ANALYSING THE INFLUENCE THAT MACROECONOMIC 

FACTORS HAVE ON THE RETURNS OF SOUTH AFRICAN 

REAL ESTATE INVESTMENT TRUSTS 

 

Research Report 
 

by 
 

JANE TAYLOR 
 

 

 

 

 

 

 

 

 

 

Submitted in partial fulfilment of the requirements for the degree 
 

Master of Commerce in  

Finance 
 

in the 

School of Economics and Finance 

at the 

University of the Witwatersrand, Johannesburg 

 

Supervisor: 

Dr Daniel Page 

 

February 2022  



 

DECLARATION 

 

I, Jane Taylor, declare that this research report is my own unaided work. It is submitted 

in partial fulfilment of the requirements for the degree of Master of Commerce in 

Finance at the University of the Witwatersrand, Johannesburg. It has not been 

submitted before for any degree or examination at this or any other university. 

 

  28 February 2022 

Signature  Date 

 



 

ACKNOWLEDGEMENTS 

 

Thank you to Professor Christo Auret and the staff from the School of Economics and 

Finance for sharing your knowledge and wisdom with me throughout this Master of 

Commerce in Finance programme. I would also like to extend my gratitude to my 

supervisor, Dr Daniel Page, for overseeing my research report. Lastly, I would like to 

express a special word of appreciation to Eileen Andrews from MSCI and Stuart Mills 

from Growthpoint Properties for granting me access to the MSCI Real Estate Analytics 

Portal. 



 
 

ABSTRACT 

 

Real estate investment trusts (REITs) have become a popular investment vehicle for 

investors seeking to gain exposure to the real estate market. The South African REIT 

regime came into effect on 1 May 2013 and since then, the South African REIT market 

has been characterised by notable return volatility. This raises the question as to 

whether changes in key macroeconomic factors influence South African REIT returns, 

and if so, to what extent do changes in macroeconomic factors have on the returns of 

South African REITs. Notably, limited empirical research has been conducted to 

analyse the impact that macroeconomic factors have on South African REIT returns. 

As such, the aim of this study is to analyse the influence that economic growth, 

inflation, interest rates, and the stock market have on the returns of South African 

REITs. Particularly, the overall South African REIT market as well as the prominent 

REIT property subtypes in South Africa including the retail, office, and industrial 

sectors are investigated.  

 

To estimate and evaluate the relationships between the stated macroeconomic factors 

and South African REIT returns, vector autoregression models and vector error 

correction models are employed. The results reveal that South African REIT returns 

are significantly positively associated with economic growth, inflation, and stock 

market returns whereas they are significantly negatively related to interest rates. 

However, changes in these macroeconomic factors only explain a small percentage 

of the variability in REIT returns. Importantly, the findings of this study are consistent 

with what has been observed in other global REIT markets except for inflation in which 

a negative association has generally been reported. The results of this study 

strengthens the field of REIT research by adding to the existing body of knowledge of 

what is currently known about South African REITs. From a practical standpoint, this 

additional insight may assist REIT asset managers, real estate collective investment 

scheme fund managers, and investors with portfolio construction and risk 

management decisions. 

 

 

 



 

 

i 

TABLE OF CONTENTS 

 

LIST OF TABLES .......................................................................................................... V 

LIST OF FIGURES ....................................................................................................... IX 

DEFINITION OF KEY TERMS ..................................................................................... XI 

ABBREVIATIONS ....................................................................................................... XIII 

CHAPTER 1 – INTRODUCTION .................................................................................. 1 

1.1 BACKGROUND .............................................................................................. 1 

1.2 PROBLEM STATEMENT ............................................................................... 3 

1.3 PURPOSE STATEMENT ............................................................................... 4 

1.4 RESEARCH OBJECTIVES ............................................................................ 4 

1.5 IMPORTANCE AND BENEFITS OF THE STUDY ........................................ 4 

1.6 DELIMITATIONS ............................................................................................ 5 

1.7 STRUCTURE OF THE STUDY ...................................................................... 5 

CHAPTER 2 – LITERATURE REVIEW ........................................................................ 6 

2.1 INTRODUCTION ............................................................................................ 6 

2.2 REAL ESTATE INVESTMENT TRUSTS ....................................................... 6 

2.2.1 What is a real estate investment trust? .................................................. 6 

2.2.2 History of REITs ...................................................................................... 7 

2.2.3 Types of REITs ........................................................................................ 7 

2.2.4 REIT property subtypes .......................................................................... 8 

2.2.5 REITs in a mixed-asset portfolio ........................................................... 11 

2.2.6 Advantages of investing in REITs ......................................................... 16 

2.2.7 Disadvantages of investing in REITs .................................................... 18 

2.2.8 Global REIT Market ............................................................................... 19 

2.2.9 South African REIT market ................................................................... 23 

2.2.10 Asset pricing theory and macroeconomic factors impacting             

REIT returns ......................................................................................................... 24 

2.3 ECONOMIC GROWTH ................................................................................ 27 



 

 

ii 

2.3.1 Theoretical relationship between economic growth and REIT returns 27 

2.3.2 Empirical relationship between economic growth and REIT returns ... 27 

2.4 INFLATION ................................................................................................... 30 

2.4.1 Theoretical relationship between inflation and REIT returns ............... 31 

2.4.2 Empirical relationship between inflation and REIT returns .................. 31 

2.5 INTEREST RATES ....................................................................................... 34 

2.5.1 Theoretical relationship between interest rates and REIT returns....... 34 

2.5.2 Empirical relationship between interest rates and REIT returns .......... 35 

2.6 STOCK MARKET ......................................................................................... 38 

2.6.1 Theoretical relationship between the stock market and REIT returns . 39 

2.6.2 Empirical relationship between the stock market and REIT returns .... 39 

2.7 SUMMARY .................................................................................................... 41 

CHAPTER 3 – RESEARCH METHOD ....................................................................... 43 

3.1 INTRODUCTION .......................................................................................... 43 

3.2 VECTOR AUTOREGRESSION MODELS ................................................... 43 

3.2.1 What is a vector autoregression model? .............................................. 43 

3.2.2 Structural form representation of the VAR model ................................ 44 

3.2.3 Reduced form representation of the VAR model ................................. 45 

3.2.4 Identification using short-run restrictions .............................................. 46 

3.2.5 Model specification and model checking .............................................. 49 

3.2.6 Uses of VAR models ............................................................................. 53 

3.3 VECTOR ERROR CORRECTION MODELS .............................................. 58 

3.3.1 Cointegrated variables .......................................................................... 58 

3.3.2 Cointegration and error correction models ........................................... 58 

3.3.3 Johansen cointegration tests ................................................................ 59 

3.3.4 Cointegrating vector and loading matrix ............................................... 61 

3.4 DATA ............................................................................................................. 62 

3.4.1 Macroeconomic data ............................................................................. 62 

3.4.2 REIT return data .................................................................................... 63 

3.5 RESEARCH METHOD LIMITATIONS ......................................................... 64 



 

 

iii 

3.6 SUMMARY .................................................................................................... 65 

CHAPTER 4 – DATA ANALYSIS ................................................................................ 67 

4.1 INTRODUCTION .......................................................................................... 67 

4.2 UNIT ROOT TESTS ..................................................................................... 67 

4.3 VECTOR AUTOGRESSION MODELS ........................................................ 67 

4.3.1 REIT market........................................................................................... 68 

4.3.2 Retail property sector ............................................................................ 76 

4.3.3 Office property sector ............................................................................ 84 

4.3.4 Industrial property sector ....................................................................... 92 

4.4 VECTOR ERROR CORRECTION MODELS ............................................ 100 

4.4.1 REIT market......................................................................................... 101 

4.4.2 Retail property sector .......................................................................... 105 

4.4.3 Office property sector .......................................................................... 108 

4.4.4 Industrial property sector ..................................................................... 112 

4.5 SUMMARY .................................................................................................. 116 

CHAPTER 5 – DISCUSSION OF RESULTS ........................................................... 118 

5.1 INTRODUCTION ........................................................................................ 118 

5.2 ECONOMIC GROWTH .............................................................................. 118 

5.2.1 Vector autoregression models ............................................................ 118 

5.2.2 Vector error correction models............................................................ 120 

5.2.3 Comparison with prior literature .......................................................... 121 

5.3 INFLATION ................................................................................................. 122 

5.3.1 Vector autoregression models ............................................................ 123 

5.3.2 Vector error correction models............................................................ 124 

5.3.3 Comparison with prior literature .......................................................... 125 

5.4 INTEREST RATES ..................................................................................... 127 

5.4.1 Vector autoregression models ............................................................ 127 

5.4.2 Vector error correction models............................................................ 129 

5.4.3 Comparison with prior literature .......................................................... 129 

5.5 STOCK MARKET ....................................................................................... 131 



 

 

iv 

5.5.1 Vector autoregression models ............................................................ 131 

5.5.2 Vector error correction models............................................................ 133 

5.5.3 Comparison with prior literature .......................................................... 134 

5.6 SUMMARY .................................................................................................. 135 

CHAPTER 6 – CONCLUSION AND RECOMMENDATIONS .................................. 136 

6.1 INTRODUCTION ........................................................................................ 136 

6.2 SUMMARY OF FINDINGS ......................................................................... 136 

6.3 CONCLUSION ............................................................................................ 137 

6.4 RECOMMENDATIONS FOR FUTURE RESEARCH ................................ 139 

REFERENCES .......................................................................................................... 141 

APPENDIX A ............................................................................................................. 150 

APPENDIX B ............................................................................................................. 154 

APPENDIX C ............................................................................................................. 158 

APPENDIX D ............................................................................................................. 162 

APPENDIX E ............................................................................................................. 164 

APPENDIX F ............................................................................................................. 166 

APPENDIX G ............................................................................................................. 170 

APPENDIX H ............................................................................................................. 174 

 



 

 

v 

LIST OF TABLES 

 

Table 1: Global REITs by property subtype held .......................................................... 9 

Table 2: Global REIT market ....................................................................................... 19 

Table 3: Americas REIT market .................................................................................. 20 

