Cyclical and Structural Components to Yield Movements: The Case of Central London Offices

Michael White, Keith Lown, and Ignas Gostautas

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]

Central London Core Office Markets

[Map Source: CBRE]

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]

Central London Core Office Markets – Current Yields

[Source: DTZ]

UK-wide - historic low yields

[Source: Savills Research]

Prime Office Yields- How low can you go?

[Source: Savills Research]

Central London Core Office Markets

Initial Yields: 1981 – 2013

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?

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)

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)

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.

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:

tDEVtDEVttt LEPLEPRRVIRRVIGYIY 543210

ttDEVtDEVttt LEPLEPRRVIRRVIGYIY 16543210

City Level Panel Data: 1981 - 2013

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.

Dependent Variable: Initial Yield Method: Pooled Least Squares Sample (adjusted): 1982 2013 Included observations: 32 after adjustments Cross-sections included: 8 Total pool (balanced) observations: 256

Variable Coefficient Std. Error t-Statistic Prob. C 5.959724 0.483930 12.31526 0.0000

ΔRent -29.30633 3.686826 -7.948930 0.0000 Δ(Rental Deviation) 24.00201 4.110937 5.838574 0.0000 Economic Activity -0.233026 0.095169 -2.448551 0.0151

Gilt Yield 2.420431 0.294811 8.210108 0.0000 Dividend Yield -2.609446 0.475600 -5.486639 0.0000

Dividend Yield Deviation 0.964915 0.513522 1.879014 0.0614 Fixed Effects (Cross)

Birmingham 0.317795 Bristol 0.306240

City of London -1.209894 Edinburgh -0.210338 Glasgow -0.121550

Leeds -0.081746 Manchester 0.238542 Newcastle 0.760952

Effects Specification Cross-section fixed (dummy variables) R-squared 0.468700 Mean dependent var 6.963863

Adjusted R-squared 0.440160 S.D. dependent var 1.291096 S.E. of regression 0.966030 Akaike info criterion 2.821892 Sum squared resid 225.8380 Schwarz criterion 3.015769 Log likelihood -347.2022 Hannan-Quinn criter. 2.899869 F-statistic 16.42207 Durbin-Watson stat 0.620791 Prob(F-statistic) 0.000000

Redundant Fixed Effects Tests Test cross-section fixed effects

Effects Test Statistic d.f. Prob. Cross-section F 10.102694 (7,242) 0.0000

Cross-section Chi-square 65.629911 7 0.0000

Short Run Error Correction Model

Dependent Variable: ΔInitial Yield Method: Pooled Least Squares Sample (adjusted): 1983 2013 Included observations: 31 after adjustments Cross-sections included: 8 Total pool (balanced) observations: 248

Variable Coefficient Std. Error t-Statistic Prob. Constant 0.018925 0.045790 0.413305 0.6798

ΔΔRent -11.74486 8.368290 -1.403495 0.1618 ΔΔRental Deviation -5.364252 0.657803 -8.154802 0.0000 ΔEconomic Activity -0.079669 0.043328 -1.838725 0.0672

ΔGilt Yield -0.215772 0.419637 -0.514187 0.6076 ΔDividend Yield 0.062494 0.427658 0.146130 0.8839 ΔDividend Yield

Deviation 0.629076 0.298257 2.109174 0.0360 Error Correction -0.302762 0.041794 -7.244123 0.0000

R-squared 0.474838 Mean dependent var 0.043439

Adjusted R-squared 0.459520 S.D. dependent var 0.795617 S.E. of regression 0.584916 Akaike info criterion 1.797030 Sum squared resid 82.11046 Schwarz criterion 1.910366 Log likelihood -214.8317 Hannan-Quinn criter. 1.842655 F-statistic 31.00022 Durbin-Watson stat 1.693057 Prob(F-statistic) 0.000000

In this model the error correction term is correctly signed and statistically significant. Of the other variables only the deviation terms are significant.

