ANALYSIS OF THE IMPACT OF INTERNATIONAL AND DOMESTIC
MACROECONOMIC SHOCKS ON THE INDEX PROPERTY
SUBSECTOR PRICE INDONESIA
Introduction:
Property as an investment in the form of physical assets is the ownership of a plot of
land and the building that stands on it, including the facilities and infrastructure in it.
Empirically, property has a relationship with a country's economy. Where the market demand
for property is high and speculation causes property prices to rise rapidly. If property prices
continue to be allowed to rise rapidly, this will trigger a property bubble. Bubble property is a
condition where there is an unreasonable increase in property prices which at a certain point
reaches the climax of the price increase then the property price falls causing the property to
become worthless. This fall in property prices will in turn lead to a recession in the national
economy.
Empirically, property bubbles experienced by Japan in 1990, China in 2005 and the
United States in 2008 have caused long recessions in all three countries. This is because
property is less liquid than other assets. For the property bubble in Japan, it started with loose
monetary policy by the Bank of Japan, ease of obtaining credit, increasing money supply,
financial deregulation and an exaggerated perspective on the economy led to aggressive
speculation in 1980. Real estate prices tripled over ten years and peaked in 1989. From 1989
to 1993, real estate prices in Japan fell by almost fifty percent (Wood 1992). This event
slowed down the growth rate of the Japanese economy. In the United States, the property
bubble started with low interest rates. Low interest rates make investment more attractive,
especially in the property sector. The existence of a risky policy to attract consumers in the
form of housing loans with no down payment or low down payment triggered many people
and speculators to buy property. Thus, property prices gradually increased. However, in
2004, the US central bank, the FED, raised its benchmark interest rate. The increase in the
benchmark interest rate was followed by an increase in lending rates. The increase in lending
rates resulted in an increase in loan defaults, especially property loans. The increase in
property loan defaults triggered the subprime mortgage crisis in the United States in 2008.
Then, the increase in lending rates also led to a property bubble in the form of a significant
decline in property prices as property defaults increased (Bianco 2008).
Property growth in Indonesia is growing rapidly. According to Schreiben (2013),
Indonesia experienced a high acceleration of the property industry, while developed countries
experienced a slowdown in the property industry sector. Even the results of Urban Land
Institue research (2013), Indonesia ranks first with the city of Jakarta as the most attractive
property investment location in Asia Pacific in 2013. Whereas in the previous year in 2012
Indonesia ranked seventh. Property growth in Indonesia occurred in residential property and
commercial property such as apartments, retail (shopping centers), industrial land, offices,
and office buildings and hotels. This can be seen from the number of residential and
commercial properties built in several regions in Indonesia other than Jabodebek (Jakarta,
Bogor, Depok and Bekasi) such as Banten, Bandung, Batam, Makassar, Palembang and Bali
(Maharso 2013).
The Indonesian people's need for property is still very high and the perception of
property as a promising form of investment is what drives the property sector to grow rapidly.
There are still 20.5% of the 251 million population in Indonesia who still do not own a home
(Faisal 2014). The change in the lifestyle of the Indonesian people who began to accept
apartments as homes is also one of the factors causing apartment-type properties to grow
rapidly. In addition, the ASEAN Economic Community (MAE) in 2015 also caused the
demand for commercial properties such as offices, industrial land and retail (shopping
centers) to grow. These conditions have caused property developers to be very aggressive in
offering property (Faisal 2014).
The Indonesian government's policy of raising fuel prices in 2014 and the depreciation
of the rupiah to IDR 13,000 per US dollar, caused property prices to rise. In addition, the
increase in Bank Indonesia's benchmark interest rate (BI rate) is an indication of higher
construction costs and higher borrowing costs. So that property developers increase the
selling price of the property to offset losses due to increased costs for construction and
borrowing costs. This increase in property prices will cause inflation in asset prices, which in
turn causes Indonesia's economic growth to slow.
There have been many studies on the macroeconomic relationship to the property price
index. However, studies on macroeconomics on the property price subsector are still very
rare. Shiratsuka (2003), Fengyun (2014), Lastrapes (2002), Meidani, Zabihi, and Ashena
(2011), Kenny (1998), Sari, Ewing, and Aydin (2007), Gabriel (2010) analyze
macroeconomics with property prices in general. Meanwhile, Gabriel (2010), Freese and
Berlemann (2012), Worthington and West (2006), Bjornland and Jacobsen (2008), Ncube and
Ndou (2011) have analyzed the macroeconomic relationship with specific prices such as
residential property prices which are divided into several subsectors based on the type of
house, namely small, medium, large and all sizes. There are also those who divide property
prices into several sectors such as house prices, office prices, retail prices, industrial land
prices and apartment prices. Research on the macroeconomic relationship to property prices
specifically is still rare in Indonesia. Therefore, this study focuses on the relationship of
macroeconomic variables to the property subsector price index in Indonesia. The difference
between this study and previous studies is that apart from the time period, researchers include
international macroeconomic variables such as world oil prices and foreign interest rates in
the variables examined. In addition, the types of property prices studied vary based on the
residential property subsector and the commercial property subsector.
Problem Formulation
Indonesia is one of the small open economies, where the domestic economy is
influenced by the world economy. The existence of international macroeconomic shocks such
as world oil prices will affect the price of domestic goods including property prices. In
addition, foreign interest rate policies such as the federal funds rate policy by the United
States central bank will be responded to by Bank Indonesia through the benchmark interest
rate policy (BI rate). This benchmark interest rate policy will be responded to by changes in
lending rates including property lending rates. The determination of this interest rate policy
will affect property prices and people's purchasing power for property. The existence of
domestic macroeconomic shocks also affects Indonesian property prices. The slow economic
growth in 2014 also caused investment in property to grow slowly. This can be seen from the
slowing growth of property sales in all property subsectors, namely residential property and
commercial property (Bank Indonesia 2014). The slowdown in property sales transactions in
turn caused property price growth to slow. The response of the property subsector to
macroeconomic shocks, both international and domestic, is of course different.
Therefore, based on the above description, the problems that will be discussed in this
study are:
1. What macroeconomic factors affect the property sub-sector price index in Indonesia and
their contribution?
2. How does the property sub-sector price index in Indonesia respond to international and
domestic macroeconomic shocks?
