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FACTORS AFFECTING REAL ESTATE PRICES -EVIDENCE FROM
MACROECONOMIC DATA IN VIETNAM 影響房地產價格的因素 -來自越南宏觀經濟
數據的證據
Article in Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University · August 2020
DOI: 10.35741/issn.0258-2724.55.4.65
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西南交通大学学报
第 55 卷 第 4 期
JOURNAL OF SOUTHWEST JIAOTONG UNIVERSITY
2020 年 8 月
ISSN: 0258-2724
Vol. 55 No. 4
Aug. 2020
DOI：10.35741/issn.0258-2724.55.4.65
Research article
Economics
FACTORS AFFECTING REAL ESTATE PRICES
– EVIDENCE FROM MACROECONOMIC DATA IN VIETNAM
影響房地產價格的因素 –來自越南宏觀經濟數據的證據
Nguyen Thi Hoang Yen a, Nguyen Thanh Hung b, Le Doan Minh Duc a, *, Vo Hoang Ngoc Thuy a,
Ngo Thi My Thuy c
a
Thu Dau Mot University
6, Tran Van On Street, Phu Hoa Ward, Thu Dau Mot City, Binh Duong Province, Vietnam,
[email protected]
b
Binh Duong University
504, Binh Duong Avenue, Hiep Thanh Ward, Thu Dau Mot City, Binh Duong Province, Vietnam
c
University of Finance – Marketing
2/4, Tran Xuan Soan Street, Tan Thuan Tay Ward, District 7, Ho Chi Minh City, Vietnam
Received: May 11, 2020 ▪ Review: June 14, 2020 ▪ Accepted: July 15, 2020
This article is an open-access article distributed under the terms and conditions of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/4.0)
Abstract
The real estate market is important in socio-economic life and is even more important for countries
with open, transition, and developing economies like Vietnam. The price of real estate is an important
factor in the market and is affected by many macroeconomic factors. Using quarterly data from the first
quarter of 2005 to the fourth quarter of 2018, the authors construct realistic test pattern matching
purposes, requires analysis and possibility of possible sources of data collected in Vietnam on the
relationship between real estate price index and factors of economic growth, inflation, money supply and
average long-term lending rate in the market. Experimental results demonstrate that these macroeconomic
factors have a significant impact on real estate prices in Vietnam. From here, the article provides
macroeconomic policy implications for the State of Vietnam in stabilizing real estate commodity prices in
particular and developing the real estate market in general.
Keywords: Real Estate, Real Estate Prices, Macro Policies, Vietnam
摘要 房地產市場在社會經濟生活中非常重要，對於像越南這樣的開放，轉型和發展中經濟體的國
家而言，房地產市場尤為重要。房地產價格是市場中的重要因素，並受到許多宏觀經濟因素的影
響。作者使用2005年第一季度至2018年第四季度的季度數據，構建了現實的測試模式匹配目的，
要求分析和確定越南收集的有關房地產價格指數與經濟增長因素之間關係的數據來源
，通貨膨脹，貨幣供應量和市場中的平均長期貸款利率。實驗結果表明，這些宏觀經濟因素對越
南的房地產價格有重大影響。從這裡開始，本文提供了對越南國家的宏觀經濟政策影響，特別是
Nguyen Thi Hoang Yen et al. / Journal of Southwest Jiaotong University / Vol.55 No.4 Aug. 2020
在穩定房地產商品價格和總體上發展房地產市場方面方面。
关键词: 越南房地產，房地產價格，宏觀政策
I.
INTRODUCTION
Real estate is central to urban sustainable
development and socio-economic policies [1].
The real estate price index not only affects
individuals but also plays an important role
under the macroeconomic level. The issue of
considering which factors affect real estate
prices has been interested in the researchers
around the world for many years, such as
empirical research in the US [2], UK [3], Hong
Kong [4], Singapore [5], Malaysia [1], China
[6], [7] or comparative situation in Hong Kong Beijing - Shanghai [8], and so on. In Vietnam,
the price of real estate goods has been a topic of
debate in many programs and seminars of
scientific research institutes and real estate
associations. Many domestic studies have
pointed out inadequacies in the management
mechanism of the real estate market and the
pricing of this particular commodity [9], [10].
