Risk Premium & Management - an Asian Direct Real Estate (Dre) Perspective

RISK PREMIUM

& MANAGEMENT

-

AN ASIAN DIRECT REAL ESTATE

(DRE) PERSPECTIVE

Ho Kim Hin/David

Copyright © 2020 by Ho Kim Hin/David.

ISBN:      Hardcover      978-1-5437-6007-1

                Softcover        978-1-5437-6005-7

                eBook              978-1-5437-6006-4

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CONTENTS

Foreword

Acknowledgements

About The Author

Introduction

Chapter 1   Direct Real Estate (DRE) Risk Premiums and Country Legal Origin

Chapter 2   International DRE Risk Premiums and the Multi-Factor Estimation Model

Chapter 3   A Risk Management Framework For International Direct real estate investing – Principles and Key Considerations

Chapter 4   The Direct Real Estate (DRE) Hurdle Rate and the

Chapter 5   Direct Real Estate (DRE) Asset Allocation and Investment Strategy

Chapter 6   A Residential Investment Fund under Modern Risk Management via Value at Risk (VaR), Incremental VaR and Risk Adjusted Return on Capital (RAROC) - An Asian Case Study

Chapter 7   The Sustainable Integrated Resort (IR) at Singapore’s Marina Bay (in the Marina South Central Business District)

Chapter 8   The Pan-Asia Commercial Real Estate Split Returns

Chapter 9   A Fuzzy Logic framework for Intelligent Building (IB) Classification for Commercial Buildings in the Tropics

Chapter 10   The Conclusion

Notes

FOREWORD

Over 100 years ago, this was a mud-flat, swamp. Today, this is a modern city. Ten years from now, this will be a metropolis. Never fear.

(The first Prime Minister of Singapore Lee Kuan Yew, 1965)

This book highlights the findings, contributions and recommendations made on several crucial issues concerning the concomitant subjects of direct real estate (DRE) risk premiums and DRE risk management. Chapter 1 examines the institutional nature of legal origin and the total returns (TRs), derived from investing in a country’s DRE, and via the adoption of a multi-factor arbitrage pricing theory (APT) model. Quarterly DRE data from the Jones Lang LaSalle Real Estate-Asia index is utilized for 13 cities in Asia and across 3 sectors (office, residential and retail real estate) are obtained. Findings confirm the existence of smoothing effects that cause a temporal bias and a seasonal lag. The 1st and 4th order autoregressive models are adopted to de-smooth the TRs. De-smoothed data is utilized in conjunction with real GDP growth rate, interest rate and vacancy rate, to form the multi-factor structural model. A pooled panel analysis is conducted with the law-system dummies, denoting British legal origin and French legal origin, and the sensitivities of the risk factor loadings to the TRs. Macroeconomic and DRE risk factors in equilibrium affect the TRs. Vacancy rate commands high and significant premium owing to its direct impact on TRs. Both the British and French legal origins have a significant relationship each on the TRs.

Chapter 2 affirms the true historical volatility to be a reasonable estimation of international DRE risk premiums, when the autoregressive lag orders of the de-smoothed returns are considered. The multi-factor model reveals that the variations of macroeconomic and DRE factors explain much more of the office and retail returns than the residential TRs. The high vacancy rate risk premium is attributable to the country-specific institutional environment. The South Asia DRE risk premium is higher than that for North Asia while the US risk premium is lower than those for Asia. Compared with the DRE risk premium model utilizing the smoothed returns, it is clear that the international DRE risk premiums for the Asia cities and the US as well as their DRE sectors, are underestimated for the smoothed TRs.

Chapter 3’s real world of international DRE investing is not short on surprises. What we on count as an effective and sustainable viable concern on international DRE investing, is our and the investing organization’s willingness and preparedness to effectively manage risk or uncertainty. We cannot know the exact consequences and the timing of the identifiable risks nor how they affect us but we can minimize, contain, avoid and seek new opportunities, early enough in the pursuit of high risk-adjusted TRs for our DRE assets. It is imperative to develop an in-depth appreciation of and to commit adequate resources to execute the risk management cycle (RMC).

