174
Finance, Accounting and Business Analysis
Volume 8 Issue 1, 2026
http://faba.bg/
ISSN 2603-5324
DOI:
https://doi.org/10.37075/FABA.2026.1.14
Macroeconomic Determinants of BRICS Property Market Returns:
A Regime-Switching Approach
Fabian Moodley
1
*
, Bertha Chipo Bangara
2
, Babatunde Lawrence
3
North-West University, Vanderbijlpark, South Africa
1
Department of Economics, University of Malawi, Zomba, Malawi
2
North-West University, Vanderbijlpark, South Africa
3
* Corresponding author
Info Articles
Abstract
History Article:
Submitted 30 April 2026
Revised 30 May 2026
Accepted 3 June 2026
Purpose: The aim of the study was to examine the effect of macroeconomic
variables on Brazil, Russia, India, China and South Africa’s (BRICS) property
market returns under changing market conditions.
Design/methodology/approach: The study utilised a Markov regime-switching
model for the period February 2011 to June 2025. The dependent variables
comprised BRICS’s property market returns, whereas the independent variables
consisted of domestic inflation growth rate, gross domestic product growth rate
and interest growth rate of each BRICS nation.
Findings: The findings revealed that macroeconomic variables have a regime-
specific effect on BRICS’s property market returns in bull and bear market
conditions. That being said, inflation growth rate had a negative significant effect
on Brazil’s, India’s and South Africa’s property returns in a bull market condition,
whereas in the bear market condition, only India’s returns were negatively
affected. Similarly, gross domestic product (GDP) had a negative significant effect
on Russia’s and China’s property returns in a bull market condition, whereas in
the bear market condition, the effect was insignificant. In contrast, interest growth
rate had only a negative significant effect on South Africa’s and Brazil’s returns in
bull and bear market conditions, respectively. The transition probabilities revealed
that the bear market condition dominated BRICS property market returns,
illustrating that the returns were decreasing across the sample period.
Practical implications: Collectively, the study presents important implications for
policymakers and investors, such that monetary policy adjustments and
investment decisions must factor in the state of the property market, as bull and
bear periods dictate the return perspective that influences investors and
policymakers’ decisions.
Originality/value: The study contributes to the literature by providing a
comprehensive asymmetrical analysis of macroeconomic fundamentals’ influence
on BRICS’s property market return. Unlike previous studies that focus
predominantly on the linear effect, this study highlights the nonlinear perspective
between macroeconomic and property market returns, offering nuanced insights
for policymakers and investors.
Paper type: Research paper
Keywords: BRICS;
Macroeconomy; Bull and
Bear Markets; Property
Returns; Markov
JEL: G1, G11, C32
Address Correspondence:
E-mail:
Fabian.moodley@nwu.ac.za
1
bbchikadza@unima.ac.mw
2
babatunde.lawrence@nwu.ac.za
3
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
175
INTRODUCTION
Property market and macroeconomic variables are seriously interlinked and intertwined. Globally,
macroeconomic fundamentals have historically been the focus of many scholars, particularly on their impact
on market returns, with early research demonstrating the procyclicality of the real estate market, indicating
a strong correlation between fluctuations in macroeconomic variables such as inflation, interest rates,
income growth and property returns (Ghent and Owyang 2010; Ling and Naranjo 2015). Research has
further enhanced the link with empirical findings on macroeconomic links to commercial property returns,
revealing that, over time, the economic relationships with property return varied, generally becoming
weaker, and lead time increased, suggesting that explanatory powers are not uniform across global markets
(Hoskins et al. 2004).
While the world continues to grapple with the effects of global shocks such as the war in Iran, the
global financial crisis, the US-China trade disputes, the COVID-19 pandemic, and the Russia-Ukraine war,
among others, global, regional and bloc growth gets revised as macroeconomic variables bear the effect of
uncertainty. Such incidents have serious impacts on the behaviour of macroeconomic variables, which, in
turn, significantly affect property market returns. The backward and forward relationship between the
property market and the economy has influenced a rise and fall in future of property returns with residential
markets being significantly influenced by macroeconomic indicators (Wahab et al. 2017).
Research has focused on the relationship between stock and bond market returns and how
macroeconomic shocks, such as fluctuations in interest rates, inflation rates, and industrial production, have
affected asset markets. Although the co-movements of real estate and other asset prices suggest similarities
in risk factors, most studies have ignored the effect on the real estate markets. However, Belo and Agbatekwe
(2002) argue that the measure of a country’s economic growth and prosperity lies in the quality and quantity
of the country’s housing stock, with real estate becoming the focal point of the government’s fiscal and
monetary policies. As such, the real estate sector has become a yardstick for realising low inflation, high
employment and balanced economic growth with reverse implications on one another (Apergi 2003; Fraser
1993). Therefore, economic instability through macroeconomic fluctuation often creates a disequilibrium in
the property market. Furthermore, Chen et al. (1986) found several US macroeconomic variables to be
significant in explaining expected stock, confirming that monetary policy shocks have a significant impact
on property prices in developed countries, with macroeconomic variables being the primary drivers of
property prices.
The extent of the interdependency of economic forces verified by the financial crises has resulted in
greater attention being placed on linkages between the real economy and financial markets. Developing
economies, on the other hand, and BRICS in particular, are largely associated with economic variations that
can potentially hinder and delay domestic growth and foreign investment. The volatility of the
macroeconomic factors associated with asset price fluctuations, unstable discount rates and risk premiums
has an additional implication on the risk perception of investors (Bansal et al. 2012). Moreover, BRICS
economies have provided investors with opportunities for international portfolio diversification. These
portfolio diversification strategies are based on the principle of low correlations in the business cycles of
different economies. A clear appreciation of the role of emerging markets’ property market returns in
international portfolio diversification is not possible without understanding the relationship between the risk
and return dynamics of property market returns and volatility in key macroeconomic variables that underlie
business cycles.
Understanding the relationship between macroeconomic variables and property market returns is
important to investors in several ways. Firstly, the relationship provides a clear and strategic implication on
real estate decision-making and portfolio management (Hoskins et al. 2004). Secondly, knowing the
relationship and whether the link is consistent or changing over time provides a useful tool in the decision-
making process as companies expose themselves in the international market in a globalised world.
LITERATURE REVIEW
Theoretical conceptualisation
The theoretical underpinnings of the study rest on the idea that property market returns are not
generated by one stable linear process. Instead, returns may shift between expansionary and contractionary
states, with macroeconomic and geopolitical variables exerting different effects across bull and bear regimes
rather than assuming one stable relationship throughout the sample. Real estate occupies a unique position
within asset markets, functioning simultaneously as a consumption good, a production input, and an
investment asset. As such, its returns are influenced by a complex interaction of macroeconomic forces,
including economic growth, interest rates, inflation, and financial conditions.
