The Power of Hedging Against Inflation with Real Estate: the Hong Kong Experience

The Power of Hedging Against Inflation with Real Estate: The Hong Kong Experience

Sammi Ka Wai HO, and Rose Neng LAI 

Submitted to the 10th Asian Real Estate Society International Conference July18 - 21, 2005 at The Swiss Grand Resort & Spa --- Bondi Beach, Sydney, Australia

Abstract: Previous literature often concludes that real estate investment is an effective hedging instrument against inflation. Some more recent research finds that the balance of supply and demand of property in the market has a direct impact on real estate hedging ability; and property values are good protection against inflation only when the vacancy rate is low. This study expands existing research to study the ability of real estate in hedging actual, expected, and unexpected inflation in the short term and long term for the Hong Kong economy. We also consider property vacancies in explaining the hedging effectiveness. In particular, we focus on the hedging power of the four private property sectors for the period of 1983 to 2004. Our results provide up-to-date suggestions to which property types are better hedging tools in various inflationary environments.

 Sammi Ka Wai Ho is graduate student of Faculty of Business Administration, University of Macau, Macau, China. Rose Neng Lai (Contact Author) is Assistant Professor, Faculty of Business Administration, University of Macau, Taipa, Macao, China. e-mail: [email protected]. Tel. No.: (853)-397-4744, Fax. No.: (853)-838-320. 1. Introduction

Real estate has historically been viewed as powerful hedging tool against inflation. This is because property values tend to increase most of the time, often at a rate that is higher than inflation. As such, the purchasing power can be preserve if real estate properties are included in the investment portfolio. Whether this perception is realistic has been widely studied over the past thirty years in many major markets. This paper serves to supplement the existing literature by studying the hedging ability of real estate against inflation in Hong Kong.

There are numbers of reasons for choosing the Hong Kong market for our study. First, the very limited land supply has led Hong Kong to be one of cities in the world with high property prices. Second, property prices tend to be more volatile in Hong Kong than in other places. These two reasons may lead to the prediction that real estate returns have been historically high enough to preserve the purchasing power. However, Hong Kong has experienced a prolonged deflation since the Asian financial crisis in July 1997. Property prices have dropped down significantly. Hence, if the first prediction that real estate returns are good hedge against inflation is correct, what will be the hedging effectiveness after the Crisis? This is the third reason for our study.

Fama and Schwert (1977) are among the first to touch the issue. They examine the relationship between inflation hedging ability of properties, equities, government bonds, and human capital in the US between 1953 and 1971. In particular, they separate inflation into two components: expected inflation and unexpected inflation. They find that only private residential properties can provide complete hedge against both expected and unexpected inflation.

Among other studies after Fama and Schwert (1977), Rubens, Bond and Webb (1989) show that farmland and residential real estate provide complete positive hedge, while Wurtzebac, Mueller and Machi (1991) find that real estate returns are effective hedge against actual and unexpected inflation in high inflation period, and against actual and expected inflation in low inflation period. In other context, Bond and Seiler (1998)

2 find that both expected and unexpected inflation are significant components of residential real estate returns.

In terms of the UK markets, Barber, Robertson and Scott (1997) suggest that commercial property in the UK is a partial hedge, and that property can hedge unexpected inflation shocks better than anticipated inflation trends. Miles (1996) concentrates on commercial properties and concludes that the returns are too sensitive to economic conditions to be good hedge against inflation. Unlike all these previous studies, Stevenson (2000) examines regional housing markets and finds that the hedging power varies among regions, and that the relationships are neither consistent nor stable. He nevertheless suggests that housing and inflation are cointegrated, and therefore exhibit a long-term relationship. Stevenson and Murray (1999) also find that the Irish market is a long-term hedge against inflation, and property returns leads inflation.

In other markets, Newell (1996) studies the hedging abilities of various property markets in Australia while taking vacancy into consideration. Vitor (2001) performs the study on the Canadian market. His findings reflect that real estate is strongly positively correlated with inflation in high inflation period but not in low inflation period.

Other researches claim that real estate is an ineffective hedge against inflation. For instance, Hamelink and Hoesli (1996) who analyze the Swiss market claim that stocks and bonds are better hedging instruments than real estate; although Hoesli (1994) addresses that the Swiss real estate market seems to provide a better hedge against inflation than common stocks in the long term. Önder (2000) also finds that real estate investment in turkey does not provide hedge under high inflationary environment, and that there is no reverse causality between real estate returns and changes in expected inflation.

For Asian markets, Sim and Choe (2002) reveal that housing in Korea provides attractive hedge against both actual and expected inflation. Sing and Low (2000 and 2001) claim that the property market provides better hedge against inflation than other financial assets in Singapore. Chu (2003) is one of the first few to study the Chinese

3 property market. Because the Chinese property market is still experiencing fast growth at its early stage, while inflation has been kept low, Chu (2003) concludes that none of the various types of properties is a good hedge against inflation, both in the long and short run. He also finds that the vacancy rates have risen dramatically, which may be a reason why the reduced returns due to vacancy cannot act as a hedge, regardless of the inflation level. In their study on the Hong Kong real estate market, Ganesan and Chiang (1998) conclude that real estate returns are not good hedge against inflation.

From the various studies on different geographical regions, we can safely assume that property market returns may be a good hedge against inflation in some regions and time periods, but not others. Furthermore, as an indicator of market disequilibrium, vacancy can play a crucial role in the real estate return, thus affecting its effectiveness against inflation hedging. We therefore consider it necessary to reexamine the hedging ability of real estate returns against inflation in the Hong Kong market from three aspects. Firstly, we attempt to contribute to the literature by updating the study by Ganesan and Chiang (1998). In particular, we will repeat the three tests that they employ by using quarterly data for the period of 1985 – 2004: (1) the Fama and Schwert framework for testing short-term hedging, (2) test for co-integration, and (3) long-term relationship between real estate returns and inflation. Notice that our data series covers almost the whole study period of Ganesan and Chiang (1998) (except for 1984). Secondly, we enrich the literature for the Hong Kong market by including vacancy as an explanatory factor into the models for short-term hedging. Finally, since our period of study is made up of high and low inflation periods, and also incorporating the remarkable Asian Financial Crisis (in which we suspect an acute structural change in the economy), we further perform individual studies for each of the periods.

