National Chengchi University (NCCU)

International Business

Leo Chen

Lisa Fu

Lola Wu

Annie Huang

Gengin Wong

Supervisor: Prof. Shieh

Jun, 2014

A study of the determinants of the

housing price in Taiwan

I. Motivation

Recently, Taiwan has been ranked as the fourth hottest housing market in

the world by CNN News. Taiwanese have suffered the soaring housing price

problem as many other countries in the world. According to the Ministry of the

Interior of Taiwan, the house price to income ratio is defined as the ratio of the

median housing price to median familial disposable incomes. It is also the basic

affordability measure for housing in a given area. In the past ten years, it has

risen to 9.2. It means that, without spending any money of his monthly salary,

it would take almost 9 years for a person to own a house in Taiwan. However,

in other countries, the ratio is 3-5 times in average. What’s more, a recent report

on the China Times shows that the house price to income ratio of Taipei city is

the highest all over the world, and the New Taipei city is on the third place.

There is a big concern that housing prices in Taiwan are already unaffordable

for most first-time buyers. It is quite hard for those first-time buyers to own a

house in urban areas. As purchasing houses is important for Taiwanese people,

the soaring housing prices indeed becomes a serious problem for our society.

When it comes to the soaring housing prices in Taiwan, many reasons

could be discussed. First, we should take the uniqueness of real estate property

in Taiwan into consideration. In Chinese traditional concept, as the old saying

goes, “Along with real estate comes about wealth”. The saying means, for

Taiwanese people, they not only view real estate property as a necessity but

also expect purchasing real estate property as an investment as well. When the

financial market is relatively unstable, people would seek to put their money

into relatively inflation-proof and possible appreciation market, such as housing

market, in order to hedge the uncertain risks. Second, compared to other

neighboring countries in Asia, the house tax and the land value tax in Taiwan

are relatively lower. So the cost of owing houses in Taiwan are lower than in

other neighboring countries. It motivates Taiwanese people to purchase houses

as investments.

Even though there are many reasons that have been discussed, we are

still curious about what are the main determinants that influence the housing

prices in Taiwan. Based on the articles we have read and the hypothesis we

set, we conduct the statistical analysis, such as collinearity tests,

autocorrelation, and the stepwise regression on some macroeconomic

determinants. We found that M1a, Real GDP, Construction Stock Price Index,

and Interest rate are the most significant determinants that affect housing prices

in Taiwan for the past 20 years.

II. Articles Review

From the past studies, they all point out that different kinds of determinants

are influencing the housing prices in Taiwan. These studies respectively choose

different determinants to test the relationship with housing prices. However,

most of them only focus on only two or three few determinants. We expect to

figure out what the most important determinants affecting Taiwan's housing

price are among all those important determinants. Based on articles we studied,

we choose M1a, M1b, M2, real GDP, interest rate, foreign exchange rate, CPI,

construction material CCI, TAIEX, and Construction Stock Price Index to study

which are more related to housing prices.

In the past studies, we found that money supply has an important influence

on the housing prices in Taiwan. For example, Chien-Wen Peng, Vickey C.C.Lin

and Ya-Ting Yang (2004) use the data from 1981Q1 to 2001Q4 and impulse

response analysis to explain structural changes of housing prices in Taipei City

and Taipei County. The empirical results revealed that housing prices in both

cities are correlated with money supply, stock market index and the allowed

floor areas specified by the building permit. The study result reveals that stock

market index has direct influence on housing prices while money supply has

indirect influence on housing prices. When money supply changes, it will affect

stock market and thus affect housing prices. Thus, we take money supply into

consideration.

Also, Chi-Wei Chen (2010) uses the data from 1994Q1 to 2010Q1 to

indicate the dynamic relationship on stock price, housing price and exchange

rate. In his article, he expects that there are abundant transactions in foreign

exchange market in Taiwan. Thus, exchange can play a role in deciding housing

prices. Also, the stock price has a positive relationship on housing prices.

Next, in macroeconomic determinants, Dokko (1999) uses GDP as a

determinant in his model. He expects that when GDP goes up apparently, the

housing prices will go up with GDP. Therefore, GDP has a positive influence on

housing prices. Yen-kuang Chen (2010) also analyzes the relationship between

real estate cycles and the macroeconomic determinants. This study uses the

data collected from Jan. 1998 to Dec. 2008. The indicators of real estate cycles

adopted in the thesis are the trading volume of real estate and the housing price

index, and the macroeconomics determinants include mortgage rates, the stock

price index, money supply and the price index. Based on cointegration Test,

they found there is a long-run equilibrium between real estate cycles and the

macroeconomic determinants. There is a two-way feedback among the stock

price index and the housing price index. Moreover, the price index affects the

housing price index. The stock price index and the price index have less effect

on the housing price index. The price index is more accountable than the stock

price index for the fluctuations of the housing price index.