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
176
A central theoretical framework for understanding property market returns is Minsky’s financial
instability hypothesis, which argues that periods of stability can generate financial fragility as investors,
lenders, and firms become more optimistic, increase leverage and take greater risks (Minsky, 1999). In this
framework, financial markets evolve endogenously from relatively stable conditions into speculative and
crisis-prone states. Therefore, financial cycles are not accidental deviations from equilibrium but intrinsic
features of capitalist financial systems, such that the property market may operate differently in expansionary
and contractionary regimes, represented as:
(1)
where:
in expansionary phases but
when leverage becomes excessive, and fragility rises.
Here,
is property market returns,
is leverage or credit conditions,
is investor expectations and
is the market regime. Therefore, the same financial conditions may support returns in a bull market but
intensify vulnerability in a bear market. The bull regime can be interpreted as a period of optimism, rising
returns, stronger liquidity and risk-taking, while the bear regime reflects deleveraging, falling confidence and
heightened sensitivity to macroeconomic shocks. Minsky’s formulation explicitly treats instability as an
internal feature of capitalist financial systems rather than merely the result of external shocks.
Second is Hamilton’s regime-switching theory, which provides the direct econometric bridge between
financial-cycle theory and empirical modelling. Hamilton proposed that economic time series may be
governed by parameters that shift according to an unobserved discrete-state Markov process (Hamilton
1989). Hamilton does not observe the regime directly but infers it probabilistically from the data. The general
Hamilton-style regime-switching model is presented as:
(2)
where
) and
is an unobserved state variable. The regime transition process is:
(3)
For two regimes:
(4)
where
and
This entails that property market returns can remain in a bull or bear state with some probability, but
can also switch between states when market conditions change. This provides the econometric bridge
between theory and method in that if financial instability theory suggests that property markets alternate
between expansionary and contractionary phases, the regime-switching model provides a formal empirical
framework for estimating those phases, their transition probabilities and their state-dependent coefficients
(Hamilton 1989).
Empirical review
Empirically, the influence of macroeconomic factors on property market returns has been extensively
examined in both developed and emerging markets, reflecting the dual nature of real estate as both a
consumption good and an investment asset, with existing literature focusing on the US property market
(Chan et al. 1990; McCue and Kling 1994) and a few others examined the effect on the UN’s property returns
(Brooks and Tsolacos 1999). The relationship between macroeconomic variables and property market
returns has been explored in the literature. While some focused on the effect of monetary policy shocks on
real estate (Anderson et al. 2012), other researchers concentrated on the effect of changes in key
macroeconomic variables (Yunus 2012). Yunus (2012) investigated the dynamic interactions among
securitised property markets, stock markets, and key macroeconomic factors for ten developed nations
throughout North America, Europe, Australia, and Asia. The results indicated that each property market is
co-integrated with its respective stock market, with key macroeconomic factors in the long run, and is also
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
177
influenced by the overall economy in the short run. Further analysis revealed that, for the overwhelming
majority of countries involved, shocks to the stock market, GDP, money supply, and inflation induce a
positive response in property returns, while shocks to long-term interest rates induce a negative response,
although the extent of the responses differs across countries/regions.
A central theme in the literature is the role of economic growth, typically proxied by GDP. Studies
such as Chen and Tzang (1988) and Fama (1990) suggest that real estate returns are positively correlated
with economic activity, as higher income levels increase demand for both residential and commercial
property. In periods of economic expansion, rising business activity stimulates demand for office and retail
space, while increased household income supports housing demand. However, the relationship is not always
linear. McGough and Tsolacos (2001) argue that property markets often lag broader economic cycles due to
adjustment costs and market rigidities.
Other studies in this area have focused on the effects of interest rate (Bernanke and Gertler 1995; Ling
and Naranjo 1997), inflation (Fama and Schwert 1977; Hoesli et al. 1997), exchange rate (Quan and Titman
1999), credit availability and financial development (Iacoviello 2005), unemployment rates and labour
market conditions (Case and Shiller 1989). While, for example, higher interest rates increase borrowing
costs, reducing demand for property investment and exerting downward pressure on returns, and
consequently, find a strong inverse relationship between interest rates and real estate returns, particularly in
commercial property markets. Others, who have focused on inflation, have found that property returns can
partially hedge against expected inflation, while other studies indicate that real estate performs poorly during
periods of unexpected inflation due to distortions in capital markets and policy responses. Furthermore,
exchange rate movements have gained increasing attention, particularly in open economies, affecting
property markets through capital flows and foreign investment. Studies such as those by Quan and Titman
(1999) highlight the growing importance of global capital flows in determining property prices, particularly
in emerging markets where foreign direct investment plays a significant role.
Credit availability and financial development have been identified as key drivers of property price
cycles. Iacoviello (2005) demonstrates that housing markets are highly sensitive to credit conditions, with
easier access to financing leading to increased demand and higher returns. Conversely, credit tightening can
trigger sharp declines in property markets, as observed during the global financial crisis of 2008. In addition,
unemployment rates and labour market conditions influence property demand in that higher unemployment
reduces household income and weakens demand for housing, leading to lower returns. Studies such as those
by Case and Shiller (1989) show that expectations about future income and employment prospects play a
critical role in housing market dynamics.
Lin (2022) employed novel data containing both capital appreciation and income components in the
US to examine whether the largest variations in house price changes are mainly driven by local or national
factors. The results show that macroeconomic factors, absorbed by time fixed effects, account for 43% of the
variation in capital gains and 2% of the variation in rental yields. Overall, the findings empirically support
the prior literature assuming that the nature of housing markets is mainly local, suggesting a greater role of
local factors for understanding cross-sectional income returns in housing markets. Erol and Ileri (2013)
investigated the macroeconomic sources of time-varying risk premia in the Turkish real estate investment
trust (REIT) industry within the arbitrage pricing theory framework and found that inflation risk appears to
be the major concern in REIT investment, with Turkish REITs behaving more like stocks than real estate.
Beyond these core variables, recent literature emphasises the role of institutional and structural
factors, including regulatory frameworks, urbanisation, and demographic trends. Glaeser et al. (2008)
highlight how land use regulations and housing supply constraints can amplify the impact of macroeconomic
shocks on property prices. Similarly, rapid urbanisation in developing countries has been associated with
sustained increases in property demand, independent of short-term macroeconomic fluctuations.
Despite the extensive body of research, several gaps remain. Firstly, much of the literature is
concentrated in developed markets, with limited empirical evidence from developing BRICS economies.
Secondly, the interaction between macroeconomic variables is often complex and nonlinear, suggesting the
need for more advanced econometric approaches such as regime-switching models. Thirdly, the increasing
integration of global financial markets implies that domestic property returns are influenced not only by local
macroeconomic conditions but also by global shocks.