Table 4: Asia-Pacific REIT market .............................................................................. 21 

Table 5: European REIT market ................................................................................. 21 

Table 6: Middle East and African REIT market .......................................................... 22 

Table 7: Granger causality test outcomes for a bivariate VAR (1) model ................. 57 

Table 8: Macroeconomic factors ................................................................................. 63 

Table 9: REIT return series ......................................................................................... 64 

Table 10: Lag order selection criteria for the REIT market ........................................ 68 

Table 11: Eigenvalues of the VAR(2) model for the REIT market ............................. 69 

Table 12: Diagnostic tests of the VAR(2) model for the REIT market ....................... 70 

Table 13: Forecast error variance decompositions of the VAR(2) model for the REIT 

market .......................................................................................................................... 74 

Table 14: Granger causality analysis of the VAR(2) model for the REIT market ...... 76 

Table 15: Lag order selection criteria for the retail property sector ........................... 77 

Table 16: Eigenvalues of the VAR(1) model for the retail property sector ................ 77 

Table 17: Diagnostic tests of the VAR(1) model for the retail property sector .......... 78 

Table 18: Forecast error variance decompositions of the VAR(1) model for the retail 

property sector ............................................................................................................. 82 

Table 19: Granger causality analysis of the VAR(1) model for the retail property sector

 ..................................................................................................................................... 83 

Table 20: Lag order selection criteria for the office property sector .......................... 84 

Table 21: Eigenvalues of the VAR(2) model for the office property sector ............... 85 

Table 22: Diagnostic tests of the VAR(2) model for the office property sector ......... 86 

Table 23: Forecast error variance decompositions of the VAR(2) model for the office 

property sector ............................................................................................................. 91 

Table 24: Granger causality analysis of the VAR(2) model for the office property sector

 ..................................................................................................................................... 91 

Table 25: Lag order selection criteria for the industrial property sector .................... 93 

Table 26: Eigenvalues of the VAR(2) model for the industrial property sector ......... 93 



 

 

vi 

Table 27: Diagnostic tests of the VAR(2) model for the industrial property sector ... 94 

Table 28: Forecast error variance decompositions of the VAR(2) model for the 

industrial property sector ............................................................................................. 99 

Table 29: Granger causality analysis of the VAR(2) model for the industrial property 

sector ......................................................................................................................... 100 

Table 30: Trace test for the REIT market ................................................................. 101 

Table 31: Maximum eigenvalue test for the REIT market ........................................ 101 

Table 32: Cointegrating vector for the REIT market ................................................. 102 

Table 33: Likelihood ratio tests for imposing restrictions on 𝛽 for the REIT market 103 

Table 34: Loading matrix for the REIT market.......................................................... 104 

Table 35: Likelihood ratio tests for imposing restrictions on 𝛼 for the REIT market 104 

Table 36: Trace test for the retail property sector .................................................... 105 

Table 37: Maximum eigenvalue test for the retail property sector ........................... 105 

Table 38: Cointegrating vector for the retail property sector .................................... 106 

Table 39: Likelihood ratio tests for imposing restrictions on 𝛽 for the retail property 

sector ......................................................................................................................... 107 

Table 40: Loading matrix for the retail property sector............................................. 108 

Table 41: Likelihood ratio tests for imposing restrictions on 𝛼 for the retail property 

sector ......................................................................................................................... 108 

Table 42: Trace test for the office property sector.................................................... 109 

Table 43: Maximum eigenvalue test for the office property sector .......................... 109 

Table 44: Cointegrating vector for the office property sector ................................... 110 

Table 45: Likelihood ratio tests for imposing restrictions on 𝛽 for the office property 

sector ......................................................................................................................... 111 

Table 46: Loading matrix for the office property sector ............................................ 112 

Table 47: Likelihood ratio tests for imposing restrictions on 𝛼 for the office property 

sector ......................................................................................................................... 112 

Table 48: Trace test for the industrial property sector .............................................. 113 

Table 49: Maximum eigenvalue test for the industrial property sector .................... 113 

Table 50: Cointegrating vector for the industrial property sector ............................. 114 

Table 51: Likelihood ratio tests for imposing restrictions on 𝛽 for the industrial property 

sector ......................................................................................................................... 115 

Table 52: Loading matrix for the industrial property sector ...................................... 116 



 

 

vii 

Table 53: Likelihood ratio tests for imposing restrictions on 𝛼 for the industrial property 

sector ......................................................................................................................... 116 

Table 54: Impulse response from a shock to economic growth ............................... 118 

Table 55: Forecast error variance decompositions of economic growth ................. 119 

Table 56: Long-run relationship between economic growth and REIT returns ....... 120 

Table 57: Impulse response from a shock to inflation .............................................. 123 

Table 58: Forecast error variance decompositions of inflation ................................ 124 

Table 59: Long-run relationship between inflation and REIT returns ...................... 124 

Table 60: Impulse response from a shock to interest rates ..................................... 127 

Table 61: Forecast error variance decompositions of interest rates ........................ 128 

Table 62: Long-run relationship between interest rates and REIT returns .............. 129 

Table 63: Impulse response from a shock to stock market returns ......................... 131 

Table 64: Forecast error variance decompositions of stock market returns............ 132 

Table 65: Long-run relationship between stock market returns and REIT returns .. 133 

Table 66: Descriptive statistics of the economic growth rate series ........................ 150 

Table 67: Unit root tests of the economic growth rate series ................................... 151 

Table 68: Descriptive statistics of the first difference of the economic growth rate series

 ................................................................................................................................... 152 

Table 69: Unit root test of the first difference of the economic growth rate series .. 153 

Table 70: Descriptive statistics of the inflation rate series ....................................... 154 

Table 71: Unit root tests of the inflation rate series .................................................. 155 

Table 72: Descriptive statistics of the first difference of the inflation rate series ..... 156 

Table 73: Unit root test of the first difference of the inflation rate series ................. 157 

Table 74: Descriptive statistics of the interest rate series ........................................ 158 

Table 75: Unit root tests of the interest rate series................................................... 159 

Table 76: Descriptive statistics of the first difference of the interest rates series.... 160 

Table 77: Unit root test of the first difference of the interest rate series .................. 161 

Table 78: Descriptive statistics of the stock market total return series .................... 162 

Table 79: Unit root tests of the stock market total return series .............................. 163 

Table 80: Descriptive statistics of the REIT market total return series .................... 164 

Table 81: Unit root tests of the REIT market total return series ............................... 165 

Table 82: Descriptive statistics of the retail property sector total return series ....... 166 

Table 83: Unit root tests of the retail property sector total return series .................. 167 



 

 

viii 

Table 84: Descriptive statistics of the first difference of the retail property sector total 

return series ............................................................................................................... 168 

Table 85: Unit root test of the first difference of the retail property sector total return 

series.......................................................................................................................... 169 

Table 86: Descriptive statistics of the office property sector total return series ...... 170 

Table 87: Unit root tests of the office property sector total return series ................. 171 

Table 88: Descriptive statistics of the first difference of the office property sector total 

return series ............................................................................................................... 172 

Table 89: Unit root test of the first difference of the office property sector total return 

series.......................................................................................................................... 173 

Table 90: Descriptive statistics of the industrial property sector total return series 174 

Table 91: Unit root tests of the industrial property sector total return series ........... 175 

Table 92: Descriptive statistics of the first difference of the industrial property sector 

total return series ....................................................................................................... 176 

Table 93: Unit root test of the first difference of the industrial property sector total return 

series.......................................................................................................................... 177 



 ix 

LIST OF FIGURES 

 

Figure 1: The efficient frontier ..................................................................................... 12 

Figure 2: The tangency portfolio ................................................................................. 13 

Figure 3: CUSUM tests of the individual equations of the VAR(2) model for the REIT 

market .......................................................................................................................... 69 

Figure 4: Orthogonal impulse response of the REIT market total return from a shock 

to economic growth ..................................................................................................... 71 

Figure 5: Orthogonal impulse response of the REIT market total return from a shock 

to inflation..................................................................................................................... 72 

Figure 6: Orthogonal impulse response of the REIT market total return from a shock 

to interest rates ............................................................................................................ 73 

Figure 7: Orthogonal impulse response of the REIT market total return from a shock 

to stock market returns ................................................................................................ 74 

Figure 8: CUSUM tests of the individual equations of the VAR(1) model for the retail 

property sector ............................................................................................................. 77 

Figure 9: Orthogonal impulse response of the retail property sector total return from a 

shock to economic growth ........................................................................................... 79 

Figure 10: Orthogonal impulse response of the retail property sector total return from 

a shock to inflation ....................................................................................................... 80 

Figure 11: Orthogonal impulse response of the retail property sector total return from 

a shock to interest rates .............................................................................................. 81 

Figure 12: Orthogonal impulse response of the retail property sector total return from 

a shock to stock market returns .................................................................................. 82 

Figure 13: CUSUM tests of the individual equations of the VAR(2) model for the office 

property sector ............................................................................................................. 85 

Figure 14: Orthogonal impulse response of the office property sector total return from 

a shock to economic growth ........................................................................................ 87 

Figure 15: Orthogonal impulse response of the office property sector total return from 

a shock to inflation ....................................................................................................... 88 

Figure 16: Orthogonal impulse response of the office property sector total return from 

a shock to interest rates .............................................................................................. 89 



 x 

Figure 17: Orthogonal impulse response of the office property sector total return from 

a shock to stock market returns .................................................................................. 90 

Figure 18: CUSUM tests of the individual equations of the VAR(2) model for the 

industrial property sector ............................................................................................. 93 

Figure 19: Orthogonal impulse response of the industrial property sector total return 

from a shock to economic growth ............................................................................... 95 

Figure 20: Orthogonal impulse response of the industrial property sector total return 

from a shock to inflation .............................................................................................. 96 

Figure 21: Orthogonal impulse response of the industrial property sector total return 

from a shock to interest rates ...................................................................................... 97 

Figure 22: Orthogonal impulse response of the industrial property sector total return 

from a shock to stock market returns .......................................................................... 98 

Figure 23: Quarterly South African economic growth rate ....................................... 150 

Figure 24: First difference of the quarterly South African economic growth rate .... 152 

Figure 25: Quarterly South African inflation rate ...................................................... 154 

Figure 26: First difference of the quarterly South African inflation rate ................... 156 

Figure 27: Quarterly South African interest rate ....................................................... 158 

Figure 28: First difference of the quarterly South African interest rate .................... 160 

Figure 29: Quarterly total return of the South African stock market ........................ 162 

Figure 30: Quarterly total return of the South African REIT market ......................... 164 

Figure 31: Semi-annual total return of the South African retail property sector ...... 166 

Figure 32: First difference of the semi-annual total return of the South African retail 

property sector ........................................................................................................... 168 

Figure 33: Semi-annual total return of the South African office property sector ..... 170 

Figure 34: First difference of the semi-annual total return of the South African office 

property sector ........................................................................................................... 172 

Figure 35: Semi-annual total return of the South African industrial property sector 174 

Figure 36: First difference of the semi-annual total return of the South African industrial 

property sector ........................................................................................................... 176 

   



 xi 

DEFINITION OF KEY TERMS 

 

Active professional 

management 

An approach to investing in which the portfolio manager 

seeks to outperform a given benchmark portfolio 

(Maginn, Tuttle, Pinto, & McLeavey, 2007). 