This paper is organized as follows. We first briefly introduce the real estate market situation in Hong Kong over the period of study in the next section. We then discuss the methodology in Section 3, and the test results in Section 4. Finally, Section 5 concludes.

2. Real Estate Market Performance in Hong Kong

4 There are four major property sectors in Hong Kong: residential, retail, office and industrial. Residential properties are made up of public and private housing. Only private housing prices are relevant to inflation because public housing benefits from the government’s supports. Housing is the most important type of savings for many households. In Hong Kong, households have always looked upon ownership not only as a social necessity, but also as the best hedge against inflation. The housing supply constraints also gave rise to high housing costs. On the other hand, retail property exhibits close relationship with consumption. When the economy is doing well, retail properties will also perform well. Therefore, it is less successful as a defensive asset. In this study, we will also include commercial properties and industrial properties. Because the data of other properties such as private specialized factories are limited, industrial properties are regarded as flatted factories, which refer to premises designed for general manufacturing processes and normally intended for sale or letting by developers.

Figure 1 compares the levels of the price indices for the four types of private property sectors. The property prices are more volatile than elsewhere, with a number of large structural changes in the past two decades. Hong Kong is well-known as a city with serious scarcity of land and highly dense population, and thus relatively high property prices compared with other parts of the world. Furthermore, as He and Webb (2000) have shown, the commercial and residential real estate markets are both very sensitive to important economic and political news, as indicated by the rapid responses in their prices and rental rates. As a result, Hong Kong real estate prices and returns are volatile.

The property prices decreased sharply with high interest rates caused by U.S. Fed policy to beat inflation and political uncertainty caused by Sino-British negotiations during 1982 to 1984. After that, the property market recovered and maintained an upward trend with peaking in 1997 after signing of the Sino-British Agreement to return Hong Kong to Chinese sovereignty in 1984. Hong Kong's property market has experienced significant downward fluctuation after 1997 Asian financial crisis and 2003 SARS outbreaks. The property values of retail, residential and office properties have drastic declined for six years because of persistent deflation.

5 Figure 1 Property Price Index of Hong Kong for 1980Q1 to 2004Q4

Per cent 250 Note: Year 1999 = 100 200

0 80 82 84 86 88 90 92 94 96 98 00 02 04 Year IND_PPI RES_PPI OFF_PPI RET_PPI

Since the Asian financial crisis in 1997, Hong Kong has witnessed an unprecedented lengthy period of gloomy economy. This is further fueled by the government adopting a housing production target of 85,000 units per year since 1998 that have driven the private property prices to fall sharply (the government eliminated the policy after a couple years). The effect of the Severe Acute Respiratory Syndrome (SARS) epidemic in 2003 (we refer it as the SARS period in the later context) further depressed domestic demand within Hong Kong. Real estate returns from different sectors have remained below or close to zero after these events, as depicted in Figure 2. The inflation rate from Figure 3 also shows similar movement. Inflation rate has experienced a gradual decline for six consecutive years starting from the fourth quarter of the 1998. The cumulative effect from these factors was to technically push the economy into the third recession in six years. As a result, the oversupply of stock has created, and vacancy rate has stood at a high level. Figure 4 plots the vacancy rates of four property sectors over 1982 to 2004. Figure 2 Property Nominal Returns of Hong Kong for 1982Q1 to 2004Q4

6 Per cen t

-30 82 84 86 88 90 92 94 96 98 00 02 04 Year

IND_RTN RES_RTN OFF_RTN RET_RTN

Figure 3 Trend of Inflation Rate in Hong Kong for the Period of 1982 to 2004

Per cent

-1 0 82 8 4 8 6 88 90 92 94 9 6 98 00 02 0 4 Year

Unlike other real estate sectors, the price index for flatted factories reached a peak much earlier than the other price indices and started to fall from the early 1990s. It reflected a decline in demand for factories because many investors move their

7 factories to Mainland China where operating costs are lower. There has also been high vacancy level in industrial market since 1994, especially in 1995. Since factories have limit potential for other applications, vacant units will usually be left idle for longer periods. Fortunately, after the SARS period in mid-2003, investors have gained confidence in the Hong Kong economy, and become more aggressive on real estate market. All property sectors are at the stage of recovery.

Figure 4 Vacancy Rates of Property in Hong Kong for 1982 to 2004

Per cen t 25

0 82 84 86 88 90 92 94 96 98 00 02 04 Year

RES_VAC IND_VAC OFF_VAC RET_VAC

As mentioned earlier, with a glance of Figure 2 and 3, it seems that the inflation rate and the rate of return from real estate are closely related. To verify this, Table 1 shows the correlation between the property returns and the inflation rates, as well as vacancy for the period of study (1982 – 2004). Also mentioned before is that the residential properties should moves most closely with inflation among the four sector. However, the correlation figure shown in Table 1 implies that their relationship is more moderate than expected. This, however, does not mean that they are not related because lead-lag relationship may still exist.

8 Furthermore, as high vacancy should imply lower return, there should be strong correlation, which is proven also from Table 1. It should also be noted that the nominal returns of four properties are negatively correlated with vacancy rates.

According to Wurtzebach, Mueller & Machi (1991), vacancy rates indicate a strong negative correlation with inflation. The nominal return on office exhibits strongest negative correlation (r = 0.721) with vacancy, while the housing nominal return has lowest negative correlation against vacancy. This is probably because Hong Kong has experienced a shortage of housing supply and sizable population, which is why a small gap between supply and demand cannot exert much pressure on domestic property prices.

