Effectiveness of Monetary Policies in Affecting
Chinese Housing Prices
Yiman Zheng (10151877)
Bachelor Thesis Economics and Business Specialization: Economics and Finance
Faculty of Economics and Business University of Amsterdam
Supervisor: Stephanie Chan
Effectiveness of Monetary Policies in
Affecting Chinese Housing Prices
Abstract
This research is based on the monthly data between June 2005 and December 2012 published by the National Bureau of Statistics in China. The researcher used linear regression model and F-tests to assess the correlation and relationships between monetary policies, including money supply and long-term mortgage interest rate, and housing prices in China. The assessment is then used to evaluate the effectiveness of monetary policies in China in controlling and easing housing prices. The regression analysis shows that money supply has significant impact on housing prices, whereas mortgage interest rates have little impact on housing prices.
Key Words
Monetary policy, mortgage interest rate, money supply, Chinese housing market, linear regression, F-test
Table of Content
Abstract ... 1
Key Words ... 1
Table of Content ... 2
Introduction ... 3
Literature Review ... Error! Bookmark not defined. Methodology ... 13
Analysis I Housing Price Analysis ... 14
Analysis II Regression Analysis ... 15
Assumption of the Model and Methodology ... 15
Indices and Data ... 16
Source of Data ... 16
Data Processing and Description ... 17
Analysis ... 23
Regression Test on Housing Prices in Shanghai ... 24
Analysis for Model on Shanghai Housing Market ... 27
Conclusion ... 28
Limitations and Further Research ... 30
Introduction
The housing market has been the most booming market in China in the past few decades. The market was firstly established in the early 90s, after the announcement of economic revolution and open-up by Deng Xiaoping. It has since then been developing rapidly and is now a sizable market and important source of GDP growth. Housing price has been increasing year by year, and has become disproportional to the wage rise. In particular, the differences in housing prices across different areas have been widening. In order to solve such structural problems and prevent housing price bubbles, the government has been applying different policies, including monetary policies and non-monetary policies. This research uses regression model to assess the effectiveness of monetary policies, including mortgage interest rates and money supply, on China’s housing market. The regression model used is linear regression model. In the final section of the report, the researcher suggests what policy makers can do to effectively control housing prices.
Literature Review
Due to the increasing importance of the real estate sector in the global economy, there has been an increasing debate on whether monetary policies should take into account property prices and whether or not such policies should be used to control property prices (Taylor, 2007; Bordo and Jeanne, 2002 and Lansing 2003). There have been a number of studies examining the interactions, correlations and effectiveness between property prices and government monetary policies. For example, economists including Bernanke and Gertler
(1999, 2001) as well as Cechetti et al. (2000) have shown that it is useful to react to asset prices in addition to the inflation forecast. Gilchrist and Leahy (2002) pointed out that monetary policies that tackle inflation and output deviations would achieve the greatest gains from reacting to asset prices. Moreover, Bernanke and Gertler (1999) as well as Kiyotaki and Moore (1997) pointed out that on the other hand, asset prices could also be one of the determinants of monetary policy decisions, due to the wealth effect and the Tobin’s Q effect. McCarthy and Peach (2002) carried out an investigation on the impact of government monetary policies on the US property prices. Their paper concluded that the residential financing system reconstruction has contributed the greatest towards the property price changes in the US. Furthermore, monetary policies also have great impacts on housing prices. Even so, it was argued that interest rates should only be used to control macroeconomic trends instead of housing prices (Taylor, 2007; Feng, 2010; Bernanke and Gertler, 2000). This helps prevent moral hazard and improves market efficiency. What’s more, Lansing (2003) argued that policy makers including central banks shouldn’t deliberately use monetary policies to prevent property bubbles, since the economy may end up in recession.
Before 1998, the Chinese government controls tightly of the distribution of properties, whereas the real estate market is only a secondary market. China’s property industry became commoditized in 1998, when the government dismantled the previous housing system. At the same time, the Chinese economy has enjoyed continuing growth at impressive rate to become the world’s second largest. Meanwhile the Chinese real estate market underwent substantial growth
as well as significant changes since then (Xu and Chen, 2012). Yao and Luo (2009) pointed out that the soaring housing prices in China was mainly caused by excessive liquidity as well as irrational and speculative consumer behaviours. Because of asymmetric reactions to gains and losses, investors normally take more risks when property prices rise and less risk when property prices drop (Yao et al., 2011).
