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The spillover effect between the real estate spot and forward markets

I-Chun Tsai*

Associate Professor

Department of Finance

National University of Kaohsiung

Ming-Chi Chen

Professor

Department of Finance

National Sun Yat-sen University

Ming-Chu Chiang

Assistant Professor

Department of Finance

National Yunlin University of Science and Technology

*Corresponding author, Department of Finance, National University of

Kaohsiung, No. 700, Kaohsiung University Rd., Nanzih District, 811. Kaohsiung,

Taiwan, R.O.C., Email: ictsai@nuk.edu.tw , Tel: 886-7- 5919767.

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The spillover effect between the real estate spot and forward markets

This paper applies Taiwan’s housing price indices from real estate existing (spot) and

pre-sale (forward) markets to study the mechanism of return transmission in these two

markets. We also estimate the spillover indices proposed by Diebold and Yilmaz

(2009) to quantify the intensity of linkage between these two markets. Previous

studies have documented the distinct effects of pre-sale housing prices. Wong, Yiu,

Tse, and Chau (2006) illustrated that the trading of pre-sale housing produced a

stabilizing effect on the spot market, whereas Wong, Yiu, and Chau (2007) showed

that the volatility of the forward market was more sensitive to shocks than the spot

market. Based on empirical evidence, the spillover is found to originate from the

existing housing market to the pre-sale housing market, especially during periods of

increased housing prices.

Keywords: pre-sale housing market, existing housing market, real estate spot and

forward markets, volatility transmission, spillover effect

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1. Introduction

Many studies in finance have closely investigated the relationship between the

spot and derivative markets. The primary benefit of derivative markets for traders is

their price discovery function and risk management through hedging (Garbade and

Silber, 1983). Price discovery refers to the process of revealing information about

future underlying spot returns through derivative markets. As the derivative markets,

especially the futures markets, usually have the advantages of low transaction costs

and high leverage (Kim, Szakmary, and Schwarz, 1999), the derivative returns are

found to lead the underlying spot returns (Herbst, McCormack, and West, 1987;

Kawaller, Koch, and Koch, 1987; Stoll and Whaley, 1990; Chan, 1992; Tse, 1995;

Chakravarty, Gulen, and Mayhew, 2004).

As to risk management, hedgers can control their spot price risk by utilizing

price discovery in futures or forward contracts, as done in the forward foreign

exchange market. Arbitragers also play important roles in the derivative market

because they constantly take advantage of the price differences between the derivative

market and the underlying spot market to generate profits. Speculators actively trade

in the derivative market to make profit from price fluctuations. These traders’

transactions help rule out arbitrage opportunities. Thus, a no-arbitrage cost-of-carry

model is proposed based on a no-arbitrage assumption in the futures and spot markets.

Stoll and Whaley (1990) documented that in a perfectly efficient and frictionless

market, the cost-of-carry relationship links the spot and futures prices. Therefore, any

innovation to asset prices will result in movements in the spot and derivative markets

and create co-movement in them. According to Fama’s (1970) definition of price

efficiency, the lead-lag relations between the spot and derivative markets found in

previous literature provide evidence, in a sense, of market inefficiency.

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MacKinlay and Ramaswamy (1988) proposed the existence of factors that can

drive the futures price away from its theoretical value1. Jiang, Fung, and Cheng (2001)

indicated that lifting short-selling restrictions can enhance the stock market’s

informational efficiency relative to the index futures. They also found the effects of

spot and futures market characteristics, market conditions, and the magnitude of

mispricing on the lead-lag relations under different short-selling regimes. Chung

(1991) stated that the inconsistent movements of the underlying and derivative market

may contribute to some other risk premiums that are not fully incorporated into the

cost-of-carry relation. In sum, although previous studies have not reached a consensus

on the transmitted relationship between the spot and derivative markets, the lead-lag

relationship found between the two markets may be the consequence of price

inefficiency resulting from these markets’ distinct characteristics and limitations.