Therefore, an understanding of macroeconomic variables and their resultant impact on the return and
evaluation of risk-return relationship in BRICS is important for both academics and investors as real estate
investments as indirect investment instruments are increasingly becoming an important part of investors’
diversified portfolio.
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
178
METHODS
Data
The primary objective of this study is to examine the effect of geopolitical uncertainty on the property
market return volatility of BRICS countries. In doing so, the study applies monthly data for the period
February 2011 to June 2025. The choice of monthly frequency is directly related to the availability of data,
as BRICS’s property market data is only available in quarterly and monthly frequencies. Similarly, the
sample period is constrained as China’s property market data is only available from January 2011.
Considering the above, the sample period remains robust as it considers three important historical events,
namely the COVID-19 pandemic, the US-China trade war and the Russia-Ukraine war. The dependent
variable comprises a proxy for each BRICS property market, as measured by the real residential property
price index. The key explanatory variables used in the study were inflation, gross domestic product and
interest rate. The geopolitical risk index, developed by Caldara and Iacoviello (2022), was used as a control
variable. The property price data was obtained from the Federal Reserve Bank of St. Louis (FRED), sourced
from the Bank for International Settlements (BIS), while the macroeconomic variables were obtained from
the World Bank and the geopolitical risk index was collected from the Economic Policy Uncertainty (EPU)
website. For Russia and India, property market data was only available at a quarterly frequency. As a result,
these series were converted to monthly observations using the quadratic interpolation technique, consistent
with the approach adopted by Dlamini (2017).
Empirical model
To achieve the study’s objective, a nonlinear modelling approach, capable of distinguishing between
bull and bear market phases, is required. Accordingly, this study employs the Markov regime-switching
model. The choice of this model is motivated by its ability to incorporate regime changes driven by an
unobserved state variable that evolves according to a first-order Markov process (Hamilton 1989). As a
result, the model accommodates regime shifts that occur at irregular time intervals, unlike many alternative
nonlinear models that rely on exogenous structural changes occurring at fixed periods (Camacho et al. 2018).
The Markov regime-switching model is specified as follows:
(5)
are the BRICS property market returns, and the state-dependent mean is given by
. The model
considers two market conditions (C
t
,), i.e. bull (1) and bear (2) market conditions The state-dependent
explanatory variables are the inflation growth rate (), long-term interest growth rate ( and real
effective growth rate () of each BRICS country. The state-dependent control variable is the
geopolitical risk index (of each BRICS country.
is the state-dependent variance.
Market conditions are assumed to evolve according to a first-order Markov process, governed by a
constant transition probability matrix. Consequently, the likelihood of transitioning between bull and bear
market regimes is expressed as follows:
(6)
Where
is the probability that the BRICS property market return is at a bullish state and will
not move,
is the probability that the returns are in a bullish state and will move to a bearish
state.
is the probability that the returns are in a bear regime and will not move
is the
probability that the returns are in a bearish regime and it will move to a bullish state (Brooks 2019).
Preliminary and diagnostic tests
Prior to the estimation of the Markov regime-switching model, it is essential that various preliminary
tests are estimated to ensure the properties of the empirical model are met. To this extent, the study considers
the variance inflation factor test (VIF) to ensure that there is no collinearity among the independent and
control variables. Similarly, the augmented Dickey-Fuller (ADF) test for unit root, the Kwiatkowski,
Phillips, Schmidt, and Shin (KPSS) test for stationarity and the ADF breakpoint unit root tests are estimated
to ensure the dependent, independent and control variables express stationarity properties in levels and in
the presence of structural breaks. Once these tests are estimated and met, the Markov regime-switching
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
179
model will be estimated and the Durbin-Watson diagnostic test for serial autocorrelation is estimated to
confirm the robustness of the model output.
RESULT AND DISCUSSION
Summary statistics
Table 1 below shows the descriptive statistics of BRICS property market returns, giving insights into
the return characteristics. Russia and South Africa reflect the countries with the highest and lowest mean
returns, with 0.005929 and 0.0000223, respectively. This indicates a huge difference in market attractiveness
in returns on investments for these two countries. Russia again comes up as the country within the bloc with
the highest standard deviation, a reflection of its volatility and high risk in returns, while South Africa reflects
the lowest standard deviation, a reflection of how volatile and stable the property market returns in both
countries are. The skewness statistics indicate that the return distribution is symmetric, with all countries
displaying a positive skewness, implying a higher likelihood of extreme positive returns (large gains). In
addition, the kurtosis values of the countries show different degrees of normality. Russia and China exhibit
kurtosis values of 13.378 and 7.9422, which are both highly leptokurtic, indicating the presence of outliers
and the presence of sharp peaks compared to the lower or normal distributions of Brazil, India and South
Africa. The Jaque-Bera value confirms the non-normality of the returns series, and also confirms volatility
clustering, thereby rejecting the null hypothesis that the data follows a normal distribution.
Table 1. Descriptive statistics of BRICS’s property market returns
BRAZIL
RUSSIA
INDIA
CHINA
RSA
Mean
0.003780
0.005929
0.002622
0.003997
2.23E-05
Median
0.004013
0.003819
0.001529
0.001998
3.79E-06
Maximum
0.014395
0.090279
0.032264
0.061790
0.003290
Minimum
-0.002525
-0.031726
-0.021725
-0.016126
-0.001888
Std. dev.
0.003844
0.013709
0.008794
0.011814
0.000824
Skewness
0.218928
2.289689
0.550992
1.695542
0.290016
Kurtosis
2.726951
13.37836
4.121601
7.942252
3.908547
Jarque-Bera
1.908289
922.2142
17.71856
257.4647
8.326920
Probability
0.385141
0.000000
0.000142
0.000000
0.015554
Notes:1. Source: Authors’ own estimation (2026)
The descriptive statistics of the macroeconomic variables of the BRICS bloc are shown in Table 2.
Among the bloc countries, Russia has the highest mean inflation growth rate with 0.57243, and China
exhibits the lowest mean of 0.13405, which indicates that China has a relatively stable growth level. Brazil
has the largest standard deviation (0.982377), indicating the most variability; South Africa (0.456712) and
China (0.494173) seem to be more stable. The skewness values reflect high asymmetry, especially for Brazil
(-10.40369) and Russia (6.038590), indicating extreme negative and positive inflation growth shocks,
respectively. The kurtosis values are exceptionally high for Brazil (127.31) and Russia (55.63), reflecting
heavy tails and extreme observations. The Jarque-Bera statistics (all significant at conventional levels except
marginally for China) confirm non-normality, justifying the choice of robust econometric models capable of
adapting to the extreme volatility and distributional irregularities.
