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A Study of the Relationship Between Singapore Property Prices and Factors that Drive Property Market

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION1. INTRODUCTION

2. RESEARCH BACKGROUND

3. RESEARCH OBJECTIVES

4. SIGNIFICANCE OF THE RESEARCH

5. RESEARCH QUESTIONS

6. STRUCTURE OF THE RESEARCH

CHAPTER 2: LITERATURE REVIEW1. THE SINGAPORE HOUSING MARKET AND RELATED

2. OVERVIEW OF THE SINGAPORE ECONOMY AND PROPERTY MARKET

2.1 ECONOMIC PERFORMANCE

2.2 LABOUR MARKET

2.3 CONSUMER PRICE INDEX

2.4 STANDARD CLASSIFICATION OF DWELLING

2.5 MARKET STRUCTURE OF SINGAPORE HOUSING MARKET

2.6 SINGAPORE DISTRICT CODE DEMARCATION

3. THE RESIDENTIAL MARKET AS OF 3Q2016

3.1 PRICES AND RENTALS

3.2 LAUNCHES AND TAKE-UP

3.3 RESALES AND SUB-SALES

3.4 SUPPLY IN THE PIPELINE

3.5 STOCK AND VACANCY

3.6 SINGAPORE POPULATION HIGHLIGHTS

4. MAJOR CRISES AND GOVERNMENT’S ROLE IN HOUSING

5. CONCLUSION

CHAPTER 3 – DATA AND METHODOLOGY1. RESEARCH DATA

2. RESEARCH METHODOLOGY

3. CORRELATIONAL RESEARCH

4. CONCLUSION

CHAPTER 4: FINDINGS AND ANALYSISPART 1: EFFECT OF GOVERNMENT POLICIES AND COOLING MEASURES

Round 1: Removal of Interest of Absorption Scheme (IAS) and Interest Only Mortgage (IOM)

1By: Kristine Kaye Cena, MBA


Round 2: LTV and Seller’s Stamp Duty

Round 3: Extension of the SSD periods

Round 4: Enhance SDD Rates and Periods / LTV

Round 5: Additional Buyer Stamp Duty

Round 6: Loan Tenure and LTV

Round 7: ABDS Rate Increase and LTV Further

Round 8: Total Debt Servicing Ratio (TDSR)

Round 9: Maximum Loan Term and Mortgage Service Ration

PART 2: CORRELATION ANALYSIS OF ECONOMIC FACTORSCorrelation Analysis between Property Price Index and Total Population of Singapore Citizens

Correlation Analysis between Property Price Index and Total Population of Permanent Residents

Correlation Analysis between Property Price Index and Total Population of Foreigners

Correlation Analysis between Property Price Index and Gross Monthly Income

Correlation Analysis between Property Price Index and Exchange Rate of SGD to USD

Correlation Analysis between Property Price Index and Prime Lending Rate

Correlation Analysis between Property Price Index and Consumer Price Index (CPI)

Correlation Analysis between Property Price Index and Vacancy Rate

Correlation Analysis between Property Price Index and Gross Domestic Product

PART 3: MULTIPLE REGRESSION AND PREDICTIVE MODEL OF PROPERTY PRICE INDEX

OF RESIDENTIAL PROPERTY

CONCLUSION

CHAPTER 5: SUMMARY AND CONCLUSION1. SUMMARY OF THE STUDY

2. ANSWERING THE RESEARCH QUESTIONS

2.1 What are the factors that drive the property price index (PPI) in Singapore?

2.2 What is the correlation of residential property price index to the economic forces that drives

the property market in Singapore?

2.3 What are the mitigation plans or recommendations to sustain the housing prices?

3. CONCLUSION

REFERENCES



CHAPTER 1: INTRODUCTION

1. INTRODUCTION

Singapore ranks first in the Asian region and ranks second in the world as the most competitive

economies, due to its outstanding performance measured by the global competitiveness index (World

Economic Forum, 2014). The country has one of the most liberal economy in the world. Singapore is also

known for its world-class technology and infrastructure, and has known for impressive place for work and

living. Over the years, Singapore has developed to be the regional center for trade, and has established

financial infrastructure to support the international trade activities.

Real estate industry is one of the key factors that drive the economic growth of Singapore. Having

a liberal economy, proficient administration, Singapore has appealed foreign investors because it

compromises benefits as business and finance center. Nevertheless, because it has limited land, the

country has become one of the most expensive properties in the world.

When Singapore attracted foreign investors and professionals, the value of real estate has begun

escalating, higher than prices in New York, London, and Paris. Due to the high prices in real estate, the

Singapore government had reacted by implementing regulations that will curb high prices and foreign

buying. Foreign buyers were charged sales tax, stamp duties, cap on total debt servicing ratio, etc.

Because of these cooling measures, the sales in 2014 declined substantially compared with previous

year, causing slump in property prices.

There are other factors that signal the weakening of residential property market in Singapore for

the next few years. The HBD resale is at worst condition due to tighter financial regulations, the primary

and secondary transaction volumes are declining, lower borrowing costs allows owners to hold on to their

units to take advantage the low interest expense, weakening purchasing power, surplus in residential

units in 2015-2016, ratio of housing to population, and government regulations. Based on these factors,

researchers and financial advisors have different views on what will greatly affect the residential property

market and what is expected in the next year.



2. RESEARCH BACKGROUND

Singapore’s housing prices show three distinct cycles for the periods from1975 to 2015. Three

peaks are clearly shown in 1983, 1996, and 2013. The price trend line suggests that the housing prices

increased gradually at an average of 0.19% since 1975. While short-range price volatility is evident,

prices increased approximately 15 times from 9.1 in 1975-Q4 to 141.6 in 2015-Q4. Many of the down

cycles in the last 40 years were triggered by the major economic crises in the markets such as economic

recession in 1985, Asian currency crises in 1997, and subprime crisis in 2007. Major events such as Gulf

war, 911 terrorist attack, SARS, Iraq war, and tsunami had influenced the rise and fall of property market

in Singapore.

The country’s economy grew rapidly from the late 70s to 80s, when political leadership opened

Singapore for foreign investments. In the late 70s, condominium living in Singapore was introduced and

new retail centers and hotels were built (Huat et al., 2016). The rapid growth of private residential market

has led to the founding of ERA Real Estate in 1982, a franchise of the US Company. The rapid growth

stopped when the country experienced recession in 1985 which led to a new approach to revitalize the

economy by recommending actions to stimulate property sector. During the 90s, change in the leadership

and major shifts in the economic strategy commenced. The 1991 Concept Plan were introduced which

has led to a faster and broader scale of developments. During this period, property prices were escalating

and eventually resulted to anti-speculative policies such as levy on capital gains for resold properties. In

1997, Singapore was hit by the Asian Financial Crisis. Combining the measured imposed in 1996 and the

impact of the financial crisis, the property prices and rentals crashed across all sectors. The government

responded to the crisis by introducing measures to stimulate the market, such as reduction in CPF

contribution rates, property taxes rebates, and corporate tax rebates. In 1998 to 2000s, Singapore

recovered from the Asian Financial Crisis and the country emerged as global financial hub. Part of the

recovery strategy was to establish the Real Estate Investment Trust (REIT). Since then, Singapore

became one of the leading REIT markets in Asia. However, it was during the millennium when the sub-

prime crisis in the US was became a full-blown global financial crisis (GFC). Though the Singapore

government mitigated the effect of GFC, the overall impact of the crisis hit real estate industry in the

country and made changes in the real estate market. Currently, the government intervened and



maintained low interest rates which stimulated real estate activities and resulted into a strong rebound in

Singapore economy in 2010. Property prices escalated across all sectors which led the government to

introduce cooling measures to curb unpredictable activities and mitigate exuberance. As new HDB flats

were introduced and promoted by the Singapore government, these units can be resold at much higher

prices and became a source of “fortuitous” wealth (Lum, 1996).

3. RESEARCH OBJECTIVES

The research will help assess and understand the fundamental factors driving the uncontained

curve in housing prices. By understanding the relationship between Singapore property prices and the

factors that drives the property market, the research can help understand the property market risks

associated with the movements in housing prices and identify mitigation plan to sustain housing prices.

The research also aims to discuss and analyze the effects of the cooling measures implemented by the

Singapore government in curbing high prices and foreign buying.

4. SIGNIFICANCE OF THE RESEARCH

The research will help to evaluate and understand the dynamic forces that drive the uncontained

curve in housing prices. By understanding its relationship to housing prices, the research can help

understand the property market risks associated with the movements in housing prices and identify

mitigation plan towards price sustainability.

5. RESEARCH QUESTIONS

The research aims to answer the following questions:

1. What are the factors that drive the property price index (PPI) in Singapore?

2. What is the correlation of PPI to the economic factors that drive the property market in

Singapore?

3. What are the mitigation plans or recommendations to sustain the housing prices?



6. STRUCTURE OF THE RESEARCH

The remaining part of this research is structured as follows.

Chapter 2: Literature review – This chapter aims to understand the current property market in Singapore

and critically review the current condition of residential property market.

