The 2008 global crisis stimulated research interest in asset or property price volatility, considering that fluctuations in the housing price sector could directly damage the domestic economy and financial stability. Furthermore, ITF policies could efficiently stabilize the pricing of basic goods and services, but they must prevent the fluctuation of asset price bubbles. Topic related to the interaction of monetary policy and asset prices has developed and been discussed in several studies. According to (Rigobon & Sack, 2003) little is known about the magnitude of the Federal Reserve’s reaction to the stock market, in part because the simultaneous response of equity prices to interest rates makes it difficult to estimate. This paper uses an identification technique based on the heteroskedasticity of stock market returns to measure the reaction of monetary policy to the stock market. We find a significant policy response, with a 5 percent rise (fall, there is a significant interaction between interest rates, asset prices, and stock prices in the short-term. This current research attempts to analyze alternative forecasts of how asset prices react to changes in monetary policy.

Figure 1 shows the fluctuation of Indonesia’s property price index’s growth. There is an opposite pattern between interest rates (as monetary policy) on housing prices in certain periods. Additionally, a decline in interest rates in 2017 and 2018 was responded to by an increase in house prices. In 2015:Q4, 2018:Q2, 2018:Q4, and 2019:Q2, house prices in Indonesia responded unfavorably to changes in interest rate rises. However, in 2017, the third and fourth quarters were the only exceptions to this tendency. The first quarter of 2021 reveals a decline in interest rates followed by a decline in the price of the housing index in Indonesia. In addition, the property price index growth has increased at most by 0.5% in recent years. Thus, the graph in Figure 1 after 2017 tends to decrease. The effect of the pandemic shows that residential property prices have decreased. This is because the welfare effect in society has diminished throughout the pandemic, resulting in a decline in property demand. In certain years, there is an inclusive pattern between Indonesia’s monetary policy and property price growth in Indonesia.

There have been several previous studies related to the property sector. (Bernanke & Kuttner, 2005) developed the sensitivity of the property sector to changes in monetary policy. (Mishkin, 2007) added the literature on the link between the property sector and transmission in monetary policy. There is a relationship between the property sector and monetary policy. Because, in some instances, the property sector is used as collateral for loans. Thus, it can affect the financial system’s stability. As a mitigating tool for modifying monetary policy on assets to maintain domestic economic stability, it is crucial to do research on the interaction between monetary policy and asset prices.

(Umar et al., 2020) explained that the direct effect of the monetary policy transmission mechanism on property prices is the income effect. When interest rates rise, the interest expense of any unpaid loan or new loan application will increase. Thus, disposable income will decrease. The fall in discretionary income can affect the level of consumption. The impact on the level of consumption is contingent on the movement of interest rates, followed by the tightening of monetary policy. The faster changes in interest rates will affect household disposable income. The size of the income effect depends on the household income proportion. The level of household consumption highly depends on the proportion of income. In that case, the household sector will have limited ability to apply for loans and may even increase the interest payments, which can reduce disposable income and consumption.

The indirect effects are the wealth effect and the credit channel effect. This research will focus on the indirect effect of monetary policy transmission through the property sector. Monetary policy transmission through credit channel effects works if interest rates are high, it can reduce the “wealth” of the housing sector, and household access to credit becomes lower. Credit-constrained households must reduce their level of consumption. (Iacoviello et al., 2008) stated that the volatility (high level of risk) of housing prices means that monetary policy shocks have a negative effect and drive the banking sector to give greater housing or property loans.

Studies on the relationship between the monetary and property sectors have been extensively developed. (Bjørnland & Jacobsen, 2010) employed the VAR approach linking monetary and macroeconomic variables with housing prices in Norway, Sweden, and the UK. The results showed that housing prices would directly respond to interest rate changes. Research by (Bredin et al., 2007) utilized the same approach and showed that housing prices (using REITs data) respond negatively to interest rate changes. Both analyses reveal that house prices are sensitive to the monetary sector. Similar results also showed by research from (Brooks & Tsolacos, 1999). The study revealed that inflation and interest rates strongly affected the UK housing market share.

