El tiempo dedicado a los cuidados

The estimated results of the multivariate regression using equation (5.2.1) are presented in the Appendix 1 and yielded these estimates:

0 ˆ1 ˆ2 ˆ3 ˆ4

t t -1 t

Hsg =β +βHsg +β Inc+β Intr+β Inf +ε (5.2.1) 1 0 .5 2 1 0 .9 6 5 3 0 .0 4 1 8 3 0 .1 6 7 4 2 .2 8

( 0 . 0 0 0 ) ( 0 . 3 0 5 2 ) ( 0 .3 9 8 4 ) ( 0 .0 4 3 1)

t t - 1

H s g = + H s g − I n c − I n t r − I n f

The values in brackets represent the p-values.

All variables have the hypothesized signs except for income, which has a negative coefficient (-0.4183) and is clearly statistically insignificant. This result is inconsistent with other findings, which report a positive relationship between house price and income (see Chen & Patel, 1998; Giussani & Hadjimatheo, 1991). The negative relationship between income and house prices in Malaysia may be due to the non- complementary consumption of other goods with housing consumption. According to Kole and Martin (2009), a house price will only increase if consumption of both goods and houses is complementary. This result indicates that for Malaysian people, buying a house is as important as buying other goods. Moreover, the relationship between house price and income is not well established by the specification in OLS due to the differences in house price appreciation and income growth recorded in Malaysia. According to Kole and Martin (2009), the relationship between income and house price is efficiently captured if the estimation uses a different size for the increase in house price and income growth. Therefore, we may conclude that the decision to purchase a house is not mainly influenced by the increase or decrease in the Malaysian household income. This may also be due to the higher amount of mortgage loans (i.e 90% to 95%) offered by bank and financial institutions in Malaysia. As for the 1.2 millions government servants in Malaysia (Ministry of

Human Resources, Malaysia, 2010), mortgage financing is 100% including stamp duty and lawyer fees (Ministry of Housing and Local Government of Malaysia, 2010).

The estimated coefficient for short-term interest rate is negative (-0.1674) and is not statistically significant even at 10% significance level. This result is consistent with Chen and Patel (1998) study, where the use of short-term interest rate is more efficient in capturing investment demand for houses. The investment demand for houses are from investors and speculators who intended to sell their housing investment during the booming market for capital gain (Chen and Patel, 1998). For Malaysia, the demand for housing is more for owners to occupy and less for investment purposes. This is consistent with the condition of the Malaysian housing market in 1991 which are predominantly, owners occupied (Cruz, 1998). Therefore, this causes short-term interest rates to exhibit less significance in capturing the relationship between housing prices and the demand for houses in Malaysia. This finding also helps to exclude the existence of speculative bubble from this study and supports the contention that Malaysian people are not influenced by the changes in short-term interest rate since they have less intention to move or sell their houses in the near future, due to factors such as financial constraints (Maki, 1993) and liquidity constraint (Stein, 1995).

The negative relationship between interest rate and house price in Malaysia is consistent with previous studies in the housing market which state that higher interest rates cause liquidity problems for households, leading to a decrease in housing demand (Follain, 1982). The increase of lending rate in Malaysia from 8.9% in 1996:Q4 to 10% in 1997:Q4 (Perkins & Woo, 2000) caused housing demand to decrease. The decrease in housing demand was caused by tight mortgage financing imposed by financial institutions which consequently reduced the money flowing to the real estate market and increased mortgage payments for borrowers (Wong et al., 2003). For example in September 1998 (during the 1997 Asian financial crisis), the amount of loans channeled into Malaysian housing sectors amounted to RM9 million, a significant drop from the highest peak of RM20 million in 1997 (Bank Negara Malaysia, 2000) (see Figure 2.5). During this period, the interest rate was reported to be at its highest peak of 11% in 1998:Q2 while the MHPI is decreased with a 99.76 index points.

The estimated coefficient for inflation is negative (-2.28) and statistically significant at 10% significant level. This result implies that Malaysian house prices are sensitive to inflation and the inflationary signal of a future increase in asset prices influences the buying decision of Malaysian people, particularly when purchasing expensive assets such as houses. This result also implies that the decrease in inflation may be perceived as a permanent decrease in the real rates of inflation. As a result, the demand for housing increases leading to higher prices at unsustainable levels, thus causing a bursting of the house price bubble. Furthermore, this shows that the demand for houses in Malaysia is based on expectations about future prices of houses. This is consistent with the underlying theory of stock-flow, which explains that the short- run demand for houses depends on the expected future prices of houses and other relevant economic variables (DiPasquale & Wheaton, 1996).