Figure 1: Average residential property prices and its annual growth rate
Source: Yao et al (2011). On China’s Monetary Policy and Asset Prices
In general, the residential property price movements are behind the property sector development stages by 1-2 years.
Source: Fangjia.com
Figure 3. Housing prices in Shanghai
Source: Fangjia.com
Figure 4. Housing prices in Shenzhen
Source: Fangjia.com
Figure 5. Housing prices in Guangzhou
Source: Fangjia.com
Cities in China are classified formally in different tiers, depending on their political and economic power, size and influence. First-tier cities refer to direct-controlled municipalities. In 2009, housing prices in Beijing, Shanghai, Guangzhou and Shenzhen, which are the so-called “first-tier cities”, increased by 50%. The prices then increased between 24% to 42% in the following year. Currently, residential properties cost 45,804 RMB/Square metre in Beijing,
35,770 RMB(5770 USD)/Square metre in Shanghai, 26,571 RMB (4286 USD)/Square metre in Shenzhen and 20,769 RMB (3350 USD)/Square metre in Guangzhou. In 2010, consumer price index (CPI) reached to the 28-month high level, which was 5.1%.
In order to control the soaring property prices, the Chinese central government has taken certain actions, including both monetary and non-monetary policies. For example, in 2006, the central bank adjusted its base-lending rate to 5.85%, up from 5.58%. Furthermore, during the same year, the central bank increased the required reserve rate (RRR) for three times: up 0.5% every time. However, despite of the implementation of the policies, the property prices show no sign in decreasing. More recently, China has injected 4 trillion RMB into its economy in 2008, in order to fight against the financial crisis. In 2009, and 2010 banks were encouraged to issue 9.5 trillion RMB and 7.95 trillion RMB of new loans respectively, in order to boost the housing market.
Due to the fact that most of the Chinese banks are state-owned, it is believed that changes and nature of monetary policies have significant impacts on asset prices (Koivu, 2012). Monetary policies in China fall into the following three categories: central bank’s price-based instruments, central bank’s quantity-based instruments and non-central bank instruments. Monetary policy instruments fall into four categories (Xie, 2004a): instruments with ratios, instruments with interest rates, quantitative instruments and other instruments. Instruments with ratios include reserve requirements; instruments with interest rates include central bank lending rates; quantitative instruments include open
market operations and other instruments include central banks bills. In terms of controlling housing prices, money supply has been used as the major method of easing the soaring of housing prices (Business Times, 2012). According to a paper published by the United Nations (2008), China has a very special economic environment. Therefore, there are several concerns that should be taken into account when analyzing the effectiveness of monetary policy in China, First of all, there are a lot non-performing loans in China. Therefore, the numbers of loans being taken do not necessarily reflect the status of the loan market. Second, many commercial or retail banks do not have enough capital. This limits the effect of monetary policy as a tool of introducing financial stability. Third, the four largest banks China, which are all state-owned, act as monopoly within the financial system. Last but not the least, the Chinese government is still controlling the interest rate. Therefore, the financial system still lacks competition and the freedom of credit decision-making.
The monetary policy development in China consists of four stages. The first stage refers to the period between 1949 and 1984, when People’s Bank of China (PBOC) was the only central bank (People’s Bank) and commercial banks such as Industrial and Commercial Bank of China in the country (Luo and Yao, 2010). The second stage refers to the period from 1984, when there were a number of banking reforms within the financial system, which helped decentralize the system (Yao et al., 2007). The third stage refers to the significant development in the housing market starting from 1993. The development of housing market made the government active in introducing monetary policies to control the stability of the economy, since such development increased the demand for loans
and the need to control interest rates. Finally, the fourth stage refers to the time when the State Council decided to use policies to “maintain currency stability as well as promote economic growth” in 1998 (Laurens and Maino, 2007).
However, there is no established or clear interest rate policy in China, unlike the Federal Reserve rate in the US. (Yao et al., 2011). Therefore, the central bank and the central government often use the money supply (M2) an intermediate measurement or target. However, the actual rate of growth of M2 can be very different from the targeted rate.