Most existing studies focus on the co-movements among the financial spot and

derivative markets. However, only a few examine related topics in the housing market

because of the heterogeneity of real estate and the absence of a central exchange

(Wong, Chau, and Yiu, 2007). As the pre-sale of unfinished units has been very

popular in Hong Kong’s real estate market2, Yiu, Hui, and Wong (2005) used pre-sale

contracts as direct real estate forward contracts to analyze the lead-lag relationship

between the spot and forward returns on direct real estate investments in Hong Kong.

Their findings suggest that during periods of low-volume ratios, the spot return

Granger causes the returns of forward contracts, and during periods of high-volume

ratios, feedback relationships occur between the two markets. As the real estate

1 For example, futures contracts with longer maturity periods have greater risk associated with unexpected changes in the yield. 2 Chau, Wong, and Yiu (2003, 2007), Yiu, Wong, and Chan (2004), Yiu, Hui, and Wong (2005), Wong, Yiu, Tse, and Chau (2006), and Wong, Chau, and Yiu (2007) present detailed descriptions and discussions of the pre-sale system in Hong Kong.

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market is regarded as less efficient (Case and Shiller, 1989; Shiller, 1993 and 2005),

the relationship between the real estate spot and forward markets today may be

different from those found in previous studies.

Moreover, Fortenbery and Zapata (1997) found that in the thinly traded market,

futures contracts do not contribute to the discovery of new information regarding

future spot prices. Compared with the other financial markets, the real estate market is

characterized by infrequent and low trading activity. Whether these housing market

characteristics cause the pre-sale market to be less informative is worthy of further

investigation.

Hence, the primary purpose of this study is to analyze the return spillover

relationship between the pre-sale and existing housing markets in Taiwan. Although

the literature has discussed the relationship between the spot and forward returns on

direct real estate investments in Hong Kong, these studies’ findings have left room for

further exploration of the relationship between the two markets. For example, Wong,

Yiu, Tse, and Chau (2006) showed that the trading of pre-sales housing produces a

stabilizing effect on the spot market. Wong, Chau, and Yiu (2007) extended the

analysis of Wong, Yiu, Tse, and Chau (2006) and provided the first study to examine

volatility spillovers between the spot and forward index returns in Hong Kong’s real

estate market. Their findings show that the volatility of the forward market makes it

more sensitive to shocks than the spot market.

The effect of derivatives trading on the underlying spot price and its

characteristics are still being debated. Some studies support the view that derivative

trading destabilizes the underlying spot market (Figlewski, 1981; Stein, 1987). Others

propose that derivative markets have a favorable effect on the underlying spot markets.

For example, Powers (1970) and Danthine (1978) claimed that futures trading

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increases market depth and reduces spot market volatility. The findings of Wong, Yiu,

Tse, and Chau (2006) were consistent with those of Powers (1970) and Danthine

(1978) in showing that the trading of pre-sales housing produced a stabilizing effect

on the spot market. Wong, Chau, and Yiu (2007) found that volatility was mainly

transmitted from the forward market to the spot market, but not vice versa. However,

the ways information spillover occurs across these two markets is still unknown.

Furthermore, the abovementioned literature does not provide evidence about the

“dynamic” relationship between the spot and forward returns on direct real estate

investments. In this paper, the spillover effects between the spot and pre-sale markets

in different periods of time and under different market conditions are investigated.

This paper employs the methodology proposed by Diebold and Yilmaz (2012) in

estimating spillover indices among the real estate spot and forward markets. This

methodology concisely describes the extent of spillovers in market volatility

considered through simple quantitative measures. Extending their work on the total

spillover in a simple vector autoregressive (VAR) model (Diebold and Yilmaz, 2009),

Diebold and Yilmaz proposed spillover indices across asset markets under a more

generalized VAR framework in which variance decompositions are invariant to the

variable ordering. The advantage of such spillover indices is that they point out the

contributions as well as the directions of volatility shocks from one market to another.

Diebold and Yilmaz (2009) applied the spillover index to measure a broad set of

global equity returns and volatilities. They also proposed that spillover intensity is

time varying, and that the nature of time variation among returns and volatilities are

remarkably different. Diebold and Yilmaz (2012) also investigated directional

volatility spillovers across US stock, bond, foreign exchange, and commodity markets.