Previous Research:
Huang and Lee (2009) analyzed the relationship between world oil price growth and
REIT (Real Estate Investment Trusts) returns with ARJI model and GARCH(1,1). The results
showed that there is a positive relationship between the growth of world oil prices and REIT
returns. The effect of oil prices is greater than other factors such as interest rates both in the
long and short term on REIT returns. This means that world oil prices have an important role
in REIT returns. Interest rates have a significant influence with a negative relationship
pattern on REIT returns. The same research by Ha Le (2015) in Malaysia with SVAR
(Structural Vector Autoregression) with the results that oil prices and labor unions have a
very positive effect on house price fluctuations. Inflation has a positive effect on house
prices. This is because high inflation causes construction costs, land prices, labor wages to
increase so that house prices increase. However, Herzbrun and Rasmussen (2015) found
different research results from the previous two studies. Herzbrun and Rasmussen found that
world oil price shocks have a negative effect on real estate prices in the USA. This is because
the decline in world oil prices causes household real income to rise so that consumption
increases and property prices rise.
Shiratsuka (2003) analyzed asset price fluctuations in financial and macroeconomic
stability in Japan in 1980 during an asset price bubble. The analysis by Shiratsuka was
conducted with qualitative observations where the results showed that the asset price bubble
in Japan was characterized by the euphoria of the asset price bubble excessive optimism in
future economic fundamentals. Further research was conducted by Fengyun (2014) by
comparing the dynamic effects of money supply on real estate prices in Japan and China
before and after the bubble with generalized VAR. The results showed that the money supply
has a positive effect on land prices in Japan. Where the money supply has a large impact on
land prices in Japan before and during the bubble period. The same results were also found in
China where the money supply also had a positive effect on real estate prices. Research by
Lastrapes (2002) also showed similar results on the positive relationship between house
prices and money supply.
Meidani, Zabihi, and Ashena (2011) analyzed the interaction between output, inflation
and house prices in Iran with Toda Yamato causality granger. The results showed that GDP
and inflation affect house prices and there is a reciprocal relationship between house prices
and GDP. High inflation in Iran causes house prices to increase due to increased demand for
housing causing inflation to be higher. The same research was also conducted by Demary
(2009) in ten OECD (Organization for Economic Co- operation and Development) countries
with cointegration panel. The results of impulse response analysis in the study showed that
monetary policy has a low influence on house prices in the ten countries. Interest rates have a
12 to 24 percent effect on house prices. Output shocks have a very large effect in nine of the
ten OECD countries. Household consumption has a positive effect on house prices. This
means that if household consumption increases then housing prices will rise.
The positive relationship of the consumer price index as a proxy for inflation to the
property price index was found in Apergis (2003) research in Greece with ECVAR (Error
Correction Vector Autoregressive). Meanwhile, loan interest rates have a negative effect on
the residential property price index. The same research was also conducted by Kenny (1998)
analyzing the housing market and macroeconomics in Ireland with VECM (Vector Error
Corection Model). However, an increase in income causes housing demand to increase. Then
in the long run the demand for houses has a negative response. Interest rates have a positive
effect on house prices when viewed from the supply side. However, an increase in interest
rates in the long run has a negative impact on housing demand on house prices. There are
similarities in the results of the effect of interest rates conducted by Kenny (1998) in Liang
and Cao's (2006) research in China with ARDL (Autoregressive Distibuted Lag). Where real
interest rates and bank credit have a significant effect on property prices with short-term
interest rates and bank credit having a positive effect on property prices while long-term
interest rates have a negative effect on property prices. The positive effect of short-term
interest rates on property prices is due to the supply side, an increase in interest rates causes
construction costs to rise so that property prices rise. Meanwhile, in the long term, an increase
in interest rates causes people's purchasing power to fall so that property prices fall. Then
there is a causality relationship both in the short and long term between long-term real
interest rates and bank credit on property prices.
Sari, Ewing, and Aydin (2007) also conducted the same research on macroeconomics
and market housing market at Turkey with VAR (Vector Autoregressive). The results showed
that interest rates, GDP and inflation have a significant effect on the housing market. Based
on the results of variance decomposition analysis, these three variables have a greater
influence on the housing market than the money supply variable. The results of research by
Mikhed and Zemcik (2009) show that in the United States there is a negative relationship
between house prices and consumption and GDP. Follain (1981) shows that inflation has a
negative effect on housing demand and investment. Panagiotidis and Printzis (2015) with
VECM, t h e results showed that the industrial production index, consumer price index, and
home loan interest rates had a positive effect on house prices in Greece. Gabriel (2010)
analyzed the impact of macroeconomic indicators on industrial rent in Central America and
Europe with OLS (Ordinary Least Square). The results showed that the reason why tenants
rent the industry is to maximize profits and take advantage of tax benefits due to the deferral
of gross profit tax. Interest rates and unemployment have a negative effect
to rental prices while GDP has a positive effect on rental prices.
Freese and Berlemann (2012) analyzed the relationship of monetary policy to prices in
each subsegment of real estate in Switzerland with VAR. The results showed that monetary
policy shocks through interest rates had a positive effect on house prices, condominium
prices and apartment rental prices in Switzerland while industrial land prices and office prices
were not affected by shocks to interest rates. The same research was also conducted by
Worthington and West (2006) on macroeconomic risks to commercial property prices,
property stock prices and property trusts in Australia with GARCH-M. Commercial property
prices analyzed were retail, office and industrial land. The results showed that
macroeconomics (inflation, short-term interest rates and industrial production index) had a
positive and significant effect on commercial property returns.
Bjornland and Jacobsen (2008) analyzed the role of house prices in the monetary policy
mechanism in the United States with SVAR. The results showed that interest rates and house
prices have a negative relationship. If there is an increase in interest rates, it will reduce house
prices. The decline in house prices will have a negative impact on output and inflation in the
United States. Bjornland and Jacobsen in 2010 also conducted the same research on small
open economy countries namely Sweden, Norway and the United Kingdom with SVAR. The
results showed that the pattern of the relationship between interest rates, house prices,
inflation and GDP is similar to the results of previous research in the United States.
Ncube and Ndou (2011) analyzed the relationship between monetary policy, house
prices and consumer spending in South Africa using SVAR. The house prices studied were
divided into 4 types, namely all types, large types, medium types and small types. The results
showed that interest rates have a negative relationship with household consumption
expenditure. This means that an increase in interest rates causes household consumption to
fall due to a decrease in people's purchasing power. House prices also have a positive
relationship with
household consumption
. This positive relationship
means that a decrease in house prices causing household consumption to fall. The
contribution of the decline in house prices to household consumption is in order from the
largest contribution to the smallest contribution, starting from the price of house types of all
sizes, large types, medium types and small types.
Raghavan and Dungey (2014) examined the effectiveness of monetary policy on asset
prices in ASEAN-5 with SVECM (Structural Vector Error Correction Model). The results
showed that monetary policy towards asset prices is still not effective. Whereas asset prices
have a close relationship with the economy. Where an uncontrolled increase in asset prices
can cause bubble prices which can lead to a recession for a country's economy.