Chapter 3: Methodology – Contains the research methods to be used in this research to achieve the

research objectives. This chapter will explain the descriptive analysis, correlation analysis, and

multivariate analysis to be used in the data analysis.

Chapter 4: Results, Analysis, and Discussion – This chapter shows the result of the descriptive analysis,

correlation analysis, and multivariate analysis and interpret the data as a result of the calculated data

analytics.

Chapter 5: Conclusions, Implications, and Recommendations – This chapter discusses the property

market risks associated with the unstable housing prices and the mitigation plan to sustain housing

prices.



CHAPTER 2: LITERATURE REVIEW

1. THE SINGAPORE HOUSING MARKET AND RELATED STUDIES

In a very small country like Singapore, the property prices are more expensive than other cities in

Europe and America. Because of its strategic location, attractive infrastructure, and efficient government,

many corporations are investing in Singapore. The demand for housing and commercial lease rapidly rise

and property prices escalated. More property developers construct residential and commercial properties

to satisfy the increasing demand. However, sometime in 2009, the property market declined and

researchers have different views about the weakening of the property sector in Singapore.

According to the current news and research studies, real estate industry in Singapore has been

shifting drastically for the past 5 years, and it is driven by numerous factors such as political, economic,

social, and technological. Different research organizations are predicting what will be the drift of

residential property market and prices in the next year. Some are forecasting upward real estate trend,

but some are saying that Singapore property price slump will remain.

Based on the research conducted by the DBS Group Research, the residential property in

Singapore is forecasted to continue in a very tight financing and regulatory environment in the year 2016

(DBS Group Research, 2015). The industry will still be on the depressed cycle and the price is anticipated

to drop by 12% to 15% in year 2016. The HDB resale market is worst, mainly due to the tighter financial

regulations by the Singapore government and selling restrictions. There is a significant collapse in primary

transaction, which was lower by 8,000 units in 2014, the softest property transactions since 2005. The low

interest rates triggered the fall in property prices and developers have launched fewer units to sell existing

inventory. The Ministry of National Development cut back on total sites available in government land

sales, giving 15 residential sites available for bidding.

Another research by the Jones Lang Lasalle said that among the classes of real estate in

Singapore, the price weakening is more apparent in residential properties than office, retail, and industrial

prices. However, in terms of mortgage payment as a percentage of household gross income, they are

estimating a healthy rate of 30% in 2016 to 2017. JLL is asserting that the curbed prices are primarily

triggered by policy changes in 2010, when the Singapore government has introduced cooling measures



(Jones Lang Lasalle, 2016). According to the Monetary Authority of Singapore, the prices should be 17%

higher, if the real estate has not been triggered by the policy changed. Policies include lower loan value,

stamp duties, cap on total debt servicing ratio, and land supply (Monetary Authority of Singapore, 2015).

However, according to Dr. Chua Yang Liang, Head of Research Southeast Asia and Singapore,

the Singapore residential property market will not be squeezed by the higher interest rates because this

has been mitigated by the policy for total debt servicing ratio (TDSR), instigated by the Monetary Authority

of Singapore. As per TDSR, banks are required to use 3.5% interest for residential property loans and

4.5% interest for non-residential property loans. Nevertheless, if the economy deteriorates further and if

the slump in the leasing market remains, the households will struggle to pay higher interest expense

(Singapore Business Review, 2016).

For the 3rd quarter of 1994 to the peak of 2nd quarter of 1996, the prices of the private residential

properties increased by 19.5%, which outpaced the growth of the average monthly earnings of all

industries at 5.92% growth. The deviation of the two factors has raised concerns on the affordability of the

private residential properties in the country (Ong, 1998 and 1999). In 1996, the government took

immediate action and implemented measures to cool the overheated market and stamp out speculative

activities (Ang, 1996). One of the programs implemented was the land sale program which was

announced in 2001(Tan, 2001) which aimed to tightened the supply and shore up sluggish property

market (Sing, 2001). After the announcement of the off-budget measures, the Minister for National

Development of Singapore commented that, “Off-budget measures to stabilize the property market will

not have an immediate effect, but will help boost confidence and help the real estate industry ride out the

downturn.” He further commented that the measures alone cannot help the real estate to recover, and

that the recovery will depend on the overall economy of Singapore (Tan, 2001).

Singapore government also implemented policies to change the loan to value of properties. It was

in year 1996 when the property value rose up to 80%, but this number has changed in 2009, 2011, 2012,

and was further tightened in 2013. In a study by Stansel and Mitchel in 195, they raised questions about

the effect and impact of the credit rationing and interest rates on housing, but they found no empirical

evidence of the correlation to the housing prices in US from 1963 to 1980. However, another study by

Burham (1972), Guttenburg (1961), and Meen (1990), concluded that the level of credit has significant



impact on the housing prices in US and UK. In another study by Tan in 1994, he emphasize that there is

a weak uni-directional relationship between property prices and interest rates as a result of economic

growth, housing policies, and high savings rate in Singapore.

The modeling of housing prices is very extensive in the developed housing markets in UK and

North America. Earlier studies of housing prices emphasized data coherency rather than theoretical

underpinning of the model(Smith, 1969, Neuburger and Nichol,1976; Mayes, 1979).In UK, the importance

of housing consumption and investment has been greatly influenced by the studies of Davidson, Srba,

Yeo, and Hendry in 1978 and Hendry in 1984. Hendry’s theory of equilibrium demand and supply

functions, the housing prices were derived as a function of household income, rental, interest rate, stock

of mortgage, tax rate, and number of household. Hendry’s model is consistent with “Catastrphe Theory”

and was extended by Dick in 1990 in UK. A study by Hsieh (1990) categorized the housing demand into

service and investment to study Taiwan’s property market. Topel and Rosen in 1988 used the investment-

based models to analyze the investment decisions of the firms. In their study, they used function of house

price and the vector of cost drivers, and the land is not considered in the factor of production. However, it

was in 1994 when Weaton and DiPasquale suggested a complete housing price model that includes

interest rates, cost of land, cost of construction, and housing stock or inventory.

The cost of capital concept was used by Breedon and Joyce (1992) the effects of the credit-

rationing in the housing prices in UK. In Cana, a study by Smith in 1969 aimed to identify the relationship

of factors that affects the Canadian housing market, by using house prices, vacancy rates, cost of

construction, land costs, mortgage, credit, availability of credit, as a function of housing starts. Whereas

he used income, price of commodities, stock of dwelling units, and credit as a function of housing prices.

In Singapore, there were various housing market studies done such as Ho and Tay’s (1993)

system of six simultaneous equations for demand and supply; Tu’s (2001) error correction term in the co-

integration model; Ong and Sing’s (2002) price discovery between private and public housing,; Sing and

Low’s (2001) characteristics of inflation hedging; and Sing’s (2001) study of demand, supply, and price

functions of condominium market. This study is to fill the gap by looking into the residential property

market of Singapore, including the public and private housing, the effect of the government intervention,

and the economic factors that drives the property prices.



2. OVERVIEW OF THE SINGAPORE ECONOMY AND PROPERTY MARKET

The Ministry of Trade and Industry Singapore is publishing a quarterly report which is the

Economic Survey of Singapore. The report is a summary of the economic performance of Singapore and

contains an analysis of the overall economy of different sectors, sources of economic growth, labor

market, investment, prices, trades, and outlook of economy (Ministry of Trade and Industry Singapore,

2016).

2.1 ECONOMIC PERFORMANCE

In the second quarter of 2016, the real GDP remained at 2.1% compared with previous quarter,

but increased compared with 1.7% in 2Q2015. The main drivers of the GDP growth in 2Q2016 are the

wholesale and retail trade at 0.4% point, transportation and storage at 0.2% point, and manufacturing

0.2% point. These top industries contributed a total of 0.8% points of GDP growth for this quarter. The

figure below shows an illustration of the GDP and the main drivers:


SOURCE: MINISTRY OF TRADE AND INDUSTRY


2.2 LABOUR MARKET

The overall unemployment rate went up from 1.9% in 1Q2016 to 2.1% in 2Q2016, as show in the

graph below. Both the resident and citizen unemployment rate increased in 2Q2016. There are about

60,300 Singapore citizens and 8,000 residents were unemployed in 2Q2016. The total number of

unemployed in 2Q2016 is higher than 1Q2016 at 60,400.


The total employment increased in 2Q2016 by 5,500 quarter-on-quarter. However, the

employment growth is much lower that 1Q2016 at 13,000 and in 2Q2015 at 9,700. The decline in the

employment was mainly a result of the employment decline in major industry sectors such as

manufacturing (-3.4), financial and insurance (-2.7), and wholesale & retail (-1.0). The first figure below

shows the quarterly change in the total employment and the second figure shows the change of

employment by industry.



2.3 CONSUMER PRICE INDEX

On a year-on-year basis, the consumer price index declined by -0.9% in 2Q2016, which was

about -0.8% in the previous quarter. Based on quarter-on-quarter seasonally-adjusted basis, the

consumer price index declined by -0.2% which about the same rate in the previous quarter. The graph

below shows the QOQ and YOY change in consumer price index.