Table 2. Descriptive statistics of BRICS’s macroeconomic variables
CPIBRAZIL
CPIRUSSIA
CPIINDIA
CPICHINA
CPISA
Mean
0.404238
0.572431
0.459687
0.134059
0.438746
Median
0.439963
0.460010
0.505334
0.093991
0.361664
Maximum
1.620050
7.613075
2.928177
1.582586
3.742204
Minimum
-11.52694
-0.540012
-1.547721
-1.231323
-0.628931
Std. dev.
0.982377
0.723626
0.670763
0.494173
0.456712
Skewness
-10.40369
6.038590
0.257183
0.364353
2.382898
Kurtosis
127.3136
55.63245
3.909106
3.542332
17.60649
Jarque-Bera
114517.5
21019.74
7.864619
5.947861
1701.615
Probability
0.000000
0.000000
0.019598
0.051102
0.000000
Source: Authors’ own estimation (2026)
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
180
The descriptive statistics of gross domestic product growth rate across BRICS economies are
discussed in Table 3, showing a significant change in economic performance and volatility. Overall, China
shows the highest mean growth rate (0.857211), followed by Russia (0.546054) and India (0.462248),
whereas Brazil (0.103168) and South Africa (0.099352) indicate average growth rates less than expected.
However, the median figures, especially for Russia (1.866529) and China (1.888500), indicate that typical
growth is greater than the mean, signalling a potential negative shock, dragging averages downwards. The
wide range between maximum and minimum values (e.g., India: 18.57193 to -22.21853) reflects extreme
economic fluctuations. China’s and Russia’s standard deviations are highest (5.200416 and 4.862814,
respectively), which reflect more macroeconomic volatility. All countries show negative skewness, which
means extreme economic downturns are more likely. Kurtosis values are well above three or very high,
particularly for South Africa (60.11) and Brazil (38.05), implying heavy tails and extreme observations.
Jarque-Bera statistics support non-normality for all these series and also corroborate the necessity for an
adequate modelling approach.
Table 3. Descriptive statistics of BRICS’s GDP
GDPBRAZIL
GDPRUSSIA
GDPINDIA
GDPCHINA
GDPSA
Mean
0.103168
0.546054
0.462248
0.857211
0.099352
Median
0.128426
1.866529
0.663266
1.888500
0.132688
Maximum
5.817076
9.029295
18.57193
18.29525
11.24857
Minimum
-6.446699
-14.97951
-22.21853
-20.58737
-12.68992
Std. dev.
0.822995
4.862814
3.371918
5.200416
1.427275
Skewness
-1.103962
-1.261255
-1.180056
-0.958058
-1.512633
Kurtosis
38.04553
4.939944
19.05132
5.827216
60.11269
Jarque-Bera
8888.338
72.99470
1897.342
84.08267
23578.54
Probability
0.000000
0.000000
0.000000
0.000000
0.000000
Source: Authors’ own estimation (2026)
Table 4 shows the descriptive statistics of interest rate growth rates in BRICS, which reveal a strong
degree of monetary variation and volatility. Russia has the highest mean interest rate growth rate of 1.219406
and a maximum value of 135.2941, indicating episodes of extreme monetary tightness or instability. More
so, Brazil and South Africa have moderate mean values of 0.388605 and 0.223527, respectively, while India
and China have means of -0.056664 and -0.370334, indicating a downturn in the monetary environment.
High standard deviations, particularly in the case of Russia (13.88589) and Brazil (6.869995), illustrate large
variability and interest growth rate instability, respectively. The distribution of skewness indicates
asymmetry, with Russia (6.236663) and Brazil (1.566049) being significantly positively skewed, suggesting
occasional large upswings in interest growth rate, while negative skewness is observed for China (-2.083063).
All countries have high kurtosis, especially Russia (57.66), suggesting leptokurtic distributions in which there
are extreme observations. The Jarque-Bera statistics are all very significant for the whole series, thereby
showing non-normality. These results indicate that BRICS’s interest growth rates are extremely volatile and
highly volatile, which highlights the need for models that can model the non-linear structures and the
clustering in volatility space.
Table 4. Descriptive statistics of BRICS’s interest rates
INTBRAZIL
INTRUSSIA
INTINDIA
INTCHINA
INTSA
Mean
0.388605
1.219406
-0.056664
-0.370334
0.223527
Median
0.000000
0.000000
0.000000
0.000000
0.000000
Maximum
37.50000
135.2941
11.36364
4.302926
15.78947
Minimum
-25.00000
-35.29412
-14.56311
-7.462687
-19.04762
Std. dev.
6.869995
13.88589
2.824503
1.468998
3.555772
Skewness
1.566049
6.236663
-0.006808
-2.083063
-0.289523
Kurtosis
11.24634
57.66249
10.77549
11.05733
14.27086
Jarque-Bera
560.8959
22659.92
435.8047
593.0813
918.1085
Probability
0.000000
0.000000
0.000000
0.000000
0.000000
Sum
67.22865
210.9572
-9.802882
-64.06775
38.67013
Sum sq.
8143.980
33421.92
1372.740
394.8946
2183.328
Sum sq. dev.
8117.855
33164.67
1372.184
371.1681
2174.684
Observations
173
173
173
173
173
Source: Authors’ own estimation (2026)
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
181
Stationarity and unit root tests
In Table 5 bellow, the ADF test and ADF breakpoint test are provided to determine whether the
timeseries exhibit stationarity properties, a requirement for the estimation of the Markov regime-switching
model. If one turns to Panel A, the associated ADF tests for all BRICS member countries, besides South
Africa, exhibit a test statistic more negative than the associated critical values as seen by the statistical
significance level. Therefore, the null hypothesis of a unit root is rejected in favour of the alternative
hypothesis that Brazil’s, Russia’s, India’s, and China’s property market returns are stationary in levels.
However, such is not confirmed for South Africa, as the test statistic is less negative than the associated
critical levels. Despite this, when the ADF test is run in first difference, it then becomes apparent that South
Africa’s property market return presents stationarity properties, which is confirmed by the ADF breakpoint
test. Similarly, in Panel B and Panel C, it is evident that BRICS’s geopolitical risk index and macroeconomic
variables (inflation, gross domestic product and interest rates growth rates) are stationary in levels, as the
ADF test statistic is more negative than the selected critical values at varying levels of significance. To this
extent, the null hypothesis is rejected in favour of the alternative hypothesis.