I-Chun Tsai and Ming-Chi Chen (2013) propose that asymmetric reactions

of the stock and housing markets will result in asymmetric relationship between

the two markets. Tsai and Chen use Sinyi Housing Price Index of Taipei city and

the Taiwan Exchange Capitalization Weighted Stock Index from 1991Q3 to

2008Q3 for empirical analysis. They reveal that regarding the integration of

housing prices and the stock price index, the correlation behavior of housing

prices toward long-term equilibrium is more significant when demand increases

significantly, but is not significant when demand declines.

Chien-Wen Peng and Chin-Oh Chang (2000) say that from 1970-1990

Taiwan has gone through three apparent cycles. In this article, the determinants

are pre-construction real estate housing prices, vacant houses, and building

permits. They use the data from 1981Q1 to 1999Q1. From their study, we

examine that in the first two booms, the insufficient house supply and increasing

oil prices cause the inflation. People have expectation of housing prices

maintaining so they increase the demand of house. Therefore, the supply is

less than demand. The cause of third housing price peak is the economics grew

too fast and the soaring stock market. Excessive money supply caused the third

cycle. From their observation, we can examine that there is a business cycle

every 7 year in Taiwan, and each cycle has its own important determinants

affecting the housing price. However, the cycle has a structural change after

1990. Thus, we will focus on this change and use the quarterly data from 1994

to 2013, total 80 data, to find the causes that are affecting Taiwan’s housing

price in this cycle. Furthermore, we also want to examine and compare among

all the determinants which are more influential to housing price.

已註解 [u1]: 建造率面積

III. Methodology

3.1 Research Framework

Being curious about what determinants affect the housing prices,

we come up with several important determinants and try to find

relationships between each one of it and the housing prices. We will first

introduce every variable we choose. Second, we will make a hypothesis

on how the determinants are going to affect the housing price. Third, we

will conduct the statistical analysis, and use methods such as collinearity

tests and autocorrelation to reduce the error that may occur. Last, we will

conduct the stepwise regression to eliminate the less-related

determinants.

3.2 Determinants

Dependent Variable

(1) Sinyi Housing Price Index

Sinyi Housing Price Index is the most common-used index in Taiwan,

which is conducted by CNCCU- SINYI Research Center for Real Estate.

The center applies the Hedonic Price Method and use the Laspeyres

formula to conduct the index. To make our data more typical, we use the

data of whole Taiwan, not just in Taipei or any other city. The graph 1

shows that the housing prices of Taiwan have been in a rise in the past

two decades.

Graph 1: show the trend of different determinants on a timely basis.

(Reference: CNCCU- SINYI Research Center for Real Estate)

Independent Determinants

(1) Money Supply(per million)

M1a (𝐗𝟏)

M1A comprises the currency held by the public plus checking accounts

and passbook deposits of enterprises and individuals.

M1b (𝐗𝟐)

M1B comprises M1A and passbook savings deposits of individuals with

other depository corporations

M2 (𝐗𝟑)

M2 comprises M1B and quasi-money. Quasi-Money comprises time

deposits, time savings deposits, foreign currency deposits (including

demand deposits and time deposits), postal savings deposits (including

giro accounts, passbook savings deposits and time savings deposits),

repurchase agreements, non-resident NT dollar deposits and money

market mutual funds of enterprises and individuals with other depository

corporations.

(2) Macroeconomic Determinants

Real GDP (𝐗𝟒)

Real GDP is defined as the market value of all officially recognized final

goods and services produced within Taiwan each year, and we collect

our data from Directorate-General of Budget, Accounting, and Statistics,

Executive Yuan, R.O.C. Unlike nominal GDP, real GDP can account for

changes in the price level, and provide a more accurate figure. In recent

years, the real GDP has been rising continuously year by year. Though

it rises and falls frequently for the time being, it has a smooth fluctuation.

