The Economics of the Modernisation of Direct Real Estate and the National Estate - a Singapore Perspective

By Kim Hin David HO

The Economics of The Modernisation of Direct Real Estate and The National Estate - A Singapore Perspective

Chapter 1 takes a close look the vector auto regression (VAR) model, offering a dynamic system of solely direct real estate variables, for international direct real estate investors and policy makers, to enable their decision-making.

Chapter 2 examines the association of residential price and aggregate consumption. A cross-spectra analysis is helps to so validate, because of its model-free characteristics

Chapter 3 is concerned with the underlying housing market dynamics and housing price time-series variation, via the Singapore (SG) generalized dynamic factor model (GDFM).

Chapter 4 is concerned with the in-depth market analysis and empirical analysis of the structural behavior of the important SG private housing sector.

Chapter 5 acknowledges that an in-depth sector analysis and an empirical analysis are imperative to better understand the structural behavior of the SG office sector.

Chapter 6 is concerned with the Main Upgrading Programme (MUP), a highly targeted subsidized Housing Development Board (HDB) policy, since the 1990s.

Chapter 7 recognizes the ‘National Estate’, denoting SG’s built environment, due to physical planning, integrated urban design, and the direct influence of the SG government in providing physical infrastructure via government ministries, statutory boards and public authorities.
Chapter 8 offers the book’s conclusion.

• Language: English
• Publisher: Partridge Publishing Singapore
• Release date: May 11, 2022
• ISBN: 9781543769739

Kim Hin David HO

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.

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Contents

Foreword

Acknowledgements

About the Author

The Introduction

Chapter 1     The Direct Real Estate Sector Cyclical Dynamics For The Kuala Lumpur, Singapore And Hong Kong Prime Office Sectors

Chapter 2     The Cross-Spectral Density Model of The Cyclical Association of Housing Price and Consumption

Chapter 3     A Dynamic Factor Model (DFM) Of Private housing sector Dynamics – The Singapore Context

Chapter 4     The Singapore (SG) Housing Sector

Chapter 5     The Singapore (SG) Prime Office Sector

Chapter 6     The Singapore Public Housing Asset Value Enhancement – The Main Upgrading Policy (MUP)

Chapter 7     The Singapore (SG) National Estate

Chapter 8     The Conclusion

Foreword

Over 100 years ago, this (Singapore) 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 following chapters, briefly outlined below.

Chapter 1 takes a close look the vector auto regression (VAR) model, offering a dynamic system of solely direct real estate variables, for international direct real estate investors and policy makers, to enable their decision-making.

Chapter 2 examines the association of residential price and aggregate consumption. A cross-spectra analysis is helps to so validate, because of its model-free characteristics

Chapter 3 is concerned with the underlying housing market dynamics and housing price time-series variation, via the Singapore (SG) generalized dynamic factor model (GDFM).

Chapter 4 is concerned with the in-depth market analysis and empirical analysis of the structural behavior of the important SG private housing sector.

Chapter 5 acknowledges that an in-depth sector analysis and an empirical analysis are imperative to better understand the structural behavior of the SG office sector.

Chapter 6 is concerned with the Main Upgrading Programme (MUP), a highly targeted subsidized Housing Development Board (HDB) policy, since the 1990s.

Chapter 7 recognizes the ‘National Estate’, denoting SG’s built environment, due to physical planning, integrated urban design, and the direct influence of the SG government in providing physical infrastructure via government ministries, statutory boards and public authorities. Chapter 8 offers the book’s conclusion.

Happy reading.

Yours sincerely,

HO, Kim Hin / David (Dr) (Professor)

Singapore

May 2022.

Acknowledgements

The Author wishes to extend his most sincere appreciation to Mr KEE Teck Koon. His support is crucial for this book to come into fruition. He is a Singaporean with diverse exposures and experiences in the public, semi-public, social enterprise and also the private sector. He started his career as a military officer in the Ministry of Defence. He retired from full-time corporate life as Chief Investment Officer from CapitaLand Group. He had helmed various entities within the Group, including as CEO of the Ascott, Chairman of CapitaLand Malaysia Mall Trust Management, and Chairman of CapitaLand Commercial Trust Management. He had also served as Chairman of Changi Airports International Pte Ltd., Chairman for Lien Aid Ltd., and he is also the Chairman of Alexandra Health Endowment Fund. He is currently a Board member of CapitaLand Investment, Mandai Park Holdings, Changi Airport Group, NTUC Enterprise, NTUC Income and NTUC FairPrice Group. Mr. Kee received a graduate degree and an undergraduate degree from the prestigious University of Oxford, England, UK."