Within Ho Chi Minh City, the study [11]
discovered the correlation of macro-financial
factors to fluctuations in housing prices.
Real estate is a characteristic commodity that
commercial values are cyclical [12]. During the
period 2000 - 2012, real estate prices in Vietnam
experienced two hot growth cycles and then
cooled in 2000 - 2006 and 2007 - 2012
respectively [13]. A sharp rise in real estate
prices often leads to a period of boom and rapid
decline when it falls into the freezing period
[14]. Recently, in 2017, business activities on
the real estate market became more active, up
4.07% with the highest level since 2011,
contributing 0.21 percentage points to the total
growth of 6.81% of national GDP. By the
second quarter of 2018, real estate had a growth
of 4.21%, which is the highest in 6 years [15].
The study of factors affecting real estate
prices is a matter of urgency in both theory and
practice. The world's researches on this issue are
conducted in countries with different
characteristics of the macroeconomic situation
compared to Vietnam, while the researches in
Vietnam are mainly based on the aggregate
method. The document aims to discuss the
development trend of the domestic real estate
market or to measure the factors affecting the
data range of a locality. In other words, there has
not been a full academic study based on the
relevant theory to explore the factors as well as
measuring the impact to provide evidence from
macroeconomic data in Vietnam. Therefore, the
authors identified the research problem as a gap
in previous studies and proceeding to set a
general objective of studying the factors
affecting real estate prices to propose policy
implications to stabilize real estate prices based
on macroeconomic tools.
II.
LITERATURE REVIEW
Residential Property Price Indices (RPPIs)
face many difficulties due to the nature of real
estate goods with irregular and heterogeneous
transaction frequency in terms of characteristics,
location, size, and facilities [16]. This leads to
differences in formulations when changing the
approach in the calculation. The estimated
characteristics approach of RPPIs from the
Hedonic model of Eurostat [14] is accepted for
common use. The point of view of RPPIs by this
method is considered the difference in the price
of real estate at the same time at time 0
compared to time t and conduct survey at
different times. Furthermore, estimates of RPPIs
or Price Index (PI) depend significantly on the
reliability of the secondary data source and the
characteristics of the real estate market in each
country are different [17]. The concept of the
basic value of real estate assets is more relevant
in theoretical analysis than empirical research
[18], [19].
The ABC (Austrian Business Cycle Theory)
theory explains changes in real factors and
investor sentiment in real estate price
movements as a result of the business cycle,
originating from the policy mistakes of the
Central Bank [20], [21]. When the central bank
implemented the monetary easing policy,
commercial banks had excess capital for lending
to compete with customers through various
credit policies and one of which was the
reduction of lending rates. This, on the one hand,
will encourage investors to borrow more to
expand their investment in real estate and carry
out higher-risk projects, thereby stimulating
rising real estate prices. The rise in real estate
prices combined with easy access to cheap
capital from banks will stimulate investors to
borrow more to continue investing in the real
estate market. As a result, a period of excessive
credit growth occurred and led to high real estate
prices for housing.
ABC economists emphasize that, as more
resources are allocated to the real estate market,
the rest of the resources will be less for other
areas of the economy. The inefficient allocation
of resources that harm the economy is a problem
to be addressed. Another view, when the central
government's efforts are aimed at curbing the
rise in property prices, preventing bankruptcy.
Besides, it can stimulate the economy by
overspending policy in the context of budget
deficit only make the allocation of resources and
Money
supply M2
Macro-economic
environment
Property
prices
inefficient investment worse. It prolongs the
recession process and the adjustment of the
economy to a stable state of growth. Moreover,
the mistake of the state is not the
implementation of the policy “fiscal austerity”
or “monetary tightening”, but the weakness of
the government (and the central bank) or the
indifferent to allow “unsustainable credit boom”
to occur in the first place.
Therefore, the existence of real estate price
fluctuations can be explained by the basic
exogenous macroeconomic variables in the
market (see Figure 1).
