The Determinants of Office Yields in European Cities

Omokolade Akinsomi et al.

Journal of Real Estate Literature2024https://doi.org/10.1080/09277544.2024.2412466article
ABDC C
Weight
0.30

What the paper says

This paper examines the drivers of prime office yields in Europe. Specifically, the paper uses 16 European cities across 8 European countries including United Kingdom, France, Germany, Italy, Spain, Belgium, Netherlands, and Ireland from Q1 2007 to Q2 2024. The premier cities include Berlin, Paris, Central London, Frankfurt, and Munich while the primary cities are Hamburg, Dusseldorf, Madrid, Milan, Amsterdam and Dublin. Lastly, the secondary cities comprised of Barcelona, Rome, Brussels, Cologne, and Lyon. For the estimation technique, we use quantile regression and OLS while accounting for country, city and time fixed effects. From the baseline results, we find that take-up, prime rent, vacancy rate and foreign investment have a negative and statistically significant effect on office prime yields. In the case of the results obtained from the quantile regression, vacancy rate is seen as the most significant determinant of office yields. Prime rent emerged as a significant determinant of office yields. The result of this work is particularly useful for investors, policy makers and analysts as it provides an in-depth understanding of how office yield varies over time in different European Cities as well as the factors which influence office yields.

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https://doi.org/https://doi.org/10.1080/09277544.2024.2412466

Or copy a formatted citation

@article{omokolade2024,
  title        = {{The Determinants of Office Yields in European Cities}},
  author       = {Omokolade Akinsomi et al.},
  journal      = {Journal of Real Estate Literature},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1080/09277544.2024.2412466},
}

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Evidence weight

0.30

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.