Tsai, Chen and Chiang (2014) examined asymmetric pricing in real estate spot and
forward markets in Taiwan using ARCH method for house price index volatility and GJR-
GARCH method to capture the impact of asymmetric positive and negative shocks on the rate
of return of house price volatility. The results show that as house prices decline, individuals
face the choice of staying in the house or renting out the house to minimize the loss of selling
price due to house price rigidity. In the housing market, if the house price decreases, the
house cannot be consumed because the house price becomes worthless.
Framework of Thought
The flow of thought scheme in Figure 1 starts from the existence of international
macroeconomic variable shocks such as world oil price shocks and foreign interest rate
shocks. This foreign interest rate shock is in the form of the United States central bank policy
in the form of a policy on the federal fund rate. The existence of international
macroeconomic variable shocks affects the Indonesian economy, which is a small open
economy. The effect of shocks to international macroeconomic variables on Indonesia has
shaken Indonesia's domestic macroeconomic variables such as economic growth, inflation,
money supply, interest rates and the rupiah exchange rate against the US dollar. This
domestic macroeconomic variable shock will then affect investment including property
investment. This shock to property investment will in turn affect prices in the property
subsector.
The price response in each property subsector to macroeconomic shocks is certainly
different. Therefore, it is necessary to analyze how property prices in each subsector respond
to macroeconomic shocks and what macroeconomic factors affect prices in the property
subsector and how much the macroeconomic variables contribute to property prices in each
subsector. The method used in this study is the VECM method because there is a
cointegration relationship on variables that are not stationary at the level. Impulse response
and variance decomposition in this method are used to see how the response of prices in the
property subsector to shocks.
Exploratory Analysis
According to Nazir (1999), exploratory analysis is an analysis in examining a group of
objects, a condition, a system of thought, or a class of events in the present. The purpose of
this analysis is to make a descriptive, systematic description or painting that is factual about
the facts and characteristics and relationships between phenomena that occur. The form of
exploratory analysis in this study is to study the relationship between the property price index
and macroeconomic variables, namely world oil prices, federal fund rate, interest rates,
economic growth, inflation, money supply and the rupiah exchange rate against the dollar in
the period 2002-2014.
VECM (Vector Error Correction Model) Method
Data that is not stationary at the level has the possibility to be cointegrated. VECM is a
restricted VAR model used for nonstationary variables that have the potential to be
cointegrated. Additional restrictions on the VECM must be imposed due to the presence of
shape and data that are not stationary at level, but cointegrated. The VECM then utilizes the
cointegration restriction information into its specification. If it is proven that there is a
cointegration relationship in the model, then the analysis is carried out using the VECM
model. The VECM estimation results are used to derive short-term and long-term
relationships with IRF (Impulse Response Function) and VD (Variance Decomposition)
analysis.
Pre-Estimation Testing
Stationarity Test
Data stationarity testing on time series data is necessary because if the time series data
is directly analyzed without testing its stationarity, it will produce spurious results because
the variables often contain a unit root (Verbeek 2000). Stationarity test can be done by using
Augmented Dicky Fuller (ADF) Test. If the ADF statistic value is smaller than the
MacKinnon Critical Value, it can be concluded that the series is stationary. If it is known that
the data is not stationary at the level, then a stationarity test can be conducted on the first
difference.
VAR Stability Test
VAR stability can be seen from the value of the inverse roots of the characteristic AR
polynomial. This can be seen from the modulus value in the AR roots table, if all AR values
are below one, then the system is stable.
Optimum Lag Test
Determining the optimal lag is very important because determining the optimal lag is
useful for eliminating autocorrelation problems in a VAR system. Determining the optimal
lag is useful to show how long the reaction of a variable to other variables (Enders 2004).
Determining the optimal lag size (lag length criteria) can be done using several criteria,
among others: Akaike Information Criteria (AIC), Schwarz Information Criterion (SIC),
Hannan Quinn Information Criterion (HQ), and Likelihood Ratio (LR). The optimal lag size
is determined by the lag that has the smallest criterion value among the four criteria. If there
are different lags from each criterion, then one of the criteria (generally AIC and SIC) can be
used.
Cointegration Test
Cointegration test aims to see whether the variables used in the system of equations
have a long-term relationship (Ilham and Siregar 2007). Cointegration is a long-term
relationship between variables that although individually not stationary but the linear
combination between these variables can be stationary (Thomas 1997). The existence of a
cointegration relationship in a system of equations indicates that there is an error correction
model in the system that describes the dynamics in the short run consistently with the long
run relationship (Verbeek 2002). Several ways to test for cointegration include: Eangle
Granger Cointegration Test, Johansen Cointegration Test, and Cointegrating Regression
Durbin Watson (CRDW). Cointegration can be seen from the cointegration rank. The
cointegration rank (r) is the sum of all cointegrating relationships. If the cointegration rank is
greater than zero, then there is cointegration so that the model used is VECM. If the
cointegration rank is equal to zero, then there is no cointegration relationship so the model
used is the VAR model (Johansen 1995).
Impulse Response Function and Variance Decomposition
Impulse Response Function (IRF) is a method used to examine the response of an
endogenous variable to a particular shock (Enders 2004). IRF can be used to examine the
effect of a one standard deviation shock from an innovation on the current or future value of
an endogenous variable. In other words, IRF measures the effect of a shock at a particular
time on an endogenous variable.
Variance Decomposition (VD) is a method used to see how changes in a variable
expressed by changes in error variance are affected by other variables. This method can also
see the strength and weakness of each variable in influencing other variables in the long run
(Enders 2004).
Exploratory Analysis of Data
The analysis begins by providing an overview of the movement of the variables that are
the object of research. For example, the money supply showed an increasing trend from 2002
to 2014. Meanwhile, the movement of world oil prices, economic growth, inflation, interest
rates both money market rate and federal funds rate and the exchange rate of the rupiah (Rp)
against the dollar (USD) fluctuated greatly from 2002 to 2014. World oil prices have
increased to 133.88 dollars per barrel in the sixth month of 2008. However, in the same year
the world oil price fell and reached 39.09 dollars per barrel in the second month of 2009.
