Q

1. Determinants of foreign and
domestic non-listed real estate
fund flows in India
Ashish Gupta
RICS School of Built Environment, Amity University, Noida, India
Graeme Newell
Faculty of Economic, Finance and Property,
School of Business, Western Sydney University, Parramatta, Australia
Deepak Bajaj
Chongqing Jianzhu College, Chongqing, China and
SKEMA Business School France,
Lille, France, and
Satya Mandal
RICS School of the Built Environment, Amity University, Noida, India
Abstract
Purpose – Real estate forms an important part of any economy and the investment in real estate, in turn, is
impacted by the macroeconomic environment of that country. The purpose of the present research is to
examine macroeconomic determinants of foreign and domestic non-listed real estate fund (NREF) flows and to
examine whether they are similar or different for an emerging economy like India.
Design/methodology/approach – The long and short-run cointegration between the time-series variables is
estimated using the autoregressive distributed lag (ARDL) bounds test and error correction model (ECM) using
quarterly data across the 2005–2017 period. ARDL is a suitable method for short time-series data.
Findings – The empirical results indicate that domestic NREF flows are positively and significantly impacted
by real GDP and performance of listed real estate stocks (i.e. BSE realty index). Whereas, foreign NREF flows
are positively and significantly impacted by the exchange rate, performance of listed real estate stocks and
domestic NREF flows.
Practical implications – The empirical results have significant implications for academicians, policy
makers and real estate market practitioners. In the context of these results, some interesting insights are gained
that would help in the implementation of the policies aimed toward increasing the fund flows in the real estate
sector, which in turn would have a significant trickle-down effect on the Indian economy.
Originality/value – The existing literature looks at macroeconomic and other drivers of foreign investment in
international real estate investments. However, there are very few studies on the determinants of domestic real
estate investment flows and on determinants of NREFs’ investment flows; particularly in emerging markets.
The present study, in contrast, evaluates simultaneously the macroeconomic determinants of the domestic and
foreign NREFs’ investment flows in India. The ARDL and ECM method used has been applied for the first time
to the study of NREFs.
Keywords Investment determinant, Non-listed real estate funds (NREFs), Foreign and domestic investment,
India, Autoregressive distributed lag (ARDL)
Paper type Research paper
1. Introduction
Real estate forms an integral part of the national economy and represents a significant part of
any nation’s wealth (Gupta et al., 2018; Sch€
atz and Sebastian, 2009). Savills (2016) have
estimated real estate assets form about 60% of the global investable asset classes including
Foreign and
domestic
NREF flows
503
The authors will like to thank the two blind reviewers and Dr. C. P. Gupta, DFS, Delhi University for
constructive feedback and support.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1463-578X.htm
Received 8 August 2019
Revised 8 January 2020
Accepted 8 January 2020
Journal of Property Investment &
Finance
Vol. 38 No. 6, 2020
pp. 503-524
© Emerald Publishing Limited
1463-578X
DOI 10.1108/JPIF-08-2019-0107

2. equities, bonds and gold. They estimated in 2015, the global property value to be about 2.7
times global GDP. The macroeconomic environment has a significant impact on the
investment flow in real estate (Rodr
ıguez and Bustillo, 2010; Mak et al., 2012). Despite the
importance of real estate in global investable assets and on the national economy, there is a
limited empirical and theoretical literature exploring linkages between real estate investment
and the countries’ macroeconomic environment (Sch€
atz and Sebastian, 2009). The present
study, in contrast, establishes linkages between the investment in real estate (both domestic
and foreign fund flows) and the country’s macroeconomic environment; for India as an
important emerging market.
Investments in real estate could be directly in buildings and land or indirectly through
publicly listed or privately owned investment vehicles. The indirect investment could be
through publicly listed REITs and shares of listed real estate operating companies (REOCs)
and privately through the non-listed real estate funds (NREFs). NREFs have gained
prominence and institutional investors have used NREFs to gain exposure to high-quality
real estate (Haran et al., 2008; Fuerst and Matysiak, 2013). In the absence of any listed REITs
[1] (until recently) and poor performance of real estate operating companies (REOCs [2]) in
India, NREFs have emerged as a preferred real estate investment vehicle for institutional
investors seeking Indian real estate exposure; particularly in the opportunity real
estate space.
The global investor’s interest in India can be understood by looking at the global
uncertainty and volatility due to many factors including a trade war between the US and
China, US and Iran and events like Brexit (or no Brexit). In these times of global uncertainty,
IMF (2018) projected the advanced [3] economies’ GDP growth to reduce in 2019 to 2.1% as
compared to 2.3% in 2017. In the same period, emerging and developing Asian [4] economies
are projected to reduce to 6.3% from 6.5%, and China in this period is expected to reduce to
6.2% from 6.9%. On the contrary, India is expected to grow faster from 6.7% in 2017 to 7.4%
in 2019. India was the seventh largest global economy in 2018 and is expected to become the
fifth largest in 2019 and the third largest by 2033 (CEBR, 2018). Global investors have played
an important role in Indian real estate markets, JLL (2018) reported that foreign investment
constituted 70% of the institutional investment in Indian real estate in 2018. The importance
of foreign investment is not difficult to understand, as most of the published literature looks at
the determinants of foreign investment (eg: Lai and Fischer, 2007; Rodr
ıguez and Bustillo,
2010; He et al., 2011; Lieser and Groh, 2011; Ross, 2011; Mak et al., 2012; Fereidouni and
Masron, 2013a, b; Salem and Baum, 2016; Mauck and Price, 2015). We were not able to find
any exclusive study on the determinants of domestic real estate funds flow. Mauck and Price,
(2015) and Fuerst et al. (2015), however, evaluated the determinants of domestic and foreign
investment simultaneously, which raises a question of relevance to the policy makers and the
real estate fund managers as to whether the determinants of foreign investment are different
from domestic investment. Given the importance of emerging markets globally, the present
study evaluates the macroeconomic determinants of domestic and foreign NREF investment
flows simultaneously in India, which is one of the fastest growing major global economies; as
well as being a key emerging market. Due to a reform-driven agenda of the present
government undertaking tax reforms, introducing a real estate regulator and REITs (for
further details refer Gupta et al. (2017)), India offers unique opportunities for investors and
was amongst the top ten transparency improvers in the world (JLL, 2018a). Past studies (e.g.
Fereidouni and Masron (2013a, b)) indicate investors while investing in emerging economies
in contrast to developed markets consider transparency as a key attribute. The present study
analyzes the time-series data over 2005–2017 using the autoregressive distributed lag (ARDL)
bounds test approach to study NREFs, which is another contribution of the present study.
The present study provides a holistic review of the macroeconomic determinants of
domestic and foreign NREF investment flows in the context of India. Subsequent sections
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3. present a discussion on the evolution of NREFs in India, review of the past literature, data,
estimation method, discussion on empirical results followed by the conclusion and the
implications at the end.
2. The evolution of NREFs in India
NREFs in India got a boost with the opening of foreign direct investment (FDI) in Indian real
estate in 2005 through an automatic route. Since then, NREFs has evolved as an alternative to
traditional bank lending to the real estate sector. For a detailed discussion on the growth of
NREFs in India for the 2005–15 period, refer to Gupta et al. (2017). They divided this growth
into the three periods of 2005–08, 2009–13 and 2014–15 based on major political, economic
and international events. During this period, they found foreign investors to be the prominent
investors, having about 77% of the total real estate investment. They, however, reported that
after the global financial crisis (GFC) during the second phase, the share of foreign investors
declined substantially, indicating the importance of domestic investors in times of crisis for
the emerging economies. Some of the largest foreign investors in Indian real estate pre-GFC
were Lehman Brothers, Deutsche AMC, De Shaw, Vornado Realty and GIC, investing in
opportunistic investments. However, in the last few years, foreign investors like Blackstone,
Brookfield and Sovereign Wealth Funds like GIC, along with pension fund investors like
CPPIB have invested in core real estate assets. Gupta et al. (2017) reported the largest foreign
investors in India are namely GIC, Blackstone, Brookfield, Xander Group, Warburg Pincus
and Morgan Stanley; whereas, the largest domestic fund managers are Piramal Fund, HDFC
Fund, ILFS Investment, Kotak Realty, ICICI Venture, ASK Property and Milestone Capital.