The inconsistency in some of the results obtained from the OLS estimation may suggest that the OLS estimators are biased and non-convergent due to the used of lagged dependant variables (Hsg_t−1) regressor as suggested by Lecat and Messonnier (2005). Therefore, to avoid any spurious results from the OLS estimation, the Breusch-Godfrey serial correlation LM test and the Bresuch- Pagan test of heteroskedasticity have been applied. These two tests help to determine any possible correlation and endogeneity in the variables. The results are summarized in Table 6.1 (see detail of Table 6.1 in the Appendix 1)

Table 6.1 Results of LM test and Heteroskedasticity test

Breusch-Godfrey Serial Correlation LM test : 2 _{38.41 0.000}

[

]

NR =

Heteroskedasticity test: Breusch-Pegan-Godfrey :NR2 =14.88 0.005

[

]

[ ]

The results in Table 6.1 indicate that in both tests, the null hypotheses of no autocorrelation (BG test) and no heteroscedasticity (BPG test) are rejected. The rejection of both hypotheses suggests the possible presence of the AutoRegression Conditional Heteroscedasticity (ARCH) effect or serial correlation in the Malaysian housing market, since the residual variance is not constant (heteroscedasticity). The

existence of serial correlation in the Malaysian house price is consistent with previous literature which tested the Efficient Market Hypothesis (EMH) in the housing market. According to Cho (1996), in the short-run house prices display a positive serial correlation which implies that the housing market is not an efficient market. Therefore, our result suggests that in the short-run, house price in Malaysia is not efficient due to its dependency on past information and trends. The inefficiency of the Malaysian housing market is due to the use of backward looking expectation or adaptive expectation hypothesis (AEH) in estimating the future price of houses.

Hence, further analysis on the residual is carried out using the ARCH test. Using the starting values from the OLS estimation, the ARCH is using Eviews 6 software. The objective of using the ARCH model is to test the null of no serial correlation in the squared residuals or ARCH effects. Table 6.2 presents the result of the ARCH effect on Malaysian house prices (see detail of Table 6.2 in the Appendix 1).

Table 6.2 ARCH test on Malaysia house price

Obs*R-squared 11.44518 Prob. Chi-Square(4) 0.0220

The result in Table 6.2 implies that the null hypothesis of no ARCH effect on the Malaysian house prices is rejected at 5% significant level. This shows that the current variance of the residual ε_t does depend on the previous period of the residual ε_t−₁. This ARCH test result also suggests that the price of houses in Malaysia may possibly be determined using adaptive expectation hypothesis (AEH) where buyers and sellers estimate the market price of a house using past information and historical trends in house prices. Therefore, it can also be concluded that the backward looking expectation or AEH can play an important role in determining the future price of houses in Malaysia.

The ARCH effect exists when the variance residuals occur in clusters (Engle, 2001). For example, in the Spanish housing market, a strong linear dependency and heteroscedasticity (volatility clustering) is displayed throughout the boom and burst cycles of the housing market (Guirguis et al., 2007).

Figure 6.4 shows the plots of MHPI standardized residual. This figure is particularly informative as it shows a certain amount of white noise and the movement of residual in four different clusters: 1990 to 1993; 1993 to 1997; 1997 to 2001 and 2001 to 2004. Among these clusters, the 1997 to 2001 period seems to decrease severely (see Figure 6.4).

Figure 6.4 Standarized residuals of Malaysian house price index from

1990 to 2004 -3 -2 -1 0 1 2 3 4 1990 1992 1994 1996 1998 2000 2002 2004 HSG Residuals

Analysis of the plots in Figure 6.4 shows that there are changes in the variance of the residuals during the expansion (boom) and contraction (burst) period in the Malaysian housing market. This finding is similar to Engle’s (1982) study who suggests that as a result of unpredictable events occurring in the real estate market house price volatility displays a clustering effect.

6.2.2 Generalized Auto Regressive Conditional Heteroscedasticity