The results of their study demonstrated that across-market volatility spillovers had

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been quite limited before the global financial crisis, and the spillovers had come from

the stock market to other markets after the collapse of the Lehman Brothers in

September 2008.

The rest of the paper is organized as follows. The methodologies applied are

briefly introduced in Section 2. The empirical results are presented in Section 3, and

the concluding remarks are provided in Section 4.

2. Methodology

Diebold and Yilmaz (2012) introduced return spillover measures based on a

generalized VAR framework in which variance decompositions are invariant to the

variable ordering. This methodology parses the forecast error variance into parts that

are attributed to various shocks. Therefore, the variance decomposition allows for the

investigation of directional spillovers across markets.

Consider a generalized N-variable VAR model, which is covariance stationary

such that xt = ∑ Aiεt−i∞i=1 , where ε~(0,Σ) is a vector of independently and

identically distributed disturbances, and Ai is N × N coefficient matrices obeying

Ai = Φ1Ai−1 + Φ2Ai−2 + ⋯+ ΦpAi−p with Ai = 0 for i < 0, and with A0 being

an N × N identity matrix. The generalized VAR framework of Koop, Pesaran, and

Potter (1996) and Pesaran and Shin (1998), or KPPS, accounted for correlated shocks

during the use of their historically observed error distribution. Under this framework,

the KPPS H-step-ahead forecast error variance decompositions are obtained. Through

variance decomposition on forecast error variances of each variable, Diebold and

Yilmaz (2012) proposed the KPPS H-step-ahead error variance for H = 1,2, … as

θijg(H) =

σjj−1 ∑ (ei

′AhΣej)2H−1h=0

∑ (ei′AhΣAh

′ ei)H−1h=0

, (1)

where Σ is the variance matrix for the error vector ε, σjj is the standard deviation of

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the error term for the jth equation, and ej is the selection vector, with one as the ith

element and zeros otherwise. As the sum of the elements in each row of variance

decomposition matrix is not equal to 1, each entry of the variance decomposition

matrix is normalized by the row sum to calculate the spillover index. The normalized

variance decompositions is

θ�ijg(H) =

θijg (H)

∑ θijg (H)N

j=1. (2)

Thus, using Eq. (2), Diebold and Yilmaz (2012) calculated the return contributions

from the KPPS variance decomposition and the proposed total spillover as

Sg(H) =∑ θ�ij

g(H)N

i,j=1,i≠j

∑ θ�ijg

(H)Ni,j=1

× 100 =∑ θ�ij

g(H)N

i,j=1,i≠j

N× 100, (3)

where θ�ijg(H) is the normalized KPPS H-step-ahead forecast error variance

decomposition by the row sum. Note that ∑ θ�ijg(H)N

j=1 = 1 and ∑ θ�ijg(H)N

i,j=1 = N.

As the generalized impulse responses and variance decompositions are invariant

to the variable ordering, the directional return spillovers received by market i from

all other markets j is

Si.g(H) =

∑ θ�ijg

(H)Nj=1,i≠j

∑ θ�ijg

(H)Ni,j=1

× 100 =∑ θ�ij

g(H)N

j=1,i≠j

N× 100 , (4)

whereas the directional return spillovers transmitted by market i to all other markets

j is

S.ig(H) =

∑ θ�ijg

(H)Nj=1,i≠j

∑ θ�ijg

(H)Ni,j=1

× 100 =∑ θ�ij

g(H)N

j=1,i≠j

N× 100. (5)

Therefore, to sum up the information on the contributions of each market to the return

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in other markets, the net return spillover from market i to all other markets j is

Sig(H) = S.i

g(H) − Si.g(H) . (6)

3. Empirical Analysis

The pre-sale housing system in Taiwan began with the United Village 2 project

in 1966. Thus, the system has been developing for over 40 years. The current housing

markets in Taiwan can be divided into two parts: the pre-sale housing market and the

existing housing market.

In the existing market, the buyers and sellers transact existing housing units.