Research by (Gupta et al., 2010) stated that house price growth in South Africa responds negatively to monetary influences, and housing price responses depend on market segmentation. Another approach developed by (Bredin et al., 2007) by using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) showed that there is a response from housing prices (REIT) to the uncertainty and volatility of monetary policy. A study by (Elbourne, 2008) utilized the SVAR approach. The result indicated a simulation of an impulse response of around a 12-15% decrease in consumption following monetary policy shocks through changes in property prices. The research results do not support evidence that credit channels and the wealth of monetary policy transmission play an important role in the variation in output in response to monetary policy changes.

The monetary policy shows its power and plays an important role in the structural analysis of property prices (Bjørnland & Jacobsen, 2013); (Gupta et al., 2012); (Rahal, 2016). Research developed by (Demary, 2010) identified three main things: a) transmission of contractionary monetary policy, which leads to an increase in interest rates. It increases housing costs. Moreover, the demand for property becomes lower. Furthermore, it will decrease housing prices, b) Transmission through inflation states that an increase in inflation has a heterogeneous impact on property prices, considering that real estate ownership is an investment and is used as a hedge against inflation. An increase in inflation will drive investment in real estate, impacting the increasing housing prices. When inflation increases, the monetary authority tends to increase interest rates which can lead to an increase in the cost of the property sector. It decreases the demand for the real estate and housing sector; c) The positive effect of the increased output is an increase in disposable income for consumption which can increase investment in the property sector. The higher economic growth will increase demand for the property sector. Thus, it can drive construction activity and property prices and interest rate have a strong relation to the housing price (Albuquerque et al., 2020).

(Assenmacher-Wesche & Gerlach, 2011) study utilizes panel data from 18 OECD countries. The results showed that monetary policy has a significant impact on housing prices. The results contrast with (Hanck & Prüser, 2020), who identified structural shocks using the BVAR approach. The estimation results showed that monetary policy strongly affects housing prices. According to (Plakandaras et al., 2020), monetary policy is a significant factor in the evolution of housing prices in the UK. Using monetary variables cannot be separated from foreign interest rates because they have implications for the development of domestic monetary policy (Dawood, 2019).

This current study applied the Autoregressive Distributed Lag Model (ARDL) approach in identifying the effect of monetary policy on asset price volatility in Indonesia to link the studies above. The first contribution consists of adopting the research developed by (Miao et al., 2022), which highlighted that monetary policy is executed through foreign interest rates. Because of this, developing countries like Indonesia are vulnerable to “bubble-driven” crises that can disrupt asset prices, investment, and capital flows. In addition, the determination of domestic interest rates considers fluctuations in foreign interest rates and to bridge the gap between research developed by (Miao et al., 2022) are to identify the affect of foreign interest rate in short and long term. The second research contribution is to analyze more comprehensively the sensitivity of domestic interest rates before the COVID-19 pandemic (modified model) and after (full model) and their effect on residential property prices in Indonesia. Third, emphasize the role of foreign and domestic interest rate on residential property price in Indonesia.

The study utilizes quarterly data from 2006Q1 to 2021Q4. The variables consist of the Residential Property Price Index (IHPR) using the base year 2002 = 100, the gross domestic product (GDP), inflation, money supply using the money supply in a broad sense, domestic interest rates, and foreign interest rates where interest rates are concerned. The Federal Reserve (The Fed) ‘s interest rate is used as the main reference for foreign central banks. The data was generated from the Central Bank of Indonesia and The Fed. This research examines the effect of monetary variables on the movement of the residential property price index in Indonesia. The model equation is as follows:

(1)

PR is the residential property price index (in logarithm), GDP is a gross domestic product using constant prices 2010=100 (in logarithm), INF is inflation, MS is money supply using the money supply in a broad sense (in logarithm), r is the domestic interest rate and r* is the foreign interest rate. The first step in estimating time- series data is the stationarity test, employing the unit root test with the Augmented Dickey-Fuller (ADF) and Philips Perron (PP) approach.

