investigating the factors affecting the investment decision in residential development

INVESTIGATING THE FACTORS AFFECTING THE

INVESTMENT DECISION IN RESIDENTIAL DEVELOPMENT

By

NARANG SOMIL

2007

An Individual Management report presented in part consideration for the degree of

MBA (Finance)

Factors affecting investment decision in residential development 2007

Acknowledgements

I would like to extend my sincere gratitude to the project supervisor Bob Berry for his

guidance, valuable comments and support throughout this project.

I would also like to thank Andrea Richardson for giving me the company project through

where I got the motivation to do research on this topic. I appreciate all the help extended

by her.

Finally I would like to thank all the survey respondents for expressing their opinions on

the management service and affordable housing obligations which helped me in coming

up with good findings. We hope the conclusions drawn in this report will assist Southreef

Properties Ltd in future or any developer and would be of help to understand the factors

affecting the purchase decision in a Residential Development.

I would like to dedicate this project to my sweet younger brother, who motivated me

throughout the project. I would also like to dedicate this project to my family and Maa.

Finally I would like to take this opportunity to thank my MBA course members, lecturers

and other staff for making this year a truly memorable experience.


Abstract

The purpose of this project is to provide a rare insight into the motivation behind

residential property investors when looking to purchase an apartment. The factors driving

demand preferences for housing are constantly changing, difficult to measure, and often

deemed to be a complex bundle of attributes. The project attempts to answer the

following questions:

What are the factors affecting the investment decision in a Residential Development? To

identify the significance and weight of such factors in the decision making process.

To what extent the rise in interest rates affect the customer’s decision of different income

profiles in purchasing a residential apartment?

The project is organized as follows. It starts with a brief outline of previous studies and

literature on rational decision making and bounded rationality framework in the

investment decision making. The literature review is followed by the primary data

collection through two questionnaires to examine the significant factors affecting the

investment decision making. The study uses factor analyses to understand the cluster of

factors affecting the investment decision of people of different income profiles and

regression analysis to identify the effect of rise in interest rates on people of different

income groups. The study involves two hypotheses, first relates to the importance of On-

site management and second relates to the effect of interest rates on the investment

decision.


CONTENTS

Chapter 1: Introduction…………………………………..……..9

1.1 Overview of UK housing system……………….10 Chapter 2: Literature Review……………………………..……13 2.1 Introduction………………………………..…...14 2.2 Bounded Rationality……………………………14 2.3 Behavioral Decision Making……………..……16 2.4 Macro factors…………………………………..18 2.4.1 Growth rate in GDP……….…………..….21 2.4.2 Inflation…………………….…………..…21 2.4.3 Interest rates……………………………...22 2.4.4 Growth in money supply……………….…22 2.4.5 Household spending………………………22 2.5 Micro factors……………………………..…....24 2.5.1 Renting Vs Buying ………..………27 2.6 Conclusions…………………………………….29 Chapter 3: Research Methodology …….………………………...30 3.1 Questionnaire…………………………………...31 3.2 Management Service Questionnaire……………32 3.3 Purchasing Behavior questionnaire…………….33


3.4 Pilot survey………………………………………….34 Chapter 4: Findings and Discussion………………………………….35 4.1 Introduction…………………………………………..36 4.2 Results of the survey……………………………..….36 4.3 Income group classification……………………….…38 4.4 Factor Analysis……………………………………...38 4.5 Results from Factor analysis………………………..40 4.6 Low Income group………………………………….41 4.7 Middle Income group……………………………….42 4.8 Higher Income group……………………………….44 4.9 On-site and Offsite management……………………45 4.10 Likelihood of purchase with rise in Interest rates…...51 4.11 Regression Analysis…………………………………54 4.12 Assumptions…………………………………………55 4.13 Results of Regression analysis………………………57 Chapter 5: Limitations………………………………………………....58 5.1 Limitations of Postal survey………………………….59 5.2 Limitations of Online survey…………………………59

5.3 Assumptions…………….……………………….……60


Chapter 6: Conclusion………………………………………………….62 6.1 Further Research……………………………….………63 Chapter 7: References………………………………..…………………65 Chapter 8: Appendix……………………………………………………71