Interest Rate (𝐗𝟓)

Here we choose the data of the weighted average bank loan interest rate

between local banks from TEJ. When interest rate drops, people can

benefit from investing other than keeping your money in the bank. Also,

the costs for borrowing money from others become lower. The downward

curve means the interest rate in Taiwan has been in a decline in general

in the past twenty years.

Foreign Exchange Rate (𝐗𝟔)

Foreign Exchange Rate is the price of a nation’s currency in terms of

another currency. It is also an important index when we talk about

macroeconomics. Here we use average foreign exchange rate in indirect

quotation method as our data (NTD/$). As a result, when New Taiwan

Dollar appreciates, the curve will go down instead of moving upward.

(3) Indices

CPI (𝐗𝟕)

http://en.wikipedia.org/wiki/Market_value

CPI reflects people’s living level of that time. As we can see the data

from Directorate-General of Budget, Accounting, and Statistics,

Executive Yuan, R.O.C, CPI has kept rising in the recent decades, which

means the burdens on Taiwanese people are getting heavier and heavier.

However, CPI only counts housing rents, housing prices are not included

in CPI. But both of the two have been in a rise these years. so we want

to see what is the difference between the trend of these two indices.

Construction Material CCI (𝐗𝟖)

To know how cost factors affect the housing market, we choose

construction material CCI as our variable. Construction Material CCI is

an index conducted by Directorate-General of Budget, Accounting, and

Statistics, Executive Yuan, R.O.C, which shows the price of materials in

the producer market. In the past two decades, the price of construction

material has risen a lot.

TAIEX (𝐗𝟗)

TAIEX = (∑𝑆𝑡𝑜𝑐𝑘 𝑃𝑟𝑖𝑐𝑒 × 𝑉𝑜𝑙𝑢𝑚𝑒

𝐵𝑎𝑠𝑒 𝑉𝑎𝑙𝑢𝑒) × 100

TAIEX is the abbreviation of Taiwan Capitalization Weighted Stock Index,

with the base year of 1966.It is conducted by the Taiwan Stock Exchange

Corporation, which is a corporation in charge of the stock market and

securities market in Taiwan. TAIEX covers all of the listed stocks

excluding preferred stocks, full-delivery stocks and newly listed stocks,

which are listed for less than one calendar month. It is one of the most

important index to see the economic condition of Taiwan, it also

represents the trend of the overall stock market.

Construction Stock Price Index (𝐗𝟏𝟎)

Construction Stock Price Index is an index which is also conducted by

the Taiwan Stock Exchange Corporation. The formula of construction

stock price index is same as the TAIEX one, but it especially take the

data of construction stock. To see how stocks influence housing prices,

we Construction Stock Price Index as our last variable.

Table 2: show the statistical data of all the determinants mentioned above.

Table 3: Statistical data of each determinant

(Source: Taiwan Economic Journal) (Source: Taiwan Economic Journal)

Mean Maximum Minimum Standard Deviation Variance Skewness Kurtosis

Sinyi Housing Price Index 144.73 286.53 96.39 50.1657689 2548.460121 1.336307 0.789112

M1A (per million) 2,694,496 5259011 1,450,296 1100193.158 1.22575E+12 0.698518 -0.77404

M1B (per million) 6,868,443 13470752 2,764,172 3170367.442 1.01785E+13 0.477472 -1.02806

M2 (per million) 22,286,655 35518863 10,553,232 6985292.492 4.9412E+13 0.152252 -1.06842

Real GDP (per million) 3,021,563 3809698 2,063,531 478863.3373 2.32213E+11 -0.43961 -0.93297

Interest Rate 5.18175 9.12 1.93 2.637429513 7.044085506 0.236063 -1.65704

Exchange Rate (NTD/$) 31.247625 35.04 25.58 2.539533542 6.53086644 -0.5663 -0.73856

CPI 92.220875 103.18 79.13 5.96729214 36.05931695 0.109401 -0.7373

Construction Material CCI 77.684875 107.17 60.8 16.01511528 259.7305494 0.431823 -1.52307

TAIEX 6856.10 9518.423 4299.03 1342.284169 1824533.46 -0.08915 -0.90989

Construction Stock Price Index 252.90 517.8767 60.26 119.6250265 14491.28805 0.181746 -0.69033

(Source: Taiwan Economic Journal) (Source: Directorate-General of Budget,

Accounting, and Statistics, Executive

Yuan, R.O.C.)

(Source: Taiwan Economic Journal) (Source: Taiwan Economic Journal)

(Source: Directorate-General of Budget (Source: Minister of the Interior, R.O.C.)