With the maturity of the Indian real estate market, during these three phase investment
preferences changed from opportunistic in the pre-GFC period, to value-added and structured
debt in the post-GFC period and to core in recent years. Preqin (2017) estimated Indian NREFs
have assets under management of about $10.2 billion; about 11% of total assets under
management in Asia–Pacific by NREFs.
JLL (2018b) estimated during 2014–18, NREFs invested about $20 billion, which is about
double the investment of $10 billion during the 2009–13 period. During the 2014–18 period
with the passage of REITs’ legislation in India in 2014 and the listing of the first Indian REITs
in 2019, investment in completed commercial real estate assets picked up momentum. This
was evident as during the 2009–13 and 2014–18 periods, the share of residential
(opportunistic investment) decreased from 62% to 37% and that of commercial (core
investment) increased from 17% to 40% of the total real estate investment for the respective
periods. There has been a commitment of over $7 billion, as estimated by JLL (2018b), in
platform/joint ventures between major Indian developers and global institutional investors
since 2012; further reinforcing the real estate investment opportunities in real estate in India.
3. Literature review
Since Markowitz proposed modern portfolio theory, many researchers have studied the
advantages of diversification. Sirmans and Worzala (2003) summarized initial studies on
international investment focusing on the advantages of diversification and currency issues.
The growing cross border investment flows integrated the international investment markets.
The global economic environment and in particular the national economic environment have
a dynamic relation to investment in real estate. One area of study that has emerged in the last
few decades and has been extensively covered by previous researchers is the study of
macroeconomic and other determinants of real estate markets. With this perspective, three
different strands of literature evolved from the study of the past literature: determinants of
real estate investment; macroeconomic linkages of real estate markets and determinants of
real estate returns.
Foreign and
domestic
NREF flows
505

4. 3.1 Determinants of real estate investment
The first strand, evaluating the determinants of real estate investment has prime relevance to
this paper. Prior studies investigated the determinants of real estate investments by
evaluating macro-economic factors only (e.g. Ross, 2011); non-macro-economic factors (e.g.
Ford et al., 1998; Moshirian and Pham, 2000; Lai and Fischer, 2007; He et al., 2011; Fereidouni
and Masron, 2013a, b; Mauck and Price, 2015) and both macro-economic and non-macro-
economic factors (e.g. Rodr
ıguez and Bustillo, 2010; Lieser and Groh, 2011; Mak et al., 2012;
Fuerst et al., 2015; Li and Chen, 2015; Salem and Baum, 2016; Devaney et al., 2017; Poon, 2017).
Most of these studies evaluated the determinants of foreign investment, with only a few
studies (e.g. Fuerst et al., 2015; Mauck and Price, 2015) evaluating determinants of both the
domestic and the foreign investments.
In one of the initial studies on the determinants of investment, Ford et al. (1998)
validated the traditional investment wisdom of profit maximization and a risk
minimization strategy. While choosing amongst the four property types (apartment,
industrial, retail and office) in the US, foreign investors were cautious of the cap rate,
transaction activity and current rentals. Lai and Fischer (2007) conducted a survey of
experts using the analytical hierarchy process (AHP) to identify key factors impacting
the localization strategies of foreign investors in Taiwan. They found the top three
decision factors to be economic, policy and market factors respectively. Rodr
ıguez and
Bustillo (2010) analyzed the determinants of the foreign real estate investment (FREI) in
the housing sector (from a tourism perspective) in Spain using time-series data from 1990
to 2007. They found GDP per capita, number of tourists, travel costs, expected returns
and housing price as determinants of FREI from the perspective of demand for tourism
services with a financial focus.
He et al. (2011) studied the factors impacting FDI in real estate development in China. They
observed the disproportionate concentration of FDI in the coastal provincial areas and a
significant spatial autocorrelation in its distribution. The investors preferred provinces with
high housing prices and well-developed institutional structures (like the developed housing
market, good governance and strong enforcement), while they avoided provinces with high
labor and financing costs. Ross (2011) examined the influence of economic factors on FDI in
real estate in Queensland, Australia. He identified GDP per capita, exchange rate, national
saving, inflation, cost of capital and 10-year bond yield as various macroeconomic indicators
of FDI. Lieser and Groh (2011) developed a composite index to determine the attractiveness of
66 countries for institutional investors investing in commercial properties using factor
analysis. They found factors such as economic activities, socio-cultural and political
structure, investor protection and investment opportunities to be important factors for
international real estate investors.
Investigating determinants of investment across various regions in China, Mak et al.
(2012) identified three major determinants of regional investment as economic,
demographic and planning factors. Fereidouni and Masron (2013a, b) using panel data
for 31 countries, examined the relationship between the FREI and the market factors. While
controlling the political stability, market size and infrastructure, the results indicate higher
transparency and lower financing costs attracted foreign investment in real estate. It was
further found that investors preferred countries/markets with higher property prices.
Investors for developed countries considered infrastructure, whereas in the emerging
countries, along with the infrastructure, investors considered landlord and leasing
practices, transparency, financing cost and real estate prices as the major determinants of
foreign investment in real estate. Lieser and Groh (2014) provided evidence that the drivers
of commercial real estate investment are growth in GDP, rate of urbanization and
demographics. Whereas, real estate investments were adversely impacted by the lack of
transparency, restricted ease of doing real estate business, unstable political regime and
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5. challenges in the socio-cultural fabric. These findings would be particularly relevant in an
emerging market context.
Mauck and Price (2015) examined the difference between foreign versus domestic
determinants of investment in real estate in multiple portfolios across 84 countries. The study
found evidence of a difference in the determinants of foreign and domestic investment. They
found listed companies, while investing in properties in foreign countries, invest in larger
assets (in comparison to their domestic investments) to acquire a smaller position and most
likely, invest along with other investors in these properties. Foreign investment was found to
be negatively related to the development of local capital markets. Li and Chen (2015)
investigated the relationship between macro-economic variables and the Chinese real estate
market. The results indicate that real estate investment has a high correlation with money
supply, employment rate and long-term interest rate, while medium levels of correlation were
observed with inflation and GDP growth.
Fuerst et al. (2015) segregated the investment inflows and outflows between domestic
and foreign investors for 24 countries. They, like Lieser and Groh (2014), were not able
to establish any significant impact of legal and institutional barriers on foreign capital
inflows. Further domestic investment inflow was significantly impacted by returns and
the macro-economic environment, whereas liquidity was found to be the most
significant driver of both foreign and domestic investments. The results indicate the
economic condition, market liquidity and returns were the major drivers of domestic
investment.
For emerging economies in the Middle Eastern and North African (MENA) region, Salem
and Baum (2016) modeled determinants of FDI in commercial real estate. The study found
evidence that country-specific (GDP growth, political stability, human development and
peace and stability parameters) and the real estate specific (size of investable real estate)
variables impact commercial real estate FDI. Overall, the institutional setup, the regulatory
framework and the political stability are likely to impact FDI in commercial real estate in
emerging economies of the region. Devaney, McAllister and Nanda (2017) found the
transaction rate was positively related to the occupancy rate, market size and economic
growth rate, while it was negatively related to the capital market risks and transaction taxes.
Poon (2017) investigated key macro-economic and real estate market determinants of foreign
investment in the London real estate markets. Pearson correlation coefficient analysis
indicated a positive correlation between foreign investment and various independent
variables like house and land price, GDP and wages; whereas, a negative relationship was
found with interest rates.
3.2 Macro-economic linkages to real estate
In the second strand of literature, the relationship between the macro-economy and real estate
was explored. Previous academic literature investigated the dynamic relationship of macro-
economic indicators with: stocks, bonds, securitized real estate and direct real estate assets
(e.g. Liow and Yang, 2005; Yunus, 2012), different property type/s (e.g. Green, 1997; Bisping
and Patron, 2008; Francesco, 2008; Li and Ge, 2008; Sch€
atz and Sebastian, 2009; Bouchouicha
and Ftiti, 2012) and locational attributes of real estate (e.g. Ho and Muhammad, 2010).