Table 1 Correlation of Real Estate Returns with Inflation and Vacancy for the period of 1982 – 2004

Variables Correlation with Inflation Rate Vacancy Rate Residential return 0.435 -0.489 Retail return 0.285 -0.596 Office return 0.187 -0.721 Industrial return 0.280 -0.494

3. The Methodology

3.1. Short-term Hedging Most studies on short-term inflation hedge follow the methodology of Fama and Schwert (1977). Fama and Schwert (1977) propose the regression tests based on the work of Fisher, in which the expected nominal rate of return has three segments: the expected real rate of return, the expected inflation, and the unexpected inflation, that is ~ ~ ~ ~ ER jt t1 ,  t  E jt t1  E( t t1 )   j  t  E( t t1 ) (1)

9 where E() refers to the expected value, “” denotes that the variables are random, R jt represents the nominal rate of return from property j at time t, t1 is the information ~ set available at time t – 1,  jt is the real rate of return from the property, and t is the inflation rate at time t. The last term in equation (1) represents the unexpected inflation rate, which is the difference between actual inflation rate and the expected inflation rate. This is the term that Fama and Schwert (1977) propose on top of the original model by Fisher (1930).

The estimation of equation (1) can be done by running the following regression equations:

R jt   j   j  t   jt (2)

R jt   j   j E( t t1 )   j ( t  E( t t1 ) jt . (3) where jt and jt are the stochastic disturbances. In the first regression equation, it is hypothesized that the return from property sector j is related to actual inflation in the same period; while equation (3) is further extended by breaking actual inflation into expected inflation (the first regressor) and unexpected inflation (the second regressor).

Furthermore, because vacancy rates have noticeable impact on real estate returns, we follow Wurtzebach, Mueller and Machi (1991) by adding vacancy rates property sector j into the regression models to form

R jt   j   j  t   jV jt   jt (4)

R jt   j   j E( t t1 )   j ( t  E( t t1 )  jV jt  jt (5) where V jt is the vacancy rate of property sector j at time t.

Rubens, Bond and Webb (1989), and Wutzebach, Mueller and Machi (1991) evaluate the hedging performance against inflation according to the magnitude of the regression coefficients of the inflation rates in equations (4) and (5). In particular, a complete positive hedge against inflation is said to be achieved when a positively signed coefficient (j or ’j and j) is not statistically different from positive one; whereas a partial hedge against inflation is obtained when a positively signed coefficient is statistically different from both positive one and zero; an indeterminant

10 hedge is obtained when the coefficient is not statistically different from zero; and finally, an effective hedge occurs when a positively signed coefficient is statistically greater than positive one. We will follow such definitions in our tests.

In addition to these commonly applied regression equations, we further extend the study to incorporate a dummy variable to account for the structural change as a result of the 1997 Asian Financial Crisis. Recall that the real estate prices in Hong Kong had suffered significant declines as the aftermath of the Crisis. We therefore define a dummy, D jt , to equal one for the observations after the Crisis, and 0 otherwise. The coefficient of the dummy thus measures the effect of the Crisis on the property returns. The estimate of equation is as follows:

R jt   j   j  t   j D jt   jt (6)

3.2. Long-term Hedging Many studies (see, for example, Ganesan and Chiang, 1998) do not support Fama and Schwert (1977) model because the assumption that real rates of return are constant is not applicable to illiquid assets such as real estate. It is therefore suspected that real estate markets respond to long term change of inflation, even if the relationship cannot be reflect in the short run. Recent researchers focus on cointegration test as a measure for assessing the long term hedging ability of properties.

Following Engle and Granger (1987), the two-step procedure in the cointegration test include firstly checking the stationarity of the two time series (return rates of property sectors and the inflation rates) using the augmented Dickey Fuller (ADF) unit root tests, and secondly checking if the residual is stationary. If so, the two series are cointegrated which means that inflation and property return have long term relationship.

Two variables are cointegrated if they are integrated of order one. In other words, each data series should be stationary in their first differences. Thus, the ADF regression test states

p yt   0  1 yt1   2t    i yti   t (7) i1

11 where y represent the series, t is the trend component, and  is the white noice. Then the cointegration test for series x and series y is given by

yt   0  1t  xt  ut . (8)

The ADF test is then applied once again to the estimates of ut to test for stationarity using

p ˆ ˆ ˆ ut  *ut1   2t  i uti  t (9) i1

3.3. Causal Relationship The long run causal relationship between inflation and nominal property returns can be tested by the Granger causality tests. Based on the test, if X causes Y, then changes in X should precede changes in Y significantly. In other words, the past value of X can help improve the prediction of current values of Y. The test involves estimating two regressions:

l l yt  1i yti   1i xti   1Et1  1t (10) i1 i1

l l xt   2i yti    2i xti   2 Et1   2t (11) i1 i1 where the third term in both equations is the error correction term which must be included if the series are cointegrated. The null hypothesis that x does not Granger cause y (that is, all s are not significantly different from zero) is then tested using the F-statistics of equations (10) and (11). In this regard, the restricted form of equation (10) is to eliminate the second term (the restricted from of equation (11) is to eliminate the first term). If the coefficients on the lagged values of x are jointly and significantly different from zero, the null hypothesis can be rejected. The series y is then said to be Granger-caused by series x.

4. The Data Series

We select the period of study that spans 1985-2004, covering 20 years of data. All the Hong Kong data are on quarterly basis except the vacancy rate, which is on annual basis. We do not incorporate the data prior to 1985 because there was a large fluctuation on the real estate performance due to economic downturn in 1982 to 1984.

12 As this study also investigates the real estate performances of different properties during high and low inflation, we choose the cutting point of high-versus-low inflation as the median of inflation during 1985 to 2004.

The major properties of Hong Kong are grouped into four categories: residential, retail, office and industrial. The price indices are based on an analysis of prices paid for units in selected developments as recorded in the Sale and Purchase Agreements. The annual total nominal return of property sector j in period t is given by

R jt  LogPjt Pjt1  (12) where R jt is property nominal return of property sector j in period t, and Pjt is the property price index of property sector j in period t. The prices and vacancy rates of various types of private properties are obtained from the Hong Kong Rating and Valuation Department and the Hong Kong Property Review. The Hong Kong Rating and Valuation Department defines that vacancy rate as available space on the market over all premises completed during the review year, and those completed earlier but not yet assessed for rating purposes, are determined by inspection at the end of each year.

