Hostname: page-component-cb9f654ff-mx8w7 Total loading time: 0 Render date: 2025-08-17T20:00:02.512Z Has data issue: false hasContentIssue false
  • English
  • Français

Two Dimensions of the Spatial Distribution of Housing: Dependency and Heterogeneity across Tennessee's Six Metropolitan Statistical Areas

Published online by Cambridge University Press:  09 September 2016

Seong-Hoon Cho ,
Christopher D. Clark  and
William M. Park
Seong-Hoon Cho
Affiliation:
Department of Agricultural Economics, the University of Tennessee
Christopher D. Clark
Affiliation:
Department of Agricultural Economics, the University of Tennessee
William M. Park
Affiliation:
Department of Agricultural Economics, the University of Tennessee
Get access Rights & Permissions [Opens in a new window]

Abstract

A two-stage multinomial logit selection model is used to model the relationship between demographic characteristics and housing density across Tennessee's six metropolitan statistical areas. The study finds that there is both spatial correlation and heterogeneity in the spatial distribution of housing both within and across the six areas. For example, Memphis, the most densely populated area, has the least amount of spatial correlation among housing density at the neighborhood level, while Johnson City, which has the lowest overall housing density, has the highest degree of spatial correlation.

Keywords

communityhousing densityspatial dependencyspatial heterogeneityC31R21

Information

Type
Invited Paper Sessions
Information
Journal of Agricultural and Applied Economics , Volume 38 , Issue 2 , August 2006 , pp. 299 - 316
Copyright
Copyright © Southern Agricultural Economics Association 2006

