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Exploiting Property Characteristics in Commercial Real Estate Portfolio Allocation

Author

Listed:
  • Alberto Plazzi

    (University of Lugano and Swiss Finance Institute)

  • Walter N. Torous

    (University of California)

  • Rossen I. Valkanov

    (University of California)

Abstract
We use a parametric portfolio approach to estimate optimal commercial real estate portfolio policies. We do so using the NCREIF data set of commercial properties over the sample period 1984:Q2 to 2009:Q1. The richness of this extensive data set and the flexibility of the parametric portfolio approach allow us to consider: (i) a large cross-section of individual properties across various regions and property types; (ii) several property-specific conditioning variables, such as cap rates, leverage, value, and vacancy rates; and (iii) various macro-economic factors. Property-specific conditioning information is found to be economically important even for portfolios that are well-diversified across geographical regions and property types.

Suggested Citation

  • Alberto Plazzi & Walter N. Torous & Rossen I. Valkanov, 2011. "Exploiting Property Characteristics in Commercial Real Estate Portfolio Allocation," Swiss Finance Institute Research Paper Series 11-08, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp1108
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    File URL: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1762788
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    Citations

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    Cited by:

    1. Philippe Bracke, 2013. "House Prices and Rents: Micro Evidence from a Matched Dataset in Central London_x0003_," ERSA conference papers ersa13p112, European Regional Science Association.
    2. Prashant Das & Patrick Smith & Paul Gallimore, 2018. "Pricing Extreme Attributes in Commercial Real Estate: the Case of Hotel Transactions," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 264-296, August.
    3. Philippe Bracke, 2015. "House Prices and Rents: Microevidence from a Matched Data Set in Central London," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(2), pages 403-431, June.
    4. Joenväärä, Juha & Kauppila, Mikko & Kahra, Hannu, 2021. "Hedge fund portfolio selection with fund characteristics," Journal of Banking & Finance, Elsevier, vol. 132(C).
    5. Hjalmarsson, Erik & Manchev, Petar, 2012. "Characteristic-based mean-variance portfolio choice," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1392-1401.
    6. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 509-580, Elsevier.
    7. Tim A. Kroencke & Felix Schindler & Bertram I. Steininger, 2018. "The Anatomy of Public and Private Real Estate Return Premia," The Journal of Real Estate Finance and Economics, Springer, vol. 56(3), pages 500-523, April.

    More about this item

    Keywords

    monetary policy; federal funds rate; yield curves; stock markets; causality; lead-lag; dependence;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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