The overall price movement of goods and services is measured by Consumer Price Index (CPI). In the case of Hong Kong, the composite CPI from the Hong Kong Census and Statistics Department is the most reliable estimate of inflation. The actual inflation is represented by the rate of change of CPI calculated on an annual basis as follows

 t  CPI t  CPI t1  CPI t1 (13) where  t is the inflation rate for period t, and CPI t is the composite CPI for period t.

The actual inflation rate,  t , is further split into two components, the expected inflation and unexpected inflation. This separation into components gives a better way of defining the hedge against inflation. It is not easily to find out the expected inflation rate. The measurement of expected inflation is an important proxy in the examination of inflation hedging ability. As mentioned in Stevenson & Murray (1999), an inappropriate proxy can lead to substantially differing results. However, the estimation procedure is not straight-forward at all (Rubens, Bond and Webb, 1989

13 mention that there is no consensus on the best method to estimate inflationary expectation).

Several methods for measuring expected inflation have been proposed in the previous literature. For instance, Fama & Schwert (1977), among other early studies, use lagged 3-month U.S. Treasury bill rate less an assumed real rate of return to measure the expected rate of inflation. The drawback of this method is that the assumption of a constant real rate of return is unrealistic. On the other hand, Hoesli (1994) and Rhim, Khayum and Schibik (1994) use the real interest rate to process an autoregressive integrated moving-average model (ARIMA) model based on the Fisherian hypothesis that inflation forecasts are embedded in the relationship between nominal and real interest rates. Others such as Sim and Choe (2002), Stevenson and Murray (1999), and Schwann and Chau (2003) use past actual inflation rates to run ARIMA models as additional proxies for expected inflation.

In the case of Hong Kong, where there is no Treasury bills rate, Ganesan & Chiang (1998) use the 12-month time deposit rate plus a constant real interest rate that fluctuates within a range of 2% since 1987. However, since Hong Kong dollar is pegged with the US dollars, the Hong Kong savings rate and time deposit rate are strongly influenced by the US interest rate. Therefore, any change in interest rate can only reflect the inflationary expectation in the US, and not Hong Kong. We therefore measure the expected inflation in Hong Kong using the ARIMA model as in Hoesli (1994)1. We first use the best lending rate in Hong Kong, obtained from the Hong Kong Monetary Authority and the Hong Kong Census and Statistics Department, as a proxy of the nominal interest rate. The real interest rate is then derived by deducting the nominal inflation rate from the nominal interest rate. The expected real interest rate is then obtained by running the ARIMA model with the real interest rate series; and subsequently, the expected inflation rate is the nominal interest rate minus the expected real interest rate. That is,

it  I t   t (14)

E( t )  I t  E(it ) (15)

1 We have also attempted other methods, and find that this is the most suitable results for our study.

14 where it is the real interest rate, I t is the nominal interest rate, E( t ) is the expected inflation rate, and E(it ) is the expected real interest rate.

The autocorrelation functions (ACFs) and partial autocorrelation functions (PACFs) of the real interest rate series depict the existence of non-stationarity. As a result, we take the first difference on the series, and find that the ARIMA(1,1,1) model is an appropriate model for estimating the expected inflation. Stevenson (2000) indicates that the effectiveness of various proxies of expected inflation can be tested by using

 t    E( t t1 )   t (16) where E(t t1) is the expected inflation rate. The proxy is good when the intercept term is not significantly different from 0, the coefficient is significantly different from 0 and is close to unity.

As shown in Table 2, we can see that that the ARIMA (1,1,1) model from the real interest rate is able to provide an effective proxy of expected inflation. At 1% significance level, all beta coefficients are not significantly difference from 1 and significantly different from 0, none of constant in the regressions are significantly different from 0. Moreover, it has a high R2 of 97%. Therefore, this proxy is adopted as good measure of expected inflation.

Table 2 Measure of Proxy for Expected Inflation   R2 F-Test t-ratio ( = 0) Proxies t-ratio ( = 1) Real interest rate 0.172 0.968*** 0.970 2,515.597*** from ARIMA(1,1,1) (1.298) (50.156) Notes: *Significant at 10% level, **at 5%, ** at 1%.

4. The Results 4.1. Preliminary Data Analysis

Before performing the actual tests on the hedging ability of property returns on inflation, it will be helpful to provide a preliminary study on the data series. Table 3 presents the mean, median and volatility of the rate of returns of various property

15 sectors and inflation series. The Table shows some very interesting results. None of the property sector can beat the inflation rate in terms of mean and median, which might have suggested that real estate is not a good hedge against inflation in Hong

Kong. However, notice that the volatilities are higher than that of the inflation. This means that, given more volatile series, the mean returns cannot tell whether hedges are effective.

Table 3 Property Performance and Inflation Trend for 1982 – 2004 Property Sectors Mean Return (%) Median Return (%) Volatility (%)

Residential 2.22 4.3 8.33 Retail 2.51 2.2 8.68 Office 0.76 -1.4 12.72 Industrial 0.3 -0.6 7.96

Inflation 5.13 6.4 5.25

In addition, Table 4 shows the correlation between inflation and vacancy rates of different property sectors during the overall period of 1982 to 2004, high inflation period (1987 to 1997), and low inflation period (1998 to 2004). The correlation with residential property vacancy rates is highest (r = - 0.65) when compared to other properties. Office vacancy rate shows a low correlation coefficient (r = - 0.20), suggesting that the relationship between two series is relatively weak. When the sample is segmented into high (1987 – 1997) and low inflation periods (1998 – 2004), somewhat different results arise. In the high inflation environment, only residential properties show positively correlated relationship with inflation. If the housing price increased roughly in step with the inflation rate, the housing demand is likely reduced because housing affordability is low during high inflationary period. Retail properties

16 with strong negative correlation (r = -0.63) with inflation implies that demand seems to outpace supply because of strong economy. On the other hand, office and industrial property have negative and little correlated relationship with inflation.

