Real Estate Investment and Vulnerability to Financial Crisis in Thailand:
A Financial Computable General Equilibrium Approach
Sutee ANANTSUKSOMSRI
Waseda Institute for Advanced Study, Waseda University, Japan

Introduction
The real estate sector and the financial sector are indeed highly interconnected. As one cansee, the 1997 financial crisis in Thailand originally stemmed from overinvestment in the real estatesector. To examine the role of real estate in the economy, an economy-wide analytical framework thatcan capture interrelations among various economic sectors, including production sectors, households,
government, and financial sectors, like a Financial Computable General Equilibrium (FCGE) model isneeded. Yet, little has been done to methodically link the real estate sector to the financial sector. In
this study, a FCGE model of Thailand, which explicitly connects the real estate sector to the financialsector, is used to analyze the importance of the real estate sector in Thai economy. To the best of myknowledge, this study is one of the first few attempts to analyze the economy-wide impacts of the realestate sector using a FCGE framework for an emerging market like Thailand.

Real Estate and Macroeconomic Growth
The real estate industry has contributed to the growth of Thailand in a rapidly growing
economy during the boom decade. During the high-growth period, the industry accounted for almost
one-third of the country’s GDP. In addition, the construction sector, which is a real-estate-relatedindustry, constituted about 20 percent of the growth in GDP. Altogether, real estate and its relatedindustry were large contributor to the growth of Thai economy during the boom period.

The Degree of Vulnerability to Financial Crisis
After 1997, many studies have investigated the economic phenomena before and during thecrisis. Azis (2002) suggests the variables signaling the degree of vulnerability to a crisis, which
include: 1) real exchange rate (RER) appreciation, 2) lending boom, and 3) low level of foreignexchange reserve.
A look back at the historical data of Thailand suggests that these three indicators ofvulnerability to financial crisis can signal overheating economic growth to some extent. Therefore,
this study uses these three indicators to measure the degree of vulnerability to another crisis.

Property and Asset Markets
Pholphirul and Rukumnuaykit (2009) estimate the duration of the real estate cycle in Thailand
to be approximately 69 months. The major leading indicators for the real estate cycle are construction
price index, money supply (M2), property stock index and post-credit finance. They also findevidence that the real estate cycle in expansion periods is always found to lead the business/economic
cycle of Thailand. The real estate business cycle in general can be explained by DiPasquale andWheaton (1992). They suggest a simple analytical framework of a four-quadrant diagram explaining
connections between the space market (property market) and real estate asset market. In this study, theframework of DiPasquale and Wheaton (1992) is incorporated as an extension of the standard FCGE
model. Figure 1 depicts the relationship between the two markets.

Figure 1: The relationship between property and asset markets

In the framework of DiPasquale and Wheaton (1992), rents in the short run are determined by
the demand for space which is equal to the stock of space in equilibrium, shown in the property
market quadrant (the northeast quadrant). The rent determination can be represented in the following
equation:,whereis a demand for space,is a rent,is economic factors, andis the stock of space.

DiPasquale and Wheaton (1994) also suggest that the supply of housing can be represented in thefollowing equation:,
where demographic characteristics and real permanent income (X), housing price (P), cost offinancing (U), and the alternative cost of renting (R).

Subsequently, in the asset market (the northwest quadrant), the rent determines a price forreal estate asset according to a capitalization rate, which includes the long-term interest rate, expected
growth in rents, risks associated with rental income stream, and the treatment of real estate in the tax
code. The real estate valuation in the asset market can be represented in the following equation:,
where P is a price for real estate asset, R is rents, and i is a capitalization rate.
In the asset market (the southwest quadrant), the price of real estate assets is related to thereplacement or construction costs. In the long run, the price of real estate in the asset market should beequal to construction costs in the equilibrium. The real estate construction in the asset market can berepresented in the following equation:,
where P is a price of real estate asset, and f(c) is a function of replacement cost.
The connections from the asset market are then back to the property market through therelation of construction costs and a long-run stock of real estate space (the southeast quadrant). The
stock depending on construction costs and the depreciation rate of stock will determine rents (NEquadrant). The stock adjustment of space in the property market can be represented in the following
equation:,whereis a long-run stock of real estate space,is a the depreciation rate of stock, andis newconstruction.

Data and Methodology
This study employs the Computable General Equilibrium (CGE) model framework based on a
Financial Social Accounting Matrix (FSAM) of Thailand, to analyze the role of real estate in theeconomy. The CGE model is then developed into the framework of a standard Financial Computable
General Equilibrium (FCGE) model based on the model developed by Puttanapong (2009). Theprinciple assumption underpinning FCGE models is the balance between total assets and liabilities
held by each institution during a given period of time. FCGE models simulate theoretical behaviors ofinstitutions in the financial market through a system of equations. The general equilibrium is achieved
when all conditions and constraints hold.
The FCGE model framework is an extension of the CGE model incorporating the flow offunds account. Vongpradhip (1987) and Rosenzweig and Taylor (1990) were among the pioneers whodeveloped a FCGE for Thailand. Consequently, influenced by the studies of Azis (2002) and Mansury

(2002) on the economy of Indonesia, Manopiniwes (2005) and Puttanapong (2008), this study
presents the FCGE model for Thailand, using the 2007 FSAM as its base year data. The model
replicates the activities in the real economy and financial transactions in Thai economy.
This study also incorporates the FCGE model with the theoretical framework relation ofproperty and asset market by DiPasquale and Wheaton (1992). This model is developed to measure
the economy-wide impact on real and financial sectors in the economy due to real estate investment.
The results of the model suggest policy implication of socio-economic impact of real estate
investments, and the degree of vulnerability to the crisis is evaluated by macroeconomic indicatorsfrom the model.