However, in the following month the world oil price also increased. The same thing also
happened to the rupiah exchange rate against the dollar, where in 2008 the rupiah depreciated
to Rp 12000/USD. But in the following year the value of the rupiah continued to appreciate
until the seventh month of 2011 reached Rp 8500/USD. The existence of movement The
volatility between 2008 and 2010 was caused by the financial crisis that hit the world due to
the subprime mortgage crisis in the United States. The federal funds rate was also volatile
from 2004 to 2006, when the Fed raised the benchmark interest rate. This increase triggered
the subprime mortgage crisis in the United States due to high-risk home loan defaults. So in
the following year the FED lowered the federal funds rate to 0.9 percent in 2014. The FED's
interest rate cut was responded by Bank Indonesia by lowering the benchmark interest rate
which was then responded by a decrease in the money market rate.
Figure 3 shows that the movement of the residential property price index and
commercial property price index showed an increasing trend from 2002 to 2014. The
financial crisis in 2008 also had an impact on the commercial property price index both on
selling prices and rental prices. This can be seen from the fluctuations in that year even
though it looks small. A significant increase in the commercial property selling price index.
Where for the last five years from 2010 to 2014 has reached more than 100 percent with the
highest increase in the selling price of industrial land reaching a 300 percent increase over the
last five years, 200 percent for condominium selling prices and 100 percent for office selling
prices. Significant increases also occurred in commercial property rental prices reaching 200
percent over the last twelve years of commercial rental price increases with the highest price
increases in retail rental prices, hotel rental prices, office rental prices, apartment rental prices
and industrial land rental prices respectively. Overall, the most significant price increase
across all property price indices occurred in 2012. This is because in 2012, Indonesia
experienced accelerated growth in the property sector supported by Indonesia's economic
growth which grew while other countries experienced slowing economic growth due to the
European crisis in 2011.
Data Generating Process
The data generating process is the first step before entering the model estimation and
analysis stage. At this stage, various pre-estimation tests will be carried out including unit
root test, VAR stability test, lag optimum test, and cointegration test.
Data Stationarity Testing
The test method used to test data stationarity is the Augmented Dickey Fuller-Test
(ADF-test). In this test, automatic lag selection is used based on the Schwarz Information
Criterion (SIC) criteria. If the ADF t-value is smaller than the MacKinnon critical value, it
can be concluded that the data used is stationary. Data testing is done at the level up to the
first difference. The stationarity test results show that almost all macroeconomic variables
both international and domestic are not stationary at the level except the federal funds rate,
inflation, interbank rates. While the results of data stationarity testing on property price index
data per subsector show that almost all property subsector variables are not stationary at the
level. However, data that is not stationary at the level has been stationary in testing at first
difference. The results of data stationarity for each variable can be seen in appendix 1, 2 and
3.
Optimum Lag Test
In this study, the determination of the optimum lag is based on the smallest Schwarz
Criterion (SC) at the lag interval used. Optimum lag testing is done in the first difference.
The results of the lag optimum test show that the residential property price index of all types,
large types, and small types and the retail rental price index are at lag one. All commercial
property selling price indexes and the selling price index of medium-type residential property
lag optimum at lag two. For hotel rental price index and apartment rental price index, lag
optimum is at lag three. The overall results of lag optimum testing in all property subsectors
in this study can be seen in appendix 4.
VAR Stability Test
The VAR stability test is conducted by calculating the roots of the characteristic
polynomial function. If all absolute values are less than one (<1) then the VAR model is
stable. Since almost all variables are not stationary at level, the VAR stability test is
conducted at first difference. The test results show that all models on prices in the property
subsector have roots of characteristic polynomial values less than one. This means that the
price test model in each property subsector has a stable VAR model. The results of the VAR
stability test can be seen in appendix 5.
Cointegration Test
In this study, the cointegration test was conducted using the Johansen cointegration
test. Cointegration can be seen from the cointegration rank. The cointegration rank (r) is the
sum of all cointegrating relationships. If the cointegration rank is greater than zero, then there
is cointegration. The cointegration test results in each property subsector show that there are
one to three cointegration relationships in the model in each property subsector. The results
of the cointegration test in all property subsectors in this study can be seen in appendix 6.
Research Model Estimation Results
There is a cointegration relationship in variables that are not stationary at the level, so
the choice of method used in this study is VECM (Vector Error Corection Model). The
estimation used in this study is at a real level of 5 percent or t-statistic greater than the
absolute value of 1.96.
VECM Estimation of Selling Price Index of Residential Property Subsector
The results of the VECM estimation in the short term, only economic growth on lag
one has a significant effect with a positive relationship pattern on the large type residential
property selling price index. Meanwhile, other domestic and international macroeconomic
variables do not significantly affect the selling price index of the residential property
subsector.
Table 2 shows the long-term VECM estimation results for the residential property
selling price index. The estimation results in Table 2 show that in the long run, the federal
funds rate has no significant effect on the residential property sub-sector selling price index.
This can be seen from the t-statistic value which is less than the absolute value of 1.96.
In the long run, world oil prices have a significant effect with a negative relationship
pattern in almost all residential property sub-sector price indices of various types except for
the residential property price index of all types. Pattern This negative relationship is not in
line with the initial research hypothesis. Where the relationship between world oil prices and
prices is positive. However, this negative relationship pattern was also found in Herzbrun and
Rasmussen (2015) in the United States. This negative relationship pattern is due to the
decline in world oil prices causing household real income to rise so that consumption
increases. Increased consumption pushes property prices up.
Economic growth has a significant effect with a positive relationship pattern on the
residential property price index in all types in the long run. An increase in economic growth
can increase property prices. This is because economic growth is an indication that the
country's economy is in a favorable climate. Rising economic growth provides a good signal
for investment. National economic growth in general, reflects an increase in economic
activity and will ultimately affect demand for the property sector. Increased demand for the
property sector can increase property prices. The interpretation of the coefficient value is that
a one percent increase in economic growth can increase by 0.15 units of residential property
price index of all types.
Inflation has a significant effect with a positive pattern on the residential property price
index across all types in the long run. This is similar to the results of research by Sari, Ewing,
and Aydin (2007) in Turkey, Panagiotidis and Printzis (2015), where an increase in inflation
can cause property prices to rise. This is caused by rising inflation through rising prices of
building materials and production costs. The increase in the price of building materials and
production costs by property developers who do not want to lose will be passed on to
consumers by increasing the selling price of residential property. The interpretation of the
coefficient value is that a one percent increase in inflation can increase by 0.031 units of the
residential property price index of all types.
The interbank rate has a significant effect with a negative relationship pattern on the
residential property price index across all types in the long run. This is similar to research by
Kenny (1998), Liang and Cao (2006) and Ncube and Ndou (2011). This is because, in the
long run, an increase in interest rates will reduce people's purchasing power. Because the
interest rate that must be paid becomes higher than the rate of return when investing so that
people's purchasing power will decrease. A decrease in purchasing power will push property
prices down. The interpretation of the coefficient value is that a one percent increase in the
interbank interest rate can reduce by 0.060 units of the residential property price index of all
types.