, Accounting, and Statistics, Executive

Yuan, R.O.C)

(Source: Taiwan Economic Journal) (Source: Taiwan Economic Journal)

3.3 Hypothesis

We have made the hypothesis on the 8 determinants that we

choose. These assumptions are based on previous articles that we have

read and the current economic situation in Taiwan. Table 4 shows our

hypothesis we made. Among the 8 determinants that we have chosen,

we make a bold assumption that the money supply will be the main

reason that affects housing price in Taiwan these years. We will clarify

and explain the reason more below.

Table 4: The hypothesis on these 8 determinants

(1) Money supply:

The reason why we regard it as the most important determinants among

Factors Money

supply

Real

GDP

Exchange

rate

Interest

rate CPI

Construction

CCI

Construction

Stock Price Index TAIEX

Change △ In

house price

index + + + - + + + -

all of our determinants is because the financial crisis that occurred in

2008. It was known as the biggest financial event during the period that

we observe from 1994-2013. And at that time, American started to

conduct the QE monetary policy. Hence, there are lots of U.S. dollar

money being issued to the world, especially into emerging markets,

including Taiwan. Because the hot money floods into Taiwan market, it

forces the central bank to raise the same level money supply to prevent

the soaring appreciation. So this event has heavily influenced the money

supply in Taiwan. Thus, we assume that as the money supply increases,

it will cause the inflation which results in the hedge in house market and

even the higher housing prices.

(2) GDP

The GDP is highly related to the mortgage. Therefore, when the

mortgage increase, it will reflect on the GDP. It also means a growing

demand on house, leading the housing price to go up.

(3) Exchange rate:

We assume that when the exchange rate goes up, which means the new

Taiwan dollar to depreciate. And it will cause the import building

materials cost more. In the end, the house price will go up.

(4) Interest rate

The unit price of house is much higher than other products in the market.

Thus, most of the purchasers need to borrow money from bank.

Therefore, when the interest rate of the mortgage goes up, it will lower

the willingness to buy the house and cause the housing price to drop.

(5) CPI

When purchasing power decreases, people will turn their currency into

inflation-proof asset in order to prevent the value of money from

shrinking. So the demand of house will increase. Thus, when the CPI

goes up, we expect the house price will go up.

(6) Construction CCI

Construction cost is a direct concern in pegging price. So we expect that

the higher construction CCI will reflect on a higher housing prices.

(7) TAIEX

As for the TAIEX, we believe that the stock market and the housing

market are two important investment markets. When the TAIEX goes up,

it means the investors choose to join the stock market which will

decrease their participant in housing market. And it will result in the

decrease of housing price.

(8) Construction stock price index

Construction has been an indicate industry in Taiwan. Hence, we

estimate that the construction stock price index can reflect the economy

in Taiwan. So when the market is prosperous, in our opinion, we expect

the purchase in house and the house price will also increase.

3.4 Multiple Regression Analysis

Multiple regression would be the core method we conduct in this

research. We mainly use this method to discuss about how the determinants

we have chosen are related to the housing price index. With multiple

regression, we could also investigate what factors may be the major causes

that affect the current housing prices, which is currently too high in Taiwan.

Multiple Regression Formula

𝑌 = α + 𝛽1‧𝑋1 + 𝛽2‧𝑋2 + 𝛽3‧X3 + 𝛽4‧X4 + 𝛽5‧X5 + 𝛽6‧X6 + 𝛽7‧X7 + 𝛽8‧X8 + 𝛽9‧

X9 + 𝛽10‧X10 + 𝜀

Y : Sinyi Housing Price Index

𝑋1 : M1a

𝑋2 : M1b

𝑋3 : M2

𝑋4 : Real GDP

𝑋5 : Interest Rate

𝑋6 : Foreign Exchange Rate (NTD/$)

𝑋7 : CPI

𝑋8 : Construction Material CCI

𝑋9 : TAIEX

𝑋10: Construction Stock Price Index

α : Intercept

β : Coefficient of each variable

𝜀 : Error

Multiple Regression Results

We ran our first regression using all the ten determinants. According to

the result, M1b, interest rate, TAIEX and Construction Stock Index are the

four significant determinants. This result has an adjusted R square of 0.98,

which means the result is able to explain 98% of the data. Moreover, the

coefficient of M1b is relatively small and positive, indicating that there is only

slightly influences between the housing price and a positive influence. When

the housing price rises, M1b also rises. The coefficient of interest rate is more

than 12, which means that this determinant can dramatically influence the

housing price, and when the housing price rises, the interest rate will rise, too.

