International money supply and real estate risk premium: The case of the London office market
Benoît Lefebvre and
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Alain Coen: University of Quebec at Montreal, Montreal, Quebec, Canada - UQAM - Université du Québec à Montréal
Benoît Lefebvre: DRM - Dauphine Recherches en Management - Université Paris-Dauphine - CNRS - Centre National de la Recherche Scientifique
Arnaud Simon: DRM - Dauphine Recherches en Management - Université Paris-Dauphine - CNRS - Centre National de la Recherche Scientifique
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The main purpose of this study is to deeply investigate the determinants of the risk premium for the Central London market between Q2-2002 and Q3-2015 using a vector autoregression (VAR) model. We shed new light on the role of central banks in the historical level of the commercial real estate risk premium. Indeed, since the global financial crisis (GFC), central banks have used unconventional monetary policies, increasing the quantity of money available in the economy and creating structural changes. Therefore, we have described the link between monetary policies and real estate using a theoretical IS/LM Mundell-Fleming framework for a small open economy with a flexible exchange rate. To empirically explore this phenomenon, we have constructed a monetary index adapted to the office market. We find that throughout the whole period (2002–2015), the vacancy rate, the employment in services, the FTSE 100, the new monetary index and the autoregressive parameter are the main determinants of the historical risk premium. However, this result hides the complex realities of different sub-periods. Finally, we study the structural changes introduced by the monetary policy using a structural VAR model and impulse-response function.
Keywords: Real estate; Direct office market; Risk premium; Monetary policies; Structural VAR (search for similar items in EconPapers)
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Published in Journal of International Money and Finance, 2018, 82, 〈10.1016/j.jimonfin.2018.01.001〉
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01778910
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