Chapter 4 recommends a model of the intuitive build-up approach in the formation of the DRE investment hurdle rates for new DRE investing. The DRE market premium of about 400 bps (basis points) reflects the systematic risk premium, estimated by Henderson Global Investors in their ‘Global Property Market Risk Premium’ report. The estimate is still acceptable. The resulting DRE risk premiums merely serve as a rough guide to ensure that the DRE hurdle rate is adequately stringent and high enough, to achieve the risk-adjusted and Sharpe-optimal portfolio return. The DRE hurdle rate is that required rate of return within the model framework of a discounted cash flow analysis, above which an investment makes sense and below which it does not. Often, this is based on the DRE investing firm’s cost of capital or weighted average cost of capital, plus or minus a risk premium, to reflect the DRE investment asset’s specific risk characteristics. It is known as the DRE required rate of return or the minimum amount of return that DRE investing firms require before they acquire a DRE investment asset. It is the minimum return to DRE investing firms to be achieved before a carry is permitted.

Chapter 5 examines the dynamic and integrated DRE investment strategy for a 13-city Pan Asia DRE portfolio, of office, industrial real estate and public listed DRE companies, which is geographically diversified, time diversified and has an optimal risk-adjusted return. The analytic hierarchy process (AHP) model and the Markowitz quadratic programming (QP) model are integrated to enable a versatile asset allocation (SAA) and a tactical asset allocation (TAA). As an appropriate interface, the AHP SAA model identifies the 13 Asia cities’ DRE markets and the proportions of these markets, which comprise the long-term, desired, normal Pan Asia DRE portfolio mix. The modern portfolio theory (MPT) QP TAA model is conducted around the AHP SAA model portfolio via imposing appropriate planned deviations i.e. tactical bands. The ‘Pan Asia Growth Investment Strategy’ is recommended, with a portfolio composition that is similar to a Sharpe-ratio-optimal TAA portfolio. Such a recommended strategy on the portfolio efficient frontier is demonstrated to achieve a high, expected TR and a low SD (14.2% and 7.18% respectively), over a long investment term of 7 years. Capital stability is a low concern while securing short-term income is not a concern at all.

Chapter 6 enables the prospective DRE institutional investor to achieve a comprehensive and in-depth return and risk assessment at the DRE level for the 4 prime Asia residential sectors of Shanghai (SH), Beijing (BJ), Bangkok (BK), and Kuala Lumpur (KL), under the modern risk management approach of the DRE VaR, the incremental DRE VaR and RAROC. While the previous Markowitz MPT QP TAA model assumes that portfolio diversification reduces market risk, the price of a DRE asset is determined by its contribution to a DRE diversified portfolio. The ex ante and ex post correlations for instance among the residential sectors are examined while the ex ante correlations are accorded more weights. The DRE VaR merely summarizes the maximum potential loss of a 4-sector prime Asia residential portfolio at the high 99% confidence level, which is US$6.648 million per year. The incremental DRE VaRs inform us of the amount of risk contributed by each Asia prime residential sector. The SH prime residential sector is the biggest risk contributor with the incremental DRE VaR of US$8.404 million. Although the BJ and BK prime residential sectors exhibit additional risks, the DRE VaR so achieved is reduced via investing in the KL prime residential sector. The risk-adjusted return on capital (RAROC) offers limits to guide the level of DRE investment activity via reviewing the decision to walk away or to continue investing for the 4 prime Asia residential sectors, to achieve high enough risk-adjusted TRs (consistent with the optimal MPT QP TAA and AHP SAA model portfolio). RAROC allows for meaningful comparisons among the 4 prime Asia residential sectors. Our RAROC analysis discovers that the SH prime residential sector is most profitable at the highest expected RAROC of 87.0% on the total RAROC capital of US$6.648 million.