TAIEX only has 0.003 on its coefficient, so the influence is not too dramatic.

However, it has a negative effect. We expect the reason is that, when the

stock market is prosperous, people will invest in stock market more and at

the same time invest less on the real estate market. The Construction Stock

Index also influences the housing prices with a coefficient of 0.058, and has

a positive effect.

Table 5: The result of the multiple regression

Adjusted R=0.985884503

Observations=80

Period=1994-2013 (quarterly)

However, we have made this regression analysis by ignoring the

collinearity problem and autocorrelation effect. Later on, we will conduct the

stepwise regression to eliminate the errors that collinearity problem and

autocorrelation effect could cause.

Collinearity Test

If the independent determinants are highly correlated with each other,

that may cause the so-called “collinearity problem”. That way, we won’t be

able to proceed the regression process. One way to testify the existence of

collinearity is to calculate the correlation coefficient. Normally, if the

Housing Price Index

(Taiwan)Y coefficient

t

StatisticsP-Value

Ｍ１Ａ X1 2.60E-05 1.89179 0.062717

Ｍ１Ｂ X2 1.39E-05 2.11087 0.038409

Ｍ２ X3 1.91E-06 1.0023 0.3197

Real GDP X4 -1.26E-05 -1.9073 0.060643

Interest Rate X5 12.89581 7.32376 3.43E-10

Foreign Exchange

Rate (NTD/$)X6 -1.256613 -1.925 0.058352

CPI X7 -0.687947 -0.8568 0.394519

Construction CCI X8 0.193131 0.92691 0.357206

TAIEX X9 -0.003228 -3.2435 0.001821

Construction Stock

Price IndexX10 0.058187 4.08791 0.000116

Intercept 3.104685 0.05505 0.956256

Money

Supply

Macroeconomic

Variables

Index

correlation coefficient between two determinants are close to 1 (perfect

correlated), that means it will be harder to use OLS (originary least squares)

method to conduct the regression analysis. The results are shown below.

Table 6: The collinearity test between each two determinants

This table above indicates that 26 out of 45 correlation coefficient are

over 0.8, and that is nearly 57%. So, first, we learn from the correlation

coefficient that we cannot use all of these 8 determinants in the regression

formula.

Autocorrelation (lag=1)

Next we calculate the autocorrelation and set the lag period for one

quarter. We wonder if one single variable has its correlation itself when doing

some time series tests. The results are demonstrated in table 7.

Table 7: The autocorrelation result (lag=1)

From table 7, we can see that almost all determinants have correlation

of more than 90%, which means that each variable itself has strong

Sinyi

Housing

Price Index

Ｍ１Ａ Ｍ１Ｂ Ｍ２Real

GDP

Interest

Rate

Foreign

Exchange

Rate

(NTD/$)

CPIConstruction

CCITAIEX

Construction

Stock Price

Index

Sinyi Housing

Price Index1.000

Ｍ１Ａ 0.936 1.000

Ｍ１Ｂ 0.897 0.993 1.000

Ｍ２ 0.839 0.968 0.985 1.000

Real GDP 0.670 0.869 0.908 0.942 1.000

Interest Rate -0.694 -0.893 -0.931 -0.943 -0.936 1.000

Foreign Exchange

Rate (NTD/$)-0.238 0.038 0.129 0.253 0.442 -0.348 1.000

CPI 0.833 0.939 0.949 0.975 0.905 -0.882 0.231 1.000

Construction CCI 0.856 0.939 0.936 0.931 0.839 -0.885 0.059 0.931 1.000

TAIEX 0.462 0.387 0.357 0.320 0.301 -0.181 -0.220 0.381 0.400 1.000

Construction Stock

Price Index0.256 0.009 -0.070 -0.176 -0.300 0.268 -0.657 -0.114 0.044 0.583 1.000

Sinyi

Housing

Price Index

Ｍ１Ａ Ｍ１Ｂ Ｍ２ Real GDPInterest

Rate

Foreign

Exchange

Rate

(NTD/$)

CPIConstruction

CCITAIEX

Construction

Stock Price

Index

lag=1 0.9975 0.9949 0.9980 0.9994 0.9661 0.9979 0.9522 0.9914 0.9928 0.8415 0.9374

relationship with its lag period.