In one of the initial studies, Green (1997) investigated the impact of non-residential and
residential investments on business cycles in the US. The empirical results indicate that GDP
causes non-residential investments but is not caused by it, whereas residential investments
cause GDP but are not caused by it. Liow and Yang (2005) found long-term equilibrium
between real estate stocks, common stocks and macro-economic variables in the Hong Kong
and Singapore markets. Thereby implying that both of these assets are substitutable in these
markets and should not be deployed in a portfolio for the benefit of diversification.
Foreign and
domestic
NREF flows
507

6. Unlike previous research, Li and Ge (2008) were not able to substantiate the inflation
hedging property of the residential real estate. Sch€
atz and Sebastian (2009) modeled the
dynamic interaction in the property markets of the UK and Germany and the regional
(country-specific) macro-economic environment and found that in the long-run, property
markets display similarities in spite of having structural differences and different
developments locally. Bouchouicha and Ftiti (2012) evaluated the relationship between
macro-economic indicators and the real estate markets in the US and the UK. The results
indicate a common trend in these regions in the long-run. However to institutional shocks, the
real estate markets within these countries behave differently.
3.3 Determinants of real estate returns [5]
The last strand of literature provides an overview of the determinants of real estate returns,
prices and cap rates. Most of the literature focusing on the macroeconomic factors of real
estate returns (in some cases, excess return and risk) investigated: multiple assets like direct
and indirect property, stocks and bonds (e.g. West and Worthington, 2006; Yunus, 2012);
direct property investment [commercial – office, retail and industrial (e.g. Dokko et al., 1991;
Ling and Naranjo, 1998; De Wit and Van Dijk, 2003; Hoskins et al., 2004; Kohlert, 2010; Lieser
and Groh, 2014; Akinsomi et al., 2018), residential/housing (e.g. Fereidouni and Bazrafshan,
2012) and both residential and non-residential (e.g. Liow, 2004; Klimczak, 2010)]; rents (e.g.
Hardin and Wolverton, 2000; Hendershott et al., 2002; Edelstein et al., 2011); cap rates (e.g.
Chuangdumrongsomsuk and Fuerst, 2017); prices of residential/housing (e.g. Borowiecki,
2009; Adams and F€
uss, 2010; Renigier-Bił ozor and Wi
s niewski, 2013); property funds (e.g.
Fuerst et al., 2013; Delfim and Hoesli, 2016); property stock markets (e.g. Liow et al., 2006) and
micro-market factors (Hardin and Wolverton, 2000).
Based on this literature, Table 1 shows the various macroeconomic indicators like
inflation, exchange rate, real GDP growth, real GDP, interest rate, money supply and stock
market performance that impact real estate fund flows.
From the study of the literature, we find evidence suggesting that the economic
environment has dynamic linkages with the real estate sector. Most of the past studies have
investigated the real estate and macroeconomic determinants of foreign investment; there are
only a few investigating only macroeconomic variables. The interest of the academic
community toward foreign investment appears to be because of large portfolios, transparent
operations and long vintages providing better data for investigation. Thillai and Doshi (2012)
found domestic investors have a better understanding of the domestic markets and are more
Parameter Previous research
Inflation De Wit and Van Dijk (2003), Hoskins et al. (2004), Liow et al. (2006), Hoesli
et al. (2008), Sch€
atz and Sebastian (2009), Ross (2011), Lieser and Groh (2014),
Delfim and Hoesli (2016)
Exchange rate Holsapple et al. (2006), Johnson et al. (2006), Liow et al. (2006), Hoesli et al.
(2004), Newell and Lee (2017)
Growth in national economy
(real GDPg)
Liow et al. (2006); Fuerst et al. (2013); Salem and Baum (2016); Delfim and
Hoesli (2016); Lieser and Groh (2014)
Size of national economy
(RGDP)
Green (1997), Olaleye et al. (2015), Poon (2017), Bao and Li (2017), Akinsomi
et al. (2018)
G Sec yield (10 years) Sch€
atz and Sebastian (2009), Fuerst et al. (2013)
Money supply (M3) Liow et al. (2006), Delfim and Hoesli (2016)
Stock market Fuerst et al. (2013), Delfim and Hoesli (2016)
Source(s): Authors’ compilation from the literature study
Table 1.
Macroeconomic
variables impacting
real estate funds flow,
price and return
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7. consistent, less volatile and diverse in their investment in comparison to foreign investors.
Gupta et al. (2017) reported during the 2005–15 period that foreign NREFs invested about
77% of total investment in India; however, after the GFC their share of investment decreased
(i.e. the share of domestic investors increased). This indicates that in emerging economies,
domestic investors may not be the dominant players; however, they reduce investment
volatility; especially in times of distress. The important question that arises is whether
domestic investment is also determined by the same factors as foreign investment in
emerging economies. However, there are hardly any studies on the determinants of domestic
investment, especially in the emerging markets. This study remedies this gap by
investigating macroeconomic determinants of domestic and foreign investments
simultaneously to real estate in India; particularly as India is a key emerging real estate
market.
4. Data
In the absence of a reliable publicly available investment database for NREFs in India, the
present study combined two secondary databases from Venture Intelligence (www.
ventureintelligence.com; from 2005 to 2017) and VCC Edge (www.vccedge.com; from 2005
to 2016). In order to create a reliable and robust database, data from these sources were
merged with other publicly available data from investor and company websites, newspaper
reports, stock market announcements and company annual reports. Duplicity and gaps in
these data were bridged from these publicly available data sources and discussions with
various NREF experts. The combined dataset had about 1,079 transactions with a value of
about $48 billion from 2005 to 2017, which represents a comprehensive and reliable database
for analysis. These data were segregated based on the source of funds (i.e. domestic and
foreign) for analysis, with about 75% of the total investment from foreign investors. Figure 1
shows the flow of domestic and foreign NREF flows across the 2005–2017 period. There was
a sudden fall in foreign NREF flows after the GFC (around 2008), investment remained
subdued during 2008–2014 period and revived in 2014 after new federal government was
elected, which boosted the investors’ confidence with this pro-reform agenda. However,
domestic NREF flows were quick to revive after the GFC, they maintained a stable funds flow
until 2014 and subsequently gradually picked up. Though both foreign and domestic NREF
flows showed similar investment patterns; foreign NREFs showed a sharp decline after
the GFC.
Domestic NREF Foreign NREF
2005q3
0
500
1000
1500
2000
2500
Investment
(USD
Million)
2008q3 2011q3 2014q3 2017q3
Figure 1.
Quarterly domestic
and foreign NREF
funds flow: Q1-2005 to
Q4-2017
Foreign and
domestic
NREF flows
509

8. In line with the literature, various determinants of NREF flows are identified as inflation
represented by the Consumer Price Index (CPI), the exchange rate between USD and INR
(EXRATE), real GDP (RGDP), 10 years of G-sec yield (GSEC), money supply (M3), the
Bombay Stock Exchange index (BSE) and the BSE realty index (BSER). The quarterly data
Independent
variable (IV) Description of IV Unit
Data
source
Expected
sign for
domestic
Expected
sign for
foreign
Consumer Price
Index (CPI)
Inflation indicates the
weighted average increase in
the price of a basket of goods
and services. It also indicates
a decrease in the purchasing
power of money
Base year
(2010 5 100)
IMF (þ) (þ)
Exchange rate
(EXRATE)
Exchange rate indicates a
change in the value of a
countries currency with
respect to benchmark
international currency (USD).
Increase in value of USD
results in a decrease in
returns at the time of exit for
the investor. In the short
term, it increases the
investment capacity of the
benchmark currency
INR/USD
(period
average)
IMF Not directly
applicable
(þ)
Real Gross
Domestic
Product (RGDP)
RGDP is the indicator of the
size of the national economy,
which in turn is the indicator
of the size of the real estate
market.
INR Billion RBI (þ) (þ)
G-Sec (GSEC) G sec indicates yield on 10
years of government
security/bonds, considered
as risk-free rate
% RBI () ()
Money Supply
(M3)
M3 is the broad money; it
includes M1 and time deposit
within the banking system. It
indicates the supply of
money/liquidity within an
economy
Base year
(2010 5 100)
OECD (þ) (þ)
SP BSE
Sensex (BSE)
SP BSE index indicates the
movement of 30 index stocks
at BSE. It is representative of
the stock market in India and
the health of its economy
Base year
(1978–
79 5 100)
BSE () ()
SP BSE Realty
Index (BSER)
SP Realty index indicates
the movement of real estate
index stocks at BSE. It is
representative of the listed
real estate stock market in
India and the health of the
real estate sector
BSE (þ) (þ)
Source(s): Authors’ analysis from data and various data sources
Table 2.