The reverse situation occurs in low inflation period. The residential vacancy rate exhibits a strong negative relationship with inflation. The previously high inflation period allow people to pool their money to invest in housing in low inflation period when the price tends to be lower. The high and positive correlation of offices implies that low demand for office units is caused by weakened economic condition.

Table 4 Correlation between Inflation and Vacancy for the period 1982 – 2004 Mean Median Correlation with Inflation Property Sector Volatility Vacancy Vacancy Overall High Inflation Low Inflation (%) (%) (%) 1982 - 2004 1987 - 1997 1998 - 2004 Residential 4.60 4.20 1.10 -0.65 0.45 -0.60

Retail 7.98 8.20 2.15 -0.49 -0.63 0.16

Office 10.73 11.10 4.25 -0.20 -0.15 0.57

Industrial 7.22 6.40 2.96 -0.56 -0.23 0.31

Furthermore, price elasticity of demand and supply are common analysis that helps in understanding how a real estate market is likely to respond to change in vacancy rate.

It can be expressed as

Pj v j e j    j * (17) V j p j

17 where e j is price elasticity of property sector j, p j is the percentage change in

property price index, V j is the percentage change in property vacancy rate,  j

represents the slope, p j is the mean of property price index, and v j is the mean of property vacancy rate. By using OLS regression, the price elasticity of vacancy for all property classes are found and presented in Table 5. All the price elasticity of vacancy is less than one, indicating all property vacancies are price inelastic. The price elasticity of vacancy rates of residential, retail and industrial properties are low and positive, which means that when vacancy rates increase 1%, the prices will be raised by the indicated percentage. Only the office price elasticity of vacancy is negative (-

0.183), implying that if the vacancy rate goes up by 1%, and the price will fall by

18.3% because vacancy stands at a high level.

Table 5 Elasticity of Property Price Index and Vacancies: 1982 - 2004 Class Slope of Mean Elasticity (%) regression line Price Index Vacancy (%) Residential 4.362 68.074 4.600 0.295 Retail 1.898 78.691 7.978 0.192 Office -1.738 101.978 10.726 -0.183 Industrial 3.636 112.517 7.217 0.233

In an attempt to test whether the correlated relationship between inflation and property returns changes due to the 1997 Asian Financial Crisis, we divide the data into pre-crisis

(1982Q1 to 1997Q2) and post-crisis (1997Q3 to 2004Q4) samples. The correlations are summarized in Table 6. All property classes have good performance during 1982Q1 to

1997Q2. However, their nominal returns are negative correlated with inflation. This is

18 not a usual relationship. The major reason is a significant drop in property returns due to political and economic shocks during 1982 to 1984. Prices were decontrolled before

1984. For this reason, the data for the period of 1982 to 1984 are excluded from our analysis.

Table 6 Property Performance Before/After Asian Financial Crisis Panel A Before Asian Financial Crisis Mean Return (%) Median Return (%) Volatility (%) Correlation with Inflation

Residential 4.885 6.000 7.438 -0.098 Retail 4.415 6.550 8.299 -0.106 Office 3.160 3.650 12.171 -0.107 Industrial 2.321 2.350 8.220 -0.156

Inflation 8.219 9.050 2.538 --- Panel B Before Asian Financial Crisis and Excluding 1982-1984 Residential 7.320 6.800 5.795 0.199 Retail 7.516 6.872 5.202 0.280 Office 6.946 7.400 9.571 0.122 Industrial 4.818 4.351 6.872 0.104

Inflation 7.826 8.454 2.541 --- Panel C: After Asian Financial Crisis Residential -3.39 -5.25 8.896 0.246 Retail -1.42 -2.05 10.387 0.206 Office -4.267 -5.1 12.044 0.030 Industrial -3.823 -3.95 6.598 0.145

Inflation -1.287 -2 3.121 ---

The Table shows that, during 1985Q1 to 1997Q2, the average returns are high for all property sectors, but the correlated relationships with inflation are low. This result is more obvious after financial crisis which adversely impacted real estate severely. In the aftermath of the Asian Financial Crisis, Hong Kong has apparently undergone to a

19 prolonged deflation period, with average negative rate of 1.287% over 1997Q3 to

2004Q4. Meanwhile, all assets have negative average return over the same period.

These figures reveal that Hong Kong real estate performance is strongly sensitive to economy situation.

4.2. Short-term Analysis

In this section, the short term hedging performance of real estate against inflation is tested. In order to compare the results with different timeframes, empirical results are grouped into quarterly and annual results because, due to data limitation, analyses incorporating vacancy can only be done on the annual data series.

4.2.1. Hedging Against Actual, Expected and Unexpected Inflation

To assess the inflation hedging abilities of property against actual inflation, equation

(2) is tested. As indicated in Table 7, the result for hedging performance of various property sectors against actual inflation over 1985Q1 to 2004Q4 show that all coefficients are positive, significantly different from zero at 1% confidence level, and inefficiently different from 1. It means that all properties are able to provide complete positive hedge against actual inflation. This finding is different from the initial analysis. Notice that, although the intercept in the regression equation for residential sector, reflecting real rate of return, is negative, it is insignificantly different from zero. On the other hand, that of the industrial property implies that the real return is negative.

The extent of inflation hedging against expected and unexpected inflation is assessed using equation (3). The results from Table 8 show that all, but the industrial, sectors are able to provide complete positive hedge against expected inflation (the

20 coefficients are significantly different from zero and insignificantly different from 1),

and effective hedge against unexpected inflation (coefficients are all significantly

greater than 1). The industrial sector is the only property type that provides partial

hedge for expected inflation, although it also provides effective hedge against

unexpected inflation. Moreover, unlike with hedging actual inflation in Table 7 above,

all intercepts are insignificantly different from zero, albeit negative, implying that the

real rates of return are zero. In other words, in the sample period, the nominal rates of

return from the properties are results of inflation.