Both the results of correlation coefficient and the autocorrelation show

the fact that some determinants must be eliminated, and in the meantime

keep the more useful ones in the formula. In order to choose the useful

determinants, we decide to conduct the Stepwise Regression.

Stepwise Regression

Since there are collinearity problems, some determinants must be

eliminated, so that we are able to run multiple regression. When it comes to

deciding which determinants should stay and which should eliminate, we

conduct the stepwise regression as a way to keep the most valuable variable

in the formula.

In our theory, we got only general direction as to which of a pool of

candidate determinants should be included in the regression model. After the

auto-correlation& correlation test we have done above, we notice that we

have to solve the collinearity problem. Thus, we have to select the final

determinants set. First, we pursue the regression model to be as

representative and realistic as possible. Second, we want to include as few

determinants as possible because each irrelevant determinants decreases

the precision of the estimated coefficients and predicted values. In order to

reach a final model, we decide to use stepwise regression method as our

way to select the determinants.

Stepwise Regression Result

We got the stepwise regression result below by using SPSS. Stepwise

regression set two significance levels: ≤0.05 for adding determinants and

≥0.1 for removing determinants. Through this criteria, we select 4 main

determinants as our determinants.

Table 8: The stepwise regression result (part1)

Determinants entered/removed (dependent determinants: house price index)

We can also take a look at the table 9. From the data below, we can

notice that the model 4 has biggest R, R square and adjusted R square value.

It represents that model 4 has a higher explanatory than the other. So that is

why we choose model 4 as our model in this paper. With the significance in

ANOVA are all less than 0.05, we can be sure that there is at least more than

one coefficient that is not equal to 0.

Table 9: The stepwise regression result (part2)

Model Determinants entered Method

1 Ｍ１Ａ(million) Stepwise（Criteria：F-to-enter = .100 possibility）。

2 Loan Interest rate of Taiwan

Bank

Stepwise（Criteria：F-to-enter = .100 possibility）。

3 Real GDP(million) Stepwise（Criteria：F-to-enter = .100 possibility）。

4 Construction stock price index Stepwise（Criteria：F-to-enter = .100 possibility）。

Model R R square Adjusted

R square

Standard

Error of

Estimate

1 .936a .875 .874 17.92953

2 .987b .975 .974 8.14861

3 .991c .983 .982 6.73167

4 .992d .985 .984 6.41296

Model Summary

a. Predictors：(Constant) M1A (million)

b. Predictors：(Constant) M1A (million), loan interest rate

c. Predictors (Constant) M1A (million), loan interest rate, Real GDP(million)

d. Predictors：(Constant) M1A (million), loan interest rate, Real GDP(million),

construction stock price index

e. Dependent: House price index

Now, in order to explain the result, let’s take a look the table 10. As we

can see from this table, the multiple regression result of model 4 is listed. It

shows that M1A has the highest correlation with house price index among all

those factors we choose. Loan interest rate and the construction stock price

also has a positive correlation, while the real GDP is negative correlated.

However, there are big differences among all the unstandardized coefficients.

M1A and GDP got comparative small number, while the others are normal

numbers. In order to even the number of these coefficients, we are going to

revise the regression model and change the arithmetic unit of M1A and GDP,

to make the result easier to elaborate.

Table 10: The stepwise regression result (part3)

Model

Unstandardized

coefficients

standardized

coefficients

T Sig. B Std. error Beta

1 （Constant） 29.773 5.303 5.615 .000

Ｍ１Ａ(million) 4.266E-05 .000 .936 23.415 .000

2 （Constant） -115.993 8.746 -13.263 .000

Ｍ１Ａ(million) 7.116E-05 .000 1.561 38.665 .000

Loan interest rate 13.312 .768 .700 17.339 .000

3 （Constant） -17.245 17.804 -.969 .336

Ｍ１Ａ(million) 7.313E-05 .000 1.604 47.040 .000

Loan interest rate 9.349 .910 .492 10.271 .000

Real GDP(million) -2.764E-05 .000 -.264 -6.069 .000

4 （Constant） -23.102 17.076 -1.353 .180

Ｍ１Ａ(million) 6.965E-05 .000 1.527 36.807 9.26E-50

Loan interest rate 8.523 .911 .448 9.355 3.11E-06

Real GDP(million) -2.317E-05 .000 -.221 -5.042 3.15E-14

Construction stock

price .024 .008 .057 2.957 .004

Dependent: House price index

Revised Multiple Regression (4 determinants)