Table 5. Stationarity and unit root test results
Country
ADF
ADF-break
Panel A: BRICS property market returns
BRAZIL
-2.974**
-6.390***
RUSSIA
-3.434**
-8.077***
INDIA
-10.253***
-15.384***
CHINA
-4.653***
-6.099***
SA
-1.921
(-6.857) ***
-3.031
(-7.687) ***
Panel B: BRICS geopolitical risk index
GPBRAZI
-5.934***
-10.966***
GPRUSSIA
-4.187***
-8.758***
GPINDIA
-10.253***
-15.384***
GPCHINA
-5.867***
-7.625***
GPSA
-7.644***
-9.737***
Panel C: Control variables
Inflation Rate
CPIBRAZI
-33.705***
-34.927***
CPIRUSSIA
-8.155***
-14.364***
CPIINDIA
-8.684***
-10.107***
CPICHINA
-9.786***
-11.755***
CPISA
-10.894***
-12.036***
GDP
GDPBRAZIL
-3.070**
-9.571***
GDPRUSSIA
-4.023***
-7.878***
GDPINDIA
-6.492***
-10.936***
GDPCHINA
-5.867857***
-7.625157***
GDPSA
-6.530***
-11.099***
Interest Rates
INTBRAZIL
-2.694*
-8.758**
INTRUSSIA
-11.422***
-16.821***
INTINDA
-5.213484***
-10.90389***
INTCHINA
-7.072044***
-13.21526***
INTSA
-4.306914***
-10.15890***
Notes:
1. *, **, and *** provide the 10%, 5% and 1% level of significance, respectively
2. Source: Authors’ own estimation (2026)
Confirming the findings of the ADF test, the ADF breakpoint test for the BRICS property market
returns, geopolitical risk index and macroeconomic variables exhibit a test statistic that is more negative
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
182
than the associated critical values at all levels of significance. Consequently, the null hypothesis that the
timeseries contains a unit root in the presence of structural breaks is rejected in favour of the alternative
hypothesis that the timeseries exhibit stationarity properties in the presence of structural breaks.
Collectively, the findings demonstrate that BRICS’s property market returns, geopolitical risk and
macroeconomic variables are stationary with and without structural breaks at levels. However, South
Africa’s property market returns are stationary at the first difference. To this extent, the differenced variable
is used henceforth.
Variance inflation factor test
A further requirement of the Markov regime-switching model is that the explanatory variables should
not exhibit colinear properties. To this end, the VIF test is estimated and presented in Table 6 below. The
findings of the VIF test associated with each dependent variable (BRICS’s property market returns), and a
set of explanatory (geopolitical risk index and macroeconomic variables) variables reveal that there exists no
colinear relationship. That being, for Brazil, Russia, India, China and South Africa’s property market
returns, the associated VIF statistic is 1, suggesting that there is no multicollinearity between geopolitical
risk and macroeconomic variables associated with each BRICS country.
Table 6. Variance inflation factor test results
Coefficient
Uncentred
Centred
Variable
variance
VIF
VIF
Dependent variable: BRAZIL
C
2.71E-07
3.155575
NA
CPIBRAZIL
9.12E-08
1.192904
1.019308
GDPBRAZIL
1.28E-07
1.016869
1.001047
INTBRAZIL
1.87E-09
1.023031
1.019749
GPBRAZIL
4.87E-05
2.966898
1.000710
Dependent variable: RUSSIA
C
2.83E-06
2.573502
NA
CPIRUSSIA
3.00E-06
2.315419
1.421014
GDPRUSSIA
5.08E-08
1.102356
1.088550
INTRUSSIA
6.06E-09
1.065476
1.057275
GPRUSSIA
1.52E-06
3.209459
1.330162
Dependent variable: INDIA
C
1.05E-06
2.603150
NA
CPIINDIA
9.52E-07
1.561104
1.060249
GDPINDIA
3.80E-08
1.089224
1.069017
INTINDIA
5.34E-08
1.055003
1.054576
GPINDIA
0.000236
1.922093
1.031582
Dependent variable: CHINA
C
5.35E-06
6.842766
NA
CPICHINA
4.07E-06
1.358525
1.264896
GDPCHINA
3.59E-08
1.268427
1.234685
INTCHINA
3.71E-07
1.083646
1.018537
GPCHINA
9.62E-06
6.350882
1.024919
Dependent variable: RSA
C
1.34E-08
3.706525
NA
CPISA
2.80E-08
2.485716
1.121807
GDPSA
2.05E-09
1.163261
1.157729
INTSA
3.35E-10
1.179506
1.174809
GPSA
2.19E-06
2.365162
1.010039
Notes: Source: Authors’ own estimation (2026)
Markov regime-switching model
Having met the requirements for the estimation of the Markov regime-switching model, it now
permits the estimation of the empirical model whereby the results are presented in Table 7. If one turns to
Panels A and B, the associated results of the bull and bear regime are presented. It is evident that the average
returns (C) associated with Brazil’s, India’s, China’s and South Africa’s property market return are positive
and significant in a bull regime. However, in a bear regime, Brazil’s, China’s and South Africa’s property
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
183
market return is negative and significant. The findings demonstrate that, on average, when the market is in
a bull regime, associated property market returns will remain positive and increasing, whereas in a bear
regime it will be negative and decreasing. These observations align closely with the properties of a bull and
bear regime, as Mayekiso and Moodley (2026) demonstrate that average returns in a bull regime should
exhibit positive and stable returns and the market is stable and less influenced by uncertainty, while in a bear
regime, returns should be decreased and negative due to the market demonstrating volatile behaviour. These
findings are further supported by the error variance (), as in a bull regime, the variance is much lower than
in a bear regime, illustrating that the bull regime is a stable market condition, whereas the bear regime is a
volatile market condition.
If one turns to the findings associated with the explanatory variables in Panel A, the bull regime, it is
evident that the inflation growth rate has a negative significant effect on Brazil’s, India’s and South Africa’s
property market returns. However, gross domestic product growth rate has a negative significant effect on
Russia’s and China’s property market returns, whereas interest growth rate and geopolitical risk only have
a negative significant effect on South Africa’s property market returns and Brazil’s property market returns,
respectively. In Panel B, the bear regime, inflation growth rate has a negative significant effect on India’s
property market returns, gross domestic product growth rate has a positive significant effect on Brazil’s and
South Africa’s property market returns, whereas it has a negative significant effect on India’s property market
returns. Interest growth rate and geopolitical risk have a positive significant effect on Brazil’s property market
returns.
Collectively, the findings reveal that the effect that macroeconomic variables and geopolitical risk
have on BRICS property market returns is not symmetrical; rather, it varies with the state of the financial
market. That being said, stable and market conditions dictate the identified effect causing the alternate with
market conditions, causing the effect to be nonlinear.