In 2Q2016, education was the top contributor of the positive consumer price index inflation at

+3.2% on a year on year basis. Though the national examination fees for Singaporeans have been

waived, this is offset by the higher school fees. The food was the second top contributor of consumer

price index inflation, having +2.2% inflation in 2Q2016. The prices of food in hawkers and restaurants are

increasing, together with the prices of non-cooked foods such as fish and vegetables.

Additionally, the prices of household durables and services went up by +2.3%, together with the

increase in salary of domestic foreign workers, despite the lower concessionary levies. The cost of

recreation and culture was also increased by 1.2% due to higher cost of holiday travels, offsetting the

effect of cheaper cinema tickets. The figure below shows the summary of the percentage change in

consumer price index from 1Q2015 to 2Q2016.




2.4 STANDARD CLASSIFICATION OF DWELLING

Singapore has established a standard classification of dwelling for the purpose of collection of

data. It provides a common statistical framework that facilities data sharing and statistics. A dwelling

refers to a building or part of building intended for one or more persons as living quarters. Figure below

shows the chart of all type of dwelling which are classified as housing units or collective dwellings.

Housing units consists of Housing and Development Board (HDB) properties, Housing and Urban

Development Corporation (HUDC) properties, landed properties, executive condominiums (EC) & other

apartments, and other housing units. Collective dwellings are grouped according to their purpose such as

institutions, hotels, services apartments, and other lodging houses. This research is focused on the

housing prices of private residential properties such as HDB, HUDC, and EC (Department of Statistics

Singapore, 2012).



The HBD (Housing and Development Boards) are public housing units which are subsidized and

regulated by the Singapore government. The government aims to provide affordable houses to

Singaporeans which became popular that more than 80% of the Singaporeans live in public housing. To

accommodate different segment of the population, HBD offers various forms of flats such as 1-Room, 2-

Room, 3-Room, 4-Room, 5-Room, Executive, and Studio Apartments. Studio apartments usually

measures 36sqm or 45sqm. These units were catered for elderly residents who want to live independently

so these units have elderly-friendly features like non-slip tiles, bathroom bars for support, and pull-for-help

cords. The 2-room flat is personalized for smaller families which usually around 45sqm which includes

living room, kitchen, bathroom, and storage room. The 3-room flat is a one-room upgrade of 2-room flats

which has a size of 60sqm to 65sqm which are tailored for families with children. The 4-room flat is ideal

for young families which offer more convenient space which measures about 90sqm. This room is

basically a 3-room plus one. The 5-room flat covers 110sqm which has an additional dining area.



Executive flats measures around 130sqm which caters more space for a study room, TV corner, and

sometimes with balcony. HDUC flats were originally built in 70s to 80s but were phased out in 1987.

Private housings were also popularized in the country as a result of strong economic growth

couples with the invasion of well-heeled foreigners and relatively limited supply of land. These factors has

led to the appreciation of private residential properties and making Singapore as hotspots for real estate

investments. The private residential property are simply classified as landed or

apartments/condominiums. Condominiums are the most popular units for private housing. These units are

more lavish and pleasant that public housing or HDB flats. Condominiums usually have amenities and

facilities such as pools, security, gym, tennis courts, gardens, function halls, clubhouse, etc. Apartments

are smaller development but similar to condominiums. These are smaller property developments and

have less recreational facilities, but more affordable than landed or condominium properties. Landed

property is regarded as the top tier of socio-economic level in Singapore. These houses are high

maintenance, expensive, incomparable size, and spacious living than public housing, condominiums, and

apartments. Terrace houses are landed properties which are part of a row of houses which are similar

and joined together by common boundary. Bungalow house are smaller and caters to the near well-offs

with a minimum size of 400sqm. The goof class bungalow are specific to the near-top-tier of well-offs

having a minimum of 400sqm, this is one of the larger estates among the landed properties.

2.5 MARKET STRUCTURE OF SINGAPORE HOUSING MARKET

Singapore has a unique market structure for the residential property markets because of

subsidized housing and laissez-faire private housing market. The government through its Housing

Development Board (HDB) builds and sells housing flats to eligible Singaporean citizens whose gross

income is not exceeding S$12,000 effective 2015. About 80% of the Singapore population occupied HDB

and 90% of which owns the flat. In 1964, the government attracted migrant population to settle families in

Singapore through home ownership. But today, only citizen families are allowed to own properties

because of Build to Order (BTO) scheme. Since then, Singapore continuously redevelops and adapts

based on the changing demography and socio-economic status of the market.



According to the latest data, the average number of persons per household size of 34 is 3.39.

Home ownership rate is 90.8% of the total number of residential households. About 80% of the home

owners dwell in HDB, 13.9% in condominiums, and 5.6% in landed properties. The total HDB dwellings is

further categorized as to the type of room, of which, 4-room flats are the most popular at 32.0%, followed

by 5-room flats at 24.1%, 3-room flats at 18.2%, and 1 to 2-room flats at 5.6%. The figure below shows

the number of resident households distributed as to the type of dwelling:

Households & Housing

Number of Resident Households '000 2015 1,225.30 2.1 1,200.00 2.2

Average Household Size 34/ Persons 2015 3.39 na 3.43 na

Home Ownership Rate 34/ % 2015 90.8 na 90.3 na

Resident Households by Type of Dwelling

%

Total 35/ 2015 100 na 100 na

Total HDB Dwellings 36/ 2015 80.1 na 80.4 na

HDB 1- & 2-Room Flats 37/ 2015 5.6 na 5.3 na

HDB 3-Room Flats 2015 18.2 na 18.3 na

HDB 4-Room Flats 2015 32 na 32.2 na

HDB 5-Room & Executive Flats 2015 24.1 na 24.4 na

Condominiums & Other Apartments2015 13.9 na 13.5 na

Landed Properties 2015 5.6 na 5.8 na

% Change (Y-o-Y)

Latest Data

% Change (Y-o-Y) 1/

Previous Period Data

Latest Period

SOURCE: DEPARTMENT OF STATISTICS

2.6 SINGAPORE DISTRICT CODE DEMARCATION

The property price index (PPI) reported by the URA in is classified as landed or non-landed. Non

landed properties are further grouped according to location such as Core Central Region (CCR), Rest of

Central Region (RCR), and Outside Central Region (OCR). The CCR refers to district 9, 10, 11,

downtown core, and Sentosa while RCR refers to other areas in CCR. The OCR refers to the remaining

16 districts in the 4 regions such as west, east, north, and north east. The figure below shows the list of

Singapore District Code and the corresponding area or location in Singapore.



SOURCE: URBAN REDEVELOPMENT AUTHORITY

3. THE RESIDENTIAL MARKET AS OF 3Q2016

The Urban Redevelopment Authority (URA) in Singapore has released the real estate statistics

for 3Q2016. The URA has shown the current status of the residential property market in Singapore at a

glance. The figure below shows the summary of the private residential comparing 2Q2016 and 3Q2016.

Both the price index and rental index decreased from last quarter at -1.5% and -1.2%, respectively. The

pipeline supply quarter on quarter also decreased by -7.5%. However, the vacancy rate for this quarter

decreased from 8.9% percent last quarter to 8.7% this quarter.




3.1 PRICES AND RENTALS

As of 3Q2016, the property price index is 137.9 and the prices of private residential property

declined by 1.5% in 3Q2016, compared with the decline in the previous quarter at 0.4%. The graph below

shows the property price index of the whole island from 4Q2011 to 3Q2016.

The price of the properties located in the Core Central Region (CCR) declined by 1.9%

comparing it with previous quarter which increased by 0.3%. In the Rest of Central Region (RCR), prices

decreased by 1.0% for both areas, compared with previous quarter at 0.2% decrease. Outside Central

Region (OCR) also decreased by 1.0% compared with previous quarter at 0.5% decrease.

3.2 LAUNCHES AND TAKE-UP

As of 3Q2016, real estate developers launched a total of 1,609 uncompleted units which are for

sale, compared with a higher number of units last quarter at 2,271. This means that numbers of units

launched were decreased this quarter by -29%.




The developers sold a total of 1,981 residential units in 3Q2016, compared with higher units in

the previous quarter at 2,256 units. The graph below shows the number of units that are launched per

quarter and the number of units sold per quarter. Evidently, the highest number of launched and sold

units happened in 1Q2012.


3.3 RESALES AND SUB-SALES

As of 3Q2016, there are 2.477 resale units, an increase compared to 2,140 units’ transaction last

quarter. The number of transactions contributed 53.9% of total sale transaction for 3Q2016. This rate is

higher than the previous quarter at 47.0%.

Total number of sub-sale transactions in 3Q2016 is 138 units, a decrease compared with 154

units in the previous quarter. The 138 units represents 3.0% of total sale transactions in 3Q2016 and 154

units represents 3.4% of total transactions in the previous quarter.

The graph below shows the total number of resale transactions and sub-sale transactions from

4Q2011 to 3Q2016.



3.4 SUPPLY IN THE PIPELINE

As of 3Q2016, total number of uncompleted private residential units or supply in the pipeline is

43,693 units and 47,250 units in the previous quarter. At the end of the 3Q2016, total number of unsold

units is 20,577. Adding the supply in the pipeline which is 11,054 units, total units in the pipeline is

54,747. Total unsold units in the EC pipeline are 4,634. Overall, there are 25,211 unsold units including

the EC units.