Confidence interval 
An interval that has a given probability of containing the 

parameter it is intended to estimate (Maginn et al., 2007). 

Diversification 

The holding of various risky assets to minimise the 

exposure to any one of them (van Wyk, Botha, & 

Goodspeed, 2015).  

Economic growth 

The expansion of productive capacity of an economy that 

results from capital accumulation and technological 

progress (DeFusco, McLeavey, Pinto, & Runkle, 2015b).  

Efficient frontier 

The set of portfolios that maximise the expected return 

for every given level of risk (Janse van Rensburg, 

McConnel, & Brue, 2015).  

First differencing  

A transformation that subtracts the value of a time series 

in period 𝑡 − 1 from its value in period 𝑡 (DeFusco et al., 

2015b).  

Inflation 
A rise in the general level of prices in an economy (Janse 

van Rensburg et al., 2015).  

Interest rates 

The cost of borrowing or the price paid for the use of 

funds expressed as a percentage per year (van Wyk, 

Botha, & Goodspeed, 2015.  

Level of significance 
The probability of rejecting a true null hypothesis 

(DeFusco et al., 2015b).  

Liquidity  

The extent to which an instrument can be readily 

acquired or disposed of at prevailing market prices (van 

Wyk, Botha, & Goodspeed, 2015.  

Mixed asset portfolio 

A collection of investment securities assembled in a 

manner designed to provide diversification, and hence, 

risk reduction benefits (van Wyk, Botha, & Goodspeed, 

2015.  



 xii 

Real estate investment 

trust 

A company that owns and manages income-producing 

real estate assets (van Wyk, Botha, & Goodspeed, 2015. 

Rebalancing  

Adjusting the actual portfolio to the strategic asset 

allocation due to price changes in portfolio holdings 

(Maginn et al., 2007).  

Stock market 
A market in which shares and other financial securities of 

public companies are issued and traded (Jones, 2018). 

Strategic asset 

allocation 

The process of allocating money to permissible asset 

classes that integrates the investor’s return objective, 

risk tolerance, and investment constraints with long-run 

capital market expectations (Maginn et al., 2007).  

Tactical asset allocation 

Asset allocation that involves making short-term 

adjustments to asset class weights based on short-term 

predictions of relative performance among asset classes 

(Maginn et al., 2007). 

Total return 

The rate of return that accounts for capital 

appreciation/deprecation and income (Maginn et al., 

2007). 

 
 

 

 

 

 

 

 

 

 

 



 xiii 

ABBREVIATIONS 

 

ADF Augmented Dickey Fuller 

AIC Akaike information criterion 

APT Arbitrage pricing theory 

ARCH Autoregressive conditional heteroskedasticity  

CAL Capital allocation line 

CAPM Capital asset pricing model 

CRSP Centre for Research in Security Prices  

CUSUM Cumulative sum of recursive residuals 

CVAR Cointegrating vector autoregression 

DF-GLS Dickey Fuller generalised least squares 

EPRA European Public Real Estate Association 

GARCH Generalised autoregressive conditional heteroskedasticity 

GMV Global minimum variance 

HQ Hannan-Quinn information criterion 

JSE Johannesburg Stock Exchange  

LM Lagrange multiplier 

MVF Minimum variance frontier  

NAREIT National Association of Real Estate Investment Trusts 

OECD Organisation for Economic Co-operation and Development 

OLS Ordinary least squares 

PLSs Property loan stocks 

PUTs Property unit trusts 

REITs Real estate investment trusts 

SARB South African Reserve Bank 

SC Schwarz criterion 

SMA Structural moving average  

StatsSA Statistics South Africa 

VAR Vector autoregression 

VECM Vector error correction model 

VMA Vector moving average  



 

 

1 

CHAPTER 1 – INTRODUCTION 

 

1.1 BACKGROUND 

As an investor, it is imperative to have an understanding of the differences among 

investment assets in order to build a properly diversified portfolio that conforms to your 

investment objectives and risk tolerance (Markowitz, 1952). Moreover, the ability to 

understand the characteristics and driving forces of these assets is essential for 

investment success. Historically, real estate investment trusts (REITs) were one of the 

most misunderstood investments and they found no natural home in diversified 

portfolios (Krewson-Kelly & Thomas, 2016). Over the last six decades, however, 

REITs have increased in popularity and have become an important feature of 

investment portfolios. Despite REITs becoming one of the world’s fastest growing real 

estate investment vehicles, there are still many poorly understood aspects of this asset 

class (Krewson-Kelly & Thomas, 2016; Parker, 2011).  

 

Since the introduction of the first REIT regime in the United States in 1960, many 

countries around the world have embraced the United States REIT approach in an 

effort to facilitate the growth and development of their domestic real estate markets 

(Paolone, O’Reilly, & Kruth, 2019). According to the European Public Real Estate 

Association, the global REIT market had a market capitalisation of $2 252.54 billion 

consisting of 974 REIT companies as at 2021-Q3 (Pekdemir, Moreno, Luiz, & Marinov, 

2021). It can be said that REITs have become an accepted form of real estate 

investment as a result of the numerous advantages associated with investing in REITs. 

Several authors have highlighted that the main advantages associated with investing 

in REITs compared to directly investing in real estate include greater liquidity, 

diversification, active professional management, earnings predictability and high 

income payout ratios, smaller capital outlay, and access to superior quality and range 

of properties (Block, 2012; Francis & Ibbotson, 2001; Paolone et al., 2019; Yau, 

Schneeweis, Robinson, & Weiss, 2007).  

 

Additionally, many studies have been undertaken to measure the benefits of including 

REITs in a mixed-asset portfolio. Although some studies have found that REITs do not 



 

 

2 

add significant performance benefits (Kuhle, 1987; Nelling & Gyourko, 1998), the 

overwhelming majority of studies provide evidence to suggest that including an 

allocation of REITs to a mixed-asset portfolio is highly advantageous to investors as it 

leads to both return-enhancement and risk-reduction benefits (Newell, Pham, & Ooi, 

2015; Ntuli & Akinsomi, 2017; Olaleye, 2011; Oyedele, 2014; Oyedele, McGreal, 

Adair, & Ogedengbe, 2013). 

 

A formalised REIT regime based on the National Association of Real Estate 

Investment Trusts (NAREIT) in the United States and the European Public Real Estate 

Association (EPRA) in Europe was introduced in South Africa on 1 April 2013 and 

came into effect on 1 May 2013 (SA REIT Association, 2021b). Since then, South 

Africa has become the largest REIT market in the Middle East and African region with 

a market capitalisation of $14.50 billion comprising 31 REIT companies as at 2021-Q3 

(Pekdemir et al., 2021).  

 

From an academic point of view, some literature relating to the South African REIT 

market has been published. This research includes, but is not limited to:   

• The initial performance of the South African REIT market (Ntuli & Akinsomi, 2017). 

• The impact of introducing REITs on foreign investments and liquidity in South 

Africa (Carstens & Freybote, 2018). 

• An analysis of the performance and diversification benefits of the South African 

REIT market (Marzuki, 2018). 

• The impact of South African REIT portfolio composition on diversification benefits 

for foreign REIT investors (Carstens, Freybote, & De Villiers, 2019; Marzuki, 2018). 

• The impact of South African REIT legislation on firm growth and firm value 

(Wesson & Carstens, 2019). 

• The resiliency of South African REITs to a pandemic (Akinsomi, 2020).  

 

Despite the information that is accessible with respect to South African REITs, to the 

author’s knowledge, limited research has been undertaken to determine the impact 

that macroeconomic factors have on South African REIT returns. It is necessary that 

research is conducted in this regard so as to enhance the understanding of the 

characteristics and driving forces of this asset class in a South African context.  



 

 

3 

1.2 PROBLEM STATEMENT 

Since the South African REIT regime came into effect on 1 May 2013, the South 

African REIT market has experienced significant return volatility. According to the 

South African REIT Association, the South African REIT market has experienced 

annual returns ranging between a high of 27.7% in 2014 to a low of −37.9% in 2020 

(SA REIT Association, 2021a). Given this extreme volatility in returns, the question 

arises as to whether changes in key macroeconomic factors influence South African 

REIT returns, and if so, to what extent do changes in macroeconomic factors have on 

the returns of South African REITs. 

 

Internationally, many researchers have undertaken studies to examine whether 

macroeconomic factors impact REIT returns. The most prominent literature in this 

regard has been conducted in the United States, however, as the global REIT market 

has expanded, the association between macroeconomic factors and REIT returns has 

attracted the attention of numerous researchers worldwide. Therefore, in addition to 

the United States, research on the influence that macroeconomic factors have on REIT 

returns has also been conducted in Europe, the Asia-Pacific region and, to a lesser 

extent, Africa. Given the findings of these studies, it can be concluded that the most 

dominant macroeconomic drivers of REIT returns include economic growth, inflation, 

interest rates, and stock market returns (Cohen & Burinskas, 2020; Ewing & Payne, 

2005; Fang, Chang, Lee, & Chen, 2016; Liow, Ibrahim, & Huang, 2006; Yunus, 2012). 