However, in the pre-sale market, the transactions involve new housing units that are

still in the construction or planning stage. In other words, the pre-sale system allows

builders to sell the housing units right after obtaining the building permits3. Actually,

pre-sales in housing can be viewed as transactions of forward contracts in real estate.

In contrast, the existing housing market is regarded as the real estate spot market

(Chau, Wong, and Yiu, 2003; Lai, Wang, and Zhou, 2004).

3.1 Preliminary Analyses

This article uses quarterly data from the Taiwan housing indices in the existing

(spot) and pre-sale (forward) markets to examine the spillover effect in these two

indices. The data cover the first quarter of 1998 to the first quarter of 2011. The

existing housing price index is obtained from the Sinyi Center for Real Estate, and the

pre-sale housing price is obtained from Cathay Real Estate Development Ltd. Apart

from the national composite index of Taiwan’s real estate market, data from three

cities—Taipei, New Taipei, and Taichung—are also applied for empirical analysis.

3 For detailed information on Taiwan’s pre-sale market, please refer to Hua, Chang, and Hsieh (2001) and Tsai (2012).

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Table 1 presents a summary of the descriptive statistics for these two housing

price indices. It also reports the outcome of tests for stationarity. An augmented

Dickey–Fuller test (Said and Dickey, 1984) and a Phillips–Perron test (1988) both

confirm that the two series are I(1). To avoid spurious regression throughout this

paper, we use the return data of these two markets to estimate the empirical models.

[Insert Table 1]

As shown in Table 1, the volatilities of existing markets are greater than those of

pre-sale markets. This finding does not indicate that the risks in the existing market

are higher. Instead, it shows that the price increases in existing markets are greater.

[Insert Figure 1]

Figures 1 and 2 depict the time series of housing prices in the existing market

and pre-sale market, respectively. The price indices of the existing market exhibit an

obvious upward trend compared with those of the pre-sale market, whereas the prices

in the pre-sale market are more volatile. Tsai (2012) concurrently used the Taiwan

housing price indices from the existing and pre-sale markets to compare the housing

price rigidity of these two indices. The empirical results from the study of Tsai (2012)

pointed out that a defensive effect, the downward housing price rigidity, exists in the

existing (spot) housing market because bad news in this market is followed by a

decreasing variance. However, in the pre-sale housing market, the volatile behavior of

housing price index is symmetric. Figures 1 and 2 are in line with the evidence found

by Tsai (2012). Wong, Chau, and Yiu (2007) discovered that volatility is mainly

transmitted from the forward market to the spot market, but they do not explain

whether the forward market is more volatile and whether the volatilities in these

markets are asymmetric. In the following analyses, we investigate whether the

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differences in volatility can contribute to differences in spillover effects.

3.2 Empirical Results

Table 2 shows the results of the VAR model. The significant parameters in Table

2 indicate that the effects are mainly from the spot market to the forward market. For

example, in Taiwan and in Taipei city, the estimated parameters of lagged returns in

the existing market are significant at 0.01 levels. In detail, the returns of Taipei’s

existing market lead pre-sale markets by at least half a year, whereas the returns of the

former lead the latter by about a quarter in Taiwan’s market as a whole.

Wong, Chau, and Yiu (2007) found that information flow between markets has

been bi-directional, yet through different channels: the return spillover is from the

spot to the forward market, whereas the volatility spillover is from the forward to the

spot market. Although Wong, Chau, and Yiu (2007) did not provide sufficient

explanation of the differences, they referred to future research for these interesting

results.

[Insert Table 2]

Based on evidence documented in Table 2, which is in line with the findings of

Wong, Chau, and Yiu (2007), we attempt to determine the causes of this outcome by

forecasting error variance decomposition using Eq. (2). The forecasting error variance

decomposition proposed by Diebold and Yilmaz (2012) facilitates our identification

of the contributions from the various system shocks. Figures 3 to 6 present the

decomposed forecasting error variance for the existing and pre-sale markets of

Taiwan and three specific cities. Comparing the figures of the existing and pre-sale

market, except that of Taichung city, we find that some proportions of error variance

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in forecasting pre-sale market come from shocks to the existing market, but not vice

versa. Among the areas studied, for example, the shocks in Taipei city’s existing

market have the strongest effect on the returns in its pre-sale market. The effect lasts

for about one and a half years. However, Taipei’s pre-sale market has less obvious

effects on its existing market. These findings echo those illustrated in Figures 1 and 2.