In 4Q2016, expected total number of completed units including the EC will be 7,077 units. Based

on the completion dates reported by the real estate developers, by the end of 2017, total expected

number of completed units is 16,167 units. And in year 2018, 15,373 units will be completed; 8,666 units




in year 2019; 6,908 units in year 2020, and 556 units after year 2020. The graph below shows the number

of units per year:

3.5 STOCK AND VACANCY

The graph below shows the change in the number of occupied units, the change in available

units, and the vacancy rate. At a glance, the change in the occupied units is very volatile, though it is

evident that the number of units increased by 4,919 units in 3Q2016 compared with the increase in

previous quarter at 8,425 units.

Moreover, the stock of occupied units goes up by 5,393 units in 3Q2016, compared with only

3.024 units increase in previous quarter, which results to a declining vacancy rate of 8.7% in 3Q2016

compared with 8.9% in the previous quarter.





3.6 SINGAPORE POPULATION HIGHIGHTS

The table below shows the Singapore Population Highlights as of June 2015, published by the

Department of Statistics Singapore. Singapore has a total population of 5.54 M, of which 70% are

citizens, 10% are permanent resident, and 20% are non-residents. The population growth of the

residents is seen to be declining since 2012 at 2.5% down to only 1.2% in 2015 while the population

growth of non-residents also dropped from 7.2% in 2012 down to 2.1% in 2015. Though the population

growth is decreasing, the number of resident households is minimally increasing by 2% comparing 2014

versus 2015. Further to the statistics below, it appears that resident household are 80% dwelling in

HDB,14% in EC, and only 5.6% in landed properties.

Singapore Population Highlights

Total Population:Population Growth of

Residents:

Population Growth of Non-

Residents:

Citizens: 3.90 M 2012: 2.5% 2012: 7.2%

Permanent Residents: 0.53 M 2013: 1.6% 2013: 4.0%

Non Residents: 1.63 M 2014: 1.3% 2014: 2.9%

Total: 5.54 M 2015: 1.2% 2015: 2.1%

Average Household Size in

2015:

Number of Resident

Households

Resident Households by

Selected Type of Dwelling:

3.39 Persons 2012: 1,152,000 2014: 80.4% HDB, 13.5%

Condo, 5.8% Landed2013: 1,174,500

2014: 1,200,000 2015: 80.1% HDB, 13.9%

Condo, 5.6% Landed2015: 1,225,000

Source: http://www.singstat.gov.sg/infographics/population/population.html

As of 2015, a total of 20% or 1.63 M of the Singapore population is non-residents. In 2012, the

population growth of non-residents soar as high as 7.2%, but after the Singapore implemented new

migration policies, the population dropped to 2.1 in 2015. According to the news article published by in

Straits Times (Whang, 2015), about 4% of the sale of non-landed private homes are contributed by the


http://www.singstat.gov.sg/infographics/population/population.html


non-residents. The 4% sale in 2015 is considered very low comparing it with 9.2% in 2013 and 10% in

2014. The Chinese nationalities are holding back because of Chinese economy slowdown and weakening

of Yuan. Indonesia market is also slowing down, as real estate in Indonesia is apparently performing

better than Singapore.

4. MAJOR CRISES AND GOVERNMENT’S ROLE IN HOUSING

In 1990, the country saw a change in leadership and created major shifts in the economic

strategies. Some of the major changes in the real estate are the regionalization of government-linked

companies and the need to restructure industrial sector arises. During 1991, the Concept Plan was

introduced to initiate decentralization of commercial activities and to spread it to other parts of Singapore

(Huat et al., 2016). During this period, the residential property market escalated specifically during

collective sale fever where residential owners sold their properties to real estate developers to redevelop

the properties. The phenomenon has led to a significant increase in HDB resale transaction volume and

prices alongside with the transaction regulations in 1993. Since then, housing prices in Singapore was

very volatile. Based on the Urban Development Authority (URA), the lowest property index in Singapore

was in Q4 1990 at 40.3 and the highest was in Q3 2013 at 154.6 indicating that housing prices went up as

high as 284% increase.

However, the longest quarter on quarter growth of the property prices was during 1990 to 1996.

The property price index (PPI) escalated from 40.3 in Q4 1990 to 129.7 in Q2 1996. In 1995, the

Singapore government imposed the executive condominium (EC), as a new form of hybrid public housing

policy. EC are 99-leasehold condominium units sold to eligible Singaporeans only within the income

ceiling set by the government at S$ 10,000. However, the income ceiling was later increase to S$12,000

in 2011. EC and HDB are both subject to the five-year minimum occupation period (MOP) which means

that they can only be transferred or resell in the open market after ten years. In 1998, the Studio

Apartments (SA) was introduced to meet the housing needs of elderly and low-income buyers. Recently,

HDB re-introduced 2-bedrooms and 3-beroom flats for lower-income groups. Additional subsidies were

given to make sure that 90% of the population can afford HDB flats. The accelerating spiral of property



prices had led to the introduction of theoretical measures which includes capital gains levy for resale of

properties within a period of three years.

It was only until 1997 that the prices went down, when the country was struck by the Asian

Financial Crisis (AFC). The crisis has started in Thailand and spread to the most countries in Southeast

Asia. Singapore’s economy was deeply hurt by the crises and the housing prices fell across all sectors

(Huat et al., 2016). According to the Department of Statistics Singapore, the PPI fell down at 71.5 in Q4

1998 from 129.7 in Q2 1996. During this period, Singapore government intervened to control the property

market by imposing anti-speculation measures to cool the overheated market in 1996. The government

imposed the 80% loan-to-value (LTV) limit on bank loan, which was previously 90% property valuations

provided by banks to purchase properties. The government also stimulated the market by reducing CPF

contributions, corporate taxes, levies on capital gain tax, and seller’s stamp duty for properties sold within

three years of purchase. The private housing supply also increased from 6,000 units up to 8,000 units as

part of the anti-speculation measures, which was alleged to contribute significantly to the decline in

property prices during the AFC until 1998.

Just when the property market was starting to stabilize in 2001 to 2006, the second housing

bubble started to form. However, it was interrupted by the Subprime Crisis and Lehman Brothers

bankruptcy in 2008, which resulted to the Global Financial Crisis (GFC). The GFC had a short-lived effect

in Singapore property market and in 2008 to 2009; the PPI took a v-shaped trend capping the lowest rate

in Q2 2009 at 95.3.

After GFC, the property prices in Singapore was starting to show a robust build up again in 2010

and the government had to intervene to keep interest rates at artificially low rates. The short downturn

was immediately followed by economic rebound in 2010, thus, property prices escalated rapidly. To

mitigate the speculative activities and exuberance, the nine rounds of anti-speculation measures were

introduced. Some of the policies implemented include macro-prudential tools such as LTV limits, total

debt servicing ratio (TDSR), seller’s stamp duty (SSD) and additional buyer’s stamp duty (ABDS). The

figure below summarizes the policies and measures imposed by the government:



Sep-2009 Round 1 Removal of Interest of Absorption Scheme (IOS) and Interest Only Mortgage (IOM)

Feb-2009 Round 2 LTV and Seller's Stamp Duty

Aug-2010 Round 3 Extension of SSD periods

Jan-2011 Round 4 Enhance SSD rate and periods / LTV

Dec-2011 Round 5 Additional Buyer's Stamp

Oct-2012 Round 6 Loan tenure and LTV

Jan-2013 Round 7 ABSD rate increase and LTV further tightened

Round 8 Total Debt servicing ration (TDSR)

Aug-2013 Round 9 Maximum loan term and Mortgage Servicing Ratio

Year Anti-speculation Measures

The policies and measures imposed by the Singapore government have an enormous impact in

the HDB resale price changes (Yong et al., 2002). However, there are studies that indicate the private

housing prices dynamics are extremely profound to fluctuations to public housing policies (Sing et al.,

2007). There are price discovery effects between price and public housing markets in the country (Ong et

al., 2002).

5. CONCLUSION

The first part of this chapter gave us an overview of the Singapore real estate industry and how a

small country developed the property industry over a short period of time. Several studies and property

outlook were also discussed and opposing opinions were deliberately expounded. This section also

explained the evolution of housing price model in US and UK, and discussed the studies made in

Singapore housing property.

The second part of this chapter discussed the overview of the Singapore economy where brief

analyses of the various sectors were discussed. To understand more about the residential property, the

standard classification of dwelling were illustrated and each type of dwelling were described how it cater

to specific type of family or resident. The market structure of Singapore residential property industry were

also discussed by presenting the statistics for housing and holdings, which reveals that 90.8% is the

ownership rate and 80.1% of which dwell in HDB flats. This section also explained the Singapore district

code demarcation.



This chapter also explained the current real estate market for 3Q2016 based on the quarterly

report published by the Urban Redevelopment Authority of Singapore. This report showed us the currently

trend on a quarter-on-quarter basis of property price index, property price index, the launches and take

up, resales and sub-sales, supply in the pipeline, and the stock and vacancy. Additionally, the Singapore

population highlights were also presented to briefly understand the local and foreign demand of the

Singapore property market.