Various other macroeconomic factors such as money supply, industrial production 

growth, oil price fluctuations, government spending, and consumer spending may also 

influence REIT returns (Bilson, Brailsford, & Hooper, 2001; Fatnassi, Slim, Ftiti, & 

Maatoug, 2014; Loo, Anuar, & Ramakrishnan, 2016; Naranjo & Ling, 1997; Razali, 

Jalil, & Nguyen, 2020). 

 

There is limited empirical research relating to the impact that the aforementioned 

macroeconomic factors have on the returns of South African REITs. As such, this 

study aims to shed light on the relationship between these macroeconomic factors and 

South African REIT returns.    

 



 

 

4 

1.3 PURPOSE STATEMENT 

The purpose of this study is to analyse the influence that numerous macroeconomic 

factors, namely economic growth, inflation, interest rates, and the stock market, have 

on the returns of South African REITs. Additionally, this study determines the degree 

to which these macroeconomic factors impact South African REIT returns. A unique 

feature of this study is that it analyses the overall South African REIT market as well 

as the prominent REIT property subtypes in South Africa including the retail, office, 

and industrial sectors. To achieve this, vector autoregression (VAR) models and 

vector error correction models (VECMs) are employed. Subsequently, a comparison 

is drawn with the findings from prior international studies to establish whether the 

relationship between macroeconomic factors and REIT returns in international 

markets is also pertinent in the South African REIT market.  

 

1.4 RESEARCH OBJECTIVES 

This study is guided by the following research objectives: 

• To analyse the influence that several macroeconomic factors have on South 

African REIT returns. 

• To identify the extent of the impact that the various macroeconomic factors have 

on South African REIT returns.  

• To compare whether the relationship between macroeconomic factors and South 

African REIT returns is in accordance with what has been observed in other 

international REIT markets. 

 

1.5 IMPORTANCE AND BENEFITS OF THE STUDY 

As mentioned previously, limited empirical research has been conducted to analyse 

the impact that macroeconomic factors have on South African REIT returns. Thus, the 

findings of this study will enhance the field of REIT research in South Africa by adding 

to the existing body of knowledge of what is currently known about South African 

REITs. Importantly, the findings of this study provide a basis for comparison between 

the South African REIT market and other global REIT markets to ascertain whether 

South African REITs are influenced by macroeconomic factors in the same manner as 

those internationally.  



 

 

5 

Additionally, from a practical standpoint, this study may be useful to REIT asset 

managers as the results could potentially assist them in making portfolio allocation 

decisions. The findings may also be beneficial to real estate collective investment 

scheme fund managers in determining an effective strategic asset allocation as well 

as in making tactical asset allocation adjustments to capitalise on changes in the 

macroeconomy. Furthermore, the results obtained from conducting this research may 

equip investors with enhanced knowledge regarding the relationship between 

macroeconomic factors and South African REIT returns which could assist them in 

making more informed investment decisions. 

 

1.6 DELIMITATIONS  

This study only analyses the influence that various macroeconomic factors have on 

South African REIT returns. Firm-specific factors such as size, leverage, dividend 

yield, and book-to-market ratio may also affect REIT returns, however, they will not be 

analysed for the purposes of this study. Moreover, this study solely investigates the 

impact that economic growth, inflation, interest rates, and the stock market have on 

South African REIT returns. Previous literature indicates that these are the most 

dominant macroeconomic drivers of REIT returns, hence, the author believes that it is 

necessary to analyse these important macroeconomic factors in this study. Other 

macroeconomic factors including money supply, industrial production growth, oil price 

fluctuations, government spending, and consumer spending will not be evaluated in 

this study. Furthermore, it is important to highlight that the findings of this study provide 

an understanding of the historical relationship between several macroeconomic 

factors and South African REIT returns which is no indication of the relationship that 

may prevail in future periods. 

 

1.7 STRUCTURE OF THE STUDY  

This research report comprises six main chapters. Chapter 2 provides an overview of 

REITs and critically evaluates prior literature relating to the influence that 

macroeconomic factors have on REIT returns. Chapter 3 outlines the methodology 

employed to conduct the study. An analysis of the data is conducted in Chapter 4 

whereafter a discussion of the findings is conferred in Chapter 5. The conclusion and 

recommendations for future research are presented in Chapter 6.   



 

 

6 

CHAPTER 2 – LITERATURE REVIEW 

 

2.1 INTRODUCTION 

The main objective of this chapter is to critically evaluate prior literature relating to the 

influence that macroeconomic factors have on the returns of real estate investment 

trusts (REITs). To provide a backdrop to the subject matter, emphasis is first placed 

on several important characteristics of REITs in section 2.2. Thereafter, the 

macroeconomic factors that form the basis of this study, namely economic growth, 

inflation, interest rates, and the stock market, are discussed in sections 2.3 to 2.6. 

Particularly, these sections expand on the economic theory behind each 

macroeconomic factor and further reviews the empirical relationship between these 

macroeconomic factors and REIT returns. A summary concludes the chapter in 

section 2.7.  

 

2.2 REAL ESTATE INVESTMENT TRUSTS 

Before investigating the relationship between macroeconomic factors and REIT 

returns, it is imperative to have an understanding of the main characteristics of REITs. 

As such, this section provides a comprehensive overview of REITs including what they 

are, how they were developed, the different types of REITs, and the various REIT 

property subtypes. Additionally, this section explains why investors invest in REITs by 

analysing how they fit into a mixed-asset portfolio as well as the advantages and 

disadvantages associated with investing in REITs. Finally, a brief discussion on asset 

pricing theory and the relationship between macroeconomic factors and REIT returns 

is provided to serve as an introduction to sections 2.3 to 2.6.  

 

2.2.1 What is a real estate investment trust? 

A REIT is an entity that derives revenue through owning, managing, or financing 

income-producing real estate (Krewson-Kelly & Thomas, 2016). Globally, REITs are 

generally exempt from paying corporate income tax if a specified majority (usually 75% 

or more depending on the country) of their income and assets pertain to income-

producing real estate and almost all of their potentially taxable income is distributed to 

their shareholders in the form of dividends (Brueggeman & Fisher, 2005). Due to this 



 

 

7 

unique feature of REITs, shareholders can expect to earn significantly higher 

distributions from investing in these entities compared to many other entities that are 

not required to distribute most of their income (Paolone et al., 2019). REITs can either 

be privately traded or publicly traded on an exchange. This study focuses on publicly 

traded REITs which allow investors to gain exposure to the real estate market without 

having to purchase, manage, or finance the properties directly (Krewson-Kelly & 

Thomas, 2016).   

 

2.2.2 History of REITs 

REITs were formed in 1960 in the United States with the introduction of the Real Estate 

Investment Trust Act. They were established to meet the needs of investors who were 

looking to gain access to income-producing real estate (Brounen & De Koning, 2012). 

Prior to the introduction of the REIT regime, the benefits associated with investing in 

commercial real estate was only available to high-net-worth individuals or accessible 

through financial intermediaries. Therefore, REITs brought about inclusivity by 

enabling regular investors to buy shares of the trust thereby allowing them to also reap 

the benefits of commercial real estate investments (Brounen & De Koning, 2012). 

Noteworthy, the Real Estate Investment Trust Act of 1960 only permitted REITs to 

own or finance commercial real estate investments. However, the United States REIT 

regime later evolved with the passing of the Tax Reform Act of 1986 which enabled 

REITs to also manage and operate commercial real estate properties instead of just 

owning or financing these investments (Brueggeman & Fisher, 2005). Important to 

highlight is that the United States REIT regime provided a model for REIT legislation 

in countries across the globe. Following the United States, the Netherlands was the 

second market to adopt the REIT regime in 1969. Subsequently, REITs have become 

increasingly popular worldwide with all G-7 nations and approximately two-thirds of 

Organisation for Economic Co-operation and Development (OECD) countries having 

embraced the United States REIT approach (Paolone et al., 2019). The global REIT 

market will be further expanded upon in section 2.2.8.  

 

2.2.3 Types of REITs 

There are three types of REITs namely equity, mortgage, and hybrid REITs (Yau et 

al., 2007). A discussion on these three types of REITs follows.  



 

 

8 

2.2.3.1 Equity REITs 

Equity REITs own and manage income-producing real estate with the primary purpose 

of leasing these properties to tenants. Hence, equity REITs predominantly derive their 

revenue from rental income received from the tenants that lease their properties 

(Brueggeman & Fisher, 2005; Yau et al., 2007). They may also generate income from 

the sale of properties although this is typically a small portion of their total revenue 

(Krewson-Kelly & Thomas, 2016). Equity REITs are the most common type of REIT 

constituting the largest percentage of the global REIT market (Paolone et al., 2019). 

 

2.2.3.2 Mortgage REITs 

Mortgage REITs provide financing to investors of income-producing real estate. They 

do this by either lending money directly through mortgages and loans or indirectly by 

acquiring mortgage-backed securities (Yau et al., 2007). As such, mortgage REITs 

obtain a significant portion of their revenue from interest income earned from lending 

money through loans or their investments in residential- or commercial-backed 

securities. To raise capital, mortgage REITs issue debt or equity in public or private 

capital markets (Krewson-Kelly & Thomas, 2016). The total market value of mortgage 

REITs is comparatively smaller to equity REITs (Paolone et al., 2019).  

 

2.2.3.3 Hybrid REITs 

Hybrid REITs adopt the investment strategies of both equity and mortgage REITs. 

Thus, these types of REITs own, manage, and finance income-producing real estate 

(Krewson-Kelly & Thomas, 2016; Yau et al., 2007). Hybrid REITs make up a small 

percentage of the global REIT market (Paolone et al., 2019).  