In the long run, money supply has a significant effect with a positive relationship
pattern only on the price index of residential properties of all types and large types. This
positive relationship is similar to research by Lastrapes (2002) and Fengyun (2014) in China.
When the money supply increases, which is reflected in the amount of money held by the
public, investment increases. The increase in investment causes the demand for housing to
increase because housing is one of the investment assets. If the demand for houses is more
than the amount of supply, the price of residential property is pushed up. A unique
phenomenon is that the significant effect of money supply only occurs in the residential
property price index of all types and large types. In general, only rich people buy houses of
all types and large types. When they have excess money, they will invest their money in these
assets. Meanwhile, the money supply has no significant effect on the price index of medium
and small type properties. This is because these types of houses are bought by middle-income
and poor people. Where they buy a house based on needs not to invest. Evidently in 2014
there were still 20.5% of the 251 million population in Indonesia who still did not own a
house (Faisal 2014). In addition, there is a public housing credit subsidy program for middle
and lower income people. The interpretation of the coefficient value is that an increase of one
unit of money supply can increase by 1.62 units of property price index of all types.
The exchange rate of the rupiah against the dollar has a significant effect with a
positive relationship pattern on the selling price index of residential property in all types in
the long run. The increase (depreciation) of the rupiah exchange rate against the dollar can
increase the price of residential property. This is because raw materials for some building
materials are still imported from abroad. Because the price of raw materials from abroad
becomes more expensive due to the depreciation of the rupiah against the dollar, the market
price for building materials will also increase. The increase in the price of building materials
causes the production cost of building a house to increase. This in turn will push house prices
up. The interpretation of the coefficient value is the depreciation of the rupiah value A one-
unit increase in the price index against the dollar can increase 0.94 units of the property price
index of all types. More complete VECM estimation results can be seen in appendix 7.
VECM Estimation of Selling Price Index of Commercial Property Subsector
In the short term, macroeconomic variables that have a significant effect on the office
selling price index are world oil prices, inflation, interbank rates and money supply. Where
this significant relationship occurs at lag one. The only macroeconomic variable that has a
significant effect on the condominium selling price index is the interbank interest rate. While
the macroeconomic variables that have a significant effect on the selling price index of
industrial land are the federal fund rate and economic growth.
Table 3 shows the long-run VECM estimation results for the commercial property
selling price index. A unique phenomenon occurs in the significant relationship between the
commercial property selling price index and the interbank rate. Where in the short term the
interest rate and the commercial property selling price index have a positive relationship.
Whereas in the long run it has a negative relationship. This phenomenon is also found in the
research of Kenny (1998), Liang and Cao (2006). The positive effect of short-term interest
rates on property prices is because from the supply side, an increase in interest rates causes
construction costs to rise so that property prices rise. Meanwhile, in the long run, an increase
in interest rates causes people's purchasing power to increase. The money market rate has a
significant effect on all commercial property selling price indices. In the long run, the
interbank rate (money market rate) has a significant effect on all commercial property selling
price indices. The way to interpret the coefficient value is that in the long run, a one percent
increase in the interbank rate can reduce by 0.22 units the office selling price index.
In the long run, world oil prices have a significant effect with a positive relationship
pattern in all commercial property subsector selling price indices. This positive relationship
pattern is also found in the research of Huang and Lee (2009), Ha Le (2015) in Malaysia. The
increase in world oil prices makes production costs increase. This means that the price of
imported goods will become expensive. Some building raw materials are imported from
abroad. This in turn causes property prices to rise. The interpretation of the coefficient value
is that an increase of one unit of world oil prices can increase by 1.02 units of the office
selling price index.
The federal fund rate has a significant effect with a negative relationship pattern only
on the industrial land selling price index in the long run. This means that the international
interest rate, namely the federal fund rate, has an influence on the company's decision to
invest or buy industrial land. A decrease in the federal fund rate followed by a decrease in
interest rates in Indonesia can increase the selling price of industrial land. This is because a
decrease in interest rates makes purchasing power increase. Because the company's rate of
return if investing is greater than the interest rate that must be paid. This will push the selling
price of industrial land up. The interpretation of the coefficient value is that a decrease of one
percent in the federal fund rate can increase by 0.35 units of the industrial land selling price
index.
Economic growth has a significant effect with a positive relationship pattern only on
the industrial land selling price index in the long run. An increase in economic growth can
increase property prices. Economic growth is an indication that the country's economy is in a
favorable climate. Economic growth reflects an increase in economic activity including the
production of goods and services. Economic activities including goods production activities
require property products. Increased production is an indication that large industrial land is
needed to increase production. Increased demand for industrial land will in turn increase the
price of industrial land. The interpretation of the coefficient value is that a one percent
decrease in economic growth can increase by 0.77 units of industrial land selling price index.
Inflation has a significant effect with a positive pattern on the office selling price index
and condominium selling price index in the long run. Inflation can cause property prices to
rise. This is because rising inflation causes building materials and production costs to
increase. The increase in the price of building materials and production costs by property
developers is covered by increasing the selling price of offices and condominiums. The
interpretation of the coefficient value is that a one percent increase in inflation can increase
by 0.087 units of office selling price index and by 0.076 units of condominium selling price
index.
In the long run, money supply has a significant effect with a positive relationship
pattern across all commercial property selling price indices. Relationship This positive result
is similar to research by Lastrapes (2002) and Fengyun (2014) in China. When the money
supply increases, the demand for commercial property increases. If the demand for
commercial property is more than the amount of supply, the price of commercial property is
pushed up. The interpretation of the coefficient value is that an increase of one unit of money
supply can increase by 9.68 units of office selling price index.
The exchange rate of the rupiah against the dollar is significant with a positive
relationship pattern only on the office selling price index. The increase (depreciation) of the
rupiah exchange rate against the dollar can increase property prices. This is because raw
materials for some building materials are still imported from abroad. Because the price of raw
materials from abroad becomes more expensive due to the depreciation of the rupiah against
the dollar, the market price for building materials will also increase. The increase in the price
of building materials causes the production cost of building an office to rise. This in turn will
push office prices up. The interpretation of the coefficient value is that the depreciation of the
rupiah against the dollar by one unit can increase by 1.38 units the office selling price index.
More complete VECM estimation results can be seen in appendix 7.
