Factors Affecting the Spread of JREITs

In order to have sufficient observations, we pool JREITs data cross sections and across time within 2005 and 2006. Table 2.8 represents the correlation matrix of the factors in the

VOL TO PRICE Size RV Life PROT1 PROT2 Panel A: JREITs in 2005 BA -0.21*** -0.21*** -0.18** -0.09 0.51*** -0.19** 0.08 -0.17** VOL 0.60*** 0.11 0.79*** 0.2** 0.61*** -0.12 -0.03 TO -0.14* -0.02 0.33*** 0.11 0.07 -0.07 PRICE 0.24*** -0.1 0.06 -0.56*** 0.14* Size -0.001 0.67*** -0.2*** 0.02 RV -0.03 -0.04 -0.21*** Life 0.22*** -0.08 PROT1 -0.29*** N 162 162 162 162 162 162 162 162 Panel B: JREITs in 2006 BA -0.05 -0.16*** 0.09 0.02 0.35*** 0.05 -0.02 -0.08 VOL 0.58*** 0.62*** 0.91*** 0.08 0.71*** -0.27*** 0.16*** TO 0.09 0.18*** 0.20*** 0.09 -0.17*** 0.04 PRICE 0.71*** -0.05 0.51*** -0.23*** 0.14** Size -0.01 0.81*** -0.24*** 0.18*** RV 0.01 0.16*** -0.12** Life -0.11* 0.12** PROT1 -0.29*** N 297 297 297 297 297 297 297 297

Table 2.8 Pearson Correlation Matrix of Factors Affecting the JREITs’ Spreads

This table presents the Pearson correlation matrix for the variables in the model of factors affecting spread of JREITs. BA is the percentage bid-ask spread. VOL is the trading volume recorded in million Japanese yen. TO is the turnover ratio. Price is the daily trading price. Size is the average market capitalization of each REIT. RV is the return volatility, measured as the variance of daily returns. Life is the life of each REIT since the first full month listed on TSE. PROT1 and PROT2 are the dummy variables for the major property types in each REIT. N denotes the number of observations. Volume, turnover, trading price, market capitalization and life are log scaled. *** denotes significance at 1% level; ** denotes significance at 5% level; * denotes significance at 10% level.

equation (10) discussed in section 4.2. Panel A shows that in 2005 most of the factors have the expected relationship with the bid-ask spread except PROT1. Trading volume, turnover, price, size, life and PROT2 are significantly and negatively correlated with the bid-ask spread at 5% or less level. Return volatility and the bid-ask spread are significantly positively correlated. Even though PROT1 is positively related to the bid-ask spread, the correlation coefficients are not statistically significant. Panel B, however, shows that only turnover and return volatility are

significantly related to the bid-ask spread at 1% level in 2006. Turnover (return volatility) is negatively (positively) associated with the bid-ask spread, which is consistent with our expectations. According to Pearson correlation coefficients, the rest of factors such as trading volume, price, size, life and property types have no linear relationship with the spread.

Meanwhile, Table 2.8 reveals some potential multicollinearity problems. Trading volume is highly and significantly related to turnover, size and life in both 2005 and 2006, with

correlation coefficients 0.60 or higher. Trading volume is also significantly correlated with price in 2006 sample. The correlation between size and life is 0.67 and 0.81 in 2005 and 2006,

respectively. Additionally, price is highly correlated with PROT1 in 2005 and with size and life in 2006. As a result, we perform the tolerance tests, a collinearity statistic, to detect

multicollinearity among these factors. A high tolerance statistic (close to 1) indicates low multicollinearity, while a tolerance statistic close to zero means that a particular variable is highly correlated with other independent variables in the model. We run the equation (10) with different versions in order to avoid variables that are highly correlated.

The pooled OLS regression results are presented in Table 2.9. Panel A reports the regression results for the 2005 sample. Version 1 is the full version of equation (10). The tolerance statistics for log trading volume, log turnover, and log size are zero, which means that these three variables are highly correlated with other independent variables. In this version, only two factors are statistically significant at 5% level – return volatility and log life. The return volatility is positively related to the bid-ask spread and the life is negatively related to the spread, which are consistent with our expectations. Version 2 of equation (10) drops log trading volume. The tolerance statistics show that there are no severe multicollinearity problems in the model.

Table 2.9 Regression Results of Factors Affecting the JREITs’ Spreads

The table reports the OLS regression results of the model of factors affecting spread. VOL is the trading volume recorded in million Japanese yen. TO is the turnover ratio. Price is the daily trading price. Size is the average market capitalization of each REIT. RV is the return volatility, measured as the variance of daily returns. Life is the life of each REIT since the first full month listed on TSE. PROT1 and PROT2 are the dummy variables for the major property types in each REIT. Tolerance is the tolerance statistics for testing collineraity. Volume, turnover, trading price, market capitalization and life are log scaled. The t- statistics are reported in parentheses. *** denotes significance at 1% level; ** denotes significance at 5% level; * denotes significance at 10% level.