Interest rate
Credit growth
in real estate
Supply and
demand of
real estate
Figure 1. The relationship between macroeconomic factors and real estate prices
Source: Compiled from the authors
Economic growth is a prominent issue in the
macroeconomy. Many studies around the world
show the impact of GDP growth on real estate
prices [5], [7]. When real GDP increases, it will
increase people's income and a large part of the
accumulated income will be invested in
preferred assets, including real estate. Because
the process of creating real estate usually lasts
long, in the short term, the sensitivity of real
estate supply is very low. However, according to
macroeconomic theory, if the actual output
exceeds potential output, it will increase
inflation, promote real estate prices to rise, and
conversely, the real estate price increases have
an impact again. From here, create a two-way
relationship between inflation and real estate
prices [5]. Effective tools to promote economic
growth and control inflation well in macromanagement policies in general and price
stability in the real estate market, in particular, is
monetary policy. Money supply growth, and
hence real estate credit growth, is the main
reason for the rise in real estate prices [4].
Empirical evidence of Xu and Chen [22] in the
Chinese market shows that the low-interest rates,
combined with faster money supply growth, and
loosening lending conditions have helped boost
real estate prices, and vice versa. Countries that
maintain a free interest rate policy, and the
impact of short-term interest rates on real estate
prices, tend to be stronger [23]. However, the
survey results of [4] in Hong Kong show that
real estate prices have a significant influence on
interest rates, while there is no evidence of the
opposite direction.
III.
METHODS/ MATERIALS
The picture of Vietnam's socio-economic
situation in 2019 takes place in the context of the
world economic situation continues to grow
slowly. Unpredictable fluctuations in the
international financial - monetary market,
complicated oil prices impact on credit growth,
psychology, and market expectations. According
to the report of the General Statistics Office of
Vietnam (2019), this is a "breakthrough year" to
strive to successfully implement the macro plan
for the 5 years 2016-2020.
Vietnam's economy grew at 6.82% in the first
quarter of 2019, lower than the quarter 4/2018
but still much higher than the previous years of
the first quarter in the 2013 - 2017 period (See
Figure 2, Chart 1). Growth of areas (i) services;
(ii) industry and construction; (iii) agriculture,
forestry, and fisheries, lower than the same
period last year, are still in good growth (See
Figure 3, Chart 2).
A. Research principles
During the study period from quarter 1, 2005
to quarter 4/2018, the growth rate of the
consumer price index fluctuated continuously,
peaked in quarter 4/2008, and bottomed in
quarter 4/2015 (See Figure 4, Chart 3). Inflation
tends to rise again in the last quarter of 2019, but
compared to the overall analysis period, it
remains stable. The price increase occurred due
to several increasing criteria in the quarter such
as (i) demand for food and foodstuffs, (ii) prices
of public transport services, (iii) prices of
construction materials; (iv) increase in tourism
products, and some essential goods such as fuel,
iron, and steel (Vietnam General Statistics
Office, 2019).
In addition to the causes of CPI increase in
2019, there are several factors that contribute to
curbing the CPI: (i) Domestic gasoline and oil
prices are affected by fluctuations in fuel prices
on the world market; (ii) All levels and sectors
actively implemented measures to ensure supply
and demand balance, prepare goods sources
well, strengthen inspection work, control the
market, perform price stabilization management
in some localities, manage the exchange rates
according to the flexible central exchange rate
mechanism (General Statistics Office of
Vietnam, 2019).
Figure 2. National growth rate
Source: CEIC database, ADB
Figure 3. Growth of economic sectors
Source: General Statistics Office of Vietnam
Figure 4. Consumer price index
Source: IMF-IFS
In general, Vietnam's economy in 2019
achieved a good growth rate, the macroeconomy
was stable, inflation was controlled at a low
level. The national economy takes place in the
context of slowing global economic growth with
increasing challenges and risks.
B. Research data
The real estate market is not like the stock
market or other investment asset markets, goods
on the market are often not bought and resold
often, so the market is of low liquidity. when a
house is traded on the market. Therefore, when
calculating the real estate price index by period.