Dependent Variable: Initial Yield Method: Pooled EGLS (Cross-section random effects) Sample (adjusted): 1982 2013 Included observations: 32 after adjustments Cross-sections included: 8 Total pool (balanced) observations: 256 Swamy and Arora estimator of component variances

Variable Coefficient Std. Error t-Statistic Prob. Constant 6.088906 0.507246 12.00385 0.0000

ΔRent -27.75832 3.628392 -7.650309 0.0000 ΔRental Deviation 22.32749 4.049777 5.513265 0.0000 Economic Activity -0.248090 0.094881 -2.614741 0.0095

Gilt Yield 2.373425 0.294094 8.070283 0.0000 Dividend Yield -2.615214 0.475567 -5.499151 0.0000

Dividend Yield Deviation 0.954059 0.513497 1.857965 0.0644 Random Effects (Cross)

Birmingham 0.268423 Bristol 0.276089

City of London -1.033786 Edinburgh -0.190559 Glasgow -0.104390

Leeds -0.077312 Manchester 0.199109 Newcastle 0.662425

Effects Specification S.D. Rho Cross-section random 0.456793 0.1827

Idiosyncratic random 0.966030 0.8173 Weighted Statistics R-squared 0.393241 Mean dependent var 2.438594

Adjusted R-squared 0.378620 S.D. dependent var 1.232209 S.E. of regression 0.971321 Sum squared resid 234.9227 F-statistic 26.89617 Durbin-Watson stat 0.596701 Prob(F-statistic) 0.000000

Unweighted Statistics R-squared 0.297289 Mean dependent var 6.963863

Sum squared resid 298.6995 Durbin-Watson stat 0.469297

• Random Effects Panel Model – Long Run Equation

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.

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob. ΔRent -29.306326 -27.758318 0.427464 0.0179

ΔRent Deviation 24.002008 22.327493 0.499106 0.0178 Economic Activity -0.233026 -0.248090 0.000055 0.0417

Gilt Yield 2.420431 2.373425 0.000422 0.0221 Dividend Yield -2.609446 -2.615214 0.000032 0.3049 Dividend Yield

Deviation 0.964915 0.954059 0.000026 0.0332

Random Effects Panel Model – Short Run Error Correction

City of LondonDependent Variable: Initial Yield Method: Least Squares Sample (adjusted): 1982 2013 Included observations: 32 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. Constant 7.253605 1.027536 7.059219 0.0000

ΔRent -21.39999 5.484539 -3.901876 0.0006 ΔRental Deviation 13.66730 6.317097 2.163541 0.0403 Economic Activity -0.146772 0.332035 -0.442037 0.6623

Gilt Yield 2.357130 0.646076 3.648381 0.0012 Dividend Yield -4.388382 1.191549 -3.682922 0.0011

Dividend Yield Deviation -0.038363 1.165804 -0.032907 0.9740 R-squared 0.758533 Mean dependent var 6.238341

Adjusted R-squared 0.700581 S.D. dependent var 1.453771 S.E. of regression 0.795492 Akaike info criterion 2.570929 Sum squared resid 15.82020 Schwarz criterion 2.891559 Log likelihood -34.13486 Hannan-Quinn criter. 2.677209 F-statistic 13.08896 Durbin-Watson stat 1.207623 Prob(F-statistic) 0.000001

Dependent Variable: ΔInitial Yield Method: Least Squares Sample (adjusted): 1983 2013 Included observations: 31 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. Constant -0.002777 0.119364 -0.023263 0.9816

ΔΔRent 6.605325 14.25578 0.463344 0.6473 ΔΔRental Deviation -6.716722 1.480595 -4.536503 0.0001 ΔEconomic Activity 0.043629 0.195880 0.222732 0.8256

ΔGilt Yield -0.012523 1.243695 -0.010069 0.9920 ΔDividend -1.231087 1.398780 -0.880115 0.3875

ΔDividend Deviation 0.382290 0.852359 0.448508 0.6580 Error Correction -0.612758 0.224675 -2.727309 0.0117

R-squared 0.698845 Mean dependent var -0.009436

Adjusted R-squared 0.623556 S.D. dependent var 0.930433 S.E. of regression 0.570868 Akaike info criterion 1.912362 Sum squared resid 7.821366 Schwarz criterion 2.236166 Log likelihood -22.64161 Hannan-Quinn criter. 2.017914 F-statistic 9.282177 Durbin-Watson stat 1.703948 Prob(F-statistic) 0.000026

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?