Appendix 1: Management service questionnaire…………....72

Appendix 2: Purchasing Behavior questionnaire……………77


List of Tables and Figures

Figure 1: Change in Real House prices in UK…………………………12

Figure 2: Macro factors………………………………………………...20

Figure 3: Factors influencing purchase decision……………………….37

Figure 4: Income bracket of the respondents…………………………..38

Figure 5: Graphical Representation of the model……………………...40

Figure 6: Factor analysis for low income group………………………..41

Figure 7: Paired sample t test for factors for low income group…….....42

Figure 8: Factor analysis for Middle income group…………....………43

Figure 9: Paired sample T test for factors for middle income group…..44

Figure 10: Factor analysis for High income group…………………….45

Figure 11: Paired sample t test for factors for High income group……45

Figure 12: Location of management company………………………...46

Figure 13: Breakdown of Factors for On-site management……….......48

Figure 14: Scores given for onsite and offsite management…………..49

Figure 15: Satisfaction levels for Onsite and Offsite management……49

Figure 16: Paired sample t test for Onsite and Offsite management…..50

Figure 17: Number of respondents and average score by respondents..52

Figure 18: Rating given by respondents of different income profiles...52

Figure 19: Likelihood of purchase in case of rise in Interest rates……53

Figure 20: Graphical Representation of the regression model………..56

Figure 21: Results of Regression analysis…………………………….57


Chapter 1: Introduction

“The key to growth is the introduction of higher dimensions of

consciousness into our awareness”

Lao Tzu


Introduction:

The foundation of this Individual management project is based on my knowledge

acquired through my group internship project at Southreef Properties Ltd. The objective

of the group project was to understand respondent’s perception about the management

service of current city centre developments and mixed tenure developments and its

impact on the buying behaviour of people. While understanding the customer purchasing

behaviour, I got encouraged to do in-depth research on the weight of various micro and

macro factors influencing the customer’s decision to invest in a Residential Development.

The study aims to look at various micro factors that affect the purchase decision. The

researcher tries to analyse this by conducting factor analyses and paired sample t test on

the compressed factors.

Through this study we also aim to test the following hypotheses:

H0.1: There is no difference between the satisfaction levels of onsite and offsite

management and hence no effect on the purchase decision.

H1.1: The satisfaction levels of onsite and offsite management differ

significantly, hence have a significant effect on the purchase decision.

H0.2: Rise in interest rates does not affect different income groups differently.

H1.2: Rise in interest rates will affect different income groups differently.


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1.1 Overview of UK housing market:

Introduction:

This section aims to provide an insight into UK housing market and makes the reader

aware of the current scenario of the market. Real estate is the world’s biggest business,

accounting for 15% of global gross domestic product (GDP) with assets of US$50 trillion

compared with US$30 trillion in equities (Bloomberg, 2004). More than 50% of the

world’s total assets are invested in direct real estate and securitized real estate investment

vehicles such as real estate investment trusts (REITs) or real estate stocks (Brown and

Matysiak, 2000). Real estate is an integral part of the economy and its returns are linked

to the macro economy and business conditions (Liu and Mei, 1992). There are a large

number of studies done to understand the risk-return performance and pricing of real

estate in the macroeconomic context. However, there is little literature on understanding

the factors affecting or drivers behind the real estate investment decision.

Housing market is an important sector in the UK economy, with a large share of

household wealth and debt held in and against nation’s home in form of investments and

mortgages or loans respectively. In the last decade average overall house prices have

increased by 180% and since 1996 national house prices have risen by 150% after

accounting for general inflation (Paul Samter, 2007). Housing demand continues to grow

faster than supply and with a strong economy prices look set to continue to increase.

Cyclical Trends in Housing Market:

UK has experienced four house price cycles since 1970. The first cycle was caused by the

Barber boom in the early 1970’s. The second cycle, at the end of the 1970’s, was caused

by general inflation which followed the 1979 oil shock. After this period till early 1990s

the economy was dogged by high inflation, high unemployment and volatile growth.

High inflation made housing an attractive investment but with house building being very


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low resulted in house price inflation. The government responded to inflationary

environment by rising interest rates and allowing sterling to appreciate against the US

dollar which caused slump in house prices (See figure 1).

The increase in the volume of credit, deregulation of the financial market and the

growing economy contributed to the late 1980’s boom in house prices. In the late 1980s

there was an inflationary boom in UK in which period house prices peak at over 30% and

general inflation went over 10%. Financial deregulation made mortgage rates more

dependent on interest rates and the reliance on variable rate mortgages made the housing

market very sensitive to macroeconomic variable via fluctuations in debt and

consumption. The volume and share of mortgage debt in GDP reached 53% in 1990

(Wilcox, 1999). But later continuous increase in housing and mortgage cost made

housing less affordable again and caused the slump in early 1990’s. The policy action

taken to reduce inflation led to a recession which intertwined with falling house prices

and the result was that many people lost their homes or faced negative equity resulting in

such circumstances that in some cases outstanding debt on the mortgage exceeded the

resale value of their home (See figure 1).

