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The Valuation of Agricultural Land and the Influence of Government Payments

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  • Feichtinger, Paul
  • Salhofer, Klaus
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    Abstract

    This study gives an overview of the theoretical foundations, empirical procedures and derived results of the literature identifying determinants of land prices. Special attention is given to the effects of different government support policies on land prices. Since almost all empirical studies on the determination of land prices refer either to the net present value method or the hedonic pricing approach as a theoretical basis, a short review of these models is provided. While the two approaches have different theoretical bases, their empirical implementation converges. Empirical studies use a broad range of variables to explain land values and we systematise those into six categories. In order to investigate the influence of different measures of government support on land prices, a meta-regression analysis is carried out. Our results reveal a significantly higher rate of capitalisation for decoupled direct payments and a significantly lower rate of capitalisation for agri-environmental payments, as compared to the rest of government support. Furthermore, the results show that taking theoretically consistent land rents (returns to land) and including non-agricultural variables like urban pressure in the regression implies lower elasticities of capitalisation. In addition, we find a significant influence of the land type, the data type and estimation techniques on the capitalisation rate.

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    Bibliographic Info

    Paper provided by Centre for European Policy Studies in its series Factor Markets Working Papers with number 112.

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    Length: 24 pages
    Date of creation: Dec 2011
    Date of revision:
    Handle: RePEc:eps:fmwppr:112

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    Cited by:
    1. Elodie Letort Author-X-Name- First: Elodie Author-X-Name- Last: Letort & Chalachew Temesgen, 2013. "Influence of environmental policies on farmland prices in the Bretagne region of France," Working Papers SMART - LERECO 13-06, INRA UMR SMART.

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