For example, every quarter, it is a fact that some
properties were used to calculate the price index
of the previous period but this period there were
no transactions. In contrast, many properties in
the previous period had no transactions but this
period appeared. To overcome this problem, the
authors calculated the average price for each
category in each segment based on the
transaction price of that segment in the reporting
period. Then based on the weight of each type to
calculate the weighted average price for that
segment. The weighted average price is used to
calculate the price index and price growth of each
segment. Such price index calculation allows us
to adjust the type of house structure in each
segment when there is a change in real estate
structure overtime, on the other hand, the way to
handle the price index as above allows to reduce
the dependence on any real estate when this
property has no repeat transactions. The house
price index data from the two sources above,
have the same calculation method and can
therefore be complementary, so the author
averages each period and uses this average price
index for the article.
The data source for PriIndex (PI) is compiled
from two sources (*): (1) Real Estate Market
Department under Housing and Real Estate
Market Management Department, Institute of
Construction Economics - Ministry of
Construction; (2) Department of Real Estate
Market under Ministry of Construction, with the
method applied to calculate real estate prices as
follows:
Real estate price index (Price Index) = (∑Pit *
Qit / ∑Pi0 * Qit) * 100.
With:
• Qit is the number of transactions of real
estate i, in m2 at the study period t
• Pit is the 1m2 price of real estate i in the
study period t
• Pi0 is the price of 1m2 of real estate i at
the base period
In particular, the price index, first of all, is
calculated separately for each segment, then
based on the proportion of each segment in the
market to determine the weight of each segment
and recalculated into a composite price index.
The data used in the quantitative model are
quarterly data series, starting from the first
quarter of 2005 to the fourth quarter of 2018.
Data of variables are described in detail in Table
1.
Table 1.
Description of variables, (%)
Variable
Source
Mean
Std. Dev.
Min
Max
Real estate price index, PI
The national growth rate of national
income, GDPG
Consumer price index, CPI
The average long-term loan interest rate in
the market, R
*
1.55
2.33
-2.55
6.75
CEIC database, ADB
6.59
1.28
3.14
9.26
IMF-IFS
8.02
6.37
0.31
27.75
IMF-IFS
10.74
3.28
6.95
20.1
Money supply, M2
CEIC database, ADB
23.8
8.84
11.94
49.11
Source: Calculations from authors
Real Estate Price Index
30.00
20.00
10.00
0.00
Price Real Estate Index, %
40.00
50.00
with GDP CPI R M2
2005q1
2010q1
2015q1
2020q1
Quarterly, %
Price Real Estate Index, %
GDP Growth (y-o-y, %)
CPI (y-o-y, %)
Lending Rate (%, y)
Broad Money Growth (%)
From 2005q1 to 2018q4
Source: ADB, IFS
Figure 5. Graph of the variability of chains in the
Source: Calculations from authors in Stata 14.2
H01
Growth rate of total national income of the whole
country (%)
GDPt
(+)
(+)
(–)
H02
M2 - money supply (VND billion)
M2t
H03
Consumer price index nationwide (%)
CPIt
H04
Average long-term loan interest rate in the market
(%)
Real estate
price index
(PIt)
(–)
Rt
Source: The authors developed
Figure 6. The proposed research model
PIt = β0 + β1GDPGt + β2M2t + β3CPIt + β4Rt +
Ut
where
U- Random errors;
β- Estimated coefficients.
The graph of variation of the chains shows the
level of change gradually decreasing from 2015
to 2018 and without trend factors (see Figure 5).
C. Research model
Approaching ABC foundation theory and
inheriting results from previous studies, the
author summarizes, proposing research models
and hypotheses about the positive (+) or opposite
(-) effect of these factors on real estate prices
(See Figure 6) as follows:
Research model is written in a general way as
follows:
IV.
RESULTS AND DISCUSSION
From the research model and the above
research data, the study conducted the stop test
(Table 2), the results show that the M2 and CPI
chains are stopped at the root level I (0); while
the variables PI, GDPG, R are stopped at the
difference, I (1).