Table 1: Targeted and actual values of the People’s Bank of China’s monetary aggregates: 1994-2006
Year M1 growth (%) M2 growth (%)
Target Actual Target Actual
1994 21 26.2 24 34.5 1995 21-23 16.8 23-25 29.5 1996 18 18.9 25 25.3 1997 18 18.9 25 25.3 1998 17 11.9 16-18 15.3 1999 14 17.7 14-15 14.7 2000 15-17 16 14-15 12.3 2001 13-14 12.7 15-16 14.4 2002 13 16.8 13 16.8 2003 16 18.7 16 19.6 2004 17 13.6 17 14.6 2005 15 11.8 15 17,6 2006 14 17.5 14 16.9
Source: This table is reproduced from Xie et al’s paper in 2004
Table 1 shows the targeted and actual values of the People’s Bank of China’s monetary aggregates between 1994 and 2006. From the graph we can see, targets were reached more easily when the target bands were formulated.
However, the problem of the targets is that the bounds are very narrow. The 1996 target was met quite precisely. It was surprising to find that the 2004 actual result was 3.4% lower than the M1 target and 2.4 percent points lower than the M2 target, when inflationary pressure was relatively low. Geiger (2006) believes that the controllability problem of monetary targets in China can be explained by its exchange rate regime and an unstable money multiplier. Xia et al. (2001) and Yu observed the problem caused by the peg of the RMB to the US $ until 2005, and the later on crawling peg, which both undervalued the currency. Besides, Goldstein (2004) and McCallum (2004) contribute the problem to the undervaluation in recent years.
Figure 7. Movements of M2, stock market index house price index and inter-bank rate (January 2008=100, all values are in natural logarithms)
Figure 7 shows that interbank rate doesn’t have high correlation with the other variables, whereas M2 seems to be the key driver during the data period.
Figure 8. Correlation between bank deposit, lending, interbank and repo rate
Figure 8 shows the movements and correlations of different interest rates. From the figure we can see, the two major market-determined interest rates (deposit and lending rates) are highly correlated, however, are less affected by the official bank rates (interbank and REPO rates). This shows that the interest rate market is still not liberalized enough in China.
Yao et al. (2011) used multiple techniques to assess the effectiveness of monetary policies on housing and share prices in China. The tools used include Granger Causality Test, Johansen’s VAR Approach and impulse analysis. Their paper concluded that housing prices have been continuously increasing, despite of the implementation of monetary tightening policies. The authors attributed
the reasons to China’s rapid urbanization, attitude towards home ownership, lack of investment channels and imperfect market competition.
Liang and Cao applied Autogressive Dynamic Lag Models (ARDL) framework to China’s quarterly data between Q1 1999 and Q2 2006. Their paper concluded that long-term interest rate and bank credit are highly related to property prices. Further, there is also long-term and short-term causality running from long-term interest rates to property prices. However, Sheng and Liu (2013) believe that housing prices are more attributed to the demand and supply relationship, rather than monetary policies. Zhang (2010) pointed out that the link between interest rates and housing prices are very weak. Further, IMF (2012) found that there wasn’t direct relationship between low interest rate and high housing prices. For instance, Australia, New Zealand and the UK have higher interest rates than the US. However, the three countries have higher housing prices.
Yet it is difficult to find the optimal monetary to respond to property prices. First of all, Borio and Lowe (1002) observed that inflation is not really a good indicator of financial imbalances. They found that overall inflation does not increase during the boom. Siebert (2004) thinks that it is difficult to characterize the optimal monetary policy to respond to housing prices. The optimal response to some extent depends on the underlying reason of the housing price increase (Smets, 1997; Dupor, 2002b). Further, the optimal response also depends on whether the underlying source of housing price increase is improved productivity or overly optimistic expectations (Siebert, 2004). According to Smets and Wouters(2003), trade-off between inflation and asset price
stabilization may occur when there is a non-fundamental housing price shock. Moreover, the optimality of a tightening policy will depend on the likelihood of a housing price bubble and the cost of such policy (Bordo and Jeanne, 2002b).
Another question is that how monetary policy should be dealing with the potential occurrence of financial imbalances typical of asset price booms. Borio and Lowe (2000) and Borio et al. (2003) pointed out that the core of the question is that whether central banks should be against the development of financial imbalances, which may eventually unwind at higher costs.