Description of the
variables
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9. for these variables along with domestic and foreign NREF flows are taken from Q1: 2005 to
Q4: 2017, and Table 2 depicts the description of these key variables.
5. Empirical methods and estimations
This section demonstrates the methodological approaches used to determine the long-run
and short-run linkages between the NREF flows and its macroeconomic determinants. In
order to develop a better understanding of the impact of these macro determinants, the flow of
NREFs was segregated based on their source of capital, i.e. domestic and foreign. The present
study tests the long and short-run cointegration between the time-series variables using the
ARDL bounds test and error correction model (ECM).
Many cointegration techniques have evolved in the last few decades that test the long-run
relationship within bivariate and multivariate economic variables. A few of the prominent
cointegration approaches proposed by Engle and Granger (1987), Johansen and Juselius
(2009), Johansen (1991) and Pesaran et al. (2001) are used extensively to analyze time-series
data. The ARDL bounds test for cointegration developed by Pesaran et al. (2001) is used in the
present study. This approach has been found to have many advantages over other
conventional cointegration approaches. First in this approach, the order of integration of the
variables is not of concern, as variables integrated of order I(0) and I(1) can be used to test the
hypothesis for cointegration. Second, unlike the conventional Johansen and Juselius (2009)
multivariate cointegration approach that uses a system of equations, ARDL uses only one
equation. Third, the ARDL method simultaneously estimates the long-run and short-run
parameters. Fourth, Narayan (2005) demonstrated that the bounds F-test values were reliable
for the small set of values between 30 and 80 observations, where other cointegration
approaches are found to be unreliable.
The ARDL has been used by various researchers in real estate for studying housing prices
(Arestis et al., 2017; Teye et al., 2017; Ozun et al., 2018); assessing housing related taxation
receipts (Smyth and McQuinn, 2016); identifying macro drivers of housing affordability
(Worthington and Higgs, 2013); determinants of mortgage defaults (Ngene et al., 2016);
studying linkages (volatility spillover effects) between various markets (Liow, 2014; Liow and
Schindler, 2017); studying the relationship between residential property and the stock market
(Lee, 2017); comparing real estate with other asset classes like bonds and shares (Szumilo
et al., 2018); modeling office rents of various markets to understand the intra-market
dependence and speed of adjustment toward long-run equilibrium (Mouzakis and Richards,
2007); studying the rental rate for pricing Islamic mortgage rates (Mohd Yusof, Bahlous and
Haniffa, 2016) and forecasting real estate pricing (An de Meulen, Micheli and Schmidt, 2014).
To establish these long and short-run relationships, the following four steps have been
used: (a) Unit root test to ensure none of the series is I(2), (b) ARDL bounds test approach for
testing cointegration, (c) ECM to estimate long and short-run dynamics and (d)
Diagnostics tests.
5.1 Unit root test
The ARDL bounds test is preferred when variables are integrated of different orders, i.e. I(0)
or I(1) or a combination of both. Although the unit root test is not a prerequisite for ARDL, this
approach will not work in the presence of I(2) variables. The present study, to ascertain the
stationarity of the variables, has used the Augmented Dickey-Fuller (ADF) test and Phillips-
Perron (PP) unit root tests.
5.2 Cointegration: ARDL bounds test
The ARDL model contains lagged values of a dependent variable, along with current and
lagged values of independent variables. By following Kripfganz and Schneider (2016) and
Adeleye et al. (2017), the generalized ARDL (p, q) model is specified in Equation (1).
Foreign and
domestic
NREF flows
511

10. Yt ¼ c0 þ c1t þ
X
p
i¼1
αi Yt−i þ
X
q
i¼0
βiXt−i þ mt (1)
where c0 is an intercept, α and β are coefficient of dependent and independent variables,
respectively; μ is the error term, and p and qi are lag order for dependent and independent
variables respectively. In this study, short and long-run dynamics are tested for two
independent variables, namely domestic NREF (DNREF) and foreign NREF (FNREF) flows.
For this, ECM is used; its equation is shown in Equation (2) and (3).
We test for the presence of cointegration, i.e. the presence of a long-run relationship
between lagged levels of explanatory variables, using the ARDL bounds test approach
developed by Pesaran et al. (2001). The test is based on joint F-statistics. Pesaran et al.
(2001) provided two sets of critical values for the lower and upper bounds, where the
independent variables are either I(0) or I(1). The lower set of critical values assume
that all the independent variables are I(0), whereas the upper set of critical values
assume all the independent variables are I(1). If the critical F-statistic is greater than
the I(1), we reject the null hypothesis of no cointegration. Alternatively, if the F-
statistic is lower than the I(0), we fail to reject the null hypothesis. If the F-statistic is
found to be between these lower and higher values, then the test is found to be
inconclusive.
5.3 Error correction model: short-run and long-run dynamics
When time-series variables are cointegrated, the ECM estimates the long-run equilibrium
relationship and short-run lag relation amongst the variables as shown in equation (2) and (3)
below.
ΔlogðDNREFtÞ ¼ α0 þ
X
p1
i¼1
α1iΔlogðDNREFt−iÞ þ
X
q1
i¼1
α2iΔlogðFNREFt−iÞ þ
X
q2
i¼1
α3iΔCPIt−i
þ
X
q3
i¼1
α4iΔlogðGDPt−iÞ þ
X
q4
i¼1
α5iΔlogðBSERt−iÞ þ λ1ECTt−1 þ μt
(2)
ΔlogðFNREFtÞ ¼ α10 þ
X
p11
i¼1
α11i ΔlogðFNREFt−iÞ þ
X
q11
i¼1
α12iΔlogðDNREFt−iÞ
þ
X
q12
i¼1
α13iΔEXRATEt−i þ
X
q13
i¼1
α14iΔlogðGDPt−iÞ
þ
X
q14
i¼1
α15iΔlogðBSERt−iÞ þ λ11ECTt−1 þ εt (3)
In equation (2) and (3) above, Δ is the difference operator, p and qi are lag order for dependent
and independent variables, α1i . . . α5i (equation 2) and α11i . . . α15i (equation 3) are the short-
run dynamic coefficients in equation (2) and (3), respectively; ECT is an error correction term
or speed of adjustment with λ being its coefficient. Variables in these equations are foreign
NREF flows (FNREF), domestic NREF flows (DNREF), inflation (CPI), exchange rate between
US dollar and Indian Rupee (EXRATE), real GDP (RGDP) and BSE realty index (BSER).
Other variables, like 10-year government security (GSEC), money supply (M3) and BSE stock
index (BSE) were not found to be significant and were dropped from these final models. In
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11. these models, λ has to be negative and statistically significant for cointegration and the
existence of a long-run equilibrium relationship between the variables.
6. Empirical results and discussion
Econometric procedures are now standard methods for examining real estate market
dynamics. Table 3 presents the descriptive statistics of the domestic and foreign NREF flows
along with selected variables. It describes quarterly mean, standard deviation, minimum and
maximum value. The data set has 52 quarterly observations between Q1 2005 to Q4 2017.
However, as the BSER was only launched in July 2007 with the availability of a few
retrospective index data, it has 48 observations.
The ARDL bounds process was used for testing for the existence of a long-run/
cointegrating relationship. Once cointegration was established by the ARDL bounds test,
ECM was used for the estimation of the long-run equilibrium and short-run lag relationship
between the variables. The empirical results are discussed in the section below.
6.1 Unit root test
The present study employed the ADF and PP unit root tests to check the stationarity in the
time-series as shown in Table 4. The null hypothesis is that there is a unit root in the series, i.e.
the series is non-stationary for these tests. It was found that various variables were integrated
at I(0) or I(1) level, which is an acceptable condition for the ARDL bounds test.