 

2.2.4 REIT property subtypes  

To achieve operational efficiency and drive business growth, most REITs focus on 

investing in a specific property subtype (Paolone et al., 2019). Table 1 reports the 

primary property subtypes with their respective percentages held in the global REIT 

market.  

 

 

 



 

 

9 

Table 1: Global REITs by property subtype held  

Property subtype Percentage 

Retail 23.3 

Office 14.5 

Residential 11.3 

Healthcare  6.8 

Industrial 4.2 

Hotel 2.7 

Self-storage 2.6 

Industrial/Office 1.0 

Diversified 33.6 

Source: Paolone et al. (2019) 

 

Evidently, the global REIT market is primarily made up of diversified REITs 

representing 33.6% of the global REIT market. The second most common property 

subtype includes retail REITs comprising 23.3% of the global REIT market. Following 

this, office and residential REITs constitute 14.5% and 11.3% of the global REIT 

market respectively. The property subtypes listed in Table 1 are expanded upon 

below.  

 

2.2.4.1 Retail REITs 

Retail REITs own and manage retail properties including regional malls, community 

shopping centres, and outlet centres (Brueggeman & Fisher, 2005; Krewson-Kelly & 

Thomas, 2016; Paolone et al., 2019). Regional malls are large, enclosed spaces in 

which higher-priced discretionary goods are typically sold. Tenants of regional malls 

generally have lease terms spanning three to ten years. Community shopping centres 

typically rent space for similar lease maturities to tenants that provide necessity goods 

and services such as food, homeware, furniture, and banking. Outlet stores, on the 

other hand, rent space to factory stores who sell products at discount prices (Paolone 

et al., 2019).  

 

 

 



 

 

10 

2.2.4.2 Office REITs 

Office REITs own and manage office properties including office parks and skyscrapers 

in central business districts of cities or suburban areas. The office buildings are leased 

to specific types of tenants including multinational corporations, banks, technology 

firms, government agencies, and law firms that need space to accommodate their 

workforce. The lease terms of these properties are usually long, spanning five to 

twenty-five years (Brueggeman & Fisher, 2005; Krewson-Kelly & Thomas, 2016; 

Paolone et al., 2019).  

 

2.2.4.3 Residential REITs 

Residential REITs own and manage various residential dwelling units including single-

family or multi-family structures such as apartment buildings, terraced houses, 

vacation homes, and student housing. These properties are solely leased for people 

to reside in and cannot be used for business purposes (Brueggeman & Fisher, 2005; 

Krewson-Kelly & Thomas, 2016). The typical lease duration for these types of 

properties tends to be approximately one year (Paolone et al., 2019).    

 

2.2.4.4 Healthcare REITs 

Healthcare REITs own health care-related properties including hospitals, rehabilitation 

centres, skilled nursing homes, assisted living and independent residential facilities for 

senior citizens, and medical office buildings. These properties are leased to and 

managed by health care providers who commonly sign long-term leases of ten or more 

years (Brueggeman & Fisher, 2005; Krewson-Kelly & Thomas, 2016; Paolone et al., 

2019).  

  

2.2.4.5 Industrial REITs 

Industrial REITs own and manage single-tenant or multi-tenant industrial properties 

including distribution centres, manufacturing facilities, and warehouses. These 

properties are generally located outside of central business districts due to the large 

space that is required for these types of buildings (Brueggeman & Fisher, 2005; 

Krewson-Kelly & Thomas, 2016). The tenants of these properties usually enter into 

long-term leases spanning five to twenty-five years (Paolone et al., 2019).  

 



 

 

11 

2.2.4.6 Hotel REITs 

Hotel REITs own hospitality-related properties including hotels and resorts. These 

properties are leased to and managed by hotel management companies who service 

a variety of guests such as vacationers and business travellers. The hotel sector is 

characterised by short-term leases (Brueggeman & Fisher, 2005; Krewson-Kelly & 

Thomas, 2016; Paolone et al., 2019). 

 

2.2.4.7 Self-storage REITs 

Self-storage REITs own and manage storage facilities comprised of different types of 

units with varying sizes. The units are leased to businesses or individuals for a short 

period of time, usually on a monthly basis (Brueggeman & Fisher, 2005; Krewson-

Kelly & Thomas, 2016; Paolone et al., 2019).  

 

2.2.4.8 Diversified REITs 

Diversified REITs own and manage numerous types of properties. For instance, a 

diversified REIT may own a combination of retail, office, and industrial properties 

(Brueggeman & Fisher, 2005; Krewson-Kelly & Thomas, 2016; Paolone et al., 2019).  

 

2.2.5 REITs in a mixed-asset portfolio 

Given that REITs have become a popular real estate investment vehicle, it is essential 

to recognise why investors include an allocation to REITs in their investment portfolios. 

However, before analysing the effect that an allocation to REITs has on an investor’s 

mixed-asset portfolio, it is important to have an understanding of efficient portfolios 

and how investors analyse the addition of a new asset class to an existing efficient 

portfolio.  

 

2.2.5.1 Efficient portfolios 

According to Markowitz (1952), an efficient portfolio offers the highest expected return 

𝐸(𝑅𝑝) for a given level of risk represented by standard deviation of return 𝜎𝑝. 

Graphically, efficient portfolios plot on the efficient frontier as seen in Figure 1. The 

efficient frontier forms part of the minimum variance frontier (MVF) which comprises 

the set of minimum-variance portfolios. Minimum-variance portfolios exhibit the lowest 

risk for a given level of expected return, hence, any feasible portfolio to the right of the 



 

 

12 

MVF would not be attractive to a risk-averse investor since these portfolios exhibit a 

higher level of risk for the same expected return as the portfolios on the MVF 

(Markowitz, 1952). The left-most point of the MVF represents the global minimum-

variance (GMV) portfolio. The GMV portfolio has the lowest level of risk of all the 

minimum-variance portfolios. The portion of the MVF beginning and continuing above 

the GMV portfolio is defined as the efficient frontier which comprises the set of efficient 

portfolios with the highest expected return for a given level of risk. Notably, the MVF 

portfolios that lie below the GMV are sub-optimal portfolios because they have a lower 

expected return for a given level of risk compared to the efficient portfolios that lie on 

the efficient frontier (Markowitz, 1952). 

 

Figure 1: The efficient frontier  

 

Source: Sharpe, Chen, Pinto, and McLeavey (2007) 

 

The portfolios described above only contain risky assets, however, the majority of 

investors are able to include risk-free assets 𝑅𝑓, such as government bonds, into their 

portfolios. By combining a risk-free asset to a portfolio of risky assets, a straight line 

called the capital allocation line (CAL) is derived as depicted in Figure 2. The point 

where the CAL intersects the efficient frontier is defined as the tangency portfolio. The 



 

 

13 

tangency portfolio is the optimal risky portfolio as it has the highest Sharpe ratio which 

is a measure of return per unit of risk (Sharpe, 1964). 

 

Figure 2: The tangency portfolio  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

Source: Adapted from Singal (2019) 

 

2.2.5.2 Addition of a new asset class to an existing efficient portfolio 

Assume that an investor holds a portfolio 𝑝 with an expected return 𝐸(𝑅𝑝) and 

standard deviation of return 𝜎𝑝. In order to determine whether an investor will realise 

a mean-variance improvement by adding an allocation of a new asset class to their 

existing portfolio, the following three inputs are required (Sharpe et al., 2007): 

1) The Sharpe ratio of the new asset class that could potentially be added to the 

existing portfolio. 

2) The Sharpe ratio of the existing portfolio. 

3) The correlation between the new asset class’ return and the existing portfolio 𝑝’s 

return expressed as 𝐶𝑜𝑟𝑟(𝑅𝑛𝑒𝑤 , 𝑅𝑝). 

 

Adding an allocation to the new asset class will only be beneficial if the new asset 

class’ Sharpe ratio is larger than the product of the existing portfolio’s Sharpe ratio and 

the correlation between the new asset class’ return and the existing portfolio’s return.  



 

 

14 

This relationship can be expressed as follows (Blume, 1984; Elton, Gruber, & Rentzler, 

1987): 

𝐸(𝑅𝑛𝑒𝑤) − 𝑅𝑓

𝜎𝑛𝑒𝑤
> (

𝐸(𝑅𝑝) − 𝑅𝑓

𝜎𝑝
)𝐶𝑜𝑟𝑟(𝑅𝑛𝑒𝑤 , 𝑅𝑝) 

 

If this relationship holds, adding an allocation of the new asset class to the existing 

portfolio will result in the investor achieving a superior efficient frontier with a tangency 

portfolio that has a higher Sharpe ratio (Sharpe et al., 2007). Important to highlight is 

that this expression does not indicate what percentage of the portfolio should be 

allocated to the new asset class. Thus, investors make use of mean-variance 

optimisation to determine the optimal weights of the respective asset classes that 

make up the efficient portfolio (Singal, 2019).  

 

2.2.5.3 Allocation of REITs to an efficient portfolio 

Many studies have been undertaken to measure the effects of including REITs in a 

mixed-asset portfolio. Although some studies have found that REITs do not add 

significant performance benefits (Kuhle, 1987; Nelling & Gyourko, 1998), the vast 

majority of studies provide evidence supporting the addition of REITs to a mixed-asset 

portfolio as it leads to both return-enhancement and risk-reduction benefits (Lee & 

Stevenson, 2005; Newell et al., 2015; Ntuli & Akinsomi, 2017; Olaleye, 2011; Oyedele, 

2014; Oyedele et al., 2013).  

 

A study in support of adding an allocation to REITs in a mixed-asset portfolio is that of 

Lee and Stevenson (2005). These authors examined the attractiveness of including 

United States REITs in a mixed-asset portfolio comprising United States equities, 

bonds, and cash as well as international equities. Using data from 1980-2000, the 

authors constructed efficient portfolios over 5-, 10-, 15- and 20-year time horizons. 

