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A Spatial and Temporal Autoregressive Local Estimation for the Paris Housing Market

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Abstract

This original study examines the potential of a spatiotemporal autoregressive Local (LSTAR) approach in modelling transaction prices for the housing market in inner Paris. We use a data set from the Paris Region notary office (“Chambre des notaires d’Île-de-France”) which consists of approximately 250,000 transactions units between the first quarter of 1990 and the end of 2005. We use the exact X -- Y coordinates and transaction date to spatially and temporally sort each transaction. We first choose to use the spatiotemporal autoregressive (STAR) approach proposed by Pace, Barry, Clapp and Rodriguez (1998). This method incorporates a spatiotemporal filtering process into the conventional hedonic function and attempts to correct for spatial and temporal correlative effects. We find significant estimates of spatial dependence effects. Moreover, using an original methodology, we find evidence of a strong presence of both spatial and temporal heterogeneity in the model. It suggests that spatial and temporal drifts in households socio-economic profiles and local housing market structure effects are certainly major determinants of the price level for the Paris Housing Market.

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Bibliographic Info

Paper provided by ESSEC Research Center, ESSEC Business School in its series ESSEC Working Papers with number DR 09004.

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Length: 25 pages
Date of creation: Jul 2009
Date of revision:
Handle: RePEc:ebg:essewp:dr-09004

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Postal: ESSEC Research Center, BP 105, 95021 Cergy, France
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Web page: http://www.essec.edu/
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Keywords: Hedonic Prices; Heterogeneity; Paris Housing Market; STAR Model;

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  1. Steven C. BOURASSA & Martin HOESLI & Vincent S. PENG, 2002. "Do Housing Submarkets Really Matter?," FAME Research Paper Series rp58, International Center for Financial Asset Management and Engineering.
  2. Alan E. Gelfand & Mark D. Ecker & John R. Knight & C. F. Sirmans, 2004. "The Dynamics of Location in Home Price," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 149-166, 09.
  3. 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.
  4. Clapp, John M & Rodriguez, Mauricio, 1999. "Erratum: Spatiotemporal Autoregressive Models of Neighborhood Effects," The Journal of Real Estate Finance and Economics, Springer, vol. 19(1), pages 85, July.
  5. 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.
  6. Bourassa, Steven C. & Hamelink, Foort & Hoesli, Martin & MacGregor, Bryan D., 1999. "Defining Housing Submarkets," Journal of Housing Economics, Elsevier, vol. 8(2), pages 160-183, June.
  7. Kelley Pace, R. & Barry, Ronald, 1997. "Sparse spatial autoregressions," Statistics & Probability Letters, Elsevier, vol. 33(3), pages 291-297, May.
  8. 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.
  9. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
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Cited by:
  1. Benchimol, Jonathan & Fourçans, André, 2012. "Money and risk in a DSGE framework: A Bayesian application to the Eurozone," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 95-111.
  2. Jean Dubé & Diègo Legros, 2013. "Dealing with spatial data pooled over time in statistical models," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 1-18, March.

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