Chapter 7 reiterates that public policies on macroeconomic management must be consistent and non-conflicting among themselves in a widely accepted ‘policy compact’. It is because the policies reinforce the fundamental investment value of large and complex developments, which affect the demand for say the integrated resort (IR)-at-Marina-Bay, Singapore, in the space market. The extent to which this IR produces favorable risk-adjusted investment returns and values, is dependent on adopting the appropriate DCF analytical approach. Net present value (NPV) and the internal rate of return (IRR) should be positive and strong enough in conjunction with the expected TRs exceeding the required returns. Under conducive DRE market conditions and economic conditions, which together with supportive institutional policies, can ultimately sustain the fundamental investment value-adding of the large and complex DRE development like the IR-at-Marina-Bay. Sensitivity analysis accordingly establishes the development’s risky factors, which impinge the IR’s sustainable viability and its physical sustainability in the long run. By way of the real estate market analysis (REMA), it is evident that Singapore’s well-established institutional plans provide the required spatial infrastructure, and help to create the foundations for human capital and financial support, for the wider DRE and IR market. Marketing strategies play an essential role in revenue generation for the purposes of the appropriate DCF analytical approach, adopted to examine the IR-at-Marina-Bay development. The DCF approach, scenario analysis, sensitivity analysis and the Monte Carlo risk simulation model clearly show that the IR-at-Marina-Bay is a viable development, financially and physically in the space market over the long run.

Chapter 8 draws due attention to the aftermath of the Asian economic crisis, terrorism and viral epidemics, that has compelled more DRE investors to risk-diversify their operations beyond their primary market into other parts of Asia. Extensive studies have been conducted to achieve high enough risk-adjusted TRs by DRE asset type, geographical or economic region, size and the DRE cyclical stage. However, limited studies examine risk-reduction diversification strategies through split returns i.e. decomposing TRs into rental-yield returns and capital value (CV) returns. Chapter 8 offers investors such alternative risk-reduction strategies of the prevailing wider market risk, via diversifying into a Pan Asia commercial DRE market portfolio, comprising office and retail real estate sectors, and with split returns for key selected Asian cities. On an ex ante basis, the rental-yield return, CV return and TR, offer immense risk-reduction diversification benefit for the 16 selected Pan Asia commercial DRE sectors. The rental-yield investment strategy is attractive for the risk-adverse investor, and is preferable for a long and steady growth strategy. The CV-appreciation investment strategy has the lowest TR despite its relatively higher market-wide risk. The TR investment strategy is suitable for the investor who prefers a more balanced growth strategy, because its market-wide risk is intermediate among all the 3 investment strategies. The portfolio TRs are significantly higher, relative to the rental-yield returns (owing to the extreme right and upward shift of the portfolio-TR efficient frontier).

Finally, Chapter 9 proposes and highly recommends the intelligent building (IB) framework, which leads to a robust measure of building intelligence, and to produce a standard guideline of a consistent performance-based structure for the promotion of the correct IB classification. Such a correct IB classification meets the different classes of users. The expediency and rigor of the fuzzy logic (FL) engine help to accurately measure the degree of IB supportiveness and responsiveness towards the expectation of users within prudent limit. The FL engine also helps to assess the impact on the micro, local and global environments. Realistic fine tuning of the performance specifications is now possible with the aid of the FL software program, which captures user changes in demand over time. With the establishment of such a performance-based building evaluation and assessment structure, the IB and other relevant development trends can well be developed.

Happy reading.

Yours sincerely,

Professor (Dr) HO, Kim Hin / David

Singapore

November 2020.

ACKNOWLEDGEMENTS

The author wishes to extend his most sincere appreciation to the School of Design & Environment, under the highly able Deanship of the Provost & Chair Professor (Dr) LAM Khee Poh, of the National University of Singapore. The same wish is extended to the University of Cambridge and the University of Hertfordshire in Hatfield, UK. These three tertiary institutions of higher learning and research are globally leading Universities, inspiring and encouraging both modern and contemporary direct real estate premiums and risk management.