Next, the Author wishes to extend his very sincere appreciation to the School of Design & Environment, under the then 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 studies of large and complex physical infrastructural provision.

About the Author

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HO, Kim Hin / David

PhD (Development Economics) (University of Cambridge), MPhil (1st Cl Hons and a Star for Distinction) (Development Studies & Land Economy) (University of Cambridge); Honorary Professor (Development Economics & Land Economy) (University of Hertfordshire); Honorary Doctorate of Letters (International Biographical Centre) (Cambridge); Systems Engineering (US Naval Postgraduate School), MRES (UK), AM NCREIF (US), FARES (US), MAEA (US), MESS, MSIM. Retired Professor (Associate) (Tenured) (International Real Estate) (Department of Real Estate) (School of Design and Environment) (National University of Singapore). Home Address: Block 220 Ang Mo Kio Avenue 1 #02-807, Singapore 560220; email address: [e-mail].

Professor HO Kim Hin / David spent 31 years across several sectors, including the military, oil refining, aerospace engineering, public housing, resettlement, land acquisition, reclamation and international real estate investing. 6 years were in Pidemco Land Ltd (now CapitaLand Ltd) and GIC Real Estate Pte Ltd. 17 years were in the NUS School of Design and Environment at the Department of Real Estate. He holds the Master of Philosophy (First Class Honours with Distinction), Doctor of Philosophy from the University of Cambridge; and the Honorary Professor from the University of Hertfordshire. He has published widely in 275 articles (inclusive of 91 articles in top peer reviewed, international journals; pertaining to real estate investment, real estate development, urban policy, consultancies, public cum private funded research projects and so also published 15 major books. He was governor of the St Gabriel’s Foundation and member (District Judge equivalent) of the Valuation Review Board under the Singapore Ministry of Finance and the Singapore Courts.

The Introduction

The Economics of The Modernisation

of Direct Real Estate and The National

Estate - A Singapore Perspective

Chapter 1 is concerned with a broad VAR model, which offers a holistic and dynamic model estimation of direct real estate variables, to better enable international direct real estate investors and policy makers, in their decision-making:

• The direct real estate market cycle is different from and partially independent of the underlying business cycle of the domestic economy.

• The direct real estate market cycle is more exaggerated in the construction and development industry in rents and vacancy.

• The vacancy cycle tends to lead the rental cycle.

• New construction completions tend to peak when vacancy peaks.

It is clear from Chapter 1 that the Singapore and Hong Kong prime office rents are more volatile than the Kuala Lumpur prime office rents. The Singapore and Hong Kong office sectors are more prone to cyclical fluctuations with prime office rents and vacancies negatively correlated; and that they are statistically significant for their lagged relationships with rents and with other variables. Similar results pertain to the relationships between prime office capital values and office initial yields. Overall, the VAR model offers an insightful set of practical and empirical models for analysing the Kuala Lumpur, Singapore and Hong Kong prime office sectors. The sectors’ price discovery process highlights the sector fundamentals, their short- and long-run explanatory factors, and the cyclical behaviour of their inefficient prime office sectors. Results are robust as evidenced by test statistics including the coefficients of determination, the Akaike and Schwarz information criterion, the diagnostic inverse roots of the characteristic polynomial, and the impulse response function.

Chapter 2 accordingly looks at the macro-level aspect of cyclical aggregate consumption on housing price, which in turn is affected by the income, substitution, and expectation effects. A frequency domain-based model, the cross-spectra analysis is appropriate because of its model-free characteristics. Such a model avoids model misspecification and parameter-estimation errors. Singapore’s housing price is significantly affected by cyclical aggregate consumption, depending on the time scale and frequency but without a consistent sign. The expectation effect, operating via the capital-value (CV) gain effect, explains the housing-price-consumption relationship, and that the CV gain effect contributes more during the expansion than the recession periods.