Late 1990’s witnessed the period of fourth cycle in house prices which was a result of

growing economy, low unemployment and low mortgage rates. House prices increased

by around 13% between 1990 and 1993 and then stabilised at the end of 1995.

Figure 1: Change in Real house prices in UK over the period 1970-1998.


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Source: Council of Mortgage lenders.

Current Housing Market:

Later in the early-mid 1990s, mortgage market experienced a lot of changes and this

resulted into a competitive lending environment. This transformation of mortgage market

with development and increased sophistication of credit risk management tools helped

lenders to identify, position themselves and target the previously underserved parts of the

market. Lenders then started making use of new techniques such as accounting for

individual borrower circumstances in affordability models in determining the amount to

be lent to an individual.

The cost of servicing debt has decreased dramatically since the early 1990s as interest

rates have fallen and competitive pressures have squeezed lenders’ margins. Lenders are

competing more strongly to attract existing mortgage holders and it has become much

easier for customers to switch between lenders to get lower interest rates. Remortgaging

activity accounted for approximately 37% in 2006 in comparison to less than 20% ten

years ago (David Miles, 2004). Fixed rate mortgages have become more popular and

accounted for over 60% of loans extended in 2006 (Miles, 2004).


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.

Chapter 2: Literature Review

“The difficulty of literature is not to write, but to write what you

mean; not to affect your reader, but to affect him precisely as you

wish”

Louis Stevenson

Why Serviced Apartments?

Literature Review

Bounded Rationality

Behavioural Decision making

Macro economic factors affecting the decision

to invest

Micro factors affecting the decision to invest

Conclusions


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2.1 Introduction:

The previous section dealt with the purpose of the study, laying down testable hypothesis

and overview of UK housing market. This chapter attempts to have a closer look at the

written literature. The study first looks at understanding the concept of bounded

rationality and behavioural decision making process. Later it looks at various macro and

micro factors that underpin the decision making process, which affect the decision to

invest in a residential property.

2.2 Bounded Rationality:

Rationality is the core behavioural assumption in the orthodox neoclassical economics.

Principles of maximisation, self-interest and consistent choice commonly underpin this

view of the rational economic factor. The proponents of the theory of rational investor

assume that an individual makes decision on the basis of these principles. It also assumes

that an investor has perfect information of his surroundings and he makes the decisions

with the sole objective of profit maximisation. This theory has been opposed by

neoclassical economic theory which proposes that every investor or every person has

limited access to the information and an individual is bounded by external constraints and

one’s own behaviour. It assumes that not all information is freely available and there are

time and cost constraints on the availability of the information.

Bounded rationality is distinguished from rationality as “the perfect human rationality

that is assumed in classical and neoclassical economic theory and the reality of human

behaviour as it is observed in economic life” (Simon,1992, p.3). Ratcliffe(1972)

understood that the essence of property is human behaviour and the premise that property

discipline is an applied science was argued strongly by Grasskaamp (1991). It has been

argued that bounded rationality perspective could be a useful supplement to the work that


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considers the role of heuristics in affecting real estate decision making in the valuation

profession (Diaz, 1990ab, 1999).

Heuristics in essence, is a cognitive data reduction process. “A heuristic is a cognitive

short-cut that allows for a reduction in the amount of information processed (Harding,

1999, p 350).Cognitive process simplification can be based on data, as well as declarative

and procedural knowledge” (Harding, 1999, p 350).

The behavioural assumption of bounded rationality embodies rejection of perfect

knowledge and optimisation on the part of economic actors, which characterises the

treatment of rationality in the neoclassical economics orthodoxy and instead involves an

element of being limited or bounded (Bruin and Hartle, 2003). The neoclassical condition

of “perfect human rationality” locates constraints in external environment, while Simon’s

concept of bounded rationality views constraints arising from the cognitive limitations of

individuals themselves (Brun and Dupuis,2000). In particular, individuals lack the

capacity to “take account of all the available information, compile exhaustive list of

alternative courses of action, and ascertain the value and probability of each of possible

outcomes” (Hindess,1998, p.69). The bounded rationality perspective shifts the emphasis

from neoclassical “Homo economics” which demonstrates characteristics of optimisation

and consistency based on perfect knowledge, to acknowledge imperfect knowledge and

satisficing behaviour (Susan Flint-Hartle, 2003). It can be seen that in real life individual

investors have limited information and imperfect knowledge while making residential

real estate decisions. These reasons affect their investment decisions and that is why

individuals are not always able to achieve optimality within the context of a dynamic and

complex property investment market. Investors tend to invest in tried investment products

and have preference for products with which they are comfortable. Investors are mindful

of past performance and they try to relate the current circumstances to past and then make

their investment decision.