𝑌 = 𝛼 + 𝛽1‧𝑋1 + 𝛽2‧𝑋2 + 𝛽3‧𝑋3 + 𝛽4‧𝑋4 + 𝜀

Y : Sinyi Housing Price Index

𝑋1 : M1a

𝑋2 : Real GDP

𝑋3 : Interest Rate

𝑋4 : Construction Stock Price Index

α : Intercept

β : Coefficient of each variable

𝜀 : Error

Revised Multiple Regression Result

From table 10, we find that the M1A and real GDP has a much smaller

unstandardized coefficient. In order to revise the coefficient to a more even

number compared to the prior one. We change the arithmetic unit of M1A and

real GDP. The sign of each determinant is still the same. And the Adjusted

R square is equal to 0.98, which represents these determinants can explain

nearly 98% of the determinants that affect housing price index.

Table 11: The revised multiple regression result

F-Value=363.661

Adjusted R= 0.94835406

Observations=80

Period=1994-2013 in quarter

Residual Plots

In order to validate our model, we check the residual plots. To see if the

points in a residual plot are randomly dispersed around the horizontal axis.

We can assess whether the observed error (residuals) is consistent with

stochastic error.

Graph 12: The residual plot

coefficientt

StatisticsP-Value

M1a X1 0.696492 36.807 9.3E-50

Real GDP X2 -0.231697 -5.0418 3.1E-06

Interest Rate X3 8.52314 9.35512 3.2E-14

Construction Stock Price Index X4 0.02375 2.95663 0.00416

Intercept -23.10229 -1.3529 0.18016

From the four residual plots above, we can find out our determinants all get

a random residual plot which means they don’t fall into symmetrical pattern.

And in the OLS context, random errors are assumed to produce residuals that

are normally distributed. So we can believe the revised regression model is

validate.

3.6 Conclusion

Taiwan often shows a type of cyclical fluctuation. To explore this

phenomenon, many papers dig into the relationship between 2-3 determinants

and housing prices and many different conclusions has been proven. Thus, the

main purpose of this study is to research which determinants affecting housing

price in Taiwan efficiently in this housing price cycle.

In our study, determinants are first selected from previous research and

then chosen by stepwise procedure. So among all those determinants, we

construct the indicators of this housing price cycle are M1A, loan interest rate,

construction stock price and GDP. We discover that the housing price is positive

correlated to M1A, loan interest rate and construction stock price, especially to

M1A. On the other hand, it is negative correlated to GDP.

However, as we can see, the interest rate and GDP is opposite from our

hypothesis. These are very interesting phenomena. As to interest rate, this is in

accordance with the conclusion as a foreign study (Summers 1981, Peiser &

Smith 1985, Harris 1989). The study points out that the change in interest rate

doesn’t have an instant reaction. Thus, we think that the lag reaction of house

price is the main reason why these two determinants have a positive correlation.

For real GDP, in our opinion, we think that difference might stem from

overvaluing housing prices and the uncertainty of the housing market which

makes buying housing price bearing more and more pressure and risk.

And in sum, we find that the money supply standing out as the most

fundamental factors influencing Taiwan’s housing price nowadays. Thus, after

our empirical analysis, we expect housing investors or buyers should pay

attention to the monetary policy and the money supply amount in Taiwan to

hedge the risk. Also, we suggest government should focus on the monetary

policy to improve the housing market environment in Taiwan.

IV. References

Chien-Wen Peng, Vickey C.C.Lin and Ya-Ting Yang. 2004. “An Analysis of Structural

Changes in Housing Prices: Changes of Taipei City and Taipei. “

Chi-Wei, Chen. 2010. “The Dynamic Relationship On Stock Price, Housing Price and

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I-Chun Tsai and Ming-Chi Chen. 2013. “Asymmetric Correlation and Difference

between the Volatility of Housing and Stock Price Indexes: Analysis Based on the

Threshold Volatility and Cointegration Model.” Journal of Financial Studies (TSSCI,

EconLit), 21(4): 25-58

Ming-Chi Chen and Kanak Patel. 2002. “An Empirical Analysis of Determinants of

Housing Prices in the Taipei Area.” Taiwan Economic Review, 30(4):563-595

Christopher Otrok, Marco E. Terrone. 2005. “House Prices, Interest Rates and

Macroeconomic Fluctuation: International Evidence”

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