Chapter 3 correspondingly focusses on the micro-structure level of housing sector dynamics with housing-price time-series variation, owing to underlying common factors. An understanding of such housing sector dynamics, based on general equilibrium theory, relates housing asset and housing sector illiquidity. Therefore, the generalized dynamic factor model (GDFM) is appropriate for validation. Utilizing a sufficiently large dataset, the GDFM captures unobservable factors like expectation, uncertainty, and transaction cost to estimate housing sector dynamics with housing-price time-series variation. Historically, Singapore affirms two common factors underlying housing sector dynamics as early as 1988 to 2007. Housing-price time-series and related financial conditions are found to have a high degree of commonality. The explanation power of housing-price time-series variation is higher when housing prices experience high volatility.

Chapter 4 is then concerned with a more in-depth market (sector) and empirical analyses of Singapore (SG)’s private housing sector structural behavior. The SG housing sector is a key sector of the domestic direct real estate market. An underlying motive is to forecast housing rents and capital values (CVs) to enable better decision making by private individual investors, households, and institutional investors for private housing accommodation. The direct and private housing sectors are subject to persistent sector disequilibria, which may well be responsible for wide deviations of prevailing rents, CVs and yields from their long run and steady state equilibrium.

It is widely acknowledged that many SG households aspire to own private housing assets. Such a natural aspiration is attributable to households’ growing and sustainable affluence. It is therefore essential to look at persistent private housing overhang (supply) to minimize or to contain it. An enhanced understanding of the oversupply problem may well help the Urban Redevelopment Authority (URA) to so fine-tune its private sale of sites provision. Despite widely publicized government policies, private housing buyers are uncertain about public policy impacts and are inclined to follow the herd instinct. Ideally, the price discovery process via CVs or transacted prices of the SG private housing sector should conform to an informationally efficient price process. The resulting informational inefficiency is attributable to the private housing sector’s illiquidity, sector timing and other institutional factors. To address such above concerns, it is hoped that with the examination of robust models for price and new construction, households and investors benefit from an enhanced discovery of the private housing price behaviour and the process generating function of new private housing construction. Households and investors are enabled to better evaluate their housing investment choices. Chapter 4 has several study objectives in mind:

• To model the structural behavior and dynamics of the price and non-price causal factors for Singapore (SG)’s private housing sector, via a structural expectation-augmented, physical stock-flow adjustment model, after correcting for serial correlation error.

• To offer post-model estimations in respect of the required forecast simulations for SG’s private housing sector.

• To model Singapore (SG)’s private housing land price inflation in terms of a unique synthetic housing land price index.

Chapter 5 is correspondingly concerned with the in-depth sector and empirical analyses to better understand SG’s prime office sector structural behavior. The SG office sector is also a key sector of the domestic direct real estate sectors for two primary reasons. First, it offers the accommodation for private firms and businesses to conduct their activities. Secondly, the office sector remains a key asset class owing to it being preferred by institutional investors and high net worth individuals. The underlying motive is to forecast prime office rents and capital values (CVs) for investors. While GDP expansion typically sustains employment for around 70% of Singapore’s workforce, the output of the financial, business, real estate (direct and indirect) and the other services sector, is just as important. The SG prime office sector’s output constitutes close to 40% of total GDP as early as year 2001 in nominal terms. Because prime office accommodation is a derived demand, the performance of the service-related sector affects SG’s prime office sector demand, via the filtering effect of multi-rounds economic demand expansion. However, there are limited empirical studies to rigorously model the structural relationships of the SG prime office sector.