The long-term existence test in the autoregressive distributed lag (ARDL) model using the bound test approach is performed after the stationarity test. (Pesaran et al., 2001) when it is not known with certainty whether the underlying regressors are trend-or first-difference stationary. The proposed tests are based on standard F-and t-statistics used to test the significance of the lagged levels of the variables in a univariate equilibrium correction mechanism. The asymptotic distributions of these statistics are non-standard under the null hypothesis that there exists no level relationship, irrespective of whether the regressors are I(0 apply the bound test cointegration model to deal with differences in the level of integration between variables. The cointegration test with the bound test approach is used to estimate the long-term coefficients with the F-test. The ARDL model equation is as follows:

(2)

The equation above uses the bound test as a cointegration test in the ARDL model by testing the F-statistic value. The initial hypothesis (null hypothesis) asserts that there is no cointegration or is described as (H0:δ1 = δ2 = δ3 = δ4 = δ5 = 0 ). Meanwhile, the alternative hypothesis holds that there is cointegration between variables. The examined F-statistics will be compared to the lower and upper band critical values, which we classify as I(1) and I(0). Based on the cointegration test, if the F-statistic value is greater than the upper limit, then H0 is rejected, indicating the existence of a long-term relationship. If the value of the F-statistic is less than the lower limit, then the long-term is not included in the model. The ARDL model based on (Pesaran et al., 2001) when it is not known with certainty whether the underlying regressors are trend-or first-difference stationary. The proposed tests are based on standard F-and t-statistics used to test the significance of the lagged levels of the variables in a univariate equilibrium correction mechanism. The asymptotic distributions of these statistics are non-standard under the null hypothesis that there exists no level relationship, irrespective of whether the regressors are I(0 requires an Error Correction Model (ECM) with the adjustment method and error correction in the short-term to get the balance in the long-term, ECM estimation aims to obtain the value of the error correction term (ECT) by performing the regression of independent variables on the dependent variable. The equation formula can be written as follows:

(3)

The value of the formula above is used to measure the speed of adjustment. The ECT value is expected to be negative and significant to obtain a long-term balance with a range of 0 and 1. A value of 0 indicates no adjustment, while a value of 1 indicates a full adjustment for one period after a shock occurs. If the ECT value is positive and significant, it indicates convergence equilibrium, and there is no external influence/ shock (independent variable). The CUSUM and CUSUMQ tests are applied to the model’s residual value to test its developed model stability. The CUSUM test is based on the cumulative sum of recursive residuals. The estimate is considered stable if the CUSUM plot is within 5 percent of the crucial value or if it does not cross the top and bottom lines on the CUSUM figure. However, the estimate is considered unstable if the CUSUM value is above the critical value of 5 percent, out of the top line, or below the CUSUM figure. The interpretation is consistent with the CUSUMQ test, based on the cumulative sum of squares of recursive residuals. Several diagnostic tests were performed to obtain a proper model (goodness of fit), as with the Jarque-Bera approach for normality and heteroscedasticity testing.

The 2008 crisis caused by asset price bubbles in the US could spread and exacerbate domestic economic conditions in the US, more than it affected foreign economies, including Indonesia; with that, it was crucial to conduct research on property assets as an early warning system for asset fluctuations in domestic economic conditions. This study identifies the effect of monetary variables through the volatility of property asset prices in Indonesia. The results of the study indicate that the income variable using the GDP variable as a proxy has a negative effect on residential property prices in Indonesia, whereas the inflation variable has a positive effect. This is because an increase in inflation will boost investment in real estate, resulting in a rise in housing prices. There was a different effect between before COVID-19 and after COVID-19, namely the influence of domestic interest rates, which directly occurred during the recession caused by the COVID-19 pandemic; this demonstrates the sensitivity of domestic interest rates to property prices during a recession. The foreign interest rate variable has a negative effect, indicating that an increase in foreign interest rates might lower the demand for domestic bonds, leading to an increase in domestic interest rates and a fall in property demand. The implications of this research show that housing price expectations are supported by anti-deflationary policies (low-interest rates) in the long-term, which strengthens monetary operations through interest rates to maintain public price expectations, especially property prices. The limitation of this study is using GDP variable as a proxy for income not using business cycle as variables.