Overall, many theoretical models and theories have shown contradicting views. Empirical studies of different countries have also given very different conclusions. The researcher believes that due to the uniqueness of the Chinese economy, China can be an interesting case study to study the relationship between government policies and housing prices.
Methodology
This paper will focus on examining the correlation between 5+years mortgage interest rate, money supply (M2) and housing prices. The tool that will be used to run regression analysis will be Stata. Following hypothesis tests will be constructed and tested:
1) 𝐻0: There is correlation between interest rate controls and housing price 𝐻1: There is no correlation between interest rate controls and housing
2) 𝐻0: There is correlation between quantitative easing/liquidity control and housing price
𝐻1: There is no correlation between quantitative easing/liquidity control and housing price
The regression analysis procedure consists three steps:
1) Plotting the dependent variable against individual independent variable respectively
2) Using linear regression analysis function in Stata for regression analysis 3) Use F-test to test the significance level
Analysis I Housing Price Analysis
Since the economic reform, China’s property prices, especially for the residential property sector, have been rising rapidly, attributing to the transformation of a government control and improvements of the free-market mechanism. The property sector was growing at a rate of 5% ever year between 1953 and 1978, which makes up 1.83% of the overall economic growth. The figures then increased to 11.4$ and 2.39% respectively between 1978 and 2005 (Source: Development Research Centre of State Council, PRC). Moreover, property prices have been very strong and resilient in the past decade. Property prices were growing at a rate of 15.1% and 19.5% in 2004 and 2005.
Figure 9. Growth rates of investments in property-related development and total fixed assets
It is obvious from Figure 9 that the growth rates in investments in property-related development are significantly higher than investments made in other fixed assets. The turning points also match the different stages of the property development in China. However, growth rate of property-related investments is lower than investments in total fixed assets in 2006, which may be due to tightening regulations since 2003.
Analysis II Regression Analysis
Assumption of the Model and Methodology
The model that is used in this research involve following variables: 1) Unknown parameters 𝛽𝑖
2) Independent variables 𝑋𝑖, which are 5+years mortgage interest rate, money supply and GDP. The choice of variables made are based on the literature review in the previous section.
Housing price will be approximated to a function of 𝑋𝑖 and 𝛽𝑖, i.e. 𝑌 ≈ 𝑓(𝛽𝑖, 𝑋𝑖). A
linear regression relationship is assumed in this model, i.e. 𝑌 = 𝛼 + 𝛽1𝑋1+𝛽2𝑋2++𝛽3𝑋3+ 𝜀,
where 𝑋1 is the M2 money supply 𝑋2 is the interest rate, 𝑋3 is the GDP. 𝛽1, 𝛽2 and
𝛽3 are respective constants, 𝛼 is a constant and 𝜀 is the standard error. The
non-monetary factor GDP is introduced in order to catch the non-non-monetary determinant of the housing price.
The linear regression analysis is firstly done on the three variables 𝑋1, 𝑋2 and 𝑋3.
Then a two-variable linear regression analysis is done on 𝑋1 and 𝑋3, as well as on
𝑋2 and 𝑋3.
Indices and Data
According to formula 𝑌 = 𝛼 + 𝛽1𝑋1+𝛽2𝑋2++𝛽3𝑋3+ 𝜀, the variables that will be included in our regression model are 5+years mortgage interest rate (LIR), money supply (MS) and a constant.
I use M2 as a measure of money supply. M2 refers to notes and coins that are in circulation which are both inside and outside Federal Reserve Banks and vaults of depository institutions, demand deposits, travelers’ checks of non-bank issuers, other checkable deposits, saving deposits, time deposits and large time deposits.
Source of Data
This research uses the aggregate housing prices per square meter of all Chinese cities, which were obtained from the National Bureau of Statistics from
2012. The average monthly housing price (RMB per square) was then calculated as:
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 ℎ𝑜𝑢𝑠𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒 = 𝐴𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒 𝑚𝑜𝑛𝑡ℎ𝑙𝑦 ℎ𝑜𝑢𝑠𝑖𝑛𝑔 𝑠𝑎𝑙𝑒𝑠 (𝑅𝑀𝐵) 𝐴𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒 𝑚𝑜𝑛𝑡ℎ𝑙𝑦 ℎ𝑜𝑢𝑠𝑖𝑛𝑔 𝑠𝑎𝑙𝑒𝑠 𝑎𝑟𝑒𝑎 (𝑚2)
The monthly data of aggregate housing sales (RMB Billion) and aggregate housing sales area (10,000 square metres) are obtained from the website. The monthly data of aggregate housing sales (RMB Billion) refers to the total contracted sales amounts during the reported period. The monthly aggregate sales area refers to the total sales areas (10,000 square metres) indicated on the contracts during the reported period.