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

American Farmland Trust. Farming on the Edge. Washington D.C.: AFT, 2002.Google Scholar
Anselin, K. Spatial Econometrics: Methods and Model. Dordrecht: Kluwer Academic, 1988.Google Scholar
Ballard, D. “Three for One: East, Middle, and West Tennessee: Why They're Different and Likely to Stay That Way.” Tennessee Alumnus (Winter 1996):9–10.Google Scholar
Can, A. “Specification and Estimation of Hedonic Housing Price Models.” Regional Science and Urban Economics 22(1992):453–74.Google Scholar
Can, A. “The Measurement of Neighborhood Dynamics in Urban House Prices.” Economic Geography 66(1990):254–72.CrossRefGoogle Scholar
Cao, T., and Cory, D.. “Mixed Land Uses, Land-Use Externalities, and Residential Property Values: A Reevaluation.” Annals of Regional Science 16(1981):1–24.Google Scholar
Casetti, E. “Generating Models by the Expansion Method: Applications to Geographic Research.” Geographic Analysis 4(1972):81–91.Google Scholar
Cliff, A., and Ord, J.. Spatial Autocorrelation. London: Pion Limited, 1973.Google Scholar
Damgaard, C., and Weiner, J.. “Describing Inequality in Plant Size or Fecundity.” Ecology 81(2000):1139–42.Google Scholar
Daniels, T., and Bowers, D. Holding Our Ground. Washington, D.C.: Island Press, 1997.Google Scholar
Dixon, P.M., Weiner, J., Mitchell-Olds, T., and Woodley, R.. “Bootstrapping the Gini Coefficient of Inequality.” Ecology 68(1987):1548–51.Google Scholar
Dubin, R. “Spatial Autocorrelation and Neighborhood Quality.” Regional Science and Urban Economics 22(1992):433–52.Google Scholar
Dubin, R. “Spatial Autocorrelation: A Primer.” Journal of Housing Economics 7(1998):304–27.Google Scholar
Fotheringham, A.S., and Brunsdon, C.. “Local Forms of Spatial Analysis.” Geographical Analysis 31(1999):341–58.CrossRefGoogle Scholar
Fotheringham, A.S., Brunsdon, C., and Charlton, M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. West Sussex, England: John Wiley & Sons Ltd., 2002.Google Scholar
Fulton, W., Pendal, R., Nguyen, M., and Harrison, A.. Who Sprawls the Most: How Growth Patterns Differ across the United States. Washington, D.C.: Brookings Institute Center on Urban and Metropolitan Policy, 2001.Google Scholar
Geoghegan, J., Waigner, L., and Nancy, B.. “Spatial Landscape Indices in a Hedonic Framework: An Ecological Economics Analysis Using GIS.” Ecological Economics 23(1997):251–64.Google Scholar
Getis, A., and Ord, K.. “The Analysis of Spatial Association by Use of Distance Statistics.” Geographical Analysis 24(1992):189–206.Google Scholar
Goodman, A. “A Comparison of Block Group and Census Tract Data in a Hedonic Housing Price Model.” Land Economics 53(1977):483–87.Google Scholar
Greene, W. LIMDEP Version 7.0 User's Manual. Econometric Software, Inc., 1995.Google Scholar
Greene, W. Econometric Analysis. Upper Saddle River, NJ: Prentice Hall, 1997.Google Scholar
Katz, B. Smart Growth: The Future of the American Metropolis? Centre for Analysis of Social Exclusion 58. London: London School of Economics, 2002.Google Scholar
Lee, L. “Generalized Econometric Models with Selectivity.” Econometrica 51(1983):507–12.Google Scholar
Lesage, J.P. “Regression Analysis of Spatial Data.” Journal of Regional Analysis and Policy 27(1997):83–94.Google Scholar
Leung, Y., Mei, C., and Zhang, W.. “Testing for Spatial Autocorrelation among the Residuals of the Geographically Weighted Regression.” Environmental and Planning A 32(2000):871–90.Google Scholar
McMillen, D. “Probit with Spatial Autocorrelation.” Journal of Regional Science 3(1992): 335–48.Google Scholar
McMillen, D. “Spatial Autocorrelation or Model Mis-specification?” International Regional Science Review 26(2003):208–17.Google Scholar
Meen, G., and Andrew, M.. “On the Aggregate Housing Market Implications of Labour Market Change.” Scottish Journal of Political Economy 45(1998):393–419.CrossRefGoogle Scholar
Moran, P.A.P. “The Interpretation of Statistical Maps.” Journal of the Royal Statistical Society Series B 10(1948):243–51.Google Scholar
Newman, P., and Kenworthy, J.. “Gasoline Consumption and Cities: A Comparison of U.S. Cities with a Global Survey.” Journal of American Planners Association 55(1989):24–37.Google Scholar
Newman, P., and Kenworthy, J.. Sustainability and Cities: Overcoming Automobile Dependence. Washington, D.C.: Island Press, 1999.Google Scholar
NRCS. Summary Report 1997 National Resources Inventory (revised December 2000). Washington, D.C.: Natural Resources Conservation Service, United States Department of Agriculture, 2000.Google Scholar
Shepard, D. “A Two-Dimensional Interpolation Function for Irregularly-Spaced Data.” Proceedings of the 23rd National Conference ACM, ACM Press, 1968, pp. 517–24.Google Scholar
Sierra Club. Solving Sprawl: 1999 Sierra Club Report. Internet site: http://www.sierraclub.org.Google Scholar
Smart Growth Online. Principles of Smart Growth. Internet site: http://www.sustainable.org.Google Scholar
Snyder, K., and Bird, L.. Paying the Costs of Sprawl: Using Fair-Share Costing to Control Sprawl. Washington, D.C.: U.S. Department of Energy, 1998.Google Scholar
Tobler, W. “Smooth Pycnophylactic Interpolation for Geographic Regions.” Journal of Statistical Association 74(1979):519–36.Google Scholar
Tse, R.C. “Estimating Neighborhood Effects in House Prices: Towards a New Hedonic Model Approach.” Urban Studies 39(2002): 1165–80.Google Scholar
U.S. Census Bureau. Internet site: http://www.census.gov.Google Scholar
U.S. Department of Agriculture. Summary Report—1997 National Resources Inventory. Washington, D.C.: United States Department of Agriculture, 1999.Google Scholar
U.S. EPA. Protecting Water Resources with Smart Growth. Washington, D.C.: United States Environmental Protection Agency, 2004.Google Scholar
Vandell, K.D. “Market Factors Affecting Spatial Heterogeneity among Urban Neighborhoods.” Housing Policy Debate 6(1995): 103–37.Google Scholar