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The house price determination process: Rational expectations with a spatial context

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  • Kiefer, Hua
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    Abstract

    Applying the rational expectations hypothesis, this essay models the current value of a house as the conditional expectation of the discounted stream of housing services accruing to the owner of the house. The value of housing services is determined by neighborhood effects as well as the physical attributes of the property itself. In the existing hedonic literature, future transactions have not been utilized to describe neighborhood effects. The rational expectations asset pricing model in this study accounts for expected future neighborhood effects as well as observed current neighborhood effects. The reduced form of the rational expectations model is a spatial autoregressive (SAR) model with two spatial lags. After employing the generalized method of moments (GMM) in estimating the spatial asset pricing model, I find that both expected future transactions and prior transactions in the neighborhood are significant. The inclusion of expected future transaction prices in the neighborhood takes into account the influence of expected changes in the community and factors these potential changes into the current house price. This is consistent with forward-looking households. The forward-looking model generates superior out-of-sample prediction performance relative to both the conventional hedonic model without considering neighborhood effects or the standard spatial hedonic model including only past transactions.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Housing Economics.

    Volume (Year): 20 (2011)
    Issue (Month): 4 ()
    Pages: 249-266

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    Handle: RePEc:eee:jhouse:v:20:y:2011:i:4:p:249-266

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    Web page: http://www.elsevier.com/locate/inca/622881

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    Keywords: Rational expectation; Spatial autoregressive model; GMM; Hedonic model; Neighborhood effects; House price;

    References

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    1. Allen C. Goodman & Thomas G. Thibodeau, 2007. "The Spatial Proximity of Metropolitan Area Housing Submarkets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 35(2), pages 209-232, 06.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    3. Kathy J. Hayes & Lori L. Taylor, 1996. "Neighborhood school characteristics: what signals quality to homebuyers?," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Q IV, pages 2-9.
    4. Can, Ayse, 1992. "Specification and estimation of hedonic housing price models," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 453-474, September.
    5. Pace, R. Kelley & Barry, Ronald & Gilley, Otis W. & Sirmans, C. F., 2000. "A method for spatial-temporal forecasting with an application to real estate prices," International Journal of Forecasting, Elsevier, vol. 16(2), pages 229-246.
    6. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
    7. Goodman, Allen C. & Thibodeau, Thomas G., 2003. "Housing market segmentation and hedonic prediction accuracy," Journal of Housing Economics, Elsevier, vol. 12(3), pages 181-201, September.
    8. Clapp, John M. & Giaccotto, Carmelo, 1998. "Residential Hedonic Models: A Rational Expectations Approach to Age Effects," Journal of Urban Economics, Elsevier, vol. 44(3), pages 415-437, November.
    9. Pace, R Kelley, et al, 1998. "Spatiotemporal Autoregressive Models of Neighborhood Effects," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 15-33, July.
    10. Blanchard, Olivier J, 1979. "Backward and Forward Solutions for Economies with Rational Expectations," American Economic Review, American Economic Association, vol. 69(2), pages 114-18, May.
    11. David M. Brasington, 1999. "Which Measures of School Quality Does the Housing Market Value?," Journal of Real Estate Research, American Real Estate Society, vol. 18(3), pages 395-414.
    12. Goodman, Allen C. & Thibodeau, Thomas G., 1998. "Housing Market Segmentation," Journal of Housing Economics, Elsevier, vol. 7(2), pages 121-143, June.
    13. Dubin, Robin A., 1998. "Spatial Autocorrelation: A Primer," Journal of Housing Economics, Elsevier, vol. 7(4), pages 304-327, December.
    14. Haurin, Donald R. & Brasington, David, 1996. "School Quality and Real House Prices: Inter- and Intrametropolitan Effects," Journal of Housing Economics, Elsevier, vol. 5(4), pages 351-368, December.
    15. Can, Ayse & Megbolugbe, Isaac, 1997. "Spatial Dependence and House Price Index Construction," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 203-22, Jan.-Marc.
    16. Basu, Sabyasachi & Thibodeau, Thomas G, 1998. "Analysis of Spatial Autocorrelation in House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 61-85, July.
    17. Pace, R Kelley & Gilley, Otis W, 1997. "Using the Spatial Configuration of the Data to Improve Estimation," The Journal of Real Estate Finance and Economics, Springer, vol. 14(3), pages 333-40, May.
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    Cited by:
    1. Huang, MeiChi, 2014. "Bubble-like housing boom–bust cycles: Evidence from the predictive power of households’ expectations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 2-16.

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