and Resource Economics, 33(2), 212–231.

    Google Scholar 

  • Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115, 1145–1161.

    Article  Google Scholar 

  • Nechyba, T. J., & Strauss, R. P. (1998). Community choice and local public services: a discrete choice approach. Regional Science and Urban Economics, 28(1), 51–73.

    Article  Google Scholar 

  • 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 

  • Pendall, R. (1999). Do land-use controls cause sprawl? Environment and Planning B: Planning and Design, 26(4), 555–571.

    Article  Google Scholar 

  • Pinkse, J., & Slade, M. E. (1998). Contracting in space: an application of spatial statistics to discrete-choice models. Journal of Econometrics, 85(1), 125–154.

    Article  Google Scholar 

  • Riitters, K. H., O’neill, R. V., Hunsaker, C. T., Wickham, J. D., Yankee, D. H., Timmins, S. P., Jones, K. B., & Jackson, B. L. (1995). A factor analysis of landscape pattern and structure metrics. Landscape Ecology, 10(1), 23–39.

    Article  Google Scholar 

  • Roth, N. E., Allan, J. D., & Erickson, D. L. (1996). Landscape influences on stream biotic integrity assessed at multiple spatial scales. Landscape Ecology, 11(3), 141–156.

    Article  Google Scholar 

  • Rusk, D. (1997). Debate on theories of David Rusk. The Regionalist, 2(3), 11–29.

    Google Scholar 

  • Schabenberger, O., & Pierce, F. J. (2002). Contemporary statistical models for the plant and soil sciences. Boca Raton: CRC Press. 738 p.

    Google Scholar 

  • Schneider, A., & Woodcock, C. E. (2008). Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Studies, 45(3), 659–692.

    Article  Google Scholar 

  • Skaburskis, A. (1995). The consequence of taxing land value. Journal of Planning Literature, 10(1), 3–21.

    Article  Google Scholar 

  • Southeast Watershed Forum (2001). Growing smarter: linking land use & water quality.

  • Turner, M. G. (1989). Landscape ecology: the effect of pattern on process. Annual Review of Ecology and Systematics, 20(1), 171–197.

    Article  Google Scholar 

  • U.S. Department of Housing and Urban Development (2000). HUD.GOV (http://www.hud.gov/).

  • U. S. Census (2015). U.S. Census of Governments, Historical Statistics of State and Local Finance, 1902–1953; U. S. Census of Governments, Governments Finances for (various years). Available at http://www.census.gov.

  • Wu, J. (2004). Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecology, 19(2), 125–138.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported in part by USDA Hatch Projects NE-1049 and W-3133.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seong-Hoon Cho.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cho, SH., Roberts, R.K. & Lambert, D.M. A Dual-rate Property Tax: Exploring the Potential for Moderating the Effects of Sprawl on Development. Appl. Spatial Analysis 9, 251–267 (2016). https://doi.org/10.1007/s12061-015-9150-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12061-015-9150-6

Keywords

JEL Classification

Navigation