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2.3 Behavioral Decision making:

As mentioned above, that the behaviour of an individual plays an important part in the

decision making process. Following section discusses that an investor decision making

behaviour does not follow a set of rules but it is subject to decision environment and

individual differences between decision takers.

The literature on investor decision making behaviour focuses on rational, normative

models that treat investor behaviour as highly structured and formalized. It is dominated

by rationalist perspectives and focused on examination of sets of rules that people should

follow, rather than studying that how decisions are actually made (Gallimore, Hansz and

Gray,2000). The limited amount of investigation of property decision making (e.q

Anderson and Settle, 1996; Farragher and Kleinman, 1996; Miles et al., 1989) has been

concerned primarily with the rules and techniques that people adopt with known

normative models of the decision process (Phyrr, 1989; Hartigay and Yu, 1993).

The behavioral decision theory has challenged rationalist approaches and looks more

closely at the decision environment and individual differences between decision takers

(Tversky and Kahneman,1974). Barkham(1996) argues that the property sector is the

most organizationally diverse in the UK economy. Much behavioral research that applies

to property, focuses on the way the individuals act as solitary actors or groups. Corporate

decision making also involves heuristics and biases similar to those displayed by

individuals because of behavioral momentum, fear of regret and an aversion to realizing

sunk costs.

Property decision making is a theory of analysis that attempts to provide a framework in

which investors can make decisions to maximize their wealth. These decisions are

generally characterized as multi-level procedures. Phyrr, 1989 has proposed a ten-step

model involving the generation of alternative ways to fulfill the initial strategy, three

stages of detailed financial feasibility research, closing with management and disposal


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issues. The literature on decision making suggests certain key behaviors exhibited by

investors at environmental, strategic and tactical levels such as the definition of goals and

objectives and a comprehensive search for alternative projects that meet the initial

criteria, formulation of a fully-defined strategy and criteria for selection and assessment

of property generated from environmental information sources. Research has shown that

the availability of information can affect the decision-making processes of investors.

Adair, 1994 found that investment most often occurs when the information is readily

available to the investor about his preferred sector. This would imply that market

imperfections, such as the heterogeneous pattern of market information, cause investors

to move away from normative models of investment behavior.

The factor that affects the property investment behavior is that not all investment is

unintentional. Investors make investment decisions with an intention to hold the property

for either long term or short term. According to study done by Judith Yates, 1996 in

Australia, a significant proportion of individual investors have shown a long term

commitment to investment in illiquid residential property yielding variable short-term but

steady long-term returns. These individual investors have been less responsive to profit

maximizing criteria and have shown a long term commitment while institutional

investors differed from them and were unwilling to invest in illiquid residential property

and preferred profit maximization over long term investment. This is partly the result of

taxation system that favored small scale investors over large-scale investors. The

potential benefits from economies of scale and risk pooling were perceived to be

insufficient to offset the effects of cumulative land taxes which reduced the returns on

residential rental property and this influenced the large-scale investors to have a short-

term objective (Yates, 1996, pp 47-48).

Behavior decision making represents what actually happens when investors make

decisions. It has been shown that investors often fail to consider long-term investment

prospects in term of short-term forecasts. They avoid booking a financial loss when the

investment in showing loss but valuation at the year end make them involve in

investment behavior as if they were investing for only one year (Thaler,1997). Investment


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decision makers have been found to overreact to current information and display

excessive optimism. They treat information received through personal contacts more

important than general market information. Investors tend to relate past incidents or

memories to present in forming their decisions. Even economic forecasters exhibits over-

reaction in responding to recent information by making large forecast changes (Ehrbeck

and Waldmann,1996). Forecasters exhibit wishful thinking when forecasting economic

events as opposed by normative investment model (Olsen, 1997). These factors interact

to reduce an investor’s adherence to a strictly normative investment model and suggest

the importance of behavioral investment model.