VECM Estimation Results of Commercial Property Rental Price Index
In the short term, all macroeconomic variables have no significant effect on the hotel
rental price index, apartment rental price index and retail rental price index. Economic growth
has a significant effect on the industrial land rental price index. Interbank rates and money
supply have a significant effect on the office rental price index.
Table 4 shows the results of the long-run estimation of commercial property rental price
indexes. In the long run, the world oil price has a significant effect on almost all commercial
property rental price indexes except for the office rental price index. Significant with negative
relationship pattern is found in retail rental price index, hotel rental price index and apartment
rental price index. This negative relationship pattern is also found in Herzbrun and
Rasmussen (2015) in the United States. This negative relationship pattern is due to the
decline in world oil prices causing household real income to rise so that consumption
increases. Increased consumption pushes property prices up. While significant with a positive
relationship pattern in the hotel rental price index and industrial land rental price index. The
interpretation of the coefficient value is that a decrease in oil prices by one unit can increase
by one unit.
1.20 units of hotel rental price index. This positive relationship pattern is also found in the
research of Huang and Lee (2009), Ha Le (2015) in Malaysia. The increase in world oil
prices makes production costs increase. This means that the price of imported goods will be
expensive. Some raw building materials are imported from abroad. This in turn causes
property prices to rise. The interpretation of the coefficient value is that an increase in oil
prices by one unit can increase by 57.58 units of industrial land rental price index.
In the long run, the federal funds rate has a significant effect with a negative
relationship pattern only on the hotel rental price index. This means that if interest rates fall,
then hotel rental prices will rise. The decline in interest rates makes people's purchasing
power increase, especially for traveling. If the desire As the number of people traveling
increases, the demand for hotels increases. This in turn will cause hotel rental prices to
increase. The interpretation of the coefficient value is that a decrease in the federal funds rate
by one percent can increase by 0.11 units of the hotel rental price index.
Economic growth has a significant effect with a positive relationship pattern on the
retail rental price index and industrial land rental price index in the long run. An increase in
economic growth can increase property prices. This is because economic growth is an
indication that the country's economy is in a favorable climate. Economic growth reflects an
increase in economic activity including the production of goods and services. Economic
activities including the production of goods and services require property products. Increased
production is an indication that large retail and industrial land is needed for these production
activities. Increased demand for retail and industrial land will in turn increase the price of
retail and industrial land. The interpretation of the coefficient value is that a one percent
increase in economic growth can increase by 9.45 units the industrial land rental price index.
Inflation has a significant effect with a positive relationship pattern in all commercial
property rental price indices. An increase in the prices of goods causes property rental prices
to rise. The interpretation of the coefficient value is that a one percent increase in inflation
can increase by 0.027 units of office rental price index.
Interbank rates have a significant effect with a negative relationship pattern in all
commercial property rental price indices except for the hotel rental price index. This is
consistent with theory and research by Kenny (1998), Liang and Cao (2006) and Ncube and
Ndou (2011). This is because, in the long run, an increase in interest rates will reduce
people's purchasing power. A decrease in purchasing power will push property rental prices
down. The interpretation of the coefficient value is that a one percent increase in interest rates
can reduce by 0.034 units the office rental price index.
Money supply has a significant effect on almost all property rental price indices except
for the retail rental price index. It has a significant effect with a positive relationship pattern
on office rental price index, industrial land rental price index and apartment rental price
index. This positive relationship is similar to the research of Lastrapes (2002) and Fengyun
(2014) in China. When the money supply increases, the demand for commercial property
increases. This is because property is one of the physical assets. If the demand for
commercial property is more than the amount of supply, the price of commercial property is
pushed up. The interpretation of the coefficient value is that an increase of one unit of money
supply can increase by 0.55 units of office rental price index. There is a negative relationship
between hotel rental price index and money supply.
The exchange rate of the rupiah against the dollar is significant with a positive
relationship pattern in almost all commercial property rental price indices except for the hotel
rental price index. The increase (depreciation) of the rupiah exchange rate against the dollar
can increase property rental prices. This is because raw materials for some building materials
are still imported from abroad. Because the price of raw materials from abroad has increased
due to the depreciation of the rupiah against the dollar, the market price for building materials
will also increase. This in turn will push up the office rental price index. On the other hand,
the depreciation of the rupiah against the dollar indicates that renting commercial property in
the country is cheaper than abroad. This in turn causes demand for commercial property to
increase so that commercial property prices are pushed up. The interpretation of the
coefficient value is that the appreciation of the rupiah against the dollar by one unit can
increase by 0.75 units the office rental price index. More complete VECM estimation results
can be seen in appendix 7.
Impulse Response Function
Impulse response function (IRF) is a method used to examine the response of an
endogenous variable to a particular shock (Enders 2004). IRF can be used to examine the
effect of a one standard deviation shock from one innovation on the current or future value of
an endogenous variable. In other words, IRF measures the effect of shocks at a certain time of
the endogenous variable. The impulse response function in this study is used to see how the
response of each price index is property subsector in Indonesia from shocks to international
macroeconomic variables and domestic macroeconomic variables.
In summary, the response of property sub-sector price indices to macroeconomic variable
shocks is shown in Table 5. The results of the impulse response analysis as a whole show that
in the long run a shock of one standard deviation of world oil prices is responded positively
by almost all property sub-sector price indices except the large house selling price index,
small house selling price index, and apartment rental price index.
A one standard deviation shock to the federal funds rate is responded negatively only in
the small house selling price index and the industrial land rental price index. A one standard
deviation shock to economic growth is responded positively in all residential property selling
price indices, almost in all property selling price indices commercial except for the
condominium selling price index, and responded positively to the retail rental price index and
industrial land rental price index. A one standard deviation inflation shock is responded
positively in almost all property price indices except for the industrial land selling price
index.
A shock of one standard deviation of interbank rates is responded negatively in almost
all property sub-sector price indices except for the hotel rental price index. A shock of one
standard deviation of money supply is responded positively in all property sub-sector price
indices except the apartment rental price index. A shock of one standard deviation of the
rupiah exchange rate is responded positively in almost all property sub-sector price indices
except in the industrial land selling price index.
Impulse Response Analysis of Price Index of Residential Property Subsector
Figure 4 is one of the impulse response output results of the residential property selling
price index of all types. Figure 4 shows that a shock of one standard deviation to
macroeconomic variables in the first month has not been responded to by the residential
property subsector selling price index. Shocks to macroeconomic variables are only
responded to by the residential property selling price index in the second month.
A one standard deviation shock to world oil prices is responded negatively to the
residential property selling price index in the short run. However, in the long run a shock to
world oil prices is responded positively by the residential property sub-sector price index
from the eighteenth month. Similar to the oil price shock, a one standard deviation shock to
the federal funds rate is negatively responded by the residential property selling price index
in the short run, but in the long run from the sixteenth month it starts to respond positively.