Table 7 Hedging Effectiveness with Actual Inflation

2 Property  j t-ratio βj t-ratio R F-Test

Sector βj = 0 βj = 1 Residential -1.137 -1.096 1.007 6.611*** 0.048 0.359 43.707*** Retail 0.274 0.253 0.883 5.536*** -0.737 0.282 30.649*** Office -1.54 -0.976 0.971 4.192*** -0.125 0.184 17.579*** Industrial -1.801 -1.780* 0.766 5.159*** -1.573 0.254 26.616*** Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%

Table 8 Hedging Effectiveness with Expected and Unexpected Inflation

 j   j t -ratio  j t-ratio 2 Property (t-ratio)   R F-Test Class   j = 0   j = 1 j = 0 j = 1 Residential -0.544 0.908 6.315*** -0.641 1.872 3.774*** 1.759* 0.417 27.551*** (-0.551) Retail 0.955 0.771 5.452*** 1.616 2.462 5.043*** 2.994*** 0.422 28.139*** (0.983) Office -0.669 0.833 4.078*** -0.819 3.478 4.936*** 3.516*** 0.352 20.909*** (-0.477) Industrial -1.178 0.663 4.952*** -2.516** 2.165 4.687*** 2.522** 0.381 23.721*** (-1.282) Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%

21 4.2.2. Hedging During High & Low Inflation Periods

The periods of high and low inflation are separated at the median inflation rate of

5.75% from 1985Q1 to 2004Q4. Sample periods with inflation rates above the median are classified as high inflation periods, while those with inflation rates below the median are classified as low inflation periods. For the entire study period, 29 quarters (1988Q3 to 1995Q3) are in the high inflation period and 37 quarters (1995Q4 to 2004Q4) are in the low inflation period. The summary inflation hedging results are contained in Table 9.

Table 9 Hedging Effectiveness with Actual Inflation During High and Low Inflation Periods Panel A: High Inflation Period: 1988 Q3 to 1995 Q3

t-ratio

2 Property Class  j t-ratio βj βj = 0 βj = 1 R F-Test Residential -0.766 -0.067 0.935 0.790 -0.055 0.023 0.624 Retail 9.938 0.964 -0.11 -0.104 -1.047 0.000 0.011 Office 44.813 2.414** -3.724 -1.951* -2.475** 0.124 3.807** Industrial 18.223 1.826* -1.286 -1.253 -2.228** 0.055 1.57 Panel B: Low Inflation Period: 1995 Q4 to 2004 Q4

t-ratio

Property Class  j t-ratio βj βj = 0 βj = 1 R2 F-Test Residential -1.784 -1.244 0.923 2.648** -0.222 0.167 7.014** Retail -0.567 -0.363 0.645 1.697* -0.933 0.076 2.881* Office -3.559 -1.900* 0.288 0.633 -1.564 0.011 0.400 Industrial -4.09 -3.989 0.018 0.074 -3.939*** 0.000 0.005 Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%

It is expected that, during high inflationary periods, increasing real estate values are expected to keep pace with inflation. However, only the office sector provides effective hedge against actual inflation and expected inflation. In fact, its negative hedge ( significantly different from zero and negative) means that its nominal return

22 declines as inflation rises. Interestingly, the office and the industrial sectors are able to generate very highly positive real rates of return (intercepts significantly different from zero) during high inflation periods. It implies that the property markets in general cannot provide effective hedge against high inflation.

Table 10 Hedging Effectiveness with Expected and Unexpected Inflation During High and Low Inflation Periods Panel A: High Inflation Period: 1988 Q3 to 1995 Q3

2  j   j t-ratio  j t-ratio R F-Test

  j = 0   j = 1  j = 0  j = 1 Residential 4.190 0.424 0.331 -0.450 -0.251 -0.190 -0.948 0.004 0.058* (0.337) Retail 16.411 -0.781 -0.696 -1.586 0.701 0.606 -0.259 0.024 0.317 (1.506) Office 55.084*** -4.798 -2.455** -2.966*** 3.071 1.525 1.028 0.201 3.265* (2.904) Industrial 23.288** -1.813 -1.691 -2.623** 0.966 0.874 -0.031 0.102 1.474 (2.237) Panel B: Low Inflation Period: 1995 Q4 to 2004 Q4

Residential -0.989 0.967 3.249*** -0.112 2.821 3.716*** 2.399** 0.367 9.836*** (-0.768) Retail 0.486 0.745 2.537** -0.870 3.609 4.819*** 3.484*** 0.426 12.611*** (0.382) Office -2.314 0.422 1.200 -1.646 4.142 4.621*** 3.505*** 0.386 10.681*** (-1.521) Industrial -3.336*** 0.117 0.661 -4.987 2.480 5.492*** 3.277*** 0.474 15.315*** (-4.352) Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%

During low inflation period, residential and retail sectors are able to provide complete positive hedge against actual and expected inflation in the low inflation period (as seen from the coefficients that are significantly different from zero and insignificantly different from 1, as well as the significant F-tests)2. In terms of the real rates of return

(see Table 9), although all intercepts are negative, only that of the office sector is

2 Note that regressions on actual inflation, and on expected and unexpected inflation, results in Durbin Watson statistics significantly less than 2, implying positively autocorrelated error terms. We therefore repeat the regressions with annual data, which are then free from autocorrelation problems, and are able to exhibit similar results (can be provided upon request).

23 significantly different from zero. However, all sectors offer positive effective hedge

against unexpected inflation ( j are significantly indifferent from 0 and 1, and significantly greater than 1). Almost all, except the retail sector, real returns remain negative, and only that of industrial property is significantly different from zero.

Therefore, it is safe to conclude that real estate hedging in Hong Kong is somewhat effective. In particular, all properties are able to provide complete positive hedge against actual inflation, complete positive hedge against expected inflation (except industrial sector) and effective hedge against unexpected inflation. Our result is therefore inconsistent with Ganesan and Chiang (1991) which concludes that real estate nominal returns in Hong Kong are imperfect hedge against actual, expected, and unexpected inflation. Furthermore, similar to Sing and Low (2000), the hedging power in low inflation is generally better than in high inflation. This is probably because the risk of properties is lower in low inflation periods than in high inflation periods.