Through their analysis, it was found that, irrespective of the time horizon, including 

REITs in a mixed-asset portfolio resulted in both return-enhancement and risk-

reduction benefits. Interestingly, these benefits tended to decline as an investor moved 

along the efficient frontier. For example, over the 5-year investment horizon, the 

efficient portfolios at the lower end of the efficient frontier achieved a return-

enhancement and risk-reduction gain ranging between 3.6-7.6 basis points and 7.3-



 

 

15 

13.4 basis points respectively. Whereas, the efficient portfolios at the upper end of the 

efficient frontier achieved a return-enhancement and risk-reduction gain of 

approximately 2 basis points and 5 basis points respectively. Evidently, the risk-

reduction gain is much larger than the return-enhancement gain, hence, the authors 

concluded that as an investor moves along the efficient frontier, the rationale for 

including REITs in a mixed-asset portfolio shifts towards the risk-reduction qualities 

rather than the return-enhancement capabilities.   

 

Similar findings relating to return-enhancement benefits were observed in a South 

African context. Ntuli and Akinsomi (2017) undertook a study to measure the effect of 

adding an allocation of South African REITs to a mixed-asset portfolio of South African 

stocks and bonds. In order to do such, the authors made use of monthly total return 

data for the period 2013-2015. In particular, the authors used the FTSE/JSE Real 

Estate Investment Trust Index (J867) as a proxy for the South African REIT market, 

the FTSE/JSE All Share Index (J203) as a proxy for the South African stock market, 

and the FTSE/JSE All Bond Index (ALBI) as a proxy for the South African bond market. 

It was found that the addition of REITs to a mixed-asset portfolio of stocks and bonds 

resulted in an improved efficient frontier. For instance, at a 3% risk level, a mixed-

asset portfolio comprising stocks and bonds had an expected return of 0.79% whereas 

a mixed-asset portfolio comprising REITs, stocks, and bonds had an expected return 

of 0.88%. As such, the authors concluded that adding an allocation of South African 

REITs to a mixed-asset portfolio of South African stocks and bonds provides return-

enhancement benefits.  

 

Other studies conducted by Newell et al. (2015), Olaleye (2011), Oyedele (2014), and 

Oyedele et al. (2013) correspond with the results of the aforementioned studies. It 

must be noted, however, that the extent of the return-enhancement and risk-reduction 

benefits varied depending on the market in which the study was conducted. 

Nevertheless, it is apparent that including an allocation of REITs to a mixed-asset 

portfolio is highly beneficial to investors as it will result in the investor achieving a 

superior efficient frontier with a tangency portfolio that has a higher Sharpe ratio.  

 



 

 

16 

2.2.6  Advantages of investing in REITs 

Despite the return-enhancement and risk-reduction benefits of including REITs in a 

mixed-asset portfolio, there are numerous other reasons as to why investors invest in 

REITs. Several authors have highlighted that the main advantages associated with 

investing in REITs include greater liquidity, diversification, active professional 

management, earnings predictability and high income payout ratios, smaller capital 

outlay, and access to superior quality and range of properties (Block, 2012; Francis & 

Ibbotson, 2001; Paolone et al., 2019; Yau et al., 2007). These advantages are 

described in more detail below. 

 

2.2.6.1 Greater liquidity 

Investing in REITs gives an investor the ability to gain exposure to the real estate 

market without assuming the liquidity risk that accompanies direct real estate 

investment (Block, 2012; Krewson-Kelly & Thomas, 2016). Investors can buy and sell 

the shares of publicly traded REITs timeously since they are publicly traded on stock 

exchanges. Contrarily, it can take several months or sometimes even years to buy or 

sell property directly. Therefore, investing in REITs allows investors to participate in 

real estate-based investments in a liquid manner by providing greater flexibility with 

regard to realising cash values and gains or losses (Paolone et al., 2019).  

 

2.2.6.2 Diversification within the real estate market 

REITs provide investors with important diversification benefits within the real estate 

market. By investing in diversified REITs or an array of specialised REITs, such as 

retail, office, industrial and residential REITs, investors can diversify their real estate 

portfolios by property subtype. Moreover, REITs generally own real estate in a variety 

of cities, provinces, and even countries allowing investors to achieve geographic 

diversification (Block, 2012; Francis & Ibbotson, 2001; Paolone et al., 2019; Yau et al., 

2007). 

 

2.2.6.3 Active professional management 

Direct investment in real estate requires intensive real estate investment and 

management expertise. Investors, however, may lack the necessary skills required to 

make optimal real estate investment decisions and efficiently manage their properties. 



 

 

17 

Thus, investing in REITs provides investors with the benefit of having their property 

interests actively managed by highly skilled and experienced real estate managers 

(Paolone et al., 2019).  

 

2.2.6.4 Earnings predictability and high income payout ratios 

Due to the contractual nature of REITs’ rental income, these companies earn a secure 

and stable stream of income over long periods, and therefore, the earnings 

predictability of REITs is higher than that of other companies (Paolone et al., 2019). 

Furthermore, as discussed previously, REITs are obliged to pay out most of their 

earnings to shareholders in the form of dividends. This characteristic enables these 

companies to be one of the most stable and highest yielding of all publicly traded 

equities (Block, 2012).   

 

2.2.6.5 Smaller capital outlay 

Direct investment in real estate requires a substantial capital outlay. For instance, 

investing in retail properties, such as regional malls, would require an investment 

outlay of billions of Rands. Most investors do not have access to the funds that are 

required to develop a well-diversified portfolio of direct real estate investments. 

Investing in REITs, on the other hand, requires a significantly smaller investment 

outlay since the shares of REITs can be bought inexpensively. Hence, REITs offer 

investors a more cost-effective way to gain access to the real estate market (Paolone 

et al., 2019). 

 

2.2.6.6 Access to superior quality and range of properties 

Investors may wish to include high quality properties with elegant architectural design 

and/or properties that are located in sought after areas in their real estate portfolios. 

However, properties with these types of characteristics rarely come on the market, 

and if they do, they are in high demand and typically overpriced. By investing in REITs 

that own such properties, investors can gain exposure to a range of superior quality 

properties (Paolone et al., 2019).  

 



 

 

18 

2.2.7 Disadvantages of investing in REITs 

In spite of the numerous advantages associated with investing in REITs, there are a 

few disadvantages that need to be pointed out. Potential disadvantages of investing 

in REITs include less control, moderate income growth potential, reliance on debt, and 

higher correlation to other asset classes (Block, 2012; Harrison, Panasian, & Seiler, 

2011; Paolone et al., 2019; Yau et al., 2007). These disadvantages are discussed 

below.   

 

2.2.7.1 Less control 

Shareholders of REITs with a minority interest have less control over property-level 

investment decisions compared to direct owners of real estate who have control over 

decisions related to property type, location, rental prices, and tenants, amongst others 

(Paolone et al., 2019).  

 

2.2.7.2 Moderate income growth potential 

As discussed previously, REITs are required to pay out a substantial portion of their 

earnings to their shareholders in the form of dividends, hence, they have a low rate of 

income retention. The high payout percentage restricts REITs in terms of reinvestment 

for future growth which tends to reduce income growth potential (Block, 2012).  

 

2.2.7.3 Reliance on debt 

Due to the high payout ratio, REITs generally rely on financial leverage to expand their 

real estate holdings. Additionally, REITs regularly turn to the debt market to refinance 

their maturing debt. The use of financial leverage increases the risk associated with 

investing in these types of entities (Harrison et al., 2011; Paolone et al., 2019).  

 

2.2.7.4 Higher correlation to other asset classes 

Historically, there has been a low correlation between direct real estate and other 

assets classes such as equities and fixed income. However, the correlation between 

REITs and other asset classes tends to be higher than that between direct property 

and other asset classes. Although REITs provide important diversification benefits, it 

can be said that investors could achieve better portfolio diversification benefits by 

investing directly in a variety of real estate compared to investing indirectly through 



 

 

19 

REITs due to the lower correlation between direct property and other asset classes  

(Block, 2012; Paolone et al., 2019; Yau et al., 2007). 

 

Despite the aforementioned disadvantages, it is evident that the advantages 

associated with investing in REITs outweigh the disadvantages, and as such, they 

remain a popular investment vehicle amongst investors looking to gain exposure to 

the real estate market.  

 

2.2.8 Global REIT Market 

Since the first REIT legislation was enacted in the United States in 1960, the listed 

real estate market has become globalised over the last six decades. Many countries 

around the world have instated similar REIT regimes to the United States in an effort 

to facilitate the growth and development of their domestic real estate markets 

(NAREIT, 2021). According to the European Public Real Estate Association, the global 

REIT market had a market capitalisation of $2 252.54 billion made up of 974 REIT 

companies as at 2021-Q3 (Pekdemir et al., 2021). Table 2 reports the distribution of 

the global REIT market amongst the four main regions: Americas, Asia-Pacific, 

Europe, and Middle East and Africa. It is apparent that the Americas region has the 

most established REIT market with a market capitalisation of $1 576.17 billion 

(representing 69.97% of the global REIT market capitalisation) with 374 REIT 

companies (representing 38.40% of REIT companies globally). 

 

Table 2: Global REIT market 

Region 

Market 

capitalisation 

($ Billion) 

Percentage of 

total market 

capitalisation (%) 

Number of 

companies 

Percentage of 

total number of 

companies (%) 

Americas 1 576.17 69.97 374 38.40 

Asia-Pacific 424.25 18.83 271 27.82 

Europe 223.06 9.90 233 23.92 

Middle East 

and Africa 
29.06 1.29 96 9.86 

Total 2 252.54 100.00 974 100.00 

Source: Adapted from Pekdemir et al. (2021) 



 

 

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The REIT markets of the four main regions presented in Table 2 are analysed in more 

detail below. 

 

2.2.8.1 Americas REIT market 

When looking at the countries in the Americas region in Table 3, it is evident that the 

United States has the largest REIT market not only in this region but also globally. The 

United States has a market capitalisation of $1 472.48 billion (representing 65.37% of 

the global REIT market capitalisation) with 193 REIT companies (representing 19.82% 

of REIT companies globally). Although the Americas region has the largest REIT 

market worldwide, it is primarily attributable to the United States which contributes 

93.42% to this region’s REIT market capitalisation.  