The SG prime office sector is also subject to persistent disequilibria, which may well be responsible for wide deviations, from the long run and steady state equilibrium prime office rents, CVs, and yields. Such wide deviations stem from demand or supply shocks, which tend to lead the SG prime office sector towards ever-changing steady-state rents, and from slow office sector adjustments toward the steady state. Such slow sector adjustments are owing to factors like long term leases that prevent tenants from swiftly adjusting their consumption to desired levels, and to landlords from speedily adjusting rents to contemporaneously reflect the unexpected changes in the office sector realities. Other factors include the delays inherent in the microeconomic process of tenant and landlord search; the slow supply responses owing to long construction lags; the delayed entry by rational investors in exercising their option to wait until the anticipated benefits outweigh the costs; and the phasing of the direct real estate development project owing to rising supply schedules or internal adjustment costs. Such foregoing considerations highlight the SG prime office sector’s inherent problems of sector inefficiency and serial correlation. Chapter 5 examines the modeling process generating functions to estimate:

■ Prime office rents and CVs via a structural expectation-augmented, physical stock-flow adjustment model, after correcting for serial correlation error; and the

■ Prime office capitalization (cap) rates via a structural model, incorporating an appropriate auto regressive (AR) model, which has similarities with an error correction model (ECM).

Consequently, the SG prime office rents, cap rates and CVs can be rigorously ascertained and forecasted. The results are meaningful and useful to office investors, domestic and foreign. The desired outcome is to substantially enhance decision making in commercial direct real estate investing.

Chapter 6 returns to SG’ mass housing sector with respect to the Main Upgrading Program (MUP), a bold and major policy implemented by SG’s Housing Development Board (HDB) since the 1990s. such a heavily subsidized policy is highly targeted to enhance the social and economic value of SG’s public housing. It benefits HDB households residing in older HDB housing estates in terms of a much-enhanced asset value of the household’s HDB flat, and a quality public housing living environment. A HDB flat owner whose precinct is selected for upgrading under the MUP policy, is envisaged to be holding a call option to upgrade his HDB flat, because this option to upgrade is valuable, and that the option has an opportunity to command a higher resale price in the HDB resale market. Chapter 6 estimates the option premiums for upgrading by adopting for the MUP policy, the explicit numerical-method solution of the binomial real option pricing model, and the Samuelson-McKean closed-form solution. The embedded real option values under the MUP policy are estimated at S$10,300 and S$2,000 for the popular 3-Room HDB flat and 4-Room HDB flat respectively. Government subsidies have a significant impact on the option values.

Chapter 7 is broadly concerned with the nationwide ‘National Estate’, which is defined by the British architect Clough Williams Ellis, and that this definition reaches Australia in the 1970s. The national estate is incorporated into the Australian Heritage Commission Act, and is used to describe a collection of buildings and sites, which are worthy of preservation for several reasons. The national estate covers natural environments, European history and Aboriginal culture. Chapter 7 looks more closely at the unique but achievable case of Singapore (SG)’s concern for its physical environment, via adopting an innovation-managing approach, to enhance the practice of effective environmental management. SG’s built environment is treated as the estate of the nation i.e. the national estate, largely because of the indirect influence of government in the physical planning regime with an emphasis on carefully integrated urban design, and of the direct influence of government in the provision of physical infrastructure, via government ministries and agencies like the statutory boards and the public authorities. These government ministries and agencies facilitate a synergistic response by the local SG community comprising policy makers, businesses, industries, citizens and residents, in making environmental management a way of life in SG, a small sized island-state nation of Southeast Asia.

Chapter 8 offers the book’s ‘Conclusion’.

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Chapter 1

The Direct Real Estate Sector

Cyclical Dynamics For The Kuala

Lumpur, Singapore And Hong

Kong Prime Office Sectors

At the broadest level, it is discernible that Malaysia’s Kuala Lumpur, Singapore and Hong Kong prime office sectors are cyclically connected. The fundamental causation is that their underlying economies are in the same sustainable, mature state like for Singapore and Hong Kong, or maturing state like for Kuala Lumpur. It is widely acknowledged that the office sector is cyclical. Such office cyclicality is typically a function of several co-mingled factors, interacting to engender long and short run office or direct real estate sector cycles. No structural changes are anticipated for at least the mid-term horizon. Their prime office capital values (CVs) and rents sustain moderately robust growth over time. It is implicit that the cycles and the sector performances (i.e. CVs, rents and, cap rates) are the signals, which well-informed and insightful direct real estate analysts and investors can utilise to their advantage. In other words, the office and direct real estate sector’ cyclicality tend to be conducive for sector players like local and international investors, depending on their understanding of the sector cycles. Given that the investors are concerned with office sector performances, it is essential for investors to have a minimum appreciation of causal factors that affect CVs (the price that the office and direct real sectors’ investors should pay), for a unit of office rent. The underlying price discovery of office rents and CVs is sourced from the asset sector micro-structure literature.