4.2.3. Effect of Asian Financial Crisis

To test the impact resulted from the Asian Financial Crisis, we add a crisis dummy variable into the regression model (2). The data are broken up into two periods: the pre-crisis period from 1985Q1 to 1997Q2, and the post-crisis period from 1997Q3 to

2004Q4, and regressions are repeated, with results shown in Table 11.

The regression results in Table 11 show that the impact of the Asian Financial Crisis is remarkable on all property sectors except the retail sector. This means that the Crisis

24 has significantly reduced (as seen from the high magnitudes of the coefficients) the nominal rate of return of the properties. In terms of inflation hedge, however, only the residential and retail sector can provide partial hedge against actual inflation. That with the office properties is indeterminate. The introduction of the dummy variables for properties reduces the significant correlation between nominal returns and inflation. The result implies that residential, office and industrial properties are more sensitive to 1997 Asian Financial Crisis compared with retail property. Notice that the positive intercept terms in all regressions show that, above all, all property types are able to provide positive real rates of return. This is different from the results shown in

Table 7, which means that the implications can be misleading if the impact from the

Crisis is not considered.

Table 11 Hedging Effectiveness with Actual Inflation by Considering Asian Financial Crisis 2 Property  j βj t-ratio λj R F-Test Class (t-ratio) (t-ratio) βj = 0 βj = 1 Residential 2.855 0.571 2.002** -1.507 -5.510* 0.385 24.110*** (1.170) (-1.803) Retail 2.606 0.627 2.074** -1.232 -3.219 0.291 15.814*** (1.007) (-0.992) Office 4.626 0.296 0.685 -1.625 -8.511* 0.218 10.736*** (1.249) (-1.833) Industrial 2.522 0.293 1.061 -2.555** -5.967** 0.292 15.854*** (1.066) (-2.013) Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%

In terms of hedging against expected and unexpected inflation, when the effect of the

Asian Financial Crisis is considered, the regression results shown in Table 12 depict the following situations. Before the Crisis, only the retail sector is able to provide complete perfect hedge against expected inflation. On the other hand, only residential sector cannot provide complete perfect hedge on unexpected inflation. The picture is

25 twisted after the Crisis, with which none can provide any hedge against expected

inflation, while all sectors can provide effective hedge on unexpected inflation, except

the industrial sector, which can completely positively hedge the unexpected inflation.

Interestingly, the real rates of return from property markets were positive before the

Crisis, but almost all (except the retail sector) are negative.

Table 12 Hedging Effectiveness with Expected and Unexpected Inflation After Considering the Asian Financial Crisis Panel A: Before the Asian Financial Crisis

2  j   j t-ratio  j t-ratio R F-Test

  j = 0   j = 1  j = 0  j = 1 Residential 4.819* 0.321 1.074 -2.273 0.831 1.316 -0.267 0.062 1.567 (1.964) Retail 4.111* 0.437 1.680* -2.161 0.937 1.702* -0.115 0.117 3.108* (1.923) Office 4.775 0.275 0.575 -1.514 2.409 2.380** 1.392 0.117 3.127* (1.214) Industrial 4.169 0.081 0.231 -2.638 1.590 2.158** 0.800 0.093 2.410 (1.457) Panel B: After the Asian Financial Crisis

Residential -2.082 0.619 1.459 -0.898 2.591 3.365*** 2.067** 0.307 5.974*** (-1.372) Retail 0.086 0.610 1.344 -0.859 3.607 4.379*** 3.165*** 0.418 9.695*** (0.053) Office -3.334* 0.085 0.158 -1.700 4.012 4.107*** 3.083*** 0.391 8.674*** (-1.731) Industrial -2.979*** 0.269 0.986 -2.685** 2.470 4.997*** 2.974 0.480 12.485*** (-3.056) Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%

Comparing Table 12 and Table 8, it is easy to see that the partial hedge against the

unexpected inflation is probably due to the successful hedge after the Crisis. It is

doubtless that the property markets are highly sensitive to fluctuations in market

confidence during the Crisis. When the economy is in the downturn, property returns

decline. However, they are still able to provide partial hedge against inflation.

Intuitively, the inflation during this period is actually negative, which is why the

26 property markets’ negative returns are hedges against what people expect, that is, expected inflation.

4.2.4. Market Disequilibrium and Hedging

When the markets are in disequilibrium, property returns will move in the same direction as the change in occupancy rate. The non-zero vacancy may hence cause the property returns to be less efficient hedges against inflation than would otherwise. To see this, we perform regression analysis of the property market hedging effectiveness against actual inflation and simultaneously accounting for vacancy using annual data series because only annual vacancy rates are available. The results are displayed in

Table 13.

Table 13 Hedging Effectiveness with Actual Inflation and Vacancy 2 Property  j βj t-ratio δj R F-Test Class (t-ratio) (t-ratio) βj = 0 βj = 1 Residential -2.812 1.088 2.323** 0.038 0.296 0.431 6.434** (-0.24) (0.136) Retail 4.77 0.733 1.581 -0.575 -0.509 0.342 4.421** (0.448) (-0.437) Office 20.58** 0.221 0.438 -0.048 -1.896** 0.413 5.990** (2.213) (-2.450) Industrial 14.63*** 0.215 0.797 -0.811 -1.866*** 0.597 12.57*** (3.123) (-3.715) Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%

From the figures in Table 13, it is obvious that our prediction is correct. The coefficients governing the vacancy effects () are almost always negative.

Nevertheless, only those for the office and industrial sectors are significantly different from zero. Interestingly, these are also the sectors with high positive real rates of return (the positive intercepts). In other words, the negative effect on the property returns due to increased vacancies is partially offset by the positive real returns. In

27 terms of hedging ability, only the residential market exhibits complete positive

effective hedge against inflation. This is also the market with which the return moves

directly with vacancy, while the real return is negative. This is very likely a result of

the choice of the data set, which is further verified by the fact that they are

insignificantly different from zero.