 

Table 3: Americas REIT market 

Country Market capitalisation ($ Billion) Number of companies 

Developed markets 

Canada 72.47 44 

United States 1 472.48 193 

Emerging markets 

Brazil 14.88 121 

Mexico 16.34 16 

Source: Adapted from Pekdemir et al. (2021) 

 

2.2.8.2 Asia-Pacific REIT market 

By analysing the Asia-Pacific REIT market in Table 4, it can be seen that Japan has 

the largest REIT market in this region and the second largest REIT market globally, 

albeit significantly smaller than that of the United States. Japan has a market 

capitalisation of $153.96 billion (representing 6.83% of the global REIT market 

capitalisation) with 64 REIT companies (representing 6.57% of REIT companies 

globally). Notably, Australia has the third largest REIT market capitalisation globally of 

$111.79 billion (representing 4.96% of the global REIT market capitalisation) with 40 

REIT companies (representing 4.12% of REIT companies globally). 

 

 



 

 

21 

Table 4: Asia-Pacific REIT market 

Country Market capitalisation ($ Billion) Number of companies 

Developed markets 

Australia 111.79 40 

Hong Kong 29.57 10 

Japan 153.96 64 

New Zealand 5.27 5 

Singapore 74.35 33 

South Korea 6.30 17 

Emerging markets 

China 8.17 6 

India 7.87 3 

Indonesia 0.60 2 

Malaysia 9.23 18 

Pakistan  0.15 1 

Philippines 3.46 5 

Taiwan 3.47 7 

Thailand 10.06 60 

Source: Adapted from Pekdemir et al. (2021) 

 

2.2.8.3 European REIT market 

When looking at the countries in the European region in Table 5, it is clear that the 

United Kingdom has the largest REIT market in this region. The United Kingdom has 

a market capitalisation of $94.60 billion (representing 4.20% of the global REIT market 

capitalisation) with 54 REIT companies (representing 5.54% of REIT companies 

globally).  

 

Table 5: European REIT market 

Country Market capitalisation ($ Billion) Number of companies 

Developed markets 

Belgium  26.23 17 



 

 

22 

France 50.40 27 

Germany 5.63 7 

Ireland 1.92 3 

Italy 0.85 3 

Netherlands 13.14 5 

Portugal  0.07 2 

Spain 26.93 77 

United Kingdom 94.60 54 

Emerging markets 

Bulgaria 0.70 34 

Greece 2.59 4 

Source: Adapted from Pekdemir et al. (2021) 

 

2.2.8.4 Middle East and African REIT market 

By analysing the Middle East and African REIT market in Table 6, one can see that 

South Africa has the largest REIT market in this region. South Africa’s REIT market 

has a market capitalisation of $14.50 billion (representing 0.64% of the global REIT 

market capitalisation) with 31 REIT companies (representing 3.18% of REIT 

companies globally).  

 

Table 6: Middle East and African REIT market 

Country Market capitalisation ($ Billion) Number of companies 

Developed markets 

Israel 2.77 6 

Emerging markets 

Bahrain 0.04 1 

Kenya 0.01 1 

Kuwait 0.09 1 

Nigeria 0.04 2 

Oman 0.05 1 

Saudi Arabia  5.11 17 

South Africa 14.50 31 



 

 

23 

Turkey 6.30 34 

United Arab 

Emirates 
0.15 2 

Source: Adapted from Pekdemir et al. (2021) 

 

2.2.9 South African REIT market 

As mentioned previously, South Africa has the largest REIT market in the Middle East 

and Africa region. Given that this study focuses on the South African REIT market, it 

is important to explore how the South African REIT regime was developed as well as 

review the regulation and taxation that South African REITs are subject to. 

 

2.2.9.1 Development 

A formalised REIT regime based on the National Association of Real Estate 

Investment Trusts (NAREIT) in the United States and the European Public Real Estate 

Association (EPRA) in Europe was introduced in South Africa on 1 April 2013 and 

came into effect on 1 May 2013 (SA REIT Association, 2021b). Prior to the introduction 

of the REIT regime, the South African listed real estate market comprised comparable 

investment vehicles known as Property Unit Trusts (PUTs) and Property Loan Stocks 

(PLSs) (Cloete, 2005). PUTs and PLSs were subject to different regulatory 

frameworks, however, as highlighted by National Treasury (2007), these regulatory 

frameworks were too restrictive which hindered the South African listed property 

market from competing on a global scale. As such, the South African REIT regime was 

introduced whereby PUTs were automatically considered to be REITs and PLSs were 

able to adopt the necessary regulatory framework to qualify as REITs. This allowed 

the South African listed real estate market to compete internationally. Since the REIT 

regime was introduced in South Africa, the South African REIT market has 

experienced considerable growth being supported by both local and international 

investors (SA REIT Association, 2021b).  

 

2.2.9.2 Regulation 

The listing requirements of South African REITs are regulated by the Johannesburg 

Stock Exchange (JSE). In addition to the normal listing rules for companies, South 

African REITs must also comply with further criteria detailed in Section 13 of the JSE 



 

 

24 

listing requirements. Specifically, a REIT must (i) own at least R300 million worth of 

property, (ii) ensure debt levels are kept below 60% of its gross asset value, (iii) earn 

at least 75% of its income from rental and/or property owned or investment income 

from indirect property ownership, (iv) adopt measures to monitor risk, (v) not 

unnecessarily enter into derivative instruments that are out of the ordinary course of 

business, and (vi) pay at least 75% of its taxable earnings available for distribution to 

its investors each year (JSE, 2015). 

 

2.2.9.3 Taxation 

After the JSE grants a listed entity REIT status, Section 25BB of the Income Tax Act 

becomes applicable to the entity (van der Zwan, 2019). Although REITs are taxpayers 

and submit tax returns, Section 25BB grants REITs a deduction from their income for 

all qualifying distributions made to shareholders. Nevertheless, distributions paid to 

shareholders are taxable at each shareholders’ marginal income tax rate as the 

dividend received does not qualify for the ordinary dividend exemption provisions. 

Section 25BB also exempts a REIT from capital gains tax on any profit earned from 

the sale of a property. However, it must be noted that REIT investors are subject to 

capital gains tax when selling their shares of these entities (van der Zwan, 2019).  

 

2.2.10 Asset pricing theory and macroeconomic factors impacting REIT returns 

Before investigating the relationship between macroeconomic factors and REIT 

returns, it is imperative to have an understanding of asset pricing theory. Accordingly, 

an overview of the two primary theories that have been derived for the valuation of 

risky assets, namely the capital asset pricing model (CAPM) and the arbitrage pricing 

theory (APT), is provided below.  

 

The development of the CAPM is attributed to Sharpe (1964), Lintner (1965), and 

Mossin (1966). The main insight of the CAPM is that investors assess the risk of an 

asset in respect of the asset’s contribution to the systematic risk of their total portfolio. 

Noteworthy, systematic risk is defined as the portion of risk that is non-diversifiable 

and is measured as the covariance of an asset’s return to the market portfolio.  

 



 

 

25 

Using the CAPM, the required return on an asset can be computed as follows (Pinto, 

Henry, Robinson, & Stowe, 2015): 

 

𝑟 = 𝑅𝑓 + 𝛽𝑖(𝐸𝑞𝑢𝑖𝑡𝑦 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚) 

 

where 𝑟 is the required return on an asset, 𝑅𝑓 is the return on a risk-free asset, 𝛽𝑖 is a 

measure of systematic risk, and 𝐸𝑞𝑢𝑖𝑡𝑦 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚 is the incremental return that 

investors require for holding equities rather than a risk-free asset.  

 

Important to note is that the CAPM is based on several unrealistic assumptions 

including that markets are in equilibrium, investors have homogenous expectations, 

and they pursue a mean-variance optimising objective, amongst others. Moreover, 

empirical tests of the CAPM have highlighted numerous anomalies that are 

inconsistent with the theory. For instance, Basu (1977) observed that stocks with low 

price-to-earnings ratios outperformed stocks with high price-to-earnings ratios, and 

Banz (1981) documented that portfolios of stocks with low market capitalisations 

outperformed portfolios of stocks with large capitalisations on a risk-adjusted basis. 

Additionally, Fama and French (1992) showed that stocks with high book-to-market 

ratios (value stocks) tend to produce larger risk-adjusted returns than stocks with low 

book-to-market ratios (growth stocks). Due to these and many other anomalies cited 

in empirical studies of the CAPM, scholars searched for an alternative asset pricing 

theory that was intuitive, required only limited assumptions, and accounted for various 

risk factors. This led to the development of a multi-factor asset pricing model known 

as the APT proposed by Ross (1976a, 1976b). Contrary to the CAPM which contends 

that the covariance of an asset’s return to the market portfolio (𝛽𝑖) is the only relevant 

risk measure, the APT contends that there are many risk factors that affect returns. 

APT models express the required return on an asset as follows (Pinto et al., 2015): 

 

𝑟 = 𝑅𝑓 + (𝑅𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚)1 + (𝑅𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚)2 + ⋯+ (𝑅𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚)𝑘 

 

where (𝑅𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚)𝑖 =  (𝐹𝑎𝑐𝑡𝑜𝑟 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦)𝑖 × (𝐹𝑎𝑐𝑡𝑜𝑟 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚)𝑖 by which 

𝐹𝑎𝑐𝑡𝑜𝑟 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 is the asset’s sensitivity to a particular factor and 

𝐹𝑎𝑐𝑡𝑜𝑟 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚 is the expected return in excess of the risk-free rate accruing to 

an asset with unit sensitivity to factor 𝑖 and zero sensitivity to all other factors. 

 



 

 

26 

Examples of risk factors included in APT models include various macroeconomic 

factors such as economic growth, inflation, or interest rates. One of the first papers to 

study the impact that various macroeconomic factors have on the performance of 

stock returns by applying an APT model was that of N.-F. Chen, Roll, and Ross (1986). 

In this study, the authors analysed the impact that seven macroeconomic factors had 

on the performance of United States stock returns during the period 1953-1984. The 

goal of this study was to explore the pricing influence of exogenous macroeconomic 

factors so as to ascertain whether these factors were related to the underlying 

variables that explain pricing in the stock market. Through this analysis, it was found 

that five macroeconomic factors were significant in explaining expected stock returns. 