Price discovery is the process by which the opinions of sector participants on the value of a direct real estate asset are synthesized into a single statistic – sector price. In the direct real estate sector context, Gong et al. (2019) and Garza et al. (2019) denote price discovery to be that process by which asset sector prices are formed through the discovery and incorporation of relevant information on asset values, by sector participants. Where two sectors have a common component of value, the relevant price information is discovered first in one sector and then transmitted to the second sector (Lizieri et al., 2018; Ho, 2007; Geltner, MacGregor and Schwann, 2003). The process is enhanced by liquidity and information efficiency of the direct real estate sector. Illiquidity fosters the widespread reliance on appraisals for direct real estate values, which results in the problem of appraisal smoothing (appraisal lag).

Chapter 1 focusses on price discovery of the Asian office sector, where heterogeneous assets are traded in dispersed local sectors, and transactions (especially prices) are shrouded in confidentiality. Such conditions improve information asymmetry to inhibit the process of price discovery in the Asian office sector. Given the peculiar nature of the direct real estate asset and the sector in which it is traded, the demand and supply for direct real estate space can be moving towards, or away from, equilibrium at any moment owing to long lead times for space construction, as shown in eqs (1) to (3):

Demand (QD) is determined by the rent (Rt) of direct real estate space and the underlying need for space (for e.g. office employment). The supply of direct real estate space (Qs) is influenced by the previous period’s rent for space (Rt-1) and the relative cost of producing it. Similarly, rent is related to the previous period’s rent (Rt-1) and the vacancy rate (VR).

Of scholarly interest is the capability of statistical, stochastic or empirical functions to generate and explain the process of the CVs and rental formation. Such process-generating functions constitute the quantitative aspect of the direct real estate price discovery process, where the information efficiency is analysed in detail. CVs and rents can be well-formed through the discovery and incorporation of relevant information, including the underlying direct real estate demand and supply, the macro- and micro- economic factors if available, by the sector participants. Overall, direct real estate sectors differ in information efficiency. Therefore, there are varying temporal lead-lag relationships in the CV, rent and fundamental sector factors for direct real estate sectors of the various cities in the Asia region.

The existence of autocorrelation for direct real estate as opposed to indirect real estate suggests that the former does not quickly respond to new information. Guirguis and Vogel (2006) and Lizieri (2013) draw attention to the importance of considering asymmetry in direct real estate prices to avoid model misspecification. Such prices exhibit some price rigidity, reacting more readily to positively lagged changes than to the negative lagged changes in prices. Consequently, the inherent sector cyclicality of the direct real estate sector should reflect the adaptive behaviour of sector participants i.e. the real estate developers, landlords and tenants. We attempt to address these concerns through a formal modelling of a system of articulated supply, demand and construction models to throw more light on the direct real estate sector cycle dynamics of Hong Kong, Kuala Lumpur and Singapore. The theoretical model for this purpose is envisaged to be a complete dynamic model system of the direct real estate space sector, comprising a unique system of six linked equations that denote the relationship among supply, demand, construction, vacancy and rent over time, as well as price response slopes and lags (Lizieri et al., 2018; Geltner and Miller, 2001). The complete dynamic model system of solely direct real estate variables will accordingly highlight the following key features:

• The direct real estate sector cycle could be different from, and partially independent of, the underlying business cycle in the local or domestic economy. Such a key feature is consistent with the study by Leung and Chen (2006) that concludes that direct real estate cycles can be intrinsic. Their dynamic general equilibrium model demonstrates that the price of commercial real estate, denoted as land in the model, can display cycles even with constant fundamentals.

• The direct real estate sector cycle may be more exaggerated in the construction and direct real estate development industry than rents and vacancy.