Loan interest rates between 2005 and 2012 were obtained from Hexun.com (http://calendar.hexun.com/global458_1.shtml).
GDP data is also from the National Bureau of Statistics from data.stats.gov.cn. However, since GDP results are only released on a quarterly basis, the average monthly GDP is obtained by dividing the quarterly GDP result by 3.
Data Processing and Description
Figure 10. Descriptive statistics of housing price, money supply and 5+years mortgage interest rates
Variable Observations Mean Std. Dev Min Max Housing Price Per Square Metre (RMB) 84 4537.825 931.4078 2973.178 6436.992 GDP (RMB 91 205343.7 122557.5 45315.83 519470.1
Billion) 5+Years Mortgage Rate (%) 91 6.631484 .6380258 5.94 7.83 M2 Money Supply (RMB Billion) 91 551331.6 210129.9 269240.5 944800
Figure 10 shows that housing price has a mean of RMB4537.825 per square metre, with a standard deviation as high as 931.4078. M2 money supply has a mean of RMB551331.6 Billion, with a standard deviation as high as 210129.9
(1) Plot
Housing prices against interest rates are firstly plotted in Figure 11. The chart shows that the correlation pattern between housing price and interest rates are not so clear.
Housing prices against money supply are plotted in Figure 12. The chart shows that the scatter follows a very clear positive correlation pattern.
Figure 12. Money supply vs Housing prices: June 2005 to December 2012
Figure 13. GDP vs Housing prices: June 2005 to December 2012
(2) Correlation and residual test
The predictors involved in the model are constant, 5-year+ mortgage interest rate, GDP and M2 money supply. The dependent variable in the model is housing price per square metre.
Figure 14 shows that residual squared is 0.1256, whereas the adjusted R-squared is 0.0892. This indicates that the data has not-so-good goodness of fit.
Figure 14. Regression test results between housing price and 5+years mortgage interest rate, GDP and M2 money supply: June 2005 to December 2012
Source SS df MS Model -0.006585693 3 0.002195231 Residual 0.045839871 72 0.000636665 Total 0.05245564 75 0.000699008 Number of observations 76 F (3, 72) 3.45 Prob > F 0.0210 R - squared 0.1256 Adjusted R – squared 0.0892 Root MSE 0.02523 Housing Price Growth Rate (%) Coefficient Std. error t 𝐏 > |𝐭| 95% CI Interest Rate -0.0028999 0.0045406 -0.64 0.525 (-0.0119514 , 0.0061515) GDP 9.76 × 10−8 3.22 × 10−8 3.04 0.003 (3.35 × 10−8, 1.62 × 10−7) M2 Money Supply −4.69 × 10−8 1.78 × 10−8 -2.63 0.01 (−8.25 × 10−8, −1.13 × 10−8) Constant 0.0175869 0.031004 0.57 0.572 (-0.0442185, 0.0793922)
However, by running two-variable regression test on 1) mortgage interest rate and GDP vs. housing price and 2) money supply and GDP vs. housing price respectively, it was found that interest rate has low correlation with housing price, whereas money supply and GDP has high correlation with housing price (Figure 15 and 16).