Variable # Observation Mean Std. Dev Minimum Maximum
Domestic NREFs flowa
(DNREF) 52 229.9 156.9 0.0 618.6
Foreign NREF flowa
(FNREF) 52 693.1 666.0 3.5 2604.2
Consumer Price Index (CPI) 52 111.0 32.9 64.5 164.1
Exchange rateb
(EXRATE) 52 52.7 9.2 39.5 67.5
SP BSE Realty Index (BSER) 48 3018.9 2309.0 1162.8 11,285.5
Real Gross Domestic Productc
(RGDP) 52 2,231,074 529,526 1,370,247 3,226,958
Note(s): a
in USD mn, b
in INR, c
in INR Billion
Source(s): Authors’ analysis from data
Variables
Augmented Dickey-Fuller (ADF) Phillips-Perron (PP)
Level First diff Decision Level First diff Decision
Log(FNREF) 3.282** - I(0) 4.965*** - I(0)
Log(DNREF) 3.185** - I(0) 4.717*** - I(0)
Log(BSER) 2.014 4.431*** I(1) 1.850 5.847*** I(1)
CPI 0.416 7.616*** I(1) 0.889 6.157*** I(1)
EXRATE 0.645 4.100*** I(1) 0.556 5.704*** I(1)
Log(RGDP) 0.975 17.419*** I(0) 0.774 13.903*** I(0)
Note(s): ***p 0.01, **p 0.05, *p 0.1, PP uses Newey-West standard errors to account for serial
correlation, whereas the ADF test uses additional lags of the first-difference variable, In ADF, one lag with the
constant term is selected using the Bayesian Information Criterion (BIC). In PP, lag is selected by default option
in Stata with constant term using Newey-West bandwidth, Ho: The variable contains a unit root
(non-stationary); Ha: The variable was generated by a stationary process
Table 3.
Data summary
Table 4.
Augmented Dickey-
Fuller (ADF) and
Phillips-Perron (PP)
unit root test
Foreign and
domestic
NREF flows
513

12. 6.2 Cointegration: the ARDL bounds testing approach
In order to conduct the bounds test for cointegration, as part of the ARDL procedure
to test the long-run relationship, maximum lags for DNREF and FNREF were selected,
using the BIC criterion. The results of the bounds test presented in Table 5 show that
the null hypothesis of no cointegration is rejected for both DNREF and FNREF at a
1% level, which indicates a long-run relationship between the variables. Once
cointegration is established between the dependent and independent variables, we
proceed to establish a long-run relationship and short-run dynamics using the
ECM below.
90% level 95% level 99% level
Panel A: DNREF
F-statistic I (0) I (1) I (0) I (1) I (0) I (1)
Critical values 2.45 3.52 2.86 4.01 3.74 5.06
Calculated value 9.140
Panel B: FNREF
F-statistic I (0) I (1) I (0) I (1) I (0) I (1)
Critical values 2.45 3.52 2.86 4.01 3.74 5.06
Calculated value 5.502
Note(s): In Panel A and B, as F-statistic critical value of I(1), we reject null hypothesis (Ho: No level
relationship), In Panel A: dependent variable is log(DNREF) and independent variables are log(FNREF),
log(BSER), CPI and log(RGDP), In Panel B: dependent variable is log(FNREF) and independent variables are
log(DNREF), log(BSER), EXRATE and log(RGDP)
Variables Coefficients
ECT t-1 0.992*** (0.155)
Long-run coefficients
log(FNREF) 0.029 (0.127)
log(BSER) 0.817*** (0.286)
CPI 0.001 (0.009)
log(RGDP) 3.576** (1.341)
Short-run coefficients
Δ log(FNREF) 0.221**(0.106)
Δ log(BSER) 0.793**(0.366)
Constant 53.021** ( 20.967 )
Observations 44
R-squared 0.633
Adj R-squared 0.562
Root MSE 0.485
Note(s): Standard errors are in parentheses, ***p 0.01, **p 0.05, *p 0.1, The dependent variable is
log(DNREF) and independent variables are log(FNREF), log(BSER), CPI, log(RGDP), ARDL (1 1 1 0 0) model
was selected using STATA’s default optimum lag section criteria
Table 5.
ARDL bound test
Table 6.
ECM results for
domestic NREF flow
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13. 6.3 Identification of drivers: error correction model—short and long-run dynamics
Table 6 shows the results of the ECM for domestic NREFs’ investment. The error
correction term (ECT) is the lag of the dependent variable, domestic NREF flow, having a
negative value and is found to be statistically significant at the 1% level. A relatively
high value of ECT indicates 99% of the deviation in the last quarter will return to its
long-run equilibrium in the present quarter. The results show that in the long-run,
domestic NREF flows are positively and significantly impacted by BSER and the RGDP.
The CPI and foreign NREF flows in the long-run were not found to be significantly
impacting the domestic NREF investments. However, in the short-run, foreign NREF and
BSER have a significant impact on the domestic NREF flows. Adjusted R2
value
indicates 56.2% of the domestic NREF investment flow is explained by these
independent variables. The results of actual and fitted values of Δ log(DNREF) is
shown in Figure A1. These results indicate the performance of the realty stocks and
national economy are important determinants of domestic NREF investment. To boost
domestic investment, suitable fiscal, regulatory and monetary policies should be
promoted to boost growth and investor confidence. In India, due to issues of corporate
governance and transparency in the real estate sector, realty stocks have performed
poorly as compared to the broader real estate markets, as reported by Gupta and
Agarwal (2019).
Table 7 shows the results of ECM for foreign NREF flows. A highly significant negative
value of ECT is obtained, indicating 54.6% speed of recovery to its long-run equilibrium
value in the next quarter. The results indicate that in the long-run, there is a positive
significant relationship between the foreign NREF flows and domestic NREF flows, BSER
and EXRATE. This indicates domestic NREFs’ investment, listed real estate and
exchange rate are significant predictors of foreign NREFs’ investment. However, in the
short-run, none of these variables are a predictor of foreign NREF flows. Adjusted R2
values show that independent variables can predict 52.2% of the foreign NREF flows. The
Variables Coefficients
ECT t-1 0.546*** (0.152)
Long-run coefficients
log(DNREF) 1.036 ** (0.471)
log(BSER) 1.203 ** (0.550)
EXRATE 0.114 ** (0.047)
log(RGDP) 3.791 (2.489)
Short-run coefficients
Δlog(FNREF)t-1 0.276**(0.127)
Constant 22.250(17.616)
Observations 44
R-squared 0.589
Adj R-squared 0.522
Root MSE 0.628
Note(s): Standard errors are in parentheses, Statistical significance levels of 1%, 5% and 10% are shown
respectively as ***p 0.01, **p 0.05, *p 0.1, The dependent variable is log(FNREF) and independent
variables are log(DNREF), log(BSER), EXRATE, log(RGDP), ARDL (2 0 0 0 0) model was selected using
STATA’s default optimum lag section criteria
Table 7.
ECM results for foreign
NREF flow
Foreign and
domestic
NREF flows
515

14. results of actual and fitted values of Δlog(FNREF) is shown in Figure A2. From these
results, we infer to boost foreign NREFs investment, the domestic NREFs investment
should be promoted and policies favorable to the realty sector should be adopted to
improve the performance of real estate stocks. In India, real estate stocks have performed
poorly [6] as compared to common stocks, but with various policy interventions [7] like
establishing a regulator in real estate, curbing black money (through demonetization) and
the introduction of REITs, it is likely to perform better in the future. The government
should adopt more reforms to improve transparency and corporate governance in the real
estate sector. Figure 2 shows the macro-economic determinants of both domestic and
foreign NREF flows in Indian real estate.
Table 8 describes the relationship between the variables and their expected versus actual
hypothesized relationship. All the achieved relationships within the models were found to be
as initially hypothesized.
6.4 Diagnostic tests
In order to evaluate the goodness of fit of the ARDL and ECM, various diagnostic tests to
evaluate the models were carried out. The series of tests evaluated the presence of
autocorrelation, heteroscedasticity and normality. Table 9 show that the models meet these
diagnostic tests. Results of CUSUMSQ test plot for DNREF (see Figure 3) and FNREF (see
Figure 4), show that the model is stable as the graph lies within the 5% significance level
boundaries.