In Figure 1, the time series of housing prices in Taipei city show the most dramatic

upward trend after 2006. Prices in pre-sale market increase remarkably after 2006, as

shown in Figure 2. The evidence supports that the prominent role of innovations in

existing market transmits to pre-sale market thereafter.

The housing price behavior in Taichung city is totally different from that of other

cities under study. Specifically, the trend of price increments is less apparent in the

existing market as shown in Figure 1 and in the pre-sale market as depicted in Figure

2. Furthermore, Figure 6 shows that less apparent shocks are transmitted between the

existing and pre-sale markets. The probable cross-market spillover, if any, may be

attributed to shocks in the pre-sale market, which are transmitted to the existing

market.

[Insert Table 3]

To investigate the interrelationship between markets in terms of return, we apply

the method proposed by Diebold and Yilmaz (2012) for further empirical

investigation. Following this method, we employ generalized VAR (Koop, Pesaran,

and Potter, 1996; Pesaran and Shin, 1998; hereafter KPPS) of order 2 and generalized

variance decompositions of 10-quarter-ahead return forecast errors to estimate the

full-sample return spillover including directional, total spillover indices for the two

markets. The results are presented in Table 3. As KPPS variance decomposition

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applied in estimation attempts to deal with correlated shocks instead of orthogonal

shocks, the row sum of contributions in Table 3 is not necessarily equal to one.

The full samples of the return spillover, shown in the lower right corner of each

panel in Table 3, indicate the extent to which the spillover causes the forecast error

variance in the existing and pre-sale markets. The estimated total spillover index for

the two markets are 12.9% for Taiwan as a whole, indicating that a 12.9% error

variance in forecasting housing returns in Taiwan come from spillovers between the

two markets. Of all the cities, the highest return spillover index is that of Taipei and

the lowest is that of Taichung. The magnitude of spillovers between the existing and

pre-sale markets in Taipei (26.2%) is about two times larger than that of Kaohsiung

(13%) and approximately three times that of Taichung (9.3%).

The findings shown in Table 3 provide only the average spillover behavior

during the period of study, and important information about cyclical spillovers

movement may be tempered by averaging. Specific events during the investigated

period may have fundamental effects on spillovers across markets (Diebold and

Yilmaz, 2012; Gaspar, 2012). To this end, we estimate the total rolling and the net

spillover index with three-year rolling windows of each area in Figures 7 and 8,

respectively. The dynamic spillover charts can help investigate how spillovers across

the existing and pre-sale markets change through time in Taiwan and in the three

cities.

The total spillover indices in Figure 7, calculated based on Eq. (3), measure the

contribution of spillovers of return shocks between the existing and pre-sale markets

to the total forecast error variance. In general, we find that the spillovers between the

two markets were more apparent after 2006. Specifically, the range of spillover index

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values in Taiwan as a whole is the widest (from 2% to 58%) among the areas

investigated. Spillovers seemed more intense during the 2007–2009 financial crises,

with the peak occurring in 2009. The highest levels of spillovers in Taipei and

Taichung cities also occurred during the global financial crisis although at different

points in time. For New Taipei city, the largest spillover was recorded in 2010.

[Insert Figure 7]

[Insert Figure 8]

Next, we perform a detailed analysis by demonstrating the directional effects of

spillover charts of each area under study in Figure 8. The net directional spillover

indices are calculated according to Eq. (6). The positive (or negative) value denotes

the net effect of the gross return shocks in the pre-sale market that have spread to (or

have been obtained from) the existing market.