2.4 Macro economic factors:

Introduction:

The last section discussed about the bounded rationality framework, importance of

decision environment and differences between individual behaviour in decision making

process. The following section discuses various macro factors that underpin these

behavioural decision making process. On the basis of these factors different individuals

make their investment decisions. An individual perceive these factors differently and then

form one’s decision to invest in a residential property.

There are a lot of demand and supply factors that drive real housing prices. Factors that

influence the demand for housing over longer horizons include growth in household

disposable income, gradual shifts in demographics (such as the relative size of older and

younger generations), permanent features of the tax system that might encourage home

ownership as oppose to other forms of wealth accumulation, and the average level of

interest rates. The availability and cost of land, the cost of construction and investments

in the improvement of the quality of the existing housing stock are longer-term

determinants of housing supply (Tsatsaronis and Zhu, 2004).


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Housing markets also depend on a number of factors that include the length of planning

and construction phases and the inertia of existing land planning schemes. Other factors

affecting the liquidity of housing markets are the transaction costs which include VAT,

stamp and registration duties and inheritance taxes and finally the uncertainty about

future prospects that follows periods of heightened volatility.

Housing investment decisions depend critically on the availability, cost and flexibility of

debt financing. A declining interest rate environment, which keeps servicing costs of

mortgages within the household budget limits imposed by current income, typically

boosts the demand for residential real estate. The residential real estate market has

benefited from the increased reliance on market-based channels of financing. The spread

of credit scoring methods and standardised mortgage contracts, coupled with a growing

appetite for tradable instruments among portfolio institutional investor, has led to the

growing securitization of mortgage assets. Credit institutions that used to hold a large

volume of mortgages on their balance sheets now have the option to sell any unwanted

exposure in the secondary market (Tsatsaronis and Zhu, 2004). Some of the benefits due

to these reduced costs and the improved liquidity of mortgage assets have been passed on

to households in the form of lower transaction fees and more flexible mortgage contract

terms. These flexible mortgage contract terms are in turn driving the demand for

residential housing.

Mortgage accounting practices can influence the creditor’s appetite for exposure to the

market and in turn feedback from house prices influences the availability of finance. The

level of ceilings on the loan-to-value (LTV) ratios and valuation methods of property

determine the ability of banks to lend against the real estate collateral. Methods that base

lending decisions on the current market values tend to increase the sensitivity of credit

availability to market conditions and helps to create a positive momentum in market

demand, valuations that are based on historical levels of prices would tend to lag current

market trends exerting a countercyclical influence on credit availability. The feedback

from property prices to credit growth is strong where there is greater prevalence of


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variable rate mortgages and more market-based property valuation practices for loan

accounting are used.

The earlier part of the section discussed the effect of macro factors on the demand and

supply in housing market. The following section describes the role and effects of macro

factors on the investment behaviour and demand for residential property.

Figure 2: Macro factors

Macro factors

Growth rate in

Money supply

InflationGDP growth

rate

Household

spending

Interest rates


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2.4.1 Growth rate in GDP:

GDP is a measure of all currently produced goods and services valued at market prices

and is thus an aggregated value of all the industries in an economy (Liow, Ibrahim and

Huang, 2005). During periods of high economic growth, there is confidence within the

economy and the individual investors and this stimulates the demand for the commercial

and residential property. Firms seeking expansion would then require more commercial

space and with the confidence in the economy individual investors might invest in

residential property with a view to make profit in future. On the contrary, in periods of

economic downturn investor’s confidence may be dampened resulting into less

investment into residential property which in turn brings down the price of the property.

2.4.2 Inflation:

Inflation rate plays a significant role in real estate investment decision. Inflation is

generally measured by changes in Consumer Price Index (CPI) which measures the retail

prices of a fixed “market basket” of several thousand goods and services purchased by

households (Liow, Ibrahim and Huang, 2005). There are two kinds of inflation, expected

and unexpected inflation rate. Unexpected inflation rate is defined as difference between

actual and the expected rate. Ferson and Harvey (1991) argue that unexpected inflation

could be a source of economic risk and a risk premium would be added for such an

investment which has exposure to unexpected inflation.

A low level of inflation helps decrease speculation in housing markets and reduces house

price volatility. The reason for this is that capital gains under the low inflation may be

lower than user costs of capital, especially if low inflation is sustained by high interest

rates.


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2.4.3 Interest Rates:

Interest rates have effects on both the future cash flow of firms and individuals and

discount rates. Interest rates are considered in calculating the discount rate or cost of

capital to appraise the investment decision. Discount rate is calculated by adding the risk

free rate and the inflation rate. This discount rate is used for property investment

appraisal decisions. Higher interest rate will increase the debt service or mortgage cost

and will affect the investment in the residential property while lower interest rate will

reduce the cost and thus will drive the investments in residential property.