Foreign
Investment
(FNREF)
Performance of
Realty Stocks
(BSER)
Exchange Rate
(EXRATE)
Domestic
Investment
(DNREF)
Size of National
Economy
(RGDP)
Note(s): Direction of the arrow shows the determinants of DNREF and FNREF
flows in India
Variable name
Domestic investment Foreign investment
Expected sign Actual sign Expected sign Actual sign
CPI (þ) Ns (þ) Ns Dp
EXRATE No direct impact Ns Dp (þ) (þ)**
RGDP (þ) (þ)** (þ) Ns
BSER (þ) (þ)*** (þ) (þ)**
BSE () Ns Dp () Ns Dp
GSEC () Ns Dp () Ns Dp
M3 (þ) Ns Dp (þ) Ns Dp
DNREF (þ) (þ)**
FNREF (þ) Ns
Note(s): ***p 0.01, **p 0.05, *p 0.1, Ns - Not significant; NsDp – Not significant dropped from the model
Figure 2.
Macroeconomic
determinants of
NREF flows
Table 8.
Significance of
variables
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15. Test Description
Test
statistic#
Conclusion
Breusch-
Godfrey
Test for autocorrelation 0.823/1.694 No serial correlation
ARCH LM Test for the presence of autoregressive
conditional heteroscedasticity
1.028/0.446 No autoregressive conditional
heteroscedasticity
White Tests for heteroscedasticity, skewness and
kurtosis
34.37/28.88 No heteroscedasticity
Jarque–Bera Test for normality 2.385/0.762 Evidence of normality
Note(s): #
DNREF/FNREF, ***p 0.01, **p 0.05, *p 0.1
Figure 3.
Domestic NREF: Plot of
CUSUMSQ for model
stability at 5% level of
significance
Figure 4.
Foreign NREF: Plot of
CUSUMSQ for model
stability at 5% level of
significance
Table 9.
Diagnostic tests
Foreign and
domestic
NREF flows
517

16. 7. Conclusion
NREFs have been a major source of institutional capital in Indian real estate in the absence of
REITs (until recently). Gupta et al. (2017) have discussed the importance of NREFs for India
and have reported about 77% of investment was from foreign NREFs across the 2005–2015
period. The previous research (eg: Rodr
ıguez and Bustillo, 2010; Mak et al., 2012) has
indicated that in the context of foreign investment, the host counties’ macroeconomic
environment plays an important role. Most of these previous research studies investigated
the determinants of foreign investment. Foreign investors are the major investors in
emerging markets like India, but the holistic study of both domestic and foreign investment
determinants assumes importance, as domestic investors have been more consistent and less
volatile in times of crisis (as reported by Thillai and Doshi, 2012; Gupta et al., 2017). We
simultaneously investigated both domestic and foreign NREF flows to examine whether
domestic and foreign investors have different macroeconomic determinants. Like Mauck and
Price (2015), the present study has been able to demonstrate that the determinants of
domestic and foreign investment in real estate by NREFs are different.
The empirical results indicate that domestic NREF flows are positively and significantly
impacted by GDP and performance of listed property stocks. However, as expected, CPI was
not found to be a significant predictor of domestic NREF flows. The results further indicate
foreign NREF flows is positively and significantly impacted by the exchange rate,
performance of listed real estate stocks (i.e. BSE realty index) and domestic NREF flows. Like
Lieser and Groh (2014), we were also not able to establish growth in GDP has a significant
relationship to foreign investment flow. Previous research (e.g. Holsapple et al., 2006; Johnson
et al., 2006; Newell and Lee, 2017) have also considered exchange rate/currency risk as a major
concern of foreign investors. To increase foreign investment in an emerging economy like
India, apart from concentrating on domestic investment, it is important to improve the
performance of listed property stocks. In the case of India, property stocks have
underperformed the common stocks. Performance of property companies can be improved
by bringing transparency and enforcing better corporate governance standards in the sector.
JLL (2018a) listed India as one of the top ten global transparency improvers, with a rank of 35;
it is now at the cusp of moving to transparent from semi-transparent rankings. This was
achieved on account of regulatory reforms like RERA and REITs, which along with high
growth have helped in increasing foreign investors’ confidence in India.
The empirical results have significant implications for academicians, policy makers and
real estate market practitioners. The results show that foreign investors are positively
impacted by the performance of the real estate stocks and the depreciation of the national
currency; indicating the importance of the capital market and its robustness for foreign
investors. Policy makers should formulate policies to increase the trust, ensure the
transparency in the real estate sector and real estate capital markets to increase the
confidence of the foreign investors. In the context of these results, some interesting insights
are gained that would help in the implementation of the policies aimed toward increasing the
NREF flows, which in turn would have a significant trickle-down effect on the Indian
economy. The limitation of the present study was the unavailability of time-series data on real
estate market factors like pricing, yield movement, vacancy and sales velocity. It would be
worthwhile to study these and other market factors that impact the NREF flows. Future
research could also be conducted for determinants of fund flows for specific asset classes and
for specific classes of investors as they seek to further expand their real estate markets
via NREFs.
Overall, this research has highlighted the critical differences in macro-economic drivers of
both domestic and international investors in Indian real estate since the introduction of
NREFs. This area is expected to take on increased importance as international investors seek
high-quality real estate exposure in India to capture its economic growth expectations. The
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17. articulation of these drivers will further facilitate the institutional investor’s investment
process in India. The identification of these critical drivers also has significant implications
for other emerging markets.
Notes
1. In India, REIT legislation was passed in 2014, but it took another 5 years for the first REIT (Embassy
Office Parks REIT Ltd., EOPRL) to list in April 2019. EOPRL was issued at an offer price of $4.28 per
unit in the start of 2nd quarter 2019, and by mid-4th quarter 2019 it provided a return of over 30%,
with a market capitalization of approximately $4.28 bn ($1 5 70 INR). EOPRL was backed by
Blackstone and Embassy group. Presently it is the only listed REITs in India; however, there a few
REIT portfolio in pipeline for listing.
2. During the stock market and real estate boom of 2006–08, many real estate firms were listed. They
were oversubscribed and got attractive valuations. However, post global financial crisis in 2008
these stocks crashed and till date have performed poorly as compared to broader stock markets
(Gupta et al., 2017; Gupta and Agarwal, 2019).
3. Comprising of the United States, Euro Area, Japan, United Kingdom, Canada and other advanced
economies.
4. Comprising of China, India and five ASEAN countries.
5. This paper focuses on real estate investment, which depends on the performance of the underlying
asset, i.e. property. Thus, apart from literature on real estate investment, in this section we provide an
overview of the literature on real estate returns to provide complete overview of literature.
6. During Q4-2008 to Q4-2017 period, BSE index appreciated by 252% whereas during the same period
BSE realty index appreciated by 26.52% (www.bseindia.com).
7. Gupta and Agarwal (2019) discussed various reforms and programs adopted by government in
recent years including REITs legislation in 2016, “Housing for all 2022”, Real Estate (Regulation and
Development) Act (RERA), demonetization of high denomination currency notes, Benami
Transaction Act, Insolvency and Bankruptcy Code (IBC) and Goods and Services Tax (GST).
References
Adams, Z. and F€
uss, R. (2010), “Macroeconomic determinants of international housing markets”,
Journal of Housing Economics, Vol. 19 No. 1, pp. 38-50.
Adeleye, N., Osabuohien, E., Bowale, E., Matthew, O. and Oduntan, E. (2017), “Financial reforms and
credit growth in Nigeria: empirical insights from ARDL and ECM techniques”, International
Review of Applied Economics, Vol. 32 No. 6, pp. 807-820.
Akinsomi, O., Mkhabela, N. and Taderera, M. (2018), “The role of macro-economic indicators in
explaining direct commercial real estate returns: evidence from South Africa”, Journal of
Property Research, Vol. 35 No. 1, pp. 28-52.
An de Meulen, P., Micheli, M. and Schmidt, T. (2014), “Forecasting real estate prices in Germany: the
role of consumer confidence”, Journal of Property Research, Vol. 31 No. 3, pp. 244-263.
Arestis, P., Gonzalez-Martinez, A.R. and Jia, L.K. (2017), “House price overvaluation in Hong Kong:
identifying the market fundamentals and understanding the bubble”, International Journal of
Housing Markets and Analysis, Vol. 10 No. 2, pp. 282-304.
Bao, H.X.H. and Li, S.H. (2017), “House price determinants: the roles of fundamentals and sentiments”,
Journal of Real Estate Practice and Education, Vol. 20 No. 1, pp. 63-77.