The effect of world oil price shocks on the federal funds rate on the residential property
selling price index is very small, almost close to 0.
Shocks to economic growth, inflation, money supply and exchange rate are responded
positively in the second month. A one standard deviation shock to economic growth causes
the residential property sale price index to increase by 0.026 percent in the fourth month. In
the same month a one standard deviation shock to inflation can increase the residential
property selling price index by 0.086 percent. A one standard deviation shock to the money
supply increases the residential property price index by 0.028 percent in the fourth month. A
one standard deviation shock to the exchange rate also increases the residential property price
index by 0.039 percent. A one standard deviation shock to the interbank rate is negatively
responded to in the long run by the residential property selling price index. A one standard
deviation shock to the interbank rate can reduce the residential property selling price index by
0.28 percent. After the fifteenth month, the average effect of macroeconomic shocks begins
to diminish. The overall results of the impulse response of the residential property sub-sector
selling price index in this study can be seen in appendix 8.
Impulse Response Analysis of Selling Price Index of Commercial Property Subsector
Figure 5 is one of the impulse response output results of the selling price index of the
commercial property subsector, namely the office selling price index. Figure 5 also shows
that a shock of one standard deviation to the macroeconomic variables in the first month has
not been responded to by the selling price index of the commercial property subsector.
Shocks to macroeconomic variables are only responded to by the average office selling price
index in the third month. A shock to the world oil price is responded positively by the office
selling price index. A one standard deviation shock to world oil prices causes the office
selling price index to continue to increase by 0.69 percent in month eleven. The effect of the
shock starts to disappear in the twenty-sixth month.
In the fourth month, a one standard deviation shock to the federal funds rate is
responded negatively by the office selling price index in the short run. However, in the long
run from the seventh month it starts to respond positively. The effect of the federal funds rate
shock in the seventh month is 0.086 percent.
Shocks to economic growth, inflation, money supply and exchange rate respond
positively in the third month. A one standard deviation shock to economic growth causes the
office selling price index to increase by 0.024 percent in the third month. In the same month a
one standard deviation shock to inflation can increase the office selling price index by 0.013
percent. A one standard deviation shock to the money supply increases the price index by
0.013 percent office sales by 0.10 percent in the third month. A one standard deviation shock
to the exchange rate also increases the office selling price index by 0.012 percent. A one
standard deviation shock to the interbank rate is negatively responded to in the long run by
the office selling price index. A one standard deviation shock to the interbank rate decreases
the office selling price index by 0.14 percent in the third month. After the twentieth month,
on average, the effect of macroeconomic shocks starts to diminish, except for shocks to world
oil prices and interbank rates, which disappear in the thirtieth month.
Impulse Response Analysis of Rental Price Index of Commercial Property Subsector
Figure 6 is one of the impulse response output results of the rental price index of the
commercial property subsector, namely the office rental price index. Figure 6 also shows that
a shock of one standard deviation on macroeconomic variables in the first month has not been
responded by the office rental price index. Shocks to macroeconomic variables are only
responded to by the office rental price index in the second month. Shocks to world oil prices
are responded positively by the office rental price index. A one standard deviation shock to
world oil prices causes the office rental price index to continue to increase by 0.11 percent in
the thirteenth month. The effect of the shock starts to diminish in the following period and
disappears in the fortieth month.
Forecast Error Variance Decomposition
Variance decomposition analysis is conducted to determine the contribution of a variable
to a shock. In addition, this method can also see the strength and weakness of each variable in
influencing other variables in the long run (Enders 2004).
The results of variance decomposition analysis show that inflation, and interbank rates
have a contribution that plays a role in almost all property subsector price indices in
Indonesia. Where Inflation has a dominant contribution to the apartment rental price index,
office rental price index and almost all residential property price indexes in all types except
large types. Interbank rates have a dominant contribution in all commercial property selling
price indices. Money supply has a large but not dominant contribution to the office selling
price index and condominium selling price index. Economic growth has a dominant
contribution to the large house selling price index and retail rental price index. World oil
prices have a dominant contribution to the industrial land sales price and rental price indices.
The exchange rate has a large but non-dominant contribution to the office selling price index.
Variance Decomposition Analysis of Selling Price Index of Residential Property
Subsector
The results of the variance decomposition analysis of the selling price index of the
residential property subsector as a whole, inflation, interbank rates, exchange rates and
economic growth have a role in the variance decomposition of the index selling price of the
residential property subsector. Figure 7 is one of the variance decomposition of the selling
price index of the residential property subsector, namely the selling price index of residential
property in all types.
Figure 7 shows that the residential property selling price index shock of all types in the
first month is influenced by itself by 100 percent. In the third month, it can be seen that other
variables begin to play a role, namely inflation by 1.67 percent. In the twelfth month, the
influence of residential property selling price index shocks began to decrease but was still
dominant at 80.69 percent. Meanwhile, other macroeconomic variables contributed
increasingly. In the same month, inflation contributed
7.38 percent, world oil prices by 0.043 percent, federal funds rate by
0.054 percent, economic growth by 2.71 percent, interbank rates by 2.97 percent, money
supply by 1.47 percent and the rupiah exchange rate by 2.92 percent.
The variance decomposition results show that over the next forty months, the
residential property selling price index contributes to the residential property selling price
index itself, reaching 77.41 percent in the fortieth month. For forty months also, the inflation
variable has a significant contribution to the residential property selling price index, which
reaches 8.73 percent in the fortieth month while the economic growth variable has a
contribution of 5.72 percent, the exchange rate of
3.47 percent and the interbank rate of 2.86 percent. Other macroeconomic variables have very
small contributions still below 1 percent in the same month. This means that inflation,
economic growth, exchange rates and interbank rates have a role in the selling price index of
residential properties of all types.
The variance decomposition results on the selling price index of other types of
residential property also show the same results as the variance decomposition results of the
selling price index of residential property of all types. In the first month it is still influenced
by itself by 100 percent. In the third month it appears The results of variance decomposition
show that over the next forty months, inflation also has a role in the three other residential
property selling price indices, which is equal to
7.91 percent on the large type residential selling price index, 9.85 percent on the medium type
residential selling price index and 2.99 percent on the small type residential selling price
index. Exchange rates and interbank rates have a large role only in the large and medium type
residential selling price indexes. Economic growth has a role of 18.54 percent on the large
type residential selling price index. The results of the variance decomposition of the
residential property selling price index in this study can be seen in appendix 9.