Table 7. Markov regime-switching model results
Variable
Brazil
Russia
India
China
South Africa
Panel A: Bull regime
C
0.008206***
(0.0000)
0.009614
(0.1744)
0.016829***
(0.0000)
0.015368***
(0.0269)
0.000544***
(0.0021)
CPI
-0.000582***
(0.0024)
-0.005982
(0.4675)
-0.006191**
(0.0471)
-0.001873
(0.5973)
-0.000448**
(0.0490)
GDP
-2.96E-05
(0.3650)
-0.003627***
(0.0013)
-8.60E-05
(0.8776)
-0.004935*
(0.0774)
1.23E-05
(0.8874)
INT
8.84E-05
(0.6990)
-1.07E-05
(0.9661)
-0.000318
(0.5429)
-0.000670
(0.6311)
-8.18E-05***
(0.0015)
GEORISK
-0.023692***
(0.0001)
0.004628
(0.4998)
-0.036177
(0.3046)
0.004300
(0.7418)
-0.002727
(0.1387)
-6.096148***
(0.0000)
-3.853929***
(0.0000)
-4.962783***
(0.0000)
-4.427803***
(0.0000
-7.205754***
(0.0000)
Panel B: Bear regime
C
-0.000485
(0.2073)
0.003179
(0.1323)
0.001556*
(0.0730)
-0.002743*
(0.0965)
-0.000582***
(0.0000)
CPI
-0.000225
(0.6670)
-0.000285
(0.8882)
-0.004002***
(0.0000)
-0.001191
(0.3473)
0.000134
(0.4440)
GDP
0.000124***
(0.0009)
0.000181
(0.5824)
-0.000501***
(0.0005)
-0.000203
(0.7806)
0.001065***
(0.0000)
INT
0.002009***
(0.0003)
7.30E-05
(0.3085)
2.59E-05
(0.8799)
-0.000605
(0.1280)
2.76E-06
(0.8497)
GEORISK
0.010980**
(0.0177)
-0.000469
(0.6709)
-0.007315
(0.5817)
0.002359
(0.2242)
0.001364
(0.7053)
-6.505835***
(0.0000)
-4.942730***
(0.0000)
-5.265519***
(0.0000)
-5.118357***
(0.0000)
-8.012737***
(0.0000)
Notes:
1. *, **, and *** provide the 10%, 5% and 1% level of significance, respectively
2. The parentheses provide the p-values
3. Source: Authors’ own estimation (2026)
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
184
Transition probabilities and expected duration
The associated transition probabilities and expected duration associated with the BRICS property
market returns are provided in Table 8. It is evident for Brazil, Russia, India and China that the transition
probabilities in a bear regime are 0.982965, 0.959556, 0.947667 and 0.975626, respectively. However, in a
bull regime, the transition probabilities are 0.968081, 0.758528, 0862054 and 0.926951, respectively. This
demonstrates that the bear market condition is more persistent for Brazil’s, Russia’s, India’s and China’s
property market returns, as they are higher in a bear regime and closer to 1, justifying the dominance of the
bear regime. These findings are further supported by the associated duration results as the duration that
Brazil’s Russia’s, India’s and China’s property market returns in a bear regime (58.70113, 24.72581,
19.10831 and 41.02776 months) is higher than the bull regime (31.32932, 4.141263, 7.249224 and 13.68947
months). Contrary to this, South Africa’s property market return transition probability is higher in a bull
regime (0.901428) than in a bear regime (0.814930), which is further supported by the duration of the bull
regime (10.14492 months) as compared to the bear regime (5.403372 months).
In sum, the bear market condition is seen to dominate the BRICS property market returns as appose
to the bull market condition, which is supported by both the transition probabilities and expected duration
parameters.
Table 8. Transition probabilities and expected duration results
Variable
Brazil
Russia
India
China
South Africa
P11
0.968081
0.758528
0.862054
0.926951
0.901428
P22
0.982965
0.959556
0.947667
0.975626
0.814930
D11
31.32932
4.141263
7.249224
13.68947
10.14492
D22
58.70113
24.72581
19.10831
41.02776
5.403372
Source: Authors’ own estimation (2026)
ROBUSTNESS TESTS
To confirm the robustness of the estimated empirical model results, the study estimates the smooth
regime probability graphs of BRICS’s property market returns. In Figure 1, it can be visualised for Brazil’s,
Russia’s, India’s, and China’s property market returns that it was dominated by the bear market condition
as Brazil’s property market returns stayed in a bear regime from 2014 to 2019 and 2022 to 2023, Russia’s
from 2012 to 2019, India’s from 2017 to 2021 and 2011 to 2017, and China’s from 2011 to 2012, 2013 to
2014, 2017 to 2020 and 2022 to 2024. However, for South Africa’s property market returns, the bull market
condition dominated returns as it stayed in the bull market condition for most of the sample period.
Similarly, it is seen that each market condition was persistent as it did not stay for prolonged periods in a
bull or bear regime; rather, once it entered a bull or bear regime, it moved into the subsequent regime, as
supported by the spikes in the graphs. These findings serve as a confirmation of the transition probability
and expected duration evidence as it supports the conclusions made therein, which confirm the robustness
of the empirical results and further validate the identified conclusions.
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 1)
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 2)
Markov Switching Filtered Regime Probabilities
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 1)
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 2)
Markov Switching Filtered Regime Probabilities
A: Brazil B: Russia
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
185
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 1)
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 2)
Markov Switching Filtered Regime Probabilities
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 1)
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 2)
Markov Switching One-step Ahead Predicted Regime Probabilities
C: India D: China
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 1)
0.0
0.2
0.4
0.6
0.8
1.0
2012 2014 2016 2018 2020 2022 2024
P(S(t)= 2)
Markov Switching Filtered Regime Probabilities
E: South Africa
Figure 1. Smooth transition probabilities of BRICS’s property market returns
Source: Authors’ own estimation (2026)
DISCUSSION OF RESULTS
The findings of this study provide strong evidence that the effect of macroeconomic variables and
geopolitical risk on BRICS property market returns is nonlinear and regime dependent. The Markov regime-
switching results demonstrate that the relationship between macroeconomic factors and property market
returns differs significantly between bull and bear market conditions. This confirms that property markets
within BRICS economies respond asymmetrically to changing economic conditions and financial
uncertainty.
The results reveal that, in the bull regime, average property market returns for Brazil, India, China,
and South Africa are positive and statistically significant, whereas bear regimes are associated with negative
and declining returns, particularly for Brazil, China, and South Africa. These findings are consistent with
Hamilton (1989), who argues that financial markets exhibit distinct dynamics across economic regimes.
Similarly, Moodley and Lawrence (2026), Baumann et al. (2026), and Moodley (2026) posit that bull regimes
are characterised by stable and positive returns, while bear regimes reflect heightened uncertainty and market
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
186
volatility.
Inflation growth rate was found to exert a significant negative effect on Brazil, India, and South Africa
during bull market conditions, implying that rising consumer prices reduce property investment
attractiveness and purchasing power. These findings are supported by Apergis (2003) and Wahab et al.