Lastly, this chapter also explained the major crises that affect the property industry and how the

Singapore government created major shifts in economic strategies to mitigate the impact of the crises.

Specifically, the nine rounds of anti-speculation measures were illustrated and discussed thoroughly.



CHAPTER 3 – DATA AND METHODOLOGY

1. RESEARCH DATA

The research uses secondary data that are publicly available. Source of data could be from

various publications of the Singapore government, publications by foreign governments, technical and

trade journals, reports of various organizations relative to study, existing reports and research by

scholars, universities, economists and specialists.

Specifically, secondary data will come from:

a. Published data and statics from the statutory boards of the Singapore government such as

Building and Construction Authority (BCA), Housing and Development Board (HDB), Monetary Authority

of Singapore (MAS), Singapore Land Authority (SLA), Urban Redevelopment Authority (URA), and

publications from Ministry of Finance (MOF)

b. Existing research studies from Nanyang Polytechnic (NYP), Ngee Ann Polytechnic (NP),

Singapore Polytechnic (SP),

c. Access to the National Library Board (NLB), WOFL online library

d. Department of Statistics (DOS) – which provides reliable and timely statistics about Singapore

e. News articles from the most reputable news sources in the world such as The Economist, BBC,

The Wall street Journal, Bloomberg, CNN, and Yahoo News.

Collection of the published secondary data is processed and analyzed. The types of analysis to

be used are descriptive analysis, correlation analysis, and multivariate analysis. Descriptive analysis aims

to describe set of data and summarize it in a significant way to analyze patterns and trends. Correlation is

a statistical measure which defines the degree to which variables fluctuate, which could either be positive

correlation or negative correlation. Multivariable analysis can use (a) multiple regression analysis (b)

multiple discriminant analysis (c) multivariate analysis (d) canonical analysis, or (e) inferential analysis.

The research will use the applicable type of analysis depending on the data collected. After in-depth

analysis of the secondary data, the data is construed by developing conclusions and explaining their

implication. The conclusion and interpretation can only be made after all relevant factors are considered.



Specifically, the researcher will use the historical data published by the Urban Redevelopment

Authority of Singapore publicly available at the government’s website. The calculation of quantitative

analysis will use the statistics from 2009 to 2015 on an annual basis for a period of 7 years. Research

data that will be used are the residential property price index, rental price index, home ownership rate,

number of HDW dwellings, annual population growth, annual population growth of Singapore citizens,

annual growth population of permanent residents, annual growth population of non-residents, gross

monthly income of households, exchange rate of SGD to USD, prime lending rate, consumer price index,

vacancy rate, supply of non-landed properties, and GDP at 2010 market price. Additionally, research data

will also use data statistics published by the Department of Statistics Singapore to get the historical data

about the number of household and Singapore population.

These are the driving forces that we will consider to understand the correlation with the country’s

residential property price index. The change in the vacancy rate of the residential properties could entail

the balance of supply and demand, hence, could indicate weather housing market is in equilibrium. For

example, when the supply of housing units are lower than the current property demand, the overage or

excess will be absorbed by the inferior or lower level of dwelling units. The private home prices in

Singapore weaken for 11th straight quarter and the vacancy rate hits 16-year high, jumping 1.4% points in

the quarter to 8.9%, the highest since 2Q2000 (Whang, 2016).

The number of supply or housing units is also a very important variable to determine and

understand the supply. Additionally, the affordability of residential properties are also driven by higher

GDP or per-capital income and the labor market.

Furthermore, this research will also perform descriptive analysis of the various government

policies and measures implemented by the government that played significant influence in the country’s

residential property market.

2. RESEARCH METHODOLOGY

Specifically, the research will use the following methods:

1. Descriptive Analysis

a. Effect of Government Policies and Cooling Measures



2. Quantitative Analysis

a. Correlation of residential property price index to various residential property factors such

as rental price index, home ownership rate, number of HDW dwellings, annual population

growth, annual population growth of Singapore citizens, annual growth population of

permanent residents, annual growth population of non-residents, gross monthly income

of households, exchange rate of SGD to USD, prime lending rate, consumer price index,

vacancy rate, supply of non-landed properties, and GDP at 2010 market price.

b. Multiple regression to calculate the future residential property price index given the

forecasted variable data.

According to the structure presented above, the first section of the next chapter will deliberately

present a descriptive analysis of the major economic crises globally and in Asia pacific region, and how

the residential property prices moves along with these major events. Along with this, various government

policies and cooling measures will also be discussed and illustrated to compare these factors with the

residential property price index in Singapore.

Aside from the descriptive analysis mentioned above, this research will also attempt to answer

the research questions by performing quantitative analysis using two statistical data analysis such as

correlation coefficient and the multiple regression.

3. CORRELATIONAL RESEARCH

In 1985, Karl Pearson introduced the correlation techniques at Royal Society meeting in London.

Using Darwin’s evolution and Galton’s heredity, he illustrated the correlation statistical model. Eventually,

the correlation techniques were improved over time until complex regression analysis of multiple

variables was made possible through computers.

A correlational study is a quantitative method of research which uses more than two variables

from the same group or classification. The objective is to determine if there is a relationship between the

two variables. It is a research method that examines how variables relate in rea world, without any

attempt by the researcher to alter or change them (Hidalgo, et al., 2014).



The data analysis and calculation can be done in Excel or a Statistical Software Program

(SPSS/NCSS/PASS) for personal computers also calculates correlations. In this research, the calculation

of correlation analysis between residential property prices will be performed using the Microsoft Excel.

The property price index will come from the publicly available data published by the Urban

Redevelopment Authority (URA) of Singapore in www.ura.gov.sg and other factors will come from the

Department of Statistics Singapore in www.singstat.gov.sg. The calculation will cover the past 10 years

from 2005 to 2015. After calculating the correlation using the historical data, multiple regression analysis

can determine the forecasted property prices. The formula will be defined as a basis of the future

outcome. Future numbers and predictions that are publicly available will be used to calculate the future

residential property price index. For instance, given the forecasted variables, we will predict the future

price index using the correlation coefficient and linear regression that we will calculate previously.

The results of the calculation can either positive correlation, negative correlation, or no

correlation. In a positive correlation, the relationship of the two variables is directly proportional which

means that as the value of one increases the value of the other also increases, or if the value of one

decreases the value of the other also decreases. In a negative correlation, the relationship is inversely

proportional, which means that as the value of one decreases the value of the other increases, or if the

value of one increases the value of the other decreases. Hence, the variables move in opposite direction.

If the result if zero, it means that there is no correlation (Taylor, 1990). The graph below shows the three

types of correlation:

The purpose of the correlational studies is to measure how variables are related using the data

that already exists. The correlation allows us to make predictions about one variable based on the given


http://www.singstat.gov.sg/

http://www.ura.gov.sg/


assumptions of the other variable, and to examine the possible cause and effect relationships (Hidalgo, et

al., 2014).

Other correlational technique is the multiple regressions that enable researchers to determine the

correlation between a criterion variable and the best of combination of predictor variable. The coefficient

of the multiple regression is symbolized by R that indicates the strength of correlation between

combination of predictor variables and criterion variables. This research will use the following formula:

Multiple regression is an extension of a linear equation which we will determine when we

calculate the correlation between price index and various driving forces. After identifying the multiple

variables that relate to dependent variable (price index), the result will be used to make more accurate

predictions. This method will be used to predict or forecast the value of price index based on the value of

two or more variables (Cohen, J. et al., 2013).

4. CONCLUSION

This chapter specifically explained the sources of research data, which will come from publicly

available statistics published by the Singapore government through its website. Main source of data are

the Urban Redevelopment Authority, Department of Statistics Singapore, and the Ministry of Trade and

Industry. These government agencies publish a quarterly report of the historical and forecasted data that

is needed to calculate the correlation coefficient and establish a formula for multiple regression for future

prediction.

Specifically, the structure of content of the next chapter was illustrated which shows that the first

part of the next chapter will be a descriptive analysis of the effects of the major crises in the Singapore

property and the effect of the cooling measures implemented by the Singapore government. The second

part of the next chapter will explain the result of the correlation between the residential property price

index to the property market factors and the economic factors. The third part will be the calculation of the

multiple regression to predict the future property price index based formula of correlation.



CHAPTER 4: FINDINGS AND ANALYSIS

PART 1: EFFECT OF GOVERNMENT POLICIES AND COOLING MEASURES

After the Global Financial Crises in 2008 to 2009, the property price index of residential

properties in Singapore showed a V-shaped trend capping the lowest rate in 2009 at 95.3 PPI. In year

2010, the property price in Singapore is becoming robust again, that’s when the government had to

arbitrate to keep the interest rates low. The short recession was instantly followed by economic recovery

in and the property prices rocketed briskly. To diminish the theoretical activities in the industry, the nine

rounds of anti-speculation methods were introduced. Below is the graph of the nine rounds plotted along

with the property price index trend from year 2009 to year 2015.