Figure 15. Regression test results between housing price, GDP and M2 money supply: June 2005 to December 2012 Source SS df MS Model 0.002185098 2 0.001092549 Residual 0.050240466 73 0.000688226 Total 0.05245564 75 0.000699008 Number of observations 76 F (2, 73) 1.59 Prob > F 0.2114 R - squared 0.0417 Adjusted R – squared 0.0154 Root MSE 0.02623 Housing Price Growth Rate (%) Coefficient Std. error t 𝐏 > |𝐭| 95% CI Interest Rate -0.0030088 0.0047206 -0.64 0.526 (-0.0124171 , 0.0063994) GDP 4.37 × 10−8 2.57 × 10−8 1.70 0.094 (−7.62 × 10−9, 9.50 × 10−8) Constant 0.0045944 0.0318229 0.14 0.886 (-0.0588285,
0.0680172)
Figure 16. Regression test results between housing price, GDP and interest rates: June 2005 to December 2012 Source SS df MS Model -0.006325995 2 0.003162997 Residual 0.046099569 73 0.000631501 Total 0.05245564 75 0.000699008 Number of observations 76 F (3, 72) 5.01 Prob > F 0.0092 R - squared 0.1207 Adjusted R – squared 0.0966 Root MSE 0.02513 Housing Price Growth Rate (%) Coefficient Std. error t 𝐏 > |𝐭| 95% CI GDP 9.69 × 10−8 3.20 × 10−8 3.03 0.003 (3.31 × 10−8, 1.61 × 10−7) M2 Money Supply −4.70 × 10−8 1.78 × 10−8 -2.65 0.01 (−8.25 × 10−8, −1.16 × 10−8) Constant 0.0014997 0.008222 -0.18 0.856 (-0.0178862, 0.0148868)
(3) F-test
Chart 13 shows that F(3, 72) is 3.45. Using the critical value table of F-test, we can see that 𝐹(3, 72)0.05= 2.73 < 3.45 . Hence the data has passed the significance test, F-test.
Analysis
The regression formula of the three-variable regression test is therefore 𝑌 = 0.0175869 − 4.69 × 10−8𝑋
1− 0.0028999 𝑋2+9.76 × 10−8𝑋3,
Where 𝑌 is the housing price, 𝑋1 is the M2 money supply, 𝑋2 is the 5+years interest rate and 𝑋3 is the GDP.
However, above results show that money supply and housing price are negatively correlated. This is an unexpected result. Increase in money supply should give buyers with greater housing purchasing power. The result may be due to the under-development of financial and banking system and other factors including shadow banking.
Through further analysis, the researcher thinks that the growth rates of housing have been uneven across China. Major cities including Beijing and Shanghai have enjoyed much rapid and significant increase in prices compared to cities that are third-tiers and beyond. Therefore, another set of regression tests on housing prices in Shanghai will be done.
Regression Test on Housing Prices in Shanghai
(1) Plot
Housing price growth rates in Shanghai and Beijing against interest rates are firstly plotted in Figure 17. The reason of why the plotted data are in columns is because interest rates are adjusted on a non-regular base and therefore the rate are constant over a period of time, whereas housing prices change. The chart shows that the correlation pattern between housing price and interest rates are not so clear.
Figure 17. 5+ Years mortgage interest rates vs Real housing price growth rate in Shanghai: August 2011 to December 2012
Housing price growth rates in Shanghai against money supply are plotted in Figure 18. The chart shows that the correlation pattern between real housing price growth rates and money supply growth rates are not so clear.
Figure 18. Money supply growth rate vs Real housing prices in Shanghai: August 2011 to December 2012
Housing price growth rates in Shanghai against GDP are plotted in Figure 19. The chart shows that the correlation pattern between real housing price growth rates and GDP is not as clear as the national data.
(2) Correlation and residual test
The predictors involved in the model are constant, 5+-year mortgage interest rate, GDP and M2 money supply growth rate. The dependent variable in the model is real housing price growth rate.
Figure 20 shows that residual squared is 0.3462, whereas the adjusted R-squared is 0.1953. This indicates that the data has better goodness of fit compared to the national level. By looking at regression tests of 5+years mortgage interest rate and GDP vs. housing price growth rate, as well as M2 money supply growth rate and GDP vs. housing price growth rate respectively, it was found that the correlation between housing price growth rates and monetary policies is low (Figure 20 and 21).
Figure 20. Regression test results between real housing price growth rate, GDP and 5+years mortgage interest rate and M2 money supply growth rate: August 2011 to December 2012
Source SS df MS Model -0.000877396 3 0.000292465 Residual 0.001656811 13 0.000636665 Total 0.002534207 16 0.000158388 Number of observations 17 F (3, 13) 2.29 Prob > F 0.1259 R - squared 0.3462 Adjusted R – squared 0.1953 Root MSE 0.01129
Price Growth Rate (%) Interest Rate -0.0222686 0.1494994 -0.15 0.884 (-0.3452424 , 0.3007051) GDP 9 × 10−9 6.09 × 10−8 0.15 0.885 (−1.22 × 10−7, 1.40 × 10−7) M2 Money Supply 8.46 × 10−8 2.84 × 10−7 0.30 0.771 (−5.29 × 10−7, 6.98 × 10−7) Constant 0.075046 1.253121 0.06 0.953 (-2.632158, 2.78225)
However, it should be noted that the model found negative correlation between 5+years mortgage interest rate and real housing price growth rate, which is expected.