(2017), who report that inflation negatively affects real estate performance through increased financing costs
and reduced affordability. However, during bear regimes, inflation only significantly affected India,
suggesting that inflationary pressures become less dominant relative to broader market uncertainty during
downturns.
Gross domestic product growth rate exhibited a negative significant effect on Russia and China during
bull regimes, while positive effects emerged for Brazil and South Africa in bear regimes. This suggests that
economic growth does not uniformly translate into stronger property returns across BRICS markets due to
structural and institutional differences. The findings align with Yunus (2012), who argues that
macroeconomic growth influences property markets differently across countries, depending on market
maturity and financial integration.
Interest growth rates negatively affected South Africa during bull regimes and positively influenced
Brazil during bear regimes. This reflects the sensitivity of property markets to monetary policy adjustments
and borrowing costs, supporting Bernanke and Gertler’s (1995) credit channel theory. Furthermore,
geopolitical risk negatively affected Brazil in the bull regime but positively affected Brazil during the bear
regime, illustrating that geopolitical uncertainty amplifies market instability and investor risk aversion.
The transition probability results further showed that bear regimes dominate most BRICS property
markets, with Brazil exhibiting a bear regime persistence probability of 0.982965 and a duration of
approximately 58.7 months. These findings imply that negative market conditions are more persistent than
positive regimes within emerging property markets. Overall, the results demonstrate that macroeconomic
variables and geopolitical risk affect BRICS’s property markets asymmetrically, reinforcing the importance
of regime-sensitive investment and policy strategies.
CONCLUSION
This study examined the effect of macroeconomic variables and geopolitical risk on BRICS property
market returns under bull and bear market conditions using a Markov regime-switching framework. The
findings reveal that macroeconomic fundamentals exert asymmetric and state-dependent effects on property
market returns, implying that the influence of inflation growth rate, gross domestic product growth rate,
interest growth rates, and geopolitical risk varies significantly across market regimes. The results further
demonstrate that bear market conditions dominate most BRICS property markets, indicating that negative
return phases are more persistent than positive market periods.
The empirical findings show that inflation growth rate significantly reduced property market returns
in Brazil, India, and South Africa during bull regimes, while its influence weakened during bear regimes.
This suggests that rising inflationary pressures diminish property market performance, primarily during
expansionary market conditions through declining purchasing power and higher financing costs. Gross
domestic product growth produced mixed effects across BRICS economies, illustrating structural
heterogeneity in property market responses to economic growth. Interest growth rate movements were also
found to exert regime-dependent effects, particularly in South Africa and Brazil, reflecting the sensitivity of
property investments to borrowing costs and monetary policy adjustments. In addition, geopolitical risk
contributed significantly to property market instability in certain regimes, confirming that uncertainty and
political tensions remain important determinants of emerging market property returns.
The transition probability results provide important insights into the persistence of market conditions.
Brazil recorded the highest bear regime persistence probability of 0.982965, with an expected duration of
approximately 58.7 months, while China’s bear regime duration was estimated at 41 months. Similarly,
Russia and India exhibited bear regime durations exceeding 19 months. In contrast, South Africa
demonstrated relatively stronger persistence in bull market conditions, with a bull regime probability of
0.901428 and an expected duration of approximately 10 months. These findings collectively suggest that
downturns in BRICS’s property markets tend to persist for prolonged periods, increasing systemic risk
exposure for investors and policymakers.
The study presents several important policy implications. Firstly, monetary authorities should
account for regime-specific market behaviour when implementing interest rate and inflation-targeting
policies. The results indicate that contractionary monetary policy during already fragile market conditions
may intensify property market downturns and prolong bearish cycles. Consequently, policymakers should
adopt more flexible and countercyclical monetary policy frameworks capable of stabilising property market
volatility during periods of economic stress. Secondly, financial regulators should strengthen
macroprudential surveillance mechanisms to monitor persistent bearish market conditions and systemic
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
187
risks within the property sector. The prolonged duration of bear regimes identified in the study highlights
the importance of early-warning systems and stress-testing frameworks to mitigate contagion risks within
financial and real estate markets.
Thirdly, institutional and portfolio investors should incorporate regime-dependent risk assessment
into investment strategies and portfolio allocation decisions. The findings demonstrate that macroeconomic
shocks affect property markets differently across market states, implying that static investment strategies may
produce suboptimal outcomes during volatile periods. Investors should therefore adopt dynamic portfolio
rebalancing strategies and diversify across asset classes to reduce exposure to prolonged bearish market
conditions. Finally, the study contributes to the broader emerging market property literature by shifting the
focus from conventional linear modelling approaches toward nonlinear and asymmetric market dynamics.
This provides deeper insights into how macroeconomic uncertainty and geopolitical tensions influence
property market behaviour across different economic conditions.
Despite the important contributions of the study, several limitations should be acknowledged. Firstly,
the study focused on a limited set of macroeconomic variables, namely inflation, GDP growth, and interest
rates, due primarily to data availability constraints across BRICS economies. Other important determinants,
such as unemployment rates, exchange rates, credit growth, housing supply indicators, and investor
sentiment measures, were not incorporated into the analysis. The exclusion of these variables may limit the
comprehensiveness of the estimated relationships.
Secondly, the study relied on aggregate national residential property indices, which may mask
important regional or sectoral differences within individual property markets. Property market dynamics
may vary substantially across commercial, industrial, and residential sectors as well as across urban and
rural regions. Future studies may therefore improve the analysis by employing disaggregated property
market data at sectoral or metropolitan levels.
Thirdly, although the Markov regime-switching framework effectively captures nonlinear regime
changes, it does not explicitly model volatility clustering and time-varying conditional variance, which are
common characteristics of financial and property market returns. Future research may therefore extend the
analysis using Markov-switching GARCH or stochastic volatility models to jointly capture regime changes
and volatility persistence.
Finally, the sample period includes major structural events such as the COVID-19 pandemic,
geopolitical conflicts, and monetary tightening cycles, which may have introduced structural breaks into the
data. Although the regime-switching framework partially captures these shifts, future studies should formally
incorporate structural break tests and crisis-specific dummy variables to improve model robustness and
empirical precision.
Acknowledgments
We are grateful to the reviewers and editor for their comments, which has enhanced the quality of the research paper.
Funding
Not applicable.
Data Available Statement
The data is available on reasonable request from the corresponding author.
Conflict of interest
The authors declare no conflict of interest.
AI Tools Statement
All authors confirm that no AI tools were used in the preparation of this manuscript.
Author contribution (as applicable):
• Conceptualization: Fabian Moodley.
• Methodology: Fabian Moodley.
• Software: Fabian Moodley.
• Validation: Bertha Chipo Bangara/Babatunde Lawrence.
• Formal analysis: Fabian Moodley
• Investigation: Fabian Moodley
• Resources: Fabian Moodley
• Data curation: Fabian Moodley
• Writing – original draft: Fabian Moodley/Bertha Chipo Bangara/Babatunde Lawrence.
