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Generalizing the OLS and Grid Estimators


  • R. Kelley Pace
  • Otis W. Gilley


The vast majority of market valuations employ either some formal estimator such as ordinary least squares (OLS) or rely upon an informal set of rules defining the grid adjustment estimator. The success of the grid adjustment estimator suggests the data do not obey the ideal assumptions underlying OLS. However, the grid adjustment estimator's lack of a formal statistical foundation makes it difficult to use for inference and other purposes. This article demonstrates how to generalize the grid estimator and OLS to potentially obtain the best features of both. Interestingly, the generalization defines a spatial autoregression. On an empirical example the spatial autoregression outperforms the grid estimator which in turn outperforms OLS. Copyright American Real Estate and Urban Economics Association.

Suggested Citation

  • R. Kelley Pace & Otis W. Gilley, 1998. "Generalizing the OLS and Grid Estimators," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 26(2), pages 331-347.
  • Handle: RePEc:bla:reesec:v:26:y:1998:i:2:p:331-347

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    References listed on IDEAS

    1. Rosen, Harvey S., 1985. "Housing subsidies: Effects on housing decisions, efficiency, and equity," Handbook of Public Economics,in: A. J. Auerbach & M. Feldstein (ed.), Handbook of Public Economics, edition 1, volume 1, chapter 7, pages 375-420 Elsevier.
    2. Ermisch, John & Di Salvo, Pamela, 1997. "The Economic Determinants of Young People's Household Formation," Economica, London School of Economics and Political Science, vol. 64(256), pages 627-644, November.
    3. Masnick, George S., 2001. "The New Demographics of Housing," Berkeley Program on Housing and Urban Policy, Working Paper Series qt9668w1w4, Berkeley Program on Housing and Urban Policy.
    4. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
    5. Haurin Donald R. & Hendershott Patric H. & Kim Dongwook, 1994. "Housing Decisions of American Youth," Journal of Urban Economics, Elsevier, vol. 35(1), pages 28-45, January.
    6. Ermisch, John, 1999. "Prices, Parents, and Young People's Household Formation," Journal of Urban Economics, Elsevier, vol. 45(1), pages 47-71, January.
    7. Haurin, Donald R & Hendershott, Patric H & Kim, Dongwook, 1993. "The Impact of Real Rents and Wages on Household Formation," The Review of Economics and Statistics, MIT Press, vol. 75(2), pages 284-293, May.
    8. Donald R. Haurin & R. Jean Haurin & Steven Garasky, 2001. "Group living decisions as youths transition to adulthood," Journal of Population Economics, Springer;European Society for Population Economics, vol. 14(2), pages 329-349.
    9. Borsch-Supan, Axel, 1986. "Household formation, housing prices, and public policy impacts," Journal of Public Economics, Elsevier, vol. 30(2), pages 145-164, July.
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    Cited by:

    1. Julia Koschinsky & Nancy Lozano-Gracia & Gianfranco Piras, 2012. "The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates," Journal of Geographical Systems, Springer, vol. 14(3), pages 319-356, July.
    2. Steven Bourassa & Eva Cantoni & Martin Hoesli, 2007. "Spatial Dependence, Housing Submarkets, and House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 35(2), pages 143-160, August.
    3. Hua Sun & Yong Tu & Shi-Ming Yu, 2005. "A Spatio-Temporal Autoregressive Model for Multi-Unit Residential Market Analysis," The Journal of Real Estate Finance and Economics, Springer, vol. 31(2), pages 155-187, September.
    4. E.-H. Yoo & P. Kyriakidis, 2009. "Area-to-point Kriging in spatial hedonic pricing models," Journal of Geographical Systems, Springer, vol. 11(4), pages 381-406, December.
    5. Marco Salvi, 2008. "Spatial Estimation of the Impact of Airport Noise on Residential Housing Prices," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 144(IV), pages 577-606, December.
    6. Ingrid Nappi‐Choulet & Tristan‐Pierre Maury, 2011. "A Spatial And Temporal Autoregressive Local Estimation For The Paris Housing Market," Journal of Regional Science, Wiley Blackwell, vol. 51(4), pages 732-750, October.
    7. Rohana Abdul Rahman, 2011. "Variations in Implementing SCM to Minimize Subjectivity and a Future Direction for Malaysia," ERES eres2011_178, European Real Estate Society (ERES).
    8. Bing Zhu & Roland Füss & Nico Rottke, 2011. "The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 42(4), pages 542-565, May.
    9. Dubin, Robin A., 1998. "Spatial Autocorrelation: A Primer," Journal of Housing Economics, Elsevier, vol. 7(4), pages 304-327, December.

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