Table 2
Augmented Dickey-Fuller (ADF) test
ADF
Z(t)
PIt
-1.67
GDPGt
-3.19
CPIt
-5.37
Rt
-3.28
M2t
-6.26
PIt
-5.24
GDPGt
-9.34
Rt
-6.86
p(*)
0.76
0.09
0.00
0.07
0.00
0.00
0.00
0.00
Source: Calculations from authors in Stata 14.2
Note: (*) MacKinnon approximate p-value
Table 3
Pesaran, Shin, and Smith Bounds test [24]
10%
1%
p-value (*)
I(0)
I(1)
I(0)
I(1)
3.79
5.11
0.01
0.03
Source: Calculations from authors in Stata 14.2
Note: (*) Kripfganz and Schneider [25] critical values and approximate p-values
Test statistics
F
I(0)
2.34
5%
I(1)
3.33
I(0)
2.78
I(1)
3.88
The existence of mixed stop chains I (0) and I
(1) is the basis for choosing the ARDL
(Autoregressive Distributed Lag) model. The
ARDL model is used to model the relationship
between (economic) variables in a time series
equation. The existence of a long-term or cointegrated relationship can be checked based on
the type of error correction. The bound testing
procedure can be used to conclude the degree of
integration of the series as I (0) or I (1) [24].
An ARDL (p, q) model has the form of
equation
1:
p
q
i 1
i 0
yt  c0   i yt i   i/ xt i  ut ,
p  1, q  0
(1)
Here, yt is the Real estate price index, PI. The
variables explained xt include the National
growth rate of national income, GDPG;
Consumer price index, CPI; The average longterm loan interest rate in the market, R; and
Money supply, M2.
ARDL model can be re-parameterized as
ECM (Error - Correction Model) correction as
follows:
p
q
i 1
i 0
yt  c0    yt 1   xt    yi yt i   xi/ xt i  ut
(2)
adjustment coefficient  measures the level of
strong response of the dependent variable to
deviations from a balanced relationship over a
period. In other words, it shows how quickly
recovery to the equilibrium position is. The short
term coefficients xi , yi explain short-term
fluctuations, not deviations from long-term
equilibrium.
At a statistically significant 5%, the Bound
test according to [24] shows that the
cointegration relationship exists between chains.
The validity of the Bound test is based on the
standard distribution assumptions of residuals, as
well as assumptions about homogeneous variance
and no autocorrelation. The Cameron & Trivedi
test results show that the residuals have standard
distribution and homogeneous variance (Table 4);
and the Durbin's alternative test (Table 5) also
showed that the remainder had no minimum
correlation to the fourth-order.
Table 4.
Cameron & Trivedi's decomposition of IM-test
Source
chi2
df
p
Heteroskedasticity
48.06
44
0.3119
Skewness
8.79
8
0.3604
Kurtosis
0.88
1
0.3482
Total
57.73
53
0.3049
Source: Calculations from authors in Stata 14.2
With

Speed-of-adjustment coefficient
p
  1   i
i 1

Long-term coefficient
q


i 0
i

The long-term coefficients in equation
(2),  describes the equilibrium states of the
independent variables on the dependent variable.
In the presence of co-integration relationships,
they correspond to negative co-integration
coefficients after standardizing the coefficients of
the dependent variable into units. The speed
Table 5.
Durbin's alternative test for autocorrelation
lags(p)
1
2
3
4
F
df
Prob > F
0.82
(1.42)
0.37
1.03
(2.41)
0.37
2.98
(3.40)
0.05
2.21
(4.39)
0.09
Source: Calculations from authors in Stata 14.2
From this result, the ARDL model estimation
results are reliable enough to explain the longterm and short-term relationships between chains
via ECM format as follows:
Table 6: Results of analyzing long-term and short-term
relationships
PI t
Coef.
Std.
p-value
Nguyen Thi Hoang Yen et al. / Journal of Southwest Jiaotong University / Vol.55 No.4 Aug. 2020
Err.