Bisping, T.O. and Patron, H. (2008), “Residential investment and business cycles in an open economy:
a generalized impulse response approach”, Journal of Real Estate Finance and Economics,
Vol. 37 No. 1, pp. 33-49.
Foreign and
domestic
NREF flows
519

18. Borowiecki, K.J. (2009), “The determinants of house prices and construction : an empirical
investigation of the Swiss housing economy”, International Real Estate Review, Vol. 12 No. 3,
pp. 193-220.
Bouchouicha, R. and Ftiti, Z. (2012), “Real estate markets and the macroeconomy: a dynamic
coherence framework”, Economic Modelling, Vol. 29 No. 5, pp. 1820-1829.
CEBR (2018), World Economic League Table 2019, CEBR, London.
Chuangdumrongsomsuk, M. and Fuerst, F. (2017), “Determinants of cap rates in US office markets”,
Journal of Real Estate Literature, Vol. 25 No. 2, pp. 265-282.
De Wit, I. and Van Dijk, R. (2003), “The global determinants of direct office real estate returns”,
Journal of Real Estate Finance and Economics, Vol. 26 No. 1, pp. 27-45.
Delfim, J.C. and Hoesli, M. (2016), “Risk factor of European non-listed real estate fund return”, Journal
of Property Research, Vol. 33 No. 3, pp. 190-213.
Devaney, S., McAllister, P. and Nanda, A. (2017), “Which factors determine transaction activity across
US metropolitan office markets?”, The Journal of Portfolio Management, Vol. 43 No. 6, pp. 90-104.
Dokko, Y., Edelstein, R.H., Pomer, M. and Urdang, E.S. (1991), “Determinants of the rate of return for
non-residential real estate: inflation expectations and market adjustment lags”, Real Estate
Economics, Vol. 19 No. 1, pp. 52-69.
Edelstein, R., Qian, W. and Tsang, D. (2011), “How do institutional factors affect international real
estate returns?”, Journal of Real Estate Finance and Economics, Vol. 43 No. 1, pp. 130-151.
Engle, R.F. and Granger, C.W.J. (1987), “Co-integration and error correction: representation, estimation,
and testing”, Econometrica, Vol. 55 No. 2, pp. 251-276.
Fereidouni, H.G. and Bazrafshan, E. (2012), “Determinants of returns on housing: the Iranian case”,
International Journal of Housing Markets and Analysis, Vol. 5 No. 4, pp. 351-360.
Fereidouni, H.G. and Masron, T.A. (2013a), “Real estate market factors and foreign real estate
investment”, Journal of Economic Studies, Vol. 40 No. 4, pp. 448-468.
Fereidouni, H.G. and Masron, T.A. (2013b), “Real estate market factors and foreign real estate
investment”, Journal of Economic Studies, Vol. 40 No. 4, pp. 448-468.
Ford, D.A., Fung, H. and Gerlowski, D.A. (1998), “Factors affecting foreign investor choice in types of
US real estate”, Journal of Real Estate Research, Vol. 16 No. 1, pp. 99-112.
Francesco, A.J. De (2008), “Time-series characteristics and long-run equilibrium for major Australian
office markets”, Real Estate Economics, Vol. 36 No. 2, pp. 371-402.
Fuerst, F. and Matysiak, G. (2013), “Analysing the performance of nonlisted real estate funds: a panel
data analysis”, Applied Economics, Vol. 45 No. 14, pp. 1777-1788.
Fuerst, F., Lim, W. and Matysiak, G. (2013), Drivers of Fund Performance, INREV, Amsterdam.
Fuerst, F., Milcheva, S. and Baum, A. (2015), “Cross-border capital flows into real estate”, Real Estate
Finance, Vol. 31 No. 3, pp. 103-122.
Green, R.K. (1997), “Follow the leader: how changes in residential and non-residential investment
predict changes in GDP”, Real Estate Economics, Vol. 25 No. 2, pp. 253-270.
Gupta, A. and Agarwal, S. (2019), “Trader to investor: evolution of real estate development in India”,
in Das, P., Aroul, R. and Freybote, J. (Eds), Real Estate in South Asia, 1st edn., Routledge.
Gupta, A., Sawhney, A., Bajaj, D. and Agarwal, S. (2017), “Significance of real estate fund management
in India”, Journal of Real Estate Literature, Vol. 25 No. 1, pp. 141-168.
Gupta, A., Newell, G., Bajaj, D. and Mandal, S. (2018), “Identifying the risk factors in Indian non-listed
real estate funds”, Journal of Property Investment and Finance, Vol. 36 No. 5, pp. 429-453.
Haran, M., McGreal, S., Adair, A. and Webb, J.R. (2008), “Unlisted property fund investment in urban
renewal/regeneration property in the U.K.: the impact of investment style on decision making”,
Journal of Real Estate Portfolio Management, Vol. 14 No. 3, pp. 185-193.
JPIF
38,6
520

19. Hardin, W. III and Wolverton, M. (2000), “Micro-market determinants of neighborhood center rental
rates”, Journal of Real Estate Research, Vol. 20 No. 3, pp. 299-322.
He, C., Wang, J. and Cheng, S. (2011), “What attracts foreign direct investment in China’s real estate
development?”, The Annals of Regional Science, Vol. 46 No. 2, pp. 267-293.
Hendershott, P., MacGregor, B. and White, M. (2002), “Explaining real commercial rents using an
error correction model with panel data”, Journal of Real Estate Finance and Economics, Vol. 24
Nos 1/2, pp. 59-87.
Ho, K.H.D. and Muhammadbin, F.I. (2010), “Explaining the macro-economy and retail real estate
sector dynamic interaction between prime and suburban retail real estate sectors”, Journal of
Property Investment and Finance, Vol. 28 No. 2, pp. 77-108.
Hoesli, M., Lekander, J. and Witkiewicz, W. (2004), “International evidence on real estate as a portfolio
diversifier”, Journal of Real Estate Research, Vol. 26 No. 2, pp. 161-206.
Hoesli, M., Lizieri, C. and MacGregor, B. (2008), “The inflation hedging characteristics of US and UK
investments: a multi-factor error correction approach”, The Journal of Real Estate Finance and
Economics, Vol. 36 No. 2, pp. 183-206.
Holsapple, E.J., Ozawa, T. and Olienyk, J. (2006), “Foreign “direct” and “portfolio” investment in real
estate: an eclectic paradigm”, Journal of Real Estate Portfolio Management, Vol. 12 No. 1,
pp. 37-47.
Hoskins, N., Higgins, D. and Cardew, R. (2004), “Macroeconomic variables and real estate returns: an
international comparison”, The Appraisal Journal, Vol. 72 No. 2, pp. 163-170.
IMF (2018), World Economic Outlook Update, January 2018, IMF, Washington D.C.
JLL (2018a), Global Real Estate Transparency Index 2018, JLL, Chicago.
JLL (2018b), Institutional Flow of Funds to Indian Real Estate: Trends and Progress, JLL, Mumbai.
Johansen, S. (1991), “Estimation and hypothesis testing of cointegration vectors in Gaussian vector
autoregressive models”, Econometrica, Vol. 59 No. 6, pp. 1551-1580.
Johansen, S. and Juselius, K. (2009), “Maximum likelihood estimation and inference on cointegration
with applications to the demand for money”, Oxford Bulletin of Economics and Statistics, Vol. 52
No. 2, pp. 169-210.
Johnson, R., Lizieri, C.M., Soenen, L. and Worzala, E.M. (2006), “Simulating currency risk on private
investments in real estate”, Journal of Real Estate Portfolio Management, Vol. 12 No. 9,
pp. 91-102.
Klimczak, K. (2010), “Determinants of real estate investment”, Economics and Sociology, Vol. 3 No. 2,
pp. 58-66.
Kohlert, D. (2010), “The determinants of regional real estate returns in the United Kingdom: a vector
error correction approach”, Journal of Property Research, Vol. 27 No. 1, pp. 87-117.
Kripfganz, S. and Schneider, D.C. (2016), “ardl: Stata module to estimate autoregressive distributed lag
models”, in Stata Conference, Chicago.