Variance Decomposition Analysis of Selling Price Index of Commercial Property
Subsector
The results of the variance decomposition analysis of the commercial property
subsector selling price index as a whole, world oil prices, inflation, interbank rates, and
money supply have a role in the variance decomposition of the commercial property
subsector selling price index. Figure 8 is the result of the variance decomposition analysis of
one of the selling price indexes of the commercial property subsector, namely the office
selling price index. Figure 8 shows that the office selling price index shock in the first month
is influenced by itself by 100 percent. In the third month, it can be seen that other variables
began to play a role, namely the interbank rate by 2.77 percent, the money supply by 1.79
percent, and the exchange rate by 1.24 percent.
In the twelfth month, the influence of the office selling price index shock diminished
but was still dominant at 41.40 percent.
Mea n whi le
, other macroeconomic
variables contributed a modest is increasing. In the same month, the contribution of economic
growth amounted to 0.61 percent, world oil prices by 3.00 percent, inflation by 6.35 percent,
rupiah exchange rate by 2.33 percent, federal funds rate by 0.094 percent, interbank rates by
28.09 percent and money supply by 18.10 percent. The variance decomposition results show
that over the next forty months, the interbank rate makes the largest contribution to the office
selling price index itself, reaching 35.66 percent in the fortieth month. Over the forty months,
the money supply variable also contributes to the office selling price index, reaching 20.01
percent in the fortieth month. The office selling price index itself reduced its contribution by
26.23 percent. Other variables that contribute to the office selling price index are the world
oil price of 7.48 percent and inflation of 8.24 percent in the fortieth month. Other variables
have a very small contribution still below 2.00 percent in the same month. This means that
interbank rates, money supply, world oil prices and inflation have a large contribution to the
office selling price index to the office selling price index
The variance decomposition results on the condominium selling price index and
industrial land selling price index also show the same results as the variance decomposition
results of the office selling price index. Where in the first month it is still influenced by itself
by 100 percent. In the third month, it can be seen that other variables began to play a role,
namely the interbank interest rate. The variance decomposition results show that over the
next forty months, the condominium selling price index and the industrial land selling price
index make a declining contribution. Over the next four months, world oil prices, interbank
rates, and money supply have a large contribution in both commercial property selling price
indices. Inflation had a large contribution only in the condominium selling price index, which
amounted to 5.0 percent while in the industrial land selling price index it amounted to 0
percent. Unlike the office selling price index, the world oil price dominated in the fortieth
month, amounting to 52.61 percent in the industrial land selling price index. The
condominium selling price index shows the same results as the office selling price index.
Where the interbank rate has a contribution of 27.55 percent to the condominium selling price
index and the money supply has a contribution of 14.90 percent to the condominium selling
price index. The results of the variance decomposition of the selling price index of the
commercial property subsector as a whole in this study can be seen in appendix 9.
Variance Decomposition Analysis of Rental Price Index of Commercial Property
Subsector
The results of the variance decomposition analysis of the commercial property
subsector rental price index as a whole, economic growth, inflation, and interbank rates have
a role in the variance decomposition of the commercial property subsector selling price index.
Figure 9 is one of the variance decomposition of the rental price index of the commercial
property subsector, namely the office rental price index. Figure 9 shows that a shock to the
office rental price index in the first month is influenced by itself by 100 percent. In the fourth
month, it can be seen that other variables began to play a role, namely inflation of 4.44
percent, the exchange rate rupiah by 3.77 percent and the federal fund rate by 2.88 percent.
While other variables have a contribution still below 2.00 percent.
The variance decomposition results for the next forty months show that the
contribution of the office rental price index to the office rental price index itself continues to
decline, reaching 22.51 percent in the fortieth month. Other macroeconomic variables
continue to increase in contribution over the next forty months. In the fortieth month, other
variables that contribute to the office rental price index are inflation at 38.80 percent,
exchange rate at 19.20 percent, money supply at 7.61 percent and interbank rates at 7.59
percent. Other variables have a very small contribution still below 3.00 percent. This means
that inflation, exchange rate, money supply and interbank rates have a large contribution to
the office rental price index.
The variance decomposition results of the other commercial property rental price
indices show that over the next four months also the four commercial property rental price
indices have a decreasing contribution. Inflation has a large contribution in all four other
property rental price indices. In the apartment price index in the fortieth month inflation has a
contribution of 14.06 percent. Economic growth contributed 22.3 percent to the retail rental
price index and 9.68 percent to the hotel rental price index in the fortieth month. In the same
month. world oil prices contributed 49.61 percent to the industrial land rental price index.
The interbank interest rate has a contribution of 30.47 to the industrial land rental price index.
The variance decomposition results of the overall commercial property sub-sector rental price
index in this study can be seen in appendix 9.
CONCLUSIONS:
This study has two main objectives, namely to analyze what macroeconomic variables
affect the price index in the property subsector and to analyze how the property subsector
price index responds to macroeconomic shocks. Based on the results of the long-term VECM
estimation, it shows that domestic macroeconomics and world oil prices have a significant
effect on almost all property subsector price indices. While the federal fund rate only has a
significant effect on the industrial land sale price index and hotel rental price index.
The results of the overall impulse response analysis show that in the long run, world oil
price shocks are responded positively in almost all property sub-sector price indices except
the large house selling price index, small house selling price index, and apartment rental price
index. Federal fund rate shocks are responded negatively only in the small house selling
price index and the industrial land rental price index. A one standard deviation shock to
economic growth is responded positively in all residential property sales price indices, almost
all commercial property sales price indices except the condominium sales price index, and
responded positively in the retail rental price index and industrial land rental price index.
Inflation shocks are responded positively in almost all property price indices except for the
industrial land selling price index. PUAB interest rate shocks are responded negatively in
almost all property sub-sector price indices except for the hotel rental price index. Money
supply shocks are responded positively in all property sub-sector price indices except the
apartment rental price index. Rupiah exchange rate shocks are responded positively in almost
all property sub-sector price indices except in the industrial land sales price index.
The results of variance decomposition analysis show that inflation, and interbank rates
have a contribution that plays a role in almost all property subsector price indices in
Indonesia. Where Inflation has a dominant contribution to the apartment rental price index,
office rental price index and almost all residential property price indexes in all types except
large types. Interbank rates have a dominant contribution in all commercial property selling
price indices. Money supply has a large but not dominant contribution to the office selling
price index and condominium selling price index. Economic growth has a dominant
contribution to the large house selling price index and retail rental price index. World oil
prices have a dominant contribution to the industrial land sales price and rental price indices.
The exchange rate has a large but non-dominant contribution to the office selling price index.