• The vacancy cycle tends to lead the rental cycle slightly.

• New construction completions tend to peak when vacancy peaks.

The model can be specified and estimated through econometric techniques like the vector auto regression (VAR) model. Other techniques that could be utilised to specify the model may include the well-known two-stage least-square model (2SLS) and seemingly unrelated regression (SUR) model, the dynamic factor model (DFM) by Forni et al. (2000 and 2001) and the spectral density model for the cyclical association of real estate price and consumption by Sun et al. (2007). Another pertinent specification is Lettau and Ludvigson’s (2004) vector error correction model (VECM) which utilizes US data in a permanent transitory variance decomposition framework to disaggregate the trend and cyclical effects that consumption has on asset values. We use the VAR model specification as it gives insights into the effect of the lagged values of all the variables in the model (Ho and Cuervo, 1999; Ho, 2005 and 2007). Such formal modelling should also enable the explicit and rigorous quantitative forecasts of say rents and CVs when the rest of the variables are forecasted beforehand.

Theoretical Considerations

Studies throughout the 2000s by Lizieri and Mekic (2018), Hendershott et al. (2010) and Sivitanidou (2002) conclude that the office sector is affected by wide deviations of prevailing rents (and other price variables) from the implicit long-run equilibrium rent. Such wide deviations subject the office sector to persistent disequilibria in the short run. Thus, it is reasonable to expect that ‘bad’ and ‘good’ equilibria would prevail in the office as well as the broad direct real estate, sector. In the short to medium-run, excess demand/supply in a ‘bad’ office sector equilibrium slowly adjust towards ‘good’ equilibrium in the long-run. The underlying dynamics of this slow adjustment process can be primarily attributed to several price and non-financial structural relationships in the sector. These relationships may include direct real estate specific factors, macroeconomic factors and the self-adjusting error corrections at work in the short run.

As early as the 1990s, DiPasquale and Wheaton (1992, 1996) seminally conceptualize a 4-quadrant representation of the two important linkages between two sectors that are in long-run equilibrium to propose a dynamic model based on stock-flow theory. In the short run, tenants’ space needs (demand) as well as the types, quantity and quality of available stock of space (supply) interact to determine the rents for real estate in the space sector. The price for space as an asset (which invariably is the sum of the discounted values of all anticipated rentals) relative to the cost of replacing or constructing space is a major determinant of the annual flow of new stock to the direct real estate sector in the long run. According to their model, sector prices should equate replacement costs in the long run where competitive equilibrium prevails in the space sector.

In the long run, adjustments to stock occur slowly over time in response to short-term prices, given the relatively long construction period. While the model is simple in terms of its variables, it poses problems when an attempt is made to link the short and long run effects as the model does not account for the intermediate stages of the sector’s movements towards its new equilibrium. Thus, a dynamic system which depicts the intermediate adjustments of the sector is required to address the limitation of the DisPaquale and Wheaton’s model.

The first two equations in a complete dynamic model system for the real estate space sector should denote the supply side of the sector. Eq (4) models construction completions and rents prevailing in the real estate space sector at the commencement of construction projects.

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Ct is the amount of new space completed in period t. R(t-L) is the rent prevailing in the sector in t-L (L being the number of lags) while K is the trigger rent which is defined as the replacement cost rent above which new construction will be started. Furthermore eq (4) assumes that there is no retirement of property, i.e. demolition rate is zero.

As buildings take time to construct, there is always a time lag between when construction decision is made and when new supply (building completions) reaches the sector. For example, it takes about two years from the date approval is received from the competent Authorities for an office development to be completed in Singapore. Therefore, data series for total completions can be adjusted two years in advance to estimate the subsequent correlation with expected returns. The office space start rate can then be expressed as in eq (5).

The completion factor is assumed to be typically growing at say 5% per quarter during a steady economic state for a matured office sector such as Singapore. Demolition is assumed to be ‘zero’ (rather than growing much more slowly at say 2.5% per quarter) as all new developments arise from the redevelopment of ‘green field’ and ‘brown field’ sites from government land sales and private en bloc land sales. This is especially true of Singapore and Hong Kong where