Year Anti-speculation Measures

Sep-2009 Round 1 Removal of Interest of Absorption Scheme (IOS) and InterestOnly Mortgage (IOM)

Feb-2009 Round 2 LTV and Seller's Stamp Duty

Aug-2010 Round 3 Extension of SSD periods

Jan-2011 Round 4 Enhance SSD rate and periods / LTV

Dec-2011 Round 5 Additional Buyer's Stamp

Oct-2012 Round 6 Loan tenure and LTV

Jan-2013 Round 7 ABSD rate increase and LTV further tightened

Jan-2013 Round 8 Total Debt servicing ration (TDSR)

Aug-2013 Round 9 Maximum loan term and Mortgage Servicing Ratio

SOURCE: URA, HDB, DBS BANK



Round 1: Removal of Interest of Absorption Scheme (IAS) and Interest Only Mortgage (IOM)

In 14th of September 2009, the interest of absorption scheme or IAS was removed. IAS is a

housing loan payment scheme offered by the developer and banks to purchase uncompleted units. It

allowed the buyer to stop or defer payments or installments until the units are completed, provided that

the buyer paid the upfront down payment. Under the IAS scheme, the buyer can avail the interest only

mortgage or IOM where the borrower can pay only the interest rate for a period of time without having to

pay the principal until the issuance of Temporary Occupation Permit or TOP. If an IOM is offered under an

IAS scheme, the developer pays the interest instead of the borrower (URA, 2009).

The IAS scheme offered by the Singapore government was abolished in 2009. This scheme

offered a very low interest rate compared with the standard payment scheme. But when the interest free

period is over, subsequent payments become higher when servicing of the principal resumes. When the

scheme was eradicated, the PPI suddenly rose from 95.3 in 2Q 2019 to 118.4 in 4Q 2009. The buyers

need to avail the regular or standard schemes which are subject to the government policies and

requirements.

Round 2: LTV and Seller’s Stamp Duty

In 20th of February 2010, the government implemented the Sellers Stamp Duty (SSD) for private

residential properties excluding the HDB flats. The SSD is 1% for the first $180,000, 2% for the next

$180,000 and 3% of the balance for properties bought and sold within one year. During this time, the loan

to valuation also decreased from 90% to 80% for private properties such as executive condominiums,

HUDC, HDB and DBSS flats. But the loans granted by the HDB to HDB flats were sustained at 90%.

During the implementation of these policies, the prices of the properties soar up to 155.1 at the

end of 2010. The SSD led the increase of property prices since the sellers need to increase the prices to

cover for the SSD. Additionally, the increase of loanable amount from 90% to 80% required the buyers to

increase down payment of the property.

Round 3: Extension of the SSD periods



In 30th of August 2010, the government extended the holding period for SSD where sellers are

required to pay 3% stamp duty for balance of property sold in the same period within three years which

was previously one year. Additionally, changes in the loan to valuation were also implemented. Buyers

who have more than one housing loan at the time of new acquisition were required to pay cash from 5%

to 10% of valuation limit. The loan to value was also decreased from 80% to 70% for buyers who have

multiple property mortgages. During this period, policies were also implemented for HDB acquisition.

Household with $8,000 to $10,000 were allowed to buy DBSS with a grant of $30,000. The project

completion of BTO flats were also shortened to 2.5 years, thus increasing the supply of units to 22,000.

The change in policy has led the sellers to hold on to their properties for at least three years,

otherwise, they will have to increase their mark up to cover for the stamp duty. The $30,000 grant offered

by the government initiated acquisition of HDB flats but at the same time, supply of HDB units were

increased which may led the accumulative trend of property prices until 2011 where prices went up to

147.4 for residential properties.

Round 4: Enhance SDD Rates and Periods / LTV

In 11th of January 2011, the government increased again the SSD period from three to four years,

and increased the SSD rates to 16%, 12%, 8%, and 4%. Lower LTV was implemented for non-individual

buyers at 50% and lower LTV for individual buyers from 70% to 60%. However, buyers who borrow

housing loan for the first time were retained at 80% LTV. During the implementation of the change in

policy, the prices continued to be in upward trend at 142.3 PPI at the beginning of January 2011 up to

145.1 during the second half of the year.

Round 5: Additional Buyer Stamp Duty

The government introduced Additional Buyers Stamp Duty or ABDS requiring the buyers to pay

10% if foreigners and non-individual buyers, 3% for Permanent Residents who are buying subsequent

properties, and 3% for Singaporeans who buy subsequent properties. This policy was introduced in 8 th of

December 2011 when the PPI was already 147.4. Subsequently, the prices continued to be in upward

trend until 3rd quarter of 2012 when the URA issues new guidelines on shoebox housing which defines the



allowable maximum number of units and sizes, depending on the location. The PPI was 148.8 during this

period.

Round 6: Loan Tenure and LTV

In October 2012, the loan tenure and LTV were again revised. The government has implemented

LTV restrictions where mortgage loan tenures were capped at 35 years for individuals and non-individual

buyers. The loan-to-valuation for non-individuals were further lowered down to 40% and subsequent loan

borrowers were also lowered to 40% for loans that are more than 29 years or extend beyond retirement

ages of 65. Furthermore, the LTV for first time borrowers were lowered at 60%. In November 2012, the

HDB increase the supply of BTO flats to 27,084 units and planned to release additional 20,000 in the

following year. The year ended having PPI of 151.5.

Round 7: ABDS Rate Increase and LTV Further

The seventh round made a lot of changes in the existing policies for ABDS and LTV. Most of the

changes made vary for Singaporean Citizens, Permanent Residents, and Foreigners. The ABSD for

private residential purchases by Singaporean buyers is 0% for first purchase, 0%-7% for second

purchase, and 3%-10% for third purchase. For private residential purchases by Permanent Residents,

ABDS is 0% for first purchase and 3%-10% for second purchase. . For private residential purchases by

Foreigners and non-individuals, the first and subsequent purchases are charged with 10% to 15% ABDS.

Changes in the LTV for private properties were also implemented during this period. The first

housing loan have LTV of 80% or 60% if the loan tenure is more than 30 years or if the borrow extends

past the retirement age of 65. For the second housing loan, LTV was lowered form 60% to 50%.

However, LTV is 30% to 40% if loan tenure is more than 30 years or if borrower is older than the 65 years

retirement age. For the third and subsequent housing loan, the LTV is lowered from 60% to 40%, or 20%

if the loan tenure is more than 30 years or if the borrow extends past the retirement age of 65.

Additionally, the cash down payment for private property acquisition were higher. Purchase of first

property requires 5% for LTV of 80% and 10% for LTV of 60%. Purchase of second and subsequent



properties require 10% to 25% cash down payment, and non-individual buyers were required 20% to 40%

cash down payment.

There were also changes in the policies for HDB. The mortgage service ratio for housing loans

were capped at 30% of the borrower’s gross monthly income or 35% for HDB loans which was previously

40%. Permanent residents (PR) who own HDB cannot sublease the whole unit, and PR who owns HDB

unit must sell their flat (private) within six months of completion from previous concurrent ownership of

minimum occupation of fulfilled from July 2013.

In 1st of February 2013, the Silver Housing Bonus (SHB) and the Lease Buyback Scheme (LBS)

were implemented by HDB. Under this scheme, the CPF top-up requirement has been lowered to

$60,000 per household and $20,000 bonus will be given fully in cash. Moreover, LBS also include lower

CPF top-up requirements and relax the criteria for eligibility to allow more elderly to qualify.

Foreigners were charged higher ABDS as high as 15%, which made it more difficult to acquire

properties in Singapore. The changes in the loan to valuation evidently encourages Singaporeans to

acquire property at earlier age, but, at the same time, discourages them to buy more than one properties.

Buyers who purchase more properties get a lower LTV. Policies for the cash down payments imply that

the lower the LTV, the higher the cash down payment is required. This policy further strengthens the

restrictions in buying more than one properties. The changes in the HBD policies denote that the

government boosts HDB market by lowering the cap for mortgage servicing ratio from 40% to 30%. This

was supported by SHB and LBS schemes which lowered the top-up requirement for CPF plus bonus, and

criteria for eligibility were relaxed to qualify elderly. Implementation of these policies at this period

resulted to 154.0 PPI during the second half of 2013.

Round 8: Total Debt Servicing Ratio (TDSR)

Just several months after the changes in the ABDS, LTV, HDB policies, CPF funding, SHB and

LBS schemes, the Singapore government introduced a new total debt servicing ratio (TDSR). The

maximum total debt limit of 60% is calculated by considering the monthly repayment of the acquired

property, together with the monthly repayment of all outstanding debt of the borrower. To calculate the

TDSR, the policy is to apply 3.5% interest rate for housing loans and 4.5% for non-housing loans, or the



prevailing market interest rate. Additionally, 30% haircut should be applied to all variable and rental

income as well as the amortization of financial assets should also be considered in the calculation. The

enhanced policy for mortgage service ratio boosted the market for HDB flats, making it more affordable

and easier to acquire. However, the PPI continued to increase minimally from 154.0 in the 3 rd quarter to

154.6 in the 3rd quarter of 2013.

Round 9: Maximum Loan Term and Mortgage Service Ration

Before the year ends in 2013, another change in the HDB polices were implemented. To make

the HDB more affordable, the government introduced the Special Housing Grant (SHG) up to $20,000 for

4-room and smaller flats, and has extended the SHG to households with earnings up t $6,500 to include

middle income families. Furthermore, this grant was also extended to singles that have $3,250 income.