As shown in Figure 8, apart from Taichung city, the net directional spillover

indices of other areas are smaller than zero in the aftermath of the global financial

crisis, meaning that shocks to the spot (existing) market play a dominant role in

predicting returns on the forward (pre-sale) market. The effect of shocks from the

existing market to the pre-sale market in Taipei city is most pronounced among all

the areas under study. In Figures 1 and 2, we find that the existing market was rather

defensive during the financial crisis, and housing prices in this market rebounded

fairly fast, whereas the pre-sale market was slower to recover after housing prices

plummeted during the crisis. As to Taichung, the main directions of impact are from

the forward to the spot market after 2008. This finding is consistent with what

Figures 1 and 2 demonstrate: that the price increases are less apparent not only in the

existing market but also in the pre-sale market.

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The main empirical results of this study are threefold. First, although the price

discovery of the forward market, which refers to the information content in forward

prices in determining cash market prices (Herbst, McCormack, and West, 1987;

Kawaller, Koch, and Koch, 1987; Stoll and Whaley, 1990; Chan, 1992; Tse, 1995;

Chakravarty, Gulen, and Mayhew, 2004), is usually found in asset markets, the

housing market is quite distinctive in this regard. Specifically, we find that price

discovery in the spot market and the information content of the spot price is useful in

predicting the forward contract price. This result is similar to those documented by

Wong, Chau, and Yiu (2007).

Second, we attribute the spillover between the spot and forward markets to the

local housing market’s performance. Taipei, the city with large price increases in the

existing market, may facilitate greater price discovery in the pre-sale market. On the

contrary, Taichung, the city with less apparent price increases in the existing market,

may have less contribution to price discovery in the pre-sale market. Finally, we find

defensive housing prices in the existing market and that a price rebound in the

pre-sale market may lead to price discovery in the spot market.

Thus, based on the second and third empirical results, we infer that the price

discovery in the existing market may stem from the market’s defensive

characteristic.4 Even though the short-term price changes in the pre-sale market are

relatively volatile because of the upward price trend in the long run and the

defensiveness of real estate, the pre-sale market may follow the existing market’s

direction. As a result, prices in the spot market are found to lead those in the forward

4 Tsai (2012) proposed that existing housing may have asset characteristics associated with both consumption and investment. As a result, when housing prices fall, the buyer may choose to live in the house or lease it to tenants to avoid losses from selling, thereby producing downward housing price rigidity in the market. In the pre-sale housing market, when prices perform poorly, the assets cannot be provided for consumption, resulting in less housing price rigidity.

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market.

4. Conclusion

This paper applies the housing price indices of the existing and pre-sale markets

of Taiwan and three other cities to study the spillover effect between the real estate

spot and forward markets. First, we estimate the mechanism of return transmission in

these two markets by decomposing the forecasting error variance. The findings reveal

that some proportions of error variance in forecasting the pre-sale market are caused

by shocks to the existing market, but not vice versa.

These results, which imply that the prominent role of innovations in the existing

market is transmitted to the pre-sale market thereafter, are distinct from the results of

related research using data from other financial markets (Herbst, McCormack, and

West, 1987; Kawaller, Koch, and Koch, 1987; Stoll and Whaley, 1990; Chan, 1992;

Tse, 1995; Chakravarty, Gulen, and Mayhew; 2004). However, the results are similar

to those documented in the study by Wong, Chau, and Yiu (2007), which proposes

that the return spillover is transmitted from the real estate spot market to the forward

market.

In addition, this paper investigates the dynamic relationship between the spot and

forward returns on direct real estate investments by demonstrating the spillover effect

between the two markets in different periods and under different market conditions.

The empirical evidence supports the observation that the spillover is mainly from the

existing housing market to the pre-sale housing market, especially during the period

when housing prices increase. The evidence shows that the price discovery of the

existing market may have resulted from the housing market’s defensive characteristic.

The results demonstrate that defensive housing prices in the existing market and price

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rebounds in the pre-sale market may lead to price discovery in the spot market. Thus,

this paper proves that the housing market is quite distinctive when it comes to price

discovery in the spot and forward markets, and also offers feasible explanation

regarding this feature of the housing market.

