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Cyclical and Structural Components to Yield Movements: The Case of Central London Offices Michael White, Keith Lown, and Ignas Gostautas.

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Presentation on theme: "Cyclical and Structural Components to Yield Movements: The Case of Central London Offices Michael White, Keith Lown, and Ignas Gostautas."— Presentation transcript:

1 Cyclical and Structural Components to Yield Movements: The Case of Central London Offices Michael White, Keith Lown, and Ignas Gostautas

2 London and Real Estate Approximately 22% of UK GDP [ONS] One of three largest global financial centres Europe’s second largest city: c.8.6 million…and growing Europe’s biggest real estate market UK lacks second city of critical mass [Source: FT.com]

3 Central London Core Office Markets [Map Source: CBRE]

4 Central London Core Office Markets in 2015 Historic low yields in every London sub sector Inflows of investment capital at an all time high Occupational demand at a new high London’s safe status haven reinforced as never before Foreign investment accounted for 66% of investment turnover [CBRE]

5 Central London Core Office Markets – Current Yields [Source: DTZ]

6 UK-wide - historic low yields [Source: Savills Research]

7 Prime Office Yields - How low can you go? [Source: Savills Research]

8 Central London Core Office Markets

9 Initial Yields: 1981 – 2013

10 London – Cycle or Trend? Is London becoming more different or are current yield reductions simply pointing to a cyclical turning point just ahead? Is the yield gap between London and regional cities persistent or temporary? Have flight-to-safety motives and increasing international investment temporarily distorted yields in London markets?

11 Yield Drivers Logically, yields are a function of the required rate of return for property and expected rental growth. Yields in cities may reflect local as well as national influences (Sivitanidou and Sivitanides, 1999) Local market characteristics may be more important than national inflation rates and stock market performance Local economic structures and supply differences can create individual city rent cycles (Orr and Jones, 2003) Such differences may imply local markets having different risk premiums (Gunnelin et al 2004)

12 Other Assets While the above discussion identifies endogenous yield drives, exogenous factors such as the performance and expected performance of other assets (Barras, 1994; Hendershott and MacGregor, 2005) In the models below we consider stocks and government debt as the main alternative investment classes The models estimated here usefully extend previous research by Dunse et al (2008)

13 Research Method In estimation the initial yield is written as a function of the gross redemption yield on long dated gilts, the yield on the FTSE all share index, and rental value growth. Following Hendershott and MacGregor (2005) we incorporate deviations from equilibrium for rent and the stock market yield variables. Mean reversion is captured by using a two year moving average of the real rent or stock market dividend from trend.

14 Model A long run reduced form equation for office yields is: If the variables in the long run model are cointegrated, there is a stationary error and then a short run yield adjustment model can be set up in an error correction form:

15 City Level Panel Data: 1981 - 2013

16 Fixed Effects Panel Model – Long Run Equation Variables have expected signs on rental value growth, rental deviation, gilt yield, and dividend yield although the dividend deviation is not significant at the 5% level. Fixed effects seem to vary across cities. This is confirmed above as we reject the null that the cross section effects are redundant.

17 Short Run Error Correction Model In this model the error correction term is correctly signed and statistically significant. Of the other variables only the deviation terms are significant.

18 Random Effects Panel Model – Long Run Equation

19 Fixed v Random Effects The Hausman panel test produced an insignificant p-value indicating that the random effects panel model is a better choice compared to the fixed effects panel model.

20 Random Effects Panel Model – Short Run Error Correction

21 City of London

22 Results and Conclusion These results indicate that initial yields respond to rents, gilts, and dividends as expected Some differences exist between London and other cities in relation to the sensitivity of initial yields to dividend yields The current gap between London and regional markets – will it persist?


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