Adjustment
Long-run
Short-run
PIt-1
-0.327
0.091
0.001
GDPGt
1.086
0.509
0.039
CPIt
0.387
0.152
0.014
Rt
-1.080
0.324
0.002
M2t
-0.111
0.061
0.078
Constant
6.167
3.977
0.128
PI t-1
-0.309
0.110
0.008
R t
0.125
0.111
0.265
R t-1
0.530
0.139
0.000
Source: Calculations from authors in Stata 14.2
With additional collected data of 2019 for
GDPG, CPI, R, and M2 variables. The study has
forecasted the change of price index for 4
quarters of 2019 (Figure 7). Forecast results show
that there is a slight fluctuation between quarters,
in which, 1 and 3 are decreasing quarters and 2.4
are recovery quarters.
Real Estate Price Index Forecast for 2019
-2
0
2
4
6
ARDL modelling with GDP CPI R M2
2005q1
2010q1
2015q1
2020q1
Quarterly
PI Forecast
PI Actual
From 2005q1 to 2019q4
Source: ADB, IFS
Figure 7. Forecast results of real estate price index 2019Q1
Source: Calculations from authors in Stata 14.2
From the above experimental results, the
macroeconomic factors have short-term and longterm relationships with Vietnam's real estate
prices as follows:
Firstly, the coefficient of adjustment of
negative errors and the statistical significance at
1% once again confirms the existence of a
cointegration relationship in the model. Besides,
the adjustment value of 0.327 indicates that the
recovery ability to the equilibrium position after
each quarter is at an average level.
Secondly, in the long term, economic growth
and inflation will increase the price index;
conversely, rising interest rates tend to reduce
only real estate prices. Although M2 harms PIs,
however, there is insufficient evidence to confirm
this effect (p-value> 0.05).
Thirdly, in the short term, changes in interest
rates and price indexes in previous quarters have
an opposite effect on changes in the current real
estate price index.
V.
CONCLUSION
Researching the factors affecting real estate
prices is an urgent issue. The research results
have theoretical implications through an
overview of domestic and foreign studies.
Moreover, empirical research also paints a
picture of the long-term and short-term
relationship of macro factors to Vietnam's real
estate price index in recent years. In the long
term, economic growth and inflation will increase
the price index; conversely, rising interest rates
tend to reduce only real estate prices. Vietnam is
a developing country that maintains a stable
economic growth rate and always has to accept
the increase in domestic real estate prices. Before
these findings, the Vietnamese government needs
Nguyen Thi Hoang Yen et al. / Journal of Southwest Jiaotong University / Vol.55 No.4 Aug. 2020
to make good use of macro tools to regulate real
estate prices in particular and to manage the
domestic real estate market in general. This study
provides evidence for factors such as economic
growth, inflation, M2 money supply, and longterm lending rates that affect real estate prices in
Vietnam. From here, the study proposes the
macro policy implications to stabilize real estate
prices, specifically as follows:
Firstly, promoting the parallel economic
growth model with 2 adequate (quantitative) appropriate (quality) objectives: (i) Ensuring the
principle of the proper, adequate and timely
collection; (ii) More drastically in combating
embezzlement and wastefulness; (iii) Promote
socialization expansion to reduce the burden on
the budget; has just exploited the resources of
society.
Secondly, control monetary policy, market
interest rates, and inflation targeting: The
government and the central bank once set the
target of how much inflation should be consistent
throughout monetary policy management. The
inflation target is very important for investors
because inflation affects interest rates, the ability
to pay debts. If the State Bank of Vietnam is not
consistent, investors are in a mood of fear and
fear of risks, so they do not dare to invest in the
long-term, causing the opposite reaction which
adversely affects economic growth. Therefore,
the management of monetary policy in the
direction of inflation targeting will help the State
Bank of Vietnam create public confidence about
the value of the local currency.
Third, complete the construction of the real
estate price index promptly at the national level
and it is necessary to improve the transparency of
current status information, transactions, and
development in the real estate market.
However, the study still exists some
limitations in the application of measurement
methods of real estate price index which are
recalculated according to popular usage formula
in the world. The authors need to expand the
study of various formulas for real estate price
indexes, collect more data in subsequent years,
and conduct interviews with local experts in the
country to select the final formula. Since then, it
helps increase the reliability of data.
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