European Business Schools Librarian's Group
ESSEC Working Papers,
ESSEC Research Center, ESSEC Business School
No DR 09003: A repeat sales index Robust to small datasets
Michel Baroni (), Fabrice Barthélémy () and Mahdi Mokrane ()
Additional contact information
Michel Baroni: ESSEC Business School, Postal: Avenue Bernard Hirsch - B.P. 50105, 95021 CERGY-PONTOISE Cedex , FRANCE,
Fabrice Barthélémy: THEMA, Université de Cergy-Pontoise, Postal: 33 bd du Port, 95011 CERGY-PONTOISE Cedex , FRANCE
Mahdi Mokrane: AEW EUROPE, Postal: 1-3 rue des Italiens, 75009 PARIS, FRANCE
Abstract: As suggested by D. Geltner, commercial properties indices have to be built using repeat sales instead of hedonic indices. The repeat sales method is a means of constructing real estate price indices based on a repeated observation of property transactions. These indices may be used as benchmarks for real estate portfolio managers. But the investors in general are also interested in introducing real estate performance in their portfolio to enhance the efficient frontier. Thus, expected return and volatility are the two key parameters. To create and to improve contracts on real estate indices, trend and volatility of these indices must be robust regarding to the periodicity of the index and the volume of transactions. This paper aims to test the robustness of the trend and volatility estimations for two indices: the classical Weighted Repeat Sales (Case & Shiller 1987) and a PCA factorial index (Baroni, Barthélémy and Mokrane 2007). The estimations are computed from a dataset of Paris commercial properties. The main findings are the trend and volatility estimates are biased for the WRS index and not for the PCA factorial index when the periodicity increases. Consequently, the level of the index at the end of the computing period is significantly different for various periodicities in the case of the WRS index. Globally, the PCA factorial seems to be more robust to the number of transactions. Firstly, we present the two methodologies and then the dataset. Finally we test the impact of the number of transactions per period on the trend and volatility estimates for each index and we give an interpretation of the results.
Keywords: Index Estimations; Property Transactions Volume; Repeat Sales Indices
20 pages, July 2009
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