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Figure 1 The housing price indices from existing markets

Figure 2 The housing price indices from pre-sale markets

40

80

120

160

200

240

280

98 99 00 01 02 03 04 05 06 07 08 09 10

Taipei CityNew Taipei City

TaichungTaiwan

50

60

70

80

90

100

110

120

98 99 00 01 02 03 04 05 06 07 08 09 10

Taipei CityNew Taipei City

Taichung CityTaiwan

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Figure 3 Decomposition of variance for series in the Taiwan housing market

99.829 86.408

79.157 76.804 75.791 75.388 75.209 75.133 75.1 75.086

0.171 13.592

20.843 23.196 24.209 24.612 24.791 24.867 24.9 24.914

1 2 3 4 5 6 7 8 9 10

Decomposition of Variance (Series: Taiwan-Rhf)

Rhf Rhs

0.171 0.607 0.913 0.913 0.942 0.943 0.947 0.947 0.948 0.948

99.829 99.393 99.087 99.087 99.058 99.057 99.053 99.053 99.052 99.052

1 2 3 4 5 6 7 8 9 10

Decomposition of Variance (Series: Taiwan-Rhs)

Rhf Rhs

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Figure 4 Decomposition of variance for series in the Taipei housing market

99.525

71.161

55.493 52.282 50.014 49.601 49.403 49.367 49.358 49.357

0.475 28.839 44.507 47.718 49.986 50.399 50.597 50.633 50.642 50.643

1 2 3 4 5 6 7 8 9 10

Decomposition of Variance (Series: Taipei-Rhf)

Rhf Rhs

0.475 1.669 1.702 1.821 1.842 1.844 1.845 1.845 1.845 1.845

99.525 98.331 98.298 98.179 98.158 98.156 98.155 98.155 98.155 98.155

1 2 3 4 5 6 7 8 9 10

Decomposition of Variance (Series: Taipei-Rhs)

Rhf Rhs

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Figure 5 Decomposition of variance for series in the New Taipei housing market

99.731 94.893 86.701 83.761 80.68 79.318 78.179 77.566 77.107 76.832

0.269 5.107 13.299 16.239 19.32 20.682 21.821 22.434 22.893 23.168

1 2 3 4 5 6 7 8 9 10

Decomposition of Variance (Series: New Taipei-Rhf)

Rhf Rhs

0.269 0.314 1.634 1.831 2.369 2.522 2.718 2.802 2.879 2.92

99.731 99.686 98.366 98.169 97.631 97.478 97.282 97.198 97.121 97.08

1 2 3 4 5 6 7 8 9 10

Decomposition of Variance (Series: New Taipei-Rhs)

Rhf Rhs

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Figure 6 Decomposition of variance for series in the Taichung housing market

96.894 93.495 93.051 93.047 93.048 93.047 93.046 93.046 93.046 93.046

3.106 6.505 6.949 6.953 6.952 6.953 6.954 6.954 6.954 6.954

1 2 3 4 5 6 7 8 9 10

Decomposition of Variance (Series: Taichung-Rhf)

Rhf Rhs

3.106 6.018 9.327 11.663 11.653 11.734 11.734 11.74 11.74 11.741

96.894 93.982 90.673 88.337 88.347 88.266 88.266 88.26 88.26 88.259

1 2 3 4 5 6 7 8 9 10

Decomposition of Variance (Series: Taichung-Rhs)

Rhf Rhs

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Figure 7 Spillover plot

Spillover plot-Taiw an2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

10

20

30

40

50

60

Spillover plot-Taipei City2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

10

15

20

25

30

35

40

45

50

55

Spillover plot-New Taipei City2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

10

15

20

25

30

35

40

45

50

Spillover plot-Taichung City2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

10

20

30

40

50

60

24

Figure 8 Net return spillover

Net return spillovers-Taiw an2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

-40

-20

0

20

40

60

Net return spillovers-Taipei2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

-50

-25

0

25

50

75

100

Net return spillovers-New Taipei2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

-100

-75

-50

-25

0

25

50

Net return spillovers-Taichung2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