(3) F-test
F(3, 13) is 2.29. Using the critical value table of F-test, we can see that 𝐹(3, 13)0.05 = 3.41 > 2.29. Hence the data has not passed the significance test,
F-test.
Analysis for Model on Shanghai Housing Market
The regression model on Shanghai housing prices shows that housing price growth rate is positively correlated to money supply (M2) growth rate and GDP, and negatively correlated to 5+years mortgage interest rates. The regression formula is therefore 𝑌 = 0.075046 + 8.46 × 10−8𝑋
1− 0.0222686𝑋2+ 9 ×
10−9𝑋 3.
Where 𝑌 is the growth rate of housing price, 𝑋1 is the growth rate of M2 money supply, 𝑋2 is the interest rate and 𝑋3 is the GDP. However, due to the limitation of available data, there are only 17 sets of data used in the regression test. Hence bias can exist for the result. The model didn’t pass the F-test.
China has been a cash-based economy for a long time. It was reported that even now many people do not have bank accounts and prefer to pay for properties in cash. This correlation results from regression tests are expected. However, the correlations don’t seem to be strong enough. Based on the regression result, monetary policies are therefore not so effective in controlling housing prices, since they may not be the most important determinants of buying incentives. On the other hand, non-monetary policies that intervene housing demand and supply may have greater impact on adjusting housing prices. It is noticed that GDP and housing price are highly correlated. Further, Chinese people view housing as a form a security and an important form of asset. Therefore, increasing housing price won’t stop Chinese people from buying properties. In contrast, a lot people buy more properties when price increases, since they think the price will continue to rise in the future.
Conclusion
Multivariable regression model enables the researcher to analyze the impact on and the correlations between monetary policies including mortgage interest rates, money supply and housing price in China. Such model is able to help readers to evaluate the effectiveness of monetary policies in terms of easing
housing prices. It is therefore useful for decision and policy makers, by helping them foresee the potential fluctuations in housing prices in response to policy changes and hence achieve better risk management and returns.
It was found in this research that M2 money supply has been a bigger cause of rising housing price, yet is still not significant enough. This may be because that money supply growth rate has greater impact on overall price levels rather than individual asset. However, even though housing prices have been seen to be positive correlated to M2 money supply, it was surprising to find that housing price growth rate is negatively correlated to M2 money supply. On the other hand, high interest rate doesn’t necessarily indicate low housing price, even though it does calm down housing price to some extent. More specifically, the correlation is strong in the long-term but not so significant in the short-term. In particular, interest rate adjustment is lagging and inflation rate has been high over the years, which have caused real interest rates to decrease. However, under the same monetary policies, housing prices do vary significantly across different cities in China. Some cities have steady housing prices, whereas the others do not. The researcher therefore believes that non-monetary policies, such as GDP, and market behaviors, such as speculative behaviors, can be greater causalities of the housing price increase.
Rising housing prices will continue to be a stress on social and economy development in China. Therefore, using relevant policies to ease the soaring of housing price in a more effective way has become particularly important. According to Sohu (2014), interest rate will carry on going up this year. However,
this will not decrease the housing price based on our regression model. Overall, the researcher thinks that monetary policy should target housing demand, affordability and to be combined non-monetary policy. The researcher has following suggestions:
1) Loan interest rate should be used wisely as a main method to control housing prices
2) However, imposing high interest rates within a short period of time will cause significant reduction in investments. Therefore, the central bank should be aware not to increase interest rates too rapidly
Limitations and Further Research
The research shows that the housing price increase was due to loan interest rate and money supply to a limited extent. Further, other literature and research papers suggest the significant differences in housing prices in different areas in China. Therefore, there are other non-monetary factors that may have greater impact on housing prices, including global economy, inflation and so on. Further research can be carried out analyzing the relationships between non-monetary policies and Chinese housing prices.
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