Simulation and results
The FCGE model is used to simulate the impact of real estate investment on the economy ofThailand. This model is based on the 2007 Thailand FSAM, which provides rich information ofeconomic activities and structure such as income distribution, transfers among institutions, production
and consumption patterns, saving and investment, and flow of funds. The fact that the model iseconomy-wide and price-endogenous makes the FCGE model suitable for analyzing the effect of realestate investment on not only macroeconomic fundamentals but also on social factors, especially on
income distribution.
In this study, the risks and opportunities from high investment speculation in real estate isexamined. There are four simulation scenarios are undertaken: (1) baseline, additional five percent
increase annually in an investment in (2) real estate, (3) agriculture, and (4) manufacturing sectors.
The baseline simulation scenario is assumed that there is the annual growth of 3.5% in real estate
investment in Thailand. Three other scenarios are assumed that there is additional five percent
increase in an investment from a rich household in alternate sectors, which represents speculative
behavior. The time frame of the simulation is 10 years. Both the risks and opportunities from thehigh speculation are measured from the simulation results.
The risk, so called the vulnerability to a crisis, is analyzed. In this study, the simulation results
can provide only two out of three indicators: real exchange rate (RER) appreciation and the level offoreign exchange reserve. On the other hand, the opportunity is indicated by macroeconomic
fundamentals, such as GDP, Real GDP (RGDP), Price Index (PINDEX), exchange rate (EXR), as
well as socio-economic indicators, such as incomes of the poor and non-poor, income distribution,and unemployment rate. The income distribution is measured by the ratio of the incomes of the poor
to those of the non-poor household.Table 1: The results of 5% investment increase on real estate, agriculture, andmanufacturing sectors at the end of year 10
Variables
Vulnerability to crisis

Baseline

Real estate

Agriculture

Manufacturing

M2/FOREX

0.238100.238190.238270.23825

RER

34.5170

34.49634.48934.482

GDP

7,214,334

7,255,572

7,291,635

7,283,635

RGDP

7,133,400

7,134,646

7,132,971

7,138,499

PINDEX

1.012

1.017

1.022

1.021

EXR

34.91335.08635.26235.191

Income of the poor

1,073,982

1,078,600

1,089,227

1,081,892

Income of the non-poor

2,953,232

2,970,756

2,981,759

2,981,416

Income distribution

0.36370.36310.36530.3629

Unemployment rate

0.01250.01170.01120.0109Macroeconomic indicators

Socioeconomic indicators

The simulation of three scenarios of a 5 percent increase in investment in real estate,
agriculture, and manufacturing sectors illustrates both the risks and opportunities from suchinvestment scenarios. Table 1 summarizes the simulation results of various indicators at the end ofyear 10. As can be seen, in comparison to the baseline, there is a marginal difference in terms ofvulnerability to crisis among three scenarios.
The results suggest that, although the investment in agriculture sector may lead to highgrowth in term of GDP, it also contributes to higher inflation, resulting in lower GDP growth in realterm. In addition, the investment in manufacturing may yield to the highest RGDP growth and lowest
unemployment rate, but it worsens income distribution between the poor and non-poor households.
Although, 5 percent additional investment in the real estate sector, on the other, may not lead to highgrowth as much as in agriculture or manufacturing sectors, it seems to bring lower economic cost—interm of inflation—as well as lower social cost—in term of income distribution—to the Thai economy.
Investment in the real estate sector contributes the least impact on increasing commodity price.
The simulation demonstrates that moderate investment in the real estate sector does notstrongly cause the country to be vulnerable to financial crisis. Such investment may also benefit theThai economy economically and socially. Therefore, monitoring mechanisms to control overheatingor speculative investment in real estate are recommended. Such policies may include introducing acapital gains tax on real estate asset investment or an excise tax on real estate properties. These tax
mechanisms are currently not imposed in Thailand, yet they could be elements of a policy that
controls overheating or unsound real estate investment.

Conclusion
Growth in the economy of Thailand is highly related with the role of the real estate industry.
While the framework of SAM and CGE show the interaction between real estate and other sectors in
the real economy, the flow of funds accounts and FSAM extend the more realistic picture ofconnection between real estate and the financial market. With the FCGE model, the simulations of therole of real estate industry on economy and social welfare can be investigated. The simulation has
shown that opportunities and risks from higher investment in real estate are not as high as higher
investment in agriculture and manufacturing sectors.
In addition, various policy implications can be applied to mitigate the negative effects fromthe real estate investment in Thailand. The analysis suggests that moderate growth in the real estate
sector is desirable. Thus, taxation policies, such as a capital gains tax on investment in real estate
assets and an excise tax on real estate properties, should be implemented to control overheating realestate investment. Upon the availability of greater detail FSAM and data on asset and liability
holdings of institutions beside households such as government and financial instructions, this FCGE
model can be extended to incorporate asset holding behaviors of such institutions.

References
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