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Spatial Clustering of Housing Construction in the Tokyo Metropolitan Area: An Application of Spatially Clustered Fixed-Effects and Spatially Correlated Random-Effects Models

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  • Atsushi Yoshida
  • Tatsuhiro Shichijo
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    Abstract

    We proposed two types of econometric models, a spatially clustered fixed-effects model (SCFEM) and a spatially correlated random-effects model (SCREM), to examine area-based panel data. We investigate what factors influence housing construction in the Tokyo Metropolitan Area, incorporating unobservable factors, local regulatory differences in housing development and spillovers of local public or private goods, which may cause spatial clustering or correlation of housing construction. The SCFEM is a type of fixed-effects model where a cluster has the same effects, so that we have to find which areas constitute a cluster. The issue of finding clusters can be regarded as a problem of model selection from too many possible models. We adopt an aggregate prediction error as a model selection criterion, which is estimated by a resampling method, namely leave-one-out cross-validation. We showed by simulations that the estimated parameters of concern are more efficient than the within estimates. The SCREM is a model where the random-effects are spatially correlated. We use the concentrated maximum likelihood method for the estimation. The unobservable area-effects are large in the east, west and north areas of the TMA but small in the south, where regulations against development are more severe than in the other areas. Clusters are found in huge cities

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    Bibliographic Info

    Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 266.

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    Date of creation: 11 Aug 2004
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    Handle: RePEc:ecm:ausm04:266

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    Keywords: cluster-effects model; housing construction; area-based panel data; spatial correlation; spillover effects;

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    1. DiPasquale Denise & Wheaton William C., 1994. "Housing Market Dynamics and the Future of Housing Prices," Journal of Urban Economics, Elsevier, vol. 35(1), pages 1-27, January.
    2. Mayer, Christopher J. & Somerville, C. Tsuriel, 2000. "Residential Construction: Using the Urban Growth Model to Estimate Housing Supply," Journal of Urban Economics, Elsevier, vol. 48(1), pages 85-109, July.
    3. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
    4. Topel, Robert H & Rosen, Sherwin, 1988. "Housing Investment in the United States," Journal of Political Economy, University of Chicago Press, vol. 96(4), pages 718-40, August.
    5. Gramlich, Edward M & Rubinfeld, Daniel L, 1982. "Micro Estimates of Public Spending Demand Functions and Tests of the Tiebout and Median-Voter Hypotheses," Journal of Political Economy, University of Chicago Press, vol. 90(3), pages 536-60, June.
    6. Aaronson, Daniel, 2001. "Neighborhood Dynamics," Journal of Urban Economics, Elsevier, vol. 49(1), pages 1-31, January.
    7. Janet S. Netz & Beck A. Taylor, 2002. "Maximum Or Minimum Differentiation? Location Patterns Of Retail Outlets," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 162-175, February.
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