ABOUT THE AUTHOR

Dr HO Kim Hin / David is Honorary Professor in Development Economics & Land Economy, awarded by the UK public university, the University of Hertfordshire. He retired end-May 2019 as Professor (Associate) (Tenured) from the National University of Singapore. Professor HO spent the last thirty-one years across several sectors, which include the military, oil refining, aerospace engineering, public housing, resettlement, land acquisition, land reclamation, real estate investment, development and international real estate investing. He spent six years in the real estate career as part of the executive management group of Singapore Technologies at Pidemco Land Limited, and as part of the senior management team of the Government of Singapore Investment Corporation’s GIC Real Estate Private Limited. Seventeen years are spent in the National University of Singapore at the then School of Building and Estate Management, the Department of Real Estate, School of Design and Environment, where his research expertise is in two areas. First is international real estate in the area of risk-return behavior behind international real estate investing in direct and indirect real estate. Secondly, is urban and public policy analysis involving real estate, sea transport, public housing, land and land use. Schooled in development economics and in land economy at the University of Cambridge, England, he has effectively extended these disciplines to examine his two expertise areas. Apart from being well versed in econometrics, his quantitative interests include real estate demand and supply, investment and finance, artificial intelligent modeling in real estate and system dynamics modeling for real estate market analysis and public policy analysis. He is the Member of the Royal Economics Society (U.K.), Academic Member of the National Council of Real Estate Investment Fiduciaries (U.S.), Fellow of the American Real Estate Society (U.S.), member of the American Economic Association (U.S.) and member of the Economic Society of Singapore and the Singapore Institute of Management. He holds the degrees of Master of Philosophy (1st Class Honors with Distinction), Honorary Doctor of Letters and the Doctor of Philosophy from the University of Cambridge, U.K. He has published widely in top international journals and conferences, in chapters of international academic book publishers. Dr Ho has written 11 major books (including this book), undertaken many consultancies and funded research projects. He has written a total of about 275 published works (with 91 in peer reviewed, reputable international journals). He is an editorial board member of the Journal of Economics & Public Finance, Real Estate Economics journal, Journal of Property Research, Journal of Property Investment & Finance, Journal of Real Estate Finance & Economics, the Property Management journal and the International Journal of Strategic Property Management. He has published widely in conferences, Finance, chapters of international academic book publishers, undertaken many consultancies and funded research projects. He is an immediate past Governor of the St Gabriel’s Foundation that oversees nine schools in Singapore; and a District Judge equivalent member of the Valuation Review Board, Ministry of Finance, Singapore, and the Singapore Courts.

INTRODUCTION

Risk Premium & Management

- An Asian Direct Real Estate Perspective

Chapter 1 ascertains the presence of appraisal smoothing. By adopting the Geltner and Miller (2007) 1st and 4th order autoregressive model to de-smooth the direct real estate TRs (total returns), a more robust set of direct real estate total returns can be obtained. The Chapter adopts the multi-factor APT (arbitrage pricing theory) model to examine the correlation of legal origins to an Asian city’s direct real estate TRs. Various sensitivities of the direct real estate TRs, i.e. the betas or the risk factor loadings, are estimated with pooled-panel data via multiple regression analysis, resolved by ordinary least-square, and from which the associated risk factor loadings are determined. The 2 main legal origins, i.e. the British legal origin and the French legal origin, are the dummy variables i.e. ‘the dummies’ in the multi-factor APT model. The coefficients are then estimated and analysed to examine the extent of the correlation.

Chapter 2 adopts a pooled-panel multi-factor least squares model to explore the provenance of international direct real estate (DRE) risk premiums for 16 cities from North and South Asia, and the US. The main objective is to ascertain the international DRE risk premiums for the sampled markets and the contributions made by macroeconomic and country-specific institutional variables to the DRE risk premiums. A secondary objective is to ascertain the effects of smoothed and de-smoothed appraised values on international DRE risk premiums. The historical DRE data deployed for our study, which spans from 2003Q1 to 2009Q2 are released free of charge by JLL REIS-Asia only for research purposes. The associated NCREIF data are obtained for the same period. We find that changes in macroeconomic and in DRE variables explain office and retail returns more than residential returns.

Chapter 3 reiterates that the real world of international DRE investing is not short on surprises. What we on count as an effective and a sustainable viable concern on international DRE investing, is our and the investing organization’s willingness and preparedness to effectively manage risk or uncertainty. We cannot know the exact consequences and the timing of the identifiable risks nor how they affect us but we can minimize, contain, avoid and seek new opportunities, early enough in pursuit of high risk-adjusted total returns for our DRE assets. Therefore, it is imperative to develop an in-depth appreciation of and to commit adequate resources to execute the risk management cycle (RMC).