-60

-40

-20

0

20

40

60

80

100

25

Table 1 Descriptive statistics

Taiwan Taipei City New Taipei City Taichung City

fRh sRh fRh sRh fRh sRh fRh sRh

Mean 0.0061 0.0092 0.0105 0.0142 0.0096 0.0122 0.0027 0.0086

Std. Dev. 0.0169 0.0404 0.0284 0.0377 0.0329 0.0333 0.0469 0.0741

Skewness 0.6255 0.6280 0.0175 -0.1325 0.2227 0.0611 0.5908 -0.6537

Kurtosis 0.7574 2.3294 0.1996

-0.1581 1.9166 -0.4797 2.7510 2.8903

PP test -3.9103

(0.00)

-8.8948

(0.00)

-4.1936

(0.00)

-6.8641

(0.00)

-4.5914

(0.00)

-6.1476

(0.00)

-8.5677

(0.00)

-8.8948

(0.00)

ADF test -3.9315

(0.00)

-8.7030

(0.00)

-4.1175

(0.00)

-6.7840

(0.00)

-4.5480

(0.00)

-6.0097

(0.00)

-8.2528

(0.00)

-8.7030

(0.00)

Notes: sRh is the existing housing price return; fRh is the pre-sale housing price return. The optimal lag length of unit root test model was chosen by the Bayesian

information criterion. Entry in parenthesis stands for the p-value.

26

Table 2 Results of VAR models

Taiwan Taipei City New Taipei City Taichung City

fRh sRh fRh sRh fRh sRh fRh sRh

1, −tfRh 0.4167

(2.87)***

-0.2030

(-0.49)

0.1536

(1.15)

0.2099

(0.82)

0.2096

(1.52)

0.0287

(0.18)

-0.1423

(-0.94)

0.3693

(1.69)*

2, −tfRh 0.1009

(0.74)

0.2433

(0.63)

0.1338

(1.16)

-0.1428

(-0.65)

0.2732

(1.95)*

0.1458

(0.93)

-0.0457

(-0.30)

0.5051

(2.30)**

1, −tsRh 0.1548

(3.28)***

-0.1038

(-0.77)

0.3326

(4.37)***

0.0064

(0.04)

0.2152

(1.73)*

0.0725

(0.52)

-0.1196

(-1.22)

-0.3397

(-2.38)**

2, −tsRh 0.0844

(1.60)

0.1544

(1.02)

0.2988

(3.39)***

0.1952

(1.16)

0.2598

(2.01)*

0.2803

(1.93)*

0.0019

(0.02)

-0.1770

(-1.25)

Constant 0.0005

(0.25)

0.0116

(1.90)*

-0.0019

(-0.59)

0.0120

(1.95)*

0.0011

(0.23)

0.0085

(1.67)

0.0037

(0.53)

0.0113

(1.13)

27

Notes: sRh is the existing housing price return; fRh is the pre-sale housing price return. Entry in parenthesis stands for the t statistics. *, ** and *** denotes significance

at the 10%, 5% and 1% level.

28

Table 3 Full sample return spillover

Taiwan

fRh sRh

Directional

From others

fRh 75.1 24.9 25

sRh 0.9 99.1 1

Directional to others 1 25 26

Directional including own 76 124 12.9%

Taipei

City fRh sRh

Directional

From others

fRh 49.4 50.6 51

sRh 1.89 98.2 2

Directional to others 2 51 52

Directional including own 51 149 26.2%

Notes: fRh and sRh denote the return of real estate forward and spot markets, respectively.

Numbers in italic represent the directional contribution from/to other markets. The lower right end

corner number in bold is the total return spillover index.

29

Table 3 Full sample return spillover (continued)

New Taipei

City fRh sRh

Directional

From others

fRh 76.8 23.2 23

sRh 2.9 97.1 3

Directional to others 3 23 26

Directional including own 80 120 13%

Taichung

City fRh sRh

Directional

From others

fRh 93 7 7

sRh 11.7 88.3 12

Directional to others 12 7 19

Directional including own 105 95 9.3%

Notes: fRh and sRh denote the return of real estate forward and spot markets, respectively.

Numbers in italic represent the directional contribution from/to other markets. The lower right end

corner number in bold is the total return spillover index.

30

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