Under the Multi Generation Priority Scheme, older parents can opt for 3-room flats. The HDB loans were

reduced from maximum of 30 years to 25 years with repayment capped at 30% of monthly gross income.

Bank loans for HDB were also lowered from 35 years to 30 years including DBSS. New loans with more

than 25 years of tenures and up to 30 years will be covered by tighter LV limits and new PR’s have to wait

three years to buy HDB resale units.

The government policies clearly signifies prioritization of Singaporeans to buy a more affordable

HDB units, by giving more government grants, by lowering the required gross monthly income, lowering

loan tenure for HDB loans and bank loans, and DE prioritization of property acquisition by PR’s. At the

end of this year, the PPI for resident property were closed at 153. 3 after which, the PPI has started to

slightly decrease having 147.0 in 2014 and 141.6 in 2015.



PART 2: CORRELATION ANALYSIS OF ECONOMIC FACTORS

Correlation Analysis between Property Price Index and Total Population of Singapore Citizens

Since 2009, the year-on-year percentage growth of Singapore Citizen is declining but the total

number of population is slightly increasing in value. The graph and table below shows the PPI trend along

with the total population of Singapore Citizens:

SOURCE: URA AND SINGAPORE DEPARTMENT OF STATISTICS

Presented below is the result of the correlation analysis between PPI and total population of

Singaporean citizens. The calculated correlation is +0.58 which means that the relation of the two

variables is directly correlated. The PPI increases when the total number of population increases, since

the fundamental demand drivers for new housing depends on household or population.

PPI Residential Properties Population SC

PPI Residential Properties 1Population Growth - SC 0.583971007 1

Correlation Analysis between Property Price Index and Total Population of Permanent Residents

The number of permanent residents in Singapore is decreasing in value. In year 2011, the

country hit a negative growth rate on a year on year basis at -1.7%. This trend continued until 2015 when

Singapore government diminished the approval of the PR residency. Figure below shows the trend

comparing it to the PPI from 2009 to 2015.




Presented below is the result of the correlation analysis between PPI and total population of

permanent residents. The calculated correlation is -0.23 which means that the relationship of the two

variables is inversely correlated or the PPI increases when the total number of population decreases. The

-0.23 correlation factor implies that the decline of the PR population did not significantly caused the

increase of the prices of properties because PR population are not the main drivers of housing demand in

the country.

PPI Residential Properties Population PR

PPI Residential Properties 1Population Growth - PR -0.227810887 1

Correlation Analysis between Property Price Index and Total Population of Foreigners

The year-on-year percentage growth of total population of foreigners is at the peak in 2011 at

6.9% and in 2012 at 7.2%. Hence, the demand for private residential properties also increased during this

year, which is evidently shown in the table below.




When the correlation factor is calculated, the result is +0.67 as shown in the figure below. The

result is interpreted to have a high positive correlation between the PPI and the population of the

foreigners.

PPI Residential Properties

Population of Foreigners

PPI Residential Properties 1Population Growth - Non Res 0.668257662 1

In year 2009 to 2015, the percentage of number of households living in condominiums remained

intact at 11-12% of the total number of households who own dwellings. Additionally, the number of

tourists jumped from 11,641 in 2010 to 13,171 in 2012 which means that the growth of tourism has also

impacted the property prices along with the increase of population of foreigners. Another significant factor

that significantly increased PPI could be attributable to the additional SSD and ADSD implemented by the

government in 2009 to 2010 particularly for foreigners who pays higher amount of duties compared with

Singapore citizens and permanent residents.

Correlation Analysis between Property Price Index and Gross Monthly Income

The gross monthly income from work per full time equivalent shows an increasing trend from year

2009 to 2015. The major shift in the gross monthly income (GMI) happened in 2011 when the GMI

suddenly went up from $ 3,000 in 2010 to $3,249 in 2011. Coincidentally, the major shift in GMI happened

the same year when the total population of foreigners also increased. The figure below shows the data:




When the correlation factor is calculated, the result is +0.62 as shown in the figure below. The

result is interpreted to have a high positive correlation between the PPI and gross monthly income. The

PPI increases when the GMI also increases, along with the increase in the foreign workers and housing

demands.


Gross Monthly Income

PPI Residential Properties 1Median (50th Percentile) 0.61373701 1

Correlation Analysis between Property Price Index and Exchange Rate of SGD to USD

Strong currency against US dollar is evident in year 2012 when the exchange rate hit the lowest

at 1.3 SGD to 1.0 USD. The stronger currency increases the purchasing power of the buyers. However,

in year 2013 onwards, the Singapore dollar started to weaken.




As shown in the calculation below, the correlation between PPI and exchange rate is -0.93, which

means that the PPI moves in the opposite direction with the exchange rate. As the property prices

increases, the rate of SGD to USD decreases. Simply put, PPI increases while the Singapore dollar

strengthens.

PPI Residential Properties SGD to USD

PPI Residential Properties 1US Dollar -0.9348617 1

Correlation Analysis between Property Price Index and Prime Lending Rate

The prime lending rate in year 2009 to 2013 remained to be constant at 5.38% and slightly

decreased in 2014-2015 at 5.35%. It was in year 2009 when the government intervened to keep interest

rates at artificially low to mitigate the effect of the global financial crisis. The interest absorption scheme

and interest only housing loans were abolished in 2009 and the loan-to-valuation was decreased from

90% high to 40% low. Despite all the changes in the government polices during these periods, the prime

lending rate remained intact over the years. The figure below shows the trend




As shown in the calculation below, the correlation between PPI and prime lending rate is very

minimal at -0.09 only. This means the impact of the lending rate do not have significant impact in the rise

and fall of property prices in Singapore.


Prime Lending Rate

PPI Residential Properties 1Prime Lending Rate -0.097781642 1

Correlation Analysis between Property Price Index and Consumer Price Index (CPI)

Consumer price index shown in the graph below represents CPI of all items such as food,

clothing and footwear, housing and utilities, household durables and services, health care, transport,

communication, recreation and culture, education, and miscellaneous goods and services. The highest

jump in CPI happened in year 2011 when the CPI went up from 87.9 in 2010 to 92.5 to 2011, and

attributable in the spike of household durables and services. The figure below shows the data from 2009

to 2015.




As shown in the calculation below, the correlation between PPI and CPI is positive 0.76 which means that

there is high direct correlation between the two variables. The increase in the property prices moves

along with the consumer price index.


Consumer Price Index

PPI Residential Properties 1Consumer Price Index 0.761617379 1

Correlation Analysis between Property Price Index and Vacancy Rate

The vacancy rate is derived by dividing the number of vacant units with the total available units. It

was in 2011 to 2013 when the supply of HBD flats and private residential units reached as high as 92,370

units in year 2012. The chart below shows how number of supply moves along with the property prices.




As shown in the calculation below, the correlation between PPI and number of supply is +0.70,

which means that the prices are significantly affected by the number of supply. As the number of supply

units increases, the PPI also increases.


Supply of Non-Landed

Properties PPI Residential Properties 1Total Supply of Non-Landed Properties0.700085772 1

Correlation Analysis between Property Price Index and Gross Domestic Product

The GDP data that is shown below represents the overall economic performance of the country

measured by the GDP at 20110 market prices. Since year 2009, economy of the Singapore shows

improving performance each year, 3-4% of which is coming from ownership of dwellings.


As shown in the calculation below, the correlation between PPI and the gross domestic product

measured in millions, resulted to +0.76, which means that the prices are significantly driven by the

country’s overall economic performance.


GDP at 2010 Market Prices

PPI Residential Properties 1GDP at 2010 Market Prices 0.762237589 1



PART 3: MULTIPLE REGRESSION AND PREDICTIVE MODEL OF PROPERTY PRICE INDEX OF

RESIDENTIAL PROPERTY

The third part of this chapter attempts to use the multiple regression analysis to predict the future

property price index of the residential property in Singapore. Using the Microsoft Excel, we will use the

five factors to formulate a linear regression. These factors are the population of foreigners, GDP at 2010

market price, total supply of non-landed properties, gross monthly income, and consumer price index.

These factors will be used to predict the PPI, using the formula for multiple regression predictive analysis

which is Y= Y = Constant + B1(X1) + B2(X2) + B3(X3).Using Microsoft Excel the figure below shows the coefficient of the 5 variables that we should to

predict the future PPI of the presidential property in Singapore.