Table 14 Hedging Effectiveness with Expected and Unexpected Inflation and Vacancy 2 Property  j   j t-ratio  j t-ratio δj R F-Test Class   j = 0   j = 1  j = 0  j = 1 Residential 0.680 0.926 2.083* -0.167 2.058 3.058*** 1.572 -0.295 0.535 6.136*** (0.062) (-0.144) Retail -2.923 0.915 2.403** -0.222 2.674 3.685*** 2.307** 0.408 0.592 7.734*** (-0.325) (0.412) Office 14.666 0.368 0.732 -1.258 1.721 1.467 0.615 -1.345 0.478 4.884** (1.470) (-1.586) Industrial 11.300** 0.273 1.164 -3.096*** 1.452 2.714** 0.845 -1.448*** 0.714 13.346*** (2.651) (-3.112)

Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%

Further breaking the actual inflation into expected and unexpected inflation, we find

from Table 14 that the complete hedge from the residential sector is mainly a

complete hedge against expected and unexpected inflation. This is in line with the real

operation in the residential market. Residential units are often perceived as an

investment tool. Naturally, its very movement will go in line with inflation. Our result

further confirms its popularity because of its ability to hedge against unexpected

inflation.

Table 14 also shows some very interesting results. Unlike Table 13, vacancy affects

only the return from the industrial sector, whose effect is however cancelled out by the

highly positive real rate of return (other sectors have insignificant real rate of return as

28 shown from the intercept terms). Furthermore, retail properties have become complete positive hedge against expected inflation and effective hedge against unexpected inflation. Note that the differences between the findings in Tables 13 and 14 are possibly due to the estimation of expected inflation using the ARIMA (1,1,1) model.

Nevertheless, given their very similar results from all the regression results above, we can safely assume that the ARIMA (1,1,1) model has generated a good proxy for expected inflation.

4.3. Cointegration

In this section, we test if the two variables, namely the property returns and the inflation rates, are cointegrated by performing the two-step procedure suggested by

Engle and Granger (1987). Stevenson (2000) only examines actual inflation in cointegration test. This is justified on the basis that the purpose of the cointegration analysis is to test for evidence of a long run relationship, and therefore it is legitimate to assume that actual and expected rates of inflation are equal (see also Summers,

1983; Moazzami, 1991; and Tarbert, 1996). To perform the unit root test, we will estimate equations (7), (8) and (9) using the nominal property returns and inflation rate series. The results are depicted in Tables 15 and 16 (we use the Schwarz information criterion (SIC) to select the most suitable lag number).

Table 15 Unit Root Tests 1985Q1 to 2004Q4 Series Lag Level Lag 1st Difference Actual inflation 4 -0.856 3 -4.217*** Residential nominal return 4 -1.474 3 -7.879*** Retail nominal return 8 -0.917 7 -5.254*** Office nominal return 4 -1.745 3 -6.804*** Industrial nominal return 4 -1.528 3 -4.749***

29 Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%; selection of lag based on SIC

Table 16 Unit Root Test of the Regression Residual Residual Series of Lag Level Residential nominal return 5 -3.354*** Retail nominal return 5 -2.745** Office nominal return 4 -2.164** Industrial nominal return 1 -1.686* Note: * represents significance at 10% level, ** is significant at 5%, *** is significant at 1%; selection of lag based on SIC

Tables 15 and 16 together show that the property return series are stationary after first differencing, and are cointegrated because the corresponding residual series are stationary of and are integrated of order zero. These results contradict Ganesan &

Chiang (1998) whose findings suggest that the estimated residuals are nonstationary and therefore no property sector is cointegrated with inflation rates.

4.4. Long-term Analysis

Investors always view real estate investments as most suitable for long-term investment strategy, which in so doing can consistently increase their long-term purchasing power of assets. With the cointegration results found in the third part, we now focus on the long term hedging power of the properties examined. This can be done by examining whether any causal relationships exist between real estate and inflation by using the Granger (1969) causality tests. Under the standard Granger test

(1969), if X causes Y, then changes in X should precede changes in Y. That is, lagged values of X can help improve the prediction of current values of Y. The Granger

Causality test results are presented in Table 17.

30 From the F-statistics shown in Table 17, the null hypothesis of no causal effects running from residential nominal return to inflation or inflation to residential in the sample are rejected at the 1% significance level. Thus these findings could be interpreted that there are feedback effects between residential property nominal return and inflation. Furthermore, the retail, office and industrial nominal returns are not

Granger-caused by inflation, while the opposite is true. In other words, it is the property returns that cause the changes in inflation rates in the long run, rather than vice versa.

Table 17 Granger Causality Results: the F-Statistic Lag Length 2 4 Residential Inflation → Return 5.496*** 3.265** Return → Inflation 8.681*** 6.288***

Retail Inflation → Return 1.882 1.474 Return → Inflation 12.907*** 7.197***

Office Inflation → Return 0.133 0.450 Return → Inflation 11.843*** 5.947***

Industrial Inflation → Return 0.932 0.246 Return → Inflation 7.640*** 3.056** Note: ** represents significance at 5%, *** is significant at 1%

5. Conclusion

This study tests the hedging power of the four property sectors in Hong Kong against inflation. Our empirical test results show that various property sectors do exhibit

31 certain abilities in short-run hedging against inflation, whether actual, expected, and/or unexpected. In terms of long-run hedging, we find that the property returns and inflation rates are cointegrated. More importantly, it is actually the return from the property markets that cause the movements in inflation, rather than the other way round. Besides, we also find that the vacancy rates have not significantly diminished the property returns’ power of hedging against inflation.

We contribute by firstly updating the literature in terms of the Hong Kong property markets, and secondly incorporating tests on (1) high versus low inflation periods, (2) impact of vacancy rate on the hedging power, and (3) structural change in the data series due to the Asian Financial Crisis. Finally, our results generate conclusions different from those of Ganesan and Chiang (1998). This may however be a result of difference in the sample period, as well as the testing approaches employed.

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