The most prominent macroeconomic factors included the change in the growth rate in 

industrial production, the change in risk premium, the change in the term structure of 

interest rates and, to a lesser extent, the unanticipated inflation rate and the change 

in expected inflation. 

 

Expanding on the work of N.-F. Chen et al. (1986), many researchers have undertaken 

studies to examine whether macroeconomic factors are also useful in explaining REIT 

returns. These studies have predominantly been conducted in the United States, 

however, as the global REIT market has grown, a multitude of researchers in other 

regions have undertaken similar studies. Therefore, research on the influence that 

macroeconomic factors have on REIT returns has also been conducted in Europe, the 

Asia-Pacific region and, to a smaller degree, Africa. The findings of these studies 

suggest that economic growth, inflation, interest rates, and the stock market have a 

significant influence on REIT returns (Cohen & Burinskas, 2020; Ewing & Payne, 2005; 

Fang et al., 2016; Yunus, 2012). Furthermore, other macroeconomic factors such as 

money supply, industrial production growth, oil price fluctuations, government 

spending, and consumer spending may also have an impact on REIT returns (Bilson 

et al., 2001; Fatnassi et al., 2014; Loo et al., 2016; Naranjo & Ling, 1997; Razali et al., 

2020). 

 

For the purposes of this study, the most dominant macroeconomic factors that drive 

REIT returns, namely economic growth, inflation, interest rates, and the stock market, 



 

 

27 

are analysed. The influence that these factors have on REIT returns is expanded upon 

in sections 2.3 to 2.6.  

 

2.3 ECONOMIC GROWTH 

Economic growth refers to the continuous expansion in the productive capacity of an 

economy between two periods (Janse van Rensburg et al., 2015; Jones, 2018). 

Studies have shown that economic growth has a significant influence on REIT returns. 

Thus, this section reviews the association between economic growth and REIT returns 

from a theoretical perspective whereafter the most prominent empirical literature in 

this regard is evaluated.  

 

2.3.1 Theoretical relationship between economic growth and REIT returns 

Economic expansion typically translates into increased business activity and demand 

for real estate which in turn results in higher occupancy rates and rental charges. In 

addition, when the economy is growing, tenants’ ability to pay rent improves, hence, 

less arrears are incurred by the lessors. Higher occupancy rates and rental charges 

coupled with reduced arrears leads to increased rental income which is the primary 

source of revenue for REITs (Paolone et al., 2019). Theoretically, it can be said that 

there should be a positive association between economic growth and REIT returns. 

The following subsection assesses various studies that have analysed the relationship 

between economic growth and REIT returns so as to ascertain whether this predicted 

positive relationship is empirically true.  

 

2.3.2 Empirical relationship between economic growth and REIT returns  

Several studies in various international REIT markets have been conducted to 

investigate the impact that economic growth has on REIT returns. Consistent with the 

theory, most authors have observed a positive relationship between these variables 

whereby higher economic growth translates into increased REIT returns (Liow et al., 

2006; McCue & Kling, 1994; Naranjo & Ling, 1997; Razali et al., 2020; Yunus, 2012). 

On the other hand, a few studies have found that an increase in economic growth 

leads to a decrease in REIT returns in which case there is a negative relationship 

between economic growth and REIT returns (Cohen & Burinskas, 2020; Ewing & 

Payne, 2005). Some other authors have concluded that there is no relationship 



 

 

28 

between economic growth and REIT returns (Chang, Chen, & Leung, 2011; Kola & 

Kodongo, 2017; Loo et al., 2016). The most prevalent studies pertaining to the 

relationship between economic growth and REIT returns are evaluated in more detail 

below.  

 

McCue and Kling (1994) explored the relationship between the macroeconomy and 

real estate returns in the United States by employing a vector autoregression (VAR) 

model to undertake forecast error variance decompositions and impulse response 

functions. To conduct their study, the authors made use of a dataset of United States 

equity REITs obtained from NAREIT as well as various macroeconomic factors for the 

period 1974-1991. The forecast error variance decompositions showed that economic 

growth explained a small proportion (9.3%) of the variation in REIT returns. 

Additionally, the impulse response functions revealed that REITs experienced a 

significant positive response to a one standard deviation shock to economic growth 

with the response peaking at approximately 1.25% ten months following the initial 

shock. Naranjo and Ling (1997) found similar results by making use of nonlinear 

multivariate regression techniques to identify macroeconomic factors that affected real 

estate returns in the United States over the period 1978-1994. The real estate return 

series used in this study comprised value weighted portfolios for five real estate related 

industries (including REITs) from the Centre for Research in Security Prices (CRSP). 

Using quarterly data from 1978-1994, the authors found that economic growth was a 

fundamental driver that positively affected REIT returns.  

 

A positive relationship between economic growth and REIT returns was also observed 

in the Asian and United Kingdom REIT markets in a study conducted by Liow et al. 

(2006). In particular, the authors analysed the association between expected risk 

premia on property stocks and various macroeconomic factors in Singapore, Hong 

Kong, Japan, and the United Kingdom from 1986-2003. Using principal component 

analysis, it was found that economic growth was able to predict positive excess returns 

for listed property stocks in all of the markets studied. Yunus (2012) also investigated 

the interaction between REITs and macroeconomic factors in a multitude of markets 

from 1990-2007. Specifically, the sample for this study comprised the following REIT 

markets: United States, Canada, Japan, Australia, Germany, France, Italy, the 



 

 

29 

Netherlands, and the United Kingdom. The REIT data consisted of publicly traded real 

estate stock price indexes retrieved from EPRA and NAREIT whilst the 

macroeconomic data was obtained from the OECD’s Main Economic Indicators 

Database and Thompson’s DataStream International. Through Granger causality 

tests, Yunus (2012) identified that international REIT returns were driven by economic 

growth in the short run. Moreover, by conducting impulse response functions, it was 

observed that a one standard deviation shock to economic growth induced a 

temporary positive response in REIT returns for the overwhelming majority of the 

countries.  

 

Similarly, Loo et al. (2016) analysed the relationship between Asian REIT markets and 

their respective macroeconomic factors. The REIT data used to conduct the study 

comprised the total return REIT indexes from Japan, Hong Kong, Singapore, 

Malaysia, Thailand, Taiwan, and South Korea from the date of availability of the data 

until December 2014. The authors discovered that only the Singaporean and 

Malaysian REIT markets were co-integrated with economic growth. Furthermore, 

Granger causality tests revealed that economic growth only had a significant influence 

on the Malaysian REIT market in the short run. For the other REIT markets, the 

relationship between economic growth and REIT returns was found to be statistically 

insignificant.  

 

Chang et al. (2011) also found no evidence of a relationship existing between 

economic growth and REIT returns for a sample of REITs operating in the United 

States. Using quarterly data covering the period 1975-2008, the authors were able to 

employ a VAR model and conduct impulse response functions to analyse the 

relationship between numerous macroeconomic factors and REIT returns in the 

United States. The impulse response functions revealed that a shock to economic 

growth had an insignificant effect on REIT returns in the thirty months following the 

shock. As a result, Chang et al. (2011) concluded that the United States REIT market 

adjusted much faster and was more forward looking than the real economy over the 

period analysed. Likewise, Kola and Kodongo (2017) also found that economic growth 

had an insignificant effect on the returns of REITs in the United States, Bulgaria, and 

South Africa. To conduct their study, the authors made use of monthly data for the 



 

 

30 

period spanning 2005-2015 for the United States, 2007-2015 for Bulgaria, and 2009-

2015 for South Africa. The following indexes were used as proxies for the respective 

REIT markets: S&P US REITs Index for the United States, SOFIA BG REIT Index for 

Bulgaria, and FTSE/JSE REITs Index for South Africa. By making use of the 

generalised method of moments model, Kola and Kodongo (2017) found that 

economic growth had no explanatory power on the conditional risk of returns and 

excess returns of the REITs in all of the markets analysed. 

 

On the contrary, Ewing and Payne (2005) observed a negative relationship between 

economic growth and REIT returns. These authors undertook a study to analyse the 

response of the United States REIT market to changes in various macroeconomic 

factors. To conduct their study, the authors made use of the NAREIT Index as a proxy 

for the United States REIT market for the period 1980-2000. Ewing and Payne (2005) 

estimated a VAR model and undertook impulse response functions to examine how 

long and to what degree REIT returns reacted to unanticipated changes in several 

macroeconomic factors. The results indicated that the response of the REIT market to 

an unanticipated increase in economic growth was insignificant in the first month 

following the shock. Thereafter, REIT returns became negative and significant from 

the second to seventh month. Accordingly, the authors concluded that an unexpected 

increase in economic growth had no immediate effect of REIT returns. However, in 

the months to follow, an increase in economic growth put pressure on the economy’s 

capacity to produce and exacerbated inflation expectations which in turn caused REIT 

returns to fall.  

 

Despite the aforementioned conflicting findings, it is evident that previous studies have 

primarily observed a significant positive relationship between economic growth and 

REIT returns implying that an increase in economic growth results in higher REIT 

returns. This observed positive relationship is in accordance with what the theory 

predicts.  

 

2.4 INFLATION 

Inflation can be defined as the percentage change in an economy’s overall price level. 

When inflation occurs, the purchasing power of an economy’s currency declines 



 

 

31 

because each unit of currency will buy fewer goods and services than it did in the prior 

period (Janse van Rensburg et al., 2015; Jones, 2018). Many researchers have 

observed that inflation has a statistically significant influence on REIT returns. 

Therefore, this section provides a theoretical overview of this association after which 

the empirical relationship between inflation and REIT returns is investigated. 

 

2.4.1 Theoretical relationship between inflation and REIT returns 

Real estate prices tend to rise when the overall price level increases, however, this is 

not necessarily the case for rental rates. REITs that enter into short-term leases have 

the ability to adjust their rental rates in accordance with the prevailing inflation rate, 

and as such, infl