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.9997R Square 0.9994Adjusted R Square 0.9963Standard Error 0.7178Observations 7.0000

ANOVAdf SS MS F Significance F

Regression 5 831.6533 166.3307 322.8318 0.0422Residual 1 0.5152 0.5152Total 6 832.1686

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 53.2867 33.5452 1.5885 0.3577 -372.9459 479.5192 -372.9459 479.5192X Variable 1 0.0000 0.0001 -0.5926 0.6594 -0.0009 0.0008 -0.0009 0.0008X Variable 2 0.0015 0.0002 9.1791 0.0691 -0.0006 0.0036 -0.0006 0.0036X Variable 3 0.0006 0.0001 6.9992 0.0903 -0.0005 0.0017 -0.0005 0.0017X Variable 4 0.0084 0.0113 0.7440 0.5928 -0.1346 0.1513 -0.1346 0.1513X Variable 5 -0.6927 1.1247 -0.6159 0.6486 -14.9831 13.5977 -14.9831 13.5977

By looking at the result of the regression analysis, the column for coefficients will be used to replace the

variable B in the predictive analysis formula:

Y = 53.2865 + 0.000 (X1) + 0.00015 (X2) + 0.0006 (X3) + 0.0084 (X4) + -0.06927 (X5)



Where X1 is the population of foreigners, X2 is the GDP at 2010 market price, X3 is the total

supply of non-landed properties, X4 is the gross monthly income, and X5 is the consumer price index. To

test the predictive formula above, we will use the forecasted X variables by calculating the compounded

annual growth rate from 2009 to 2015, and applying the rate to predict 2017. By doing this, presented

below is the calculation of PPI forecast 2016 which is estimated at 145.5.

CONCLUSION

This chapter deliberately discussed the findings and analysis in three different sections, the first

part showed the descriptive analysis of the effects of the Singapore Government policies and cooling

measures to intervene and mitigate the increasing demand of residential properties; the discussion

emphasized the nine rounds of cyclical measures comparing it with the annual PPI from 2009 to 2015.

The second section focused on the quantitative research by calculating the correlation of PPI to various

economic factors such as population of Singapore citizen, population of permanent residents, population

of foreigners, gross monthly income, exchange rate of Singapore Dollar to US Dollar, prime lending rate,

consumer price index, total supply of non-landed properties, and gross domestic product or GDP at 2010

market prices. The third section of this chapter attempted to calculate the forecasted PPI by using the

multiple progression analysis. To be able to create a more accurate formula only five factors were

considered in the calculations which are the population of foreigners, GDP, supply, GMI, and CPI.


YOY Growth 2009 vs 2010

2010 vs 2011

2011 vs 2012

2012 vs 2013

2013 vs 2014

2014 vs 2015 CAGR 2016 Coefficient Result

Population of Foreigners 4% 6% 7% 4% 3% 2% 4% 1,702,296 (0.00004) (66.7) Gross Monthly Income Median 2% 8% 7% 6% 2% 5% 5% 4,140.3 0.00837 34.7 Consumer Price Index 3% 5% 4% 2% 1% -1% 2% 102.0 (0.69269) (70.6) Total Supply 18% 12% 7% -7% -20% -25% -2% 56,450.3 0.00061 34.5 GDP at 2010 Market Prices 13% 4% 4% 5% 3% 2% 5% 105,002.3 0.00153 160.4 Intercept 53.3 PPI Residential Properties - Forecast 145.5


CHAPTER 5: SUMMARY AND CONCLUSION

1. SUMMARY OF THE STUDY

The chapter one of this research expounded the overview of the Singapore economy and real

estate industry. The chapter also explained that real estate industry is one of the factors that drive the

economic growth of Singapore. Furthermore, the research objectives, significance of the research, and

the research questions were clearly explained in the first chapter.

The second chapter which is the literature review explained and illustrated the Singapore

economy using the quarterly reports published by various Singapore Government agencies. The

economic performance as of the second quarter of the current year were illustrated and explained by

looking into the gross domestic product, labor market, consumer price index, and other economic drivers.

This chapter also reviewed the Singapore housing market quoting market outlook by economists,

research group, government agencies, and outlooks from various journals. Other topics covered by this

chapter are the standard classification dwelling, structure of housing market, and Singapore district code

demarcation. The residential market as of the third quarter of the current year was also discussed based

on the report released by Urban Redevelopment Authority (URA). In this section the key indicators were

explained such as price index, rental index, take-up, pipeline supply, and vacancy rate. The comparative

analysis is discussed by analyzing the change on a quarter-on-quarter basis and year-on-year basis.

Additionally, the Singapore population highlights were also illustrated as well as the major crises such as

Asian Financial Crisis (AFC) and Global Financial Crisis (GFC). The government’s role in crises mitigation

and implementation of cooling measures were also tackled in this chapter.

In Chapter three, the research data and methodology were presented. The sources of research

data were specifically identified and the method of quantitative analysis is explained. This research used

qualitative and quantitative analysis. The effect of the government policies and cooling measures were

analyzed based on the research data. The correlation coefficient of the factors that drives the property

market was calculated using the formula for correlation as well as multiple regression analysis.



The fourth chapter intensely explained the findings and analysis. This chapter consists of three

parts. The first part presented a descriptive analysis of the effect of government policies and cooling

measures to the PPI of residential property. The second part showed quantitative analysis by calculating

the correlation coefficient of the nine variables which drives the property market. The third part explained

how to use the linear regression formula to predict the future PPI of residential property market.

The last chapter of the research explains the summary of the research, answering the research

questions, and discussion of key findings and conclusion.

2. ANSWERING THE RESEARCH QUESTIONS

2.1 What are the factors that drive the property price index (PPI) in Singapore?

There were several economic and social variables that were considered in this research. Among

these factors, the main indicator and driver of the PPI are the nine rounds of cyclical cooling measures

implemented by the Singapore government, the population of the foreigners, the gross domestic product,

total supply of the non-landed properties, the gross monthly income, and the consumer price index. All

other factors such as currency appreciation or devaluation, prime lending rate, population of the

Singapore citizens, and population of the permanent residents, have insignificant impact in the PPI

residential property in Singapore.

2.2 What is the correlation of residential property price index to the economic forces that

drives the property market in Singapore?

The figure below summarizes the result of the correlation analysis between PPI of residential

properties and nine economic factors.



Key Metrics of

Correlation

Population of

Foreigners

GDP at 2010

Market Prices

Total Supply of

Non-Landed

Properties

Gross Monthly Income

Consumer Price Index

US Dollar Prime Lending

Rate

Population SC

Population PR

2009 1,253,697 72,567 62,240 2,927 85.4 1.4545 5.38 3,200,693 533,183 2010 1,305,011 83,027 75,514 3,000 87.8 1.3635 5.38 3,230,719 541,002 2011 1,394,437 86,835 85,724 3,249 92.5 1.2579 5.38 3,257,228 532,023 2012 1,494,232 90,409 92,370 3,480 96.7 1.2497 5.38 3,285,140 533,065 2013 1,554,411 95,442 86,541 3,705 99 1.2513 5.38 3,313,507 531,244 2014 1,598,985 98,121 72,279 3,770 100 1.2671 5.35 3,343,030 527,709 2015 1,632,312 99,890 57,867 3,949 99.5 1.3748 5.35 3,375,023 527,667 Result 0.668258 0.762238 0.700086 0.613737 0.761617 (0.934862) (0.097782) 0.583971 (0.227811) High/Low high high high high high high low high lowRelationship positive positive positive positive positive negative negative positive negative

The correlation coefficient of each factors were classified if there is a high or low correlation and if

the relationship is positive or negative correlation. Based on the summary presented above, GDP has the

highest positive correlation at +0.762, followed by the CPI at +0.761, then supply of units at +0.700,

population of the foreigners at +0.66, and the gross monthly income at +0.614. Using the formula Y =

53.2865 + 0.000 (X1) + 0.00015 (X2) + 0.0006 (X3) + 0.0084 (X4) + -0.06927 (X5), the forecasted PPI for

2016 is forecasted at 145.5 or increased from last year of 3%.

2.3 What are the mitigation plans or recommendations to sustain the housing prices?

The implementation of the cyclical measures by the Singapore government affected the

transaction volumes. The government has thrived in raising the property prices and implementing quite a

few policies to prioritize housing affordability for Singaporeans. However, overall effect is decreased

number of transactions especially in private properties, while increasing the number of HDB flats for sale.

The implementation of the SSD and ABSD has significantly led the increase of prices, together with the

lower LTV where buyers need to meet certain requirements to be eligible. On the other hand, the

government succeeded in making HDB flats more affordable for Singaporeans, by enhancing the CPF

policies, improving LTV, and lower SSD and ABSD compared with PR’s and foreigners.

The rise and fall of the property prices are also driven by the economic factors such as population

of the foreigners, GDP, supply, GMI, and CPI. These factors move in the same direction with the property

prices. However, this research suggests that the government policies and measures have the highest

impact in the prices of the properties. In order to sustain the property price in Singapore, the demand for



housing must be balanced and the cooling measures implemented by the government must be relaxed to

stabilize the prices. Furthermore, the number of supply in the pipeline must be analyzed to mitigate

vacancy rates and weakening rental rates, and to balance the demand and supply fundamentals.

3. CONCLUSION

This research has successfully answered the three research questions and has accomplished the

research objectives. This research profoundly analyzed the fundamental factors that drive the residential

property prices by understanding the Singapore economic performance, the real estate industry, and the

impact of the government policies and cooling measures. This research also helped to deeply analyze the

impact of the major crises and how the government responded to mitigate the effect of the predicaments.

The reader of this research will have a meaningful understanding of the relationship of various economic

factors and more or less have a prediction of the performance of the residential property industry in

Singapore.



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