• Writing – review & editing: Fabian Moodley/Bertha Chipo Bangara/Babatunde Lawrence.
• Visualization: Fabian Moodley/Bertha Chipo Bangara/Babatunde Lawrence.
• Supervision: Fabian Moodley
• Project administration: Fabian Moodley
• Funding acquisition: Not applicable.
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
188
REFERENCE
Anderson, R., V. Boney, and H. Guirguis. 2012. The Impact of Switching Regimes and Monetary Shocks:
An Empirical Analysis of REITs. Journal of Real Estate Research, 34 (2): 157–182.
https://doi.org/10.1080/10835547.2012.12091332
Apergis, N. 2003. Housing Price and Macroeconomic Factors: Prospects within the European Monetary
Union. International Real Estate Review, 6 (1): 63–74.
Bansal, P., S. Bertels, T. Ewart, P. MacConnachie, and J. O'Brien. 2012. Bridging the research–practice gap.
Belo, M., and A. Abgatekwe. 2002. Project Management in Property Development: The Nigeria Experience.
Ibadan: University Press PLC.
Bernanke, B., and M. Gertler. 1995. Inside the Black Box: The Credit Channel of Monetary Policy
Transmission. Journal of Economic Perspectives, 9 (4): 27–48.
Baumann, K., F. Moodley, and S. Ferreira-Schenk. 2026. The effect of investor sentiment on South African
bank returns: A comparative analysis of economic conditions. Journal of Business and Environmental
Sciences, 2(5): 105-127.
Brooks, C. 2019. Introductory Econometrics for Finance. New York: Cambridge University Press.
Brooks, C., and S. Tsolacos. 1999. The Impact of Economic and Financial Factors on UK Property
Performance. Journal of Property Research, 16 (2): 139–152.
Caldara, D. and Iacoviello, M. 2022. Measuring geopolitical risk. American economic review, 112(4): 1194-
1225.
Camacho, M., G. Perez-Quiros, and P. Poncela. 2018. Markov-Switching Dynamic Factor Models in Real
Time. International Journal of Forecasting, 34 (4): 598–611.
Case, K., and R. Shiller. 1989. The Efficiency of the Market for Single-Family Homes. American Economic
Review, 79 (1): 125–137.
Chan, S.H., J. D. Martin, and J. W. Kensinger. 1990. Corporate research and development expenditures and
share value. Journal of Financial Economics, 26(2): 255-276.
Chen, K., and D. Tzang. 1988. Interest-rate sensitivity of real estate investment trusts. Journal of Real Estate
Research, 3(3): 13-22.
Chen, N., R. Roll, and S. A. Ross. 1986. Economic Forces and the Stock Market. Journal of Business, 59 (3):
383–403.
Erol, I., and A. Ileri. 2013. What Determines REIT Returns in Turkey? Iktisat Isletme ve Finans, 28 (331): 9–
32.
Fama, E., and G. W. Schwert. 1977. Asset returns and inflation. Journal of financial economics, 5(2): 115-146.
Dlamini, C.S. 2017. The relationship between macroeconomic indicators and stock returns: evidence from the JSE
sectoral indices. Unpublished master’s dissertation. South Africa: University of the Witwatersrand.
Fama, E. 1990. Stock Returns, Expected Returns, and Real Activity. Journal of Finance, 45 (4): 1089–1108.
Fraser, W. D. 1993. Principles of Property Investment and Pricing. London: Macmillan.
McCue, T., and J. Kling. 1994. Real estate returns and the macroeconomy: some empirical evidence from
real estate investment trust data, 1972-1991. Journal of Real Estate Research, 9(3): 277-287.
Glaeser, E., J. Gyourko, and A. Saiz. 2008. Housing Supply and Housing Bubbles. Journal of Urban
Economics, 64 (2): 198–217.
Ghent, A.C., and M. T. Owyang. 2010. Is housing the business cycle? Evidence from US cities. Journal of
Urban Economics, 67(3): 336-351.
Hamilton, J. D. 1989. A New Approach to the Economic Analysis of Nonstationary Time Series and the
Business Cycle. Econometrica, 57 (2): 357–384.
Hoesli, M., B. MacGregor, G. Matysiak, and N. Nanthakumaran. 1997. The Short-Term Inflation-Hedging
Characteristics of UK Real Estate. Journal of Real Estate Finance and Economics, 15 (1): 27–57.
Hoskins, N., D. Higgins, and R. Cardew. 2004. Macroeconomic Variables and Real Estate Returns: An
International Comparison. The Appraisal Journal, 72 (2): 163–170.
Iacoviello, M. 2005. House Prices, Borrowing Constraints, and Monetary Policy. American Economic Review,
95 (3): 739–764.
Lin, P. 2022. Do Macroeconomic Factors Matter in Housing Markets? Paper presented at the 28th Annual
European Real Estate Society Conference, Milan, Italy.
Ling, D., and A. Naranjo. 1997. Economic Risk Factors and Commercial Real Estate Returns. Journal of
Real Estate Finance and Economics, 14 (3): 283–307.
Mayekiso, M., and F. Moodley. 2026. The effect of investor sentiment on returns of JSE size-based indices
under changing market conditions: Evidence from a Markov regime-switching model. Economics,
Management and Sustainability, 11(1): 116-135.
Moodley, Bangara, Lawrence/ Finance, Accounting and Business Analysis, Volume 8, Issue 1, 2026
189
McGough, T., and S. Tsolacos. 2001. Do Yields Reflect Property Market Fundamentals? Journal of Property
Research, 14 (2): 102–114.
Mehrling, P. 1999. The vision of Hyman P. Minsky. Journal of Economic Behavior & Organization, 39(2): 129-
158.
Moodley, F., and B. Lawrence. 2026. The impact of market dynamics and geopolitical uncertainty on
property return: A comparative analysis of BRICS countries. Risks, 14(3): 55.
Moodley, F. 2026. The effect of geopolitical risk on South African government bonds returns: a Markov
regime-switching framework. EUREKA: Social and Humanities, (2): 21-32.
Quan, D., and S. Titman. 1999. Do Real Estate Prices and Stock Prices Move Together? Real Estate
Economics, 27 (2): 183–207.
Wahab, M. B., A. S. Adeogun, G. B. Morenikeji, M. Mammah, and S. O. Abdulkareem. 2017. Effect of
Macroeconomic Factors on Residential Property Returns in Abuja, Nigeria. LAÜ Sosyal Bilimler
Dergisi, 8 (2): 133–149.
Yunus, N. 2012. Modeling Relationships among Securitized Property Markets, Stock Markets, and
Macroeconomic Variables. Journal of Real Estate Research, 34 (2): 127–156.