Lai, P.P. and Fischer, D. (2007), “The determinants of foreign real estate investment in Taiwan”,
Pacific Rim Property Research Journal, Vol. 13 No. 3, pp. 263-279.
Lee, K.N.H. (2017), “Residential property price-stock price nexus in Hong Kong: new evidence from
ARDL bounds test”, International Journal of Housing Markets and Analysis, Vol. 10 No. 2,
pp. 204-220.
Li, J. and Chen, X. (2015), “Analysis of China’s real estate prices and macroeconomy based on
evolutionary co-spectral method”, Journal of Industrial Engineering and Management, Vol. 8
No. 2, pp. 598-614.
Li, L.H. and Ge, C.L. (2008), “Inflation and housing market in Shanghai”, Property Management,
Vol. 26 No. 4, pp. 273-288.
Foreign and
domestic
NREF flows
521

20. Lieser, K. and Groh, A.P. (2011), “The attractiveness of 66 countries for institutional real estate
investments”, Journal of Real Estate Portfolio Managment, Vol. 17 No. 3, pp. 191-211.
Lieser, K. and Groh, A.P. (2014), “The determinants of international commercial real estate
investment”, Journal of Real Estate Finance and Economics, Vol. 48 No. 4, pp. 611-659.
Ling, D.C. and Naranjo, A. (1998), “The fundamental determinants of commercial real estate returns”,
Real Estate Finance, Vol. 14 No. 4, pp. 13-24.
Liow, K.H. (2004), “Time-varying macroeconomic risk and commercial real estate: an asset pricing
perspective”, Journal of Real Estate Portfolio Management, Vol. 10 No. 1, pp. 47-57.
Liow, K.H. (2014), “The dynamics of return co-movements and volatility spillover effects in China
public property markets and international linkages”, Journal of Property Investment and
Finance, Vol. 32 No. 6, pp. 610-641.
Liow, K.H. and Schindler, F. (2017), “Linkages between office markets in Europe: a volatility spillover
perspective”, Journal of Property Investment and Finance, Vol. 35 No. 1, pp. 3-25.
Liow, K.H. and Yang, H. (2005), “Long-term co-memories and short-run adjustment: securitized real
estate and stock markets”, Journal of Real Estate Finance and Economics, Vol. 31 No. 3,
pp. 283-300.
Liow, K.H., Ibrahim, M.F. and Huang, Q. (2006), “Macroeconomic risk influences on the property stock
market”, Journal of Property Investment and Finance, Vol. 24 No. 4, pp. 295-323.
Mak, S., Choy, L. and Ho, W. (2012), “Region-specific estimates of the determinants of real estate
investment in China”, Urban Studies, Vol. 49 No. 4, pp. 741-755.
Mauck, N. and Price, S.M. (2015), “Determinants of foreign versus domestic real estate investment:
property level evidence from listed real estate investment firms”, The Journal of Real Estate
Finance and Economics, Vol. 54 No. 1, pp. 17-57.
Mohd Yusof, R., Bahlous, M. and Haniffa, R. (2016), “Rental rate as an alternative pricing for Islamic
home financing: an empirical investigation on the UK market”, International Journal of Housing
Markets and Analysis, Vol. 9 No. 4, pp. 601-626.
Moshirian, F. and Pham, T. (2000), “Determinants of US investment in real estate abroad”, Journal of
Multinational Financial Management, Vol. 10 No. 1, pp. 63-72.
Mouzakis, F. and Richards, D. (2007), “Panel data modelling of prime office rents: study of 12 major
European markets”, Journal of Property Research, Vol. 24 No. 1, pp. 31-53.
Narayan, P.K. (2005), “The saving and investment nexus for China: evidence from cointegration tests”,
Applied Economics, Vol. 37 No. 17, pp. 1979-1990.
Newell, G. and Lee, C.L. (2017), The Impact of Currency on the Performance of European Non-listed
Real Estate Funds, INREV, Amsterdam.
Ngene, G.M., Hassan, M.K., Hippler, W.J. III and Julio, I. (2016), “Determinants of mortgage default
rates: pre-crisis and crisis period dynamics and stability”, Journal of Housing Research, Vol. 25
No. 1, pp. 39-64.
Olaleye, B.A., Bello, O.B. and Ayodele, T.O. (2015), “Determinants of listed property stock prices in
Nigeria: a macro economic perspective”, Real Estate Finance, Vol. 32 No. 2, pp. 70-76. No.
Ozun, A., Ertugrul, H.M. and Coskun, Y. (2018), “A dynamic model for housing price spillovers with
an evidence from the US and the UK markets”, Journal of Capital Markets Studies, Vol. 2 No. 1,
pp. 70-81.
Pesaran, M.H., Shin, Y. and Smith, R.J. (2001), “Bounds testing approaches to the analysis of level
relationships”, Journal of Applied Econometrics, Vol. 16 No. 3, pp. 289-326.
Poon, J. (2017), “Foreign direct investment in the UK real estate market”, Pacific Rim Property
Research Journal, Vol. 23 No. 3, pp. 249-266, Routledge.
Preqin (2017), Preqin Insight: Alternative Assets in India, Preqin, London.
JPIF
38,6
522

21. Renigier-Biłozor, M. and Wi
sniewski, R. (2013), “The impact of macroeconomic factors on residential
property price indices in Europe”, Folia Oeconomica Stetinensia, Vol. 12 No. 2, pp. 103-125.
Rodr
ıguez, C. and Bustillo, R. (2010), “Modelling foreign real estate investment: the Spanish case”,
Journal of Real Estate Finance and Economics, Vol. 41 No. 3, pp. 354-367.
Ross, S. (2011), “A model for examining foreign direct investment in real estate”, Journal of new
business ideas and trends, Vol. 9 No. 2, pp. 23-33.
Salem, M. and Baum, A. (2016), “Determinants of foreign direct real estate investment in selected
MENA countries”, Journal of Property Investment and Finance, Vol. 34 No. 2, pp. 116-142.
Savills (2016), Around the World in Dollars and Cents, Savills, London.
Sch€
atz, A. and Sebastian, S. (2009), “The links between property and the economy – evidence from the
British and German markets”, Journal of Property Research, Vol. 26 No. 2, pp. 171-191.
Sirmans, C.F. and Worzala, E. (2003), “International direct real estate investment: a review of the
literature”, Urban Studies, Vol. 40 Nos 5–6, pp. 1081-1114.
Smyth, D.A. and McQuinn, K. (2016), “Assessing the sustainable nature of housing-related taxation
receipts: the case of Ireland”, Journal of European Real Estate Research, Vol. 9 No. 2,
pp. 193-214.
Szumilo, N., Wiegelmann, T., Łaszkiewicz, E., Pietrzak, M.B. and Balcerzak, A.P. (2018), “The real
alternative? A comparison of German real estate returns with bonds and stocks”, Journal of
Property Investment and Finance, Vol. 36 No. 1, pp. 19-31.
Teye, A.L., Knoppel, M., Haan, J.D. and Elsinga, M.G. (2017), “Amsterdam house price ripple effects in
The Netherlands”, Journal of European Real Estate Research, Vol. 10 No. 3, pp. 331-345.
Thillai, R.A. and Doshi, M. (2012), “Beyond capital: private equity and real estate development in
India”, The Journal of Private Equity, Vol. 15 No. 3, pp. 62-76.
West, T. and Worthington, A.C. (2006), “Macroeconomic risk factors in Australian commercial real
estate, listed property trust and property sector stock returns”, Journal of Financial
Management of Property and Construction, Vol. 11 No. 2, pp. 105-116.
Worthington, A. and Higgs, H. (2013), “Macro drivers of Australian housing affordability, 1985-2010:
an autoregressive distributed lag approach”, Studies in Economics and Finance, Vol. 30 No. 4,
pp. 347-369.
Yunus, N. (2012), “Modeling relationships among securitized property markets, stock markets, and
macroeconomics variables”, Journal of Real Estate Research, Vol. 34 No. 2, pp. 127-156.
Foreign and
domestic
NREF flows
523

22. Annexure
Corresponding author
Ashish Gupta is the corresponding author and can be contacted at: agupta@ricssbe.edu.in
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Figure A2.
Actual and fitted value
for Δ log(FNREF)
Figure A1.
Actual and fitted value
for Δ log(DNREF)
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