Chapter 4 recommends a model of the intuitive build-up approach in the formation of the(direct real estate (DRE) investment hurdle rates for new DRE investing. The DRE market premium of about 400 bps (basis points) is meant to reflect the systematic risk premium as estimated by Henderson Global Investors in their ‘Global Property Market Risk Premium’. The estimate is still acceptable. The building-up approach of DRE risk premiums serves only as a rough guide to ensure that ultimately the DRE hurdle rate is adequately stringent and high enough, to achieve the risk-adjusted and Sharpe-optimal portfolio return.

The DRE hurdle rate is the required rate of return within the model framework of a discounted cash flow analysis, above which an investment makes sense and below which it does not. Often, this is based on the DRE investing firm’s cost of capital or weighted average cost of capital, plus or minus a risk premium, to reflect the DRE asset investment’s specific risk characteristics. It is also called the DRE required rate of return or the minimum amount of return that DRE investing firms require before they make a DRE asset investment. It is the minimum return to DRE investing firms to be achieved before a carry is permitted.

Chapter 5 affirms in general the existence of appraisal smoothing for international direct real estate (DRE), via adopting the Geltner and Miller (2007) first and fourth order autoregressive model, to de-smooth the DRE total returns. The good fit of this autoregressive model with high adjusted R² values is duly noted. The set of total-weighted evaluation percentages by city and country, under the 3-factor AHP (analytic hierarchy process) SAA (strategic asset allocation) model, offers a consistently derived strategic asset allocation, which represents what an investor desires to achieve over a longer-term investment horizon. In other words, the long-term normal portfolio composition of say a Pan Asia Office portfolio is recommended by the AHP SAA model, on the basis of expert judgment for the thirteen Asian cities. The AHP SAA model portfolio is also geographically diversified.

As an appropriate interface, the AHP SAA model in effect identifies the thirteen Asia cities’ DRE markets and the proportions for these markets that comprise the long-term, desired normal Pan Asia DRE portfolio mix. The subsequent MPT QP TAA model is conducted around the Pan Asia AHP SAA model portfolio via imposing appropriate tactical bands.The Pan Asia growth investment strategy is recommended in Chapter 5, with a portfolio composition that is similar to the Sharpe-ratio-optimal tactical asset allocation (TAA) portfolio.

Chapter 6 shows that from the performance review and forecasts of the 4 prime Asia residential sectors, the Shanghai (SH) prime residential sector is much more attractive than other 3 sectors. Its next 5-year mean total return (TR) is nearly 50% more than the total return of the other 3 prime Asia residential sectors. Comparing the 2 prime residential sectors of SH and BJ (Beijing), it is observed that the SH prime residential sector has a higher TR even though the volatility is lower than that of BJ. Considering market competitiveness, we may well infer that the potential risk associated with the former 5-year TR forecasts of the SH prime residential sector is under estimated. Utilizing DRE data raises the issue of how to deal with the valuation smoothing error problem. While studies are divided as to whether or not the smoothing error exists and whether or not it is appropriately corrected, DRE data de-smoothing is of more concern only when comparing DRE with market-based equity and bond TRs. The Markowitz MPT constrained optimization model is based on the assumption that diversification reduces portfolio risk and that the price of an asset is determined by its contribution to a DRE diversified portfolio.

The DRE value at risk (VaR) of Chapter 6 merely summarizes the maximum potential loss of the 4-sector prime Asia residential portfolio at the high 99 percent confidence level, which is US$6.648 million per year. The incremental DRE VaRs informs us of the amount of risk contributed by each Asia prime residential sector. The SH prime residential sector is the biggest risk contributor with the incremental DRE VaR contribution amount of US$8.404 million. Such a contribution is due to the biggest capital amount invested in SH prime residential sector’s inherent high risk. Although the BJ and BK prime residential sectors exhibit additional risks, the final DRE VaR is reduced to US$6.648 million via risk diversification by investing in the KL prime residential sector. RAROC offers limits to guide the level of DRE investment activity by reviewing the decision to walk away or continue investing in the 4 prime Asia residential sectors, to achieve risk-adjusted TRs that are consistent with the optimal asset allocation. RAROC allows for meaningful comparisons among the 4 prime Asia residential sectors. RAROC analysis discovers that the Shanghai (SH) prime residential sector is most profitable at the highest expected RAROC of 87.0%