Panel A: JREITs 2005

Coefficient Tolerance Coefficient Tolerance Coefficient Tolerance Coefficient Tolerance

Constant 0.276 0.897* 1.209** 0.844* (0.123) (1.861) (2.485) (1.717) VOL 0.275 0.000 -0.138*** 0.318 0.024 0.269 (0.283) (-6.731) (1.058) TO -0.411 0.000 -0.137*** 0.844 -0.161*** 0.448 (-0.425) (-6.741) (-5.749) PRICE -0.053 0.630 -0.052 0.633 -0.052 0.633 -0.052 0.633 (-1.467) (-1.457) (-1.447) (-1.459) Sice -0.252 0.000 0.023 0.418 0.162*** 0.262 (-0.259) (1.051) (5.736) RV 0.128*** 0.787 0.127*** 0.821 0.127*** 0.823 0.127*** 0.821 (9.809) (9.991) (9.969) (9.998) Life -0.057** 0.397 -0.057** 0.398 -0.057** 0.398 -0.057** 0.398 (-2.354) (-2.351) (-2.344) (-2.355) PROT1 0.043 0.474 0.044 0.478 0.044 0.478 0.044 0.479 (1.544) (1.579) (1.592) (1.581) PROT2 -0.008 0.862 -0.008 0.862 -0.008 0.862 -0.008 0.862 (-0.403) (-0.401) (-0.399) (-0.401) Adj. R2

Version 1 Version 2 Version 3 Version 4

Table 2.9 continued

Panel B: JREITs 2006

Coefficient Tolerance Coefficient Tolerance Coefficient Tolerance Coefficient Tolerance

Constant -1.038 0.736*** -0.93 0.334***

(-1.544) (10.617) (-1.455) (3.876)

VOL drop -0.023* 0.902 -0.039 0.147 drop

(-1.878) (-1.299) TO -0.132*** 0.884 -0.094** 0.356 -0.136*** 0.89 (-4.725) (-2.13) (-4.878) PRICE 0.112** 0.478 0.11** 0.479 (2.058) (2.027) Sice -0.04 0.215 -0.005 0.319 (-1.352) (-0.212) RV 0.059*** 0.911 0.052*** 0.949 0.059*** 0.911 0.058*** 0.912 (7.795) (6.737) (7.781) (7.707) Life 0.041 0.334 0.04 0.333 0.031 0.342 (1.245) (1.206) (0.94) PROT1 -0.076 0.821 -0.067** 0.843 -0.076** 0.821 -0.081*** 0.826 (-2.475) (-2.131) (-2.469) (-2.618) PROT2 -0.044 0.897 -0.035 0.9 -0.044 0.897 -0.045 0.897 (-1.199) (-0.917) (-1.206) (-1.21) Adj. R2 0.197 0.132 0.196 0.188

Log turnover, return volatility, and log life are significant at 5% level, all with expected signs. Log size remains insignificant. Version 3 of equation (10) deletes log turnover. Log trading volume becomes a significant factor at 1% level and is negatively related to the spread. Even though log size becomes significant, however, it has wrong sign. The tolerance statistic of 0.262 indicates that it is somewhat correlated with other factors, which may cause the wrong sign. Version 4 of equation (10) eliminates log size. With the presence of log turnover, log trading volume is no longer significant. The coefficient of log turnover is significant at 1% level and has the expected negative sign. The property dummies remains insignificant in four different

versions. Through all four versions, return volatility and log life remains the significant and right relationship with the spread. For the spread of JREITs in 2005, return volatility, life and turnover are dominant factors.

Panel B of Table 2.9 shows the regression results for 2006 JREITs. Since trading volume is highly correlated (0.91) with size, these two factors are not able to exist in the same equation. Version 1 of equation (10) excludes log trading volume. Log turnover and return volatility have the right sign and are significant at 1%. Version 2 of equation (10) drops log turnover, log price, and log size since all of them are highly correlated with log trading volume. Return volatility remains significant. The coefficient of log trading volume becomes significant at 10% level. Additionally, PROT1 is significantly and negatively related to the spread. This indicates that JREITs holding two different types of properties have smaller spreads than those holding only single type of properties. Version 3 leaves out log size. Again with the presence of log turnover, log trading volume is no longer a significant factor. The tolerance statistic shows that log trading volume is correlated with other variable. Return volatility and PROT1 remains significant. Version 4 eliminates log trading volume and log price. Log turnover, return volatility and

PROT1 are the only significant factors. Log size remains insignificant throughout the four version of equation (10). The tolerance statistics associated with log size indicate that it remain somewhat correlated with other factors in the model. The fundamental factors that affect the spread of JREITs in 2006 are return volatility, turnover, and property dummy representing holding two types of properties.

Overall for JREITs, the most important factors affecting the spread are return volatility and turnover. The higher the return volatility is, the higher the spread is. Since the JREIT market is such a young market, returns are expected to be somewhat volatile. There are also two minor factors affecting the spread – life and property type. The life of JREITs is negatively related to the spread in 2005, indicating the existence of learning effect. However, it is puzzled that it is no longer a significant factor in 2006 sample. This may be due to shorter lives of some of JREITs in the 2006 sample. The property dummy, specifically, the dummy representing two-type

properties, is negatively correlated with the spread. This implies that JREITs holding two different type properties have smaller spreads.