2.4.4 Growth in Money Supply:

Money supply is represented by M2 and is a broad measure of money in an economy.

Increases in money supply will give rise to greater inflation uncertainty and will have an

adverse impact on real estate markets. The excessive growth in money supply may lead

to an inflationary environment and might affect the investments because of higher

discount rates (Liow, Ibrahim and Huang, 2005).

2.4.5 Household spending:

House prices influence household spending in various ways. An increase in house prices

increases wealth and consumers spend more with increase in house prices. Another effect

of increase in house prices is that it raises the collateral available to certain households

and reduces credit constraints placed upon them, allowing them to buy more. Most

importantly, prices and consumption are influenced by common factors and these factors

can change income expectations which might drive an increase or decrease in both house

prices and consumer spending simultaneously rather than one leading the other

(Attansio, Blow, Hamilton and Leicester; Diana Kasparova and Michael White, 2001).


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Previous studies:

According to study done by Anne de Bruin and Susan Flint- Hartle, 2003 in New Zeland,

there are various reasons motivating the property investment decision. Economic reasons

motivating the property investment decision which were highlighted in the study were

return on investment, wealth accumulation through long-term capital gain/growth and

attitude to risk. The availability of cheap mortgages was proposed as another important

factor which increases the chances of long term capital appreciation. According to the

concept “real wealth increase” highlighted in the study by Dupuis(1992) , the smaller the

outlay of the investor’s own equity in the property, the size of the deposit, the greater is

the wealth increase. Hence, “it is even possible to make real wealth gains from nothing

but capacity to pay a mortgage, to the extent that if all of the purchase price of a house

can be borrowed and upon resale of that house all the relative increase goes to the owner”

(Dupuis, 1992). With financial institutions increasingly willing to lend on smaller sized

deposits, the scope for real wealth gain does however increase. Intense competition

among financial institution for share of residential mortgage market ensures ample

availability of funds at competitive interest rates. Exemption of capital gains tax on

housing is another motivating factor influencing property investment decisions.

A study was also done by Anne de Bruin and Susan Flint-Hartle, 2003 in New Zeland, to

understand the implications of low inflation on property investment decisions. In this

questions were asked by way of postal survey and interviews to assess the implications of

low inflation on the decision to invest in a property. The bounded rationality concept sees

constraints arising from the cognitive limitations of individuals themselves, particularly

their lack of ability to embrace all available information. The results suggested that some

lacked the specific knowledge about the impact of low inflation while some had the

knowledge that low inflation erodes the potential for capital gain but this did not alter

their belief that the decision to invest in a property was a sound one. People were rather

more comfortable with the investment which leads to interpret this as satisficing

behaviour. The study also focused on that how portfolio diversification and attitude to

risk influences the decision to invest. A diversified portfolio to spread the risk is a


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standard rule of investment. Respondents were asked to rank their investment categories

in order of importance to them. Investment in property was highlighted as the most

important investment by majority of respondents. There were various motives which

guided the investment in property such as some perceived property to be a safe

investment, some made losses in share market so they started to invest in property and

some acted purely on advice of a financial planner. Therefore these investors may said to

be rational, albeit “boundedly rational”. Low risk and good investment returns were other

reasons for investing in residential property. Finally, optimisation is not the primary

decision making criteria but there are other social and contextual factors in operation

guiding the investment decision. The bounded rationality framework permits recognition

of composite constraints as an integral part of the decision-making environment.

2.5 Micro factors:

The investment decision of an investor is affected by Macro and Micro factors, the above

section dealt with analysing the effect of macro factors, the following section discuses

about the effect of micro factors.

The factors driving demand preferences for housing are constantly changing, difficult to

measure and often deemed to be a complex bundle of attributes (Richard & Reed, 2007).

The benefits of housing ownership can be said as twofold, first to provide shelter for the

entire household, second, as an investment for the owner occupier (Hutchison, 1994).

Other than providing shelter, housing also provides a long-term investment and security

of tenure.

Zangerle (1927) and Henderson (1931) had done pioneering studies on real estate

appraisal and had paid attention to the effects of environmental and building factors such

as landscape views, vegetation, noise, air pollution and building patterns on property

values. The study by Boris A. Portnov, Yakov Odish and Larissa Fleishman, 2003


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proposes that Environmental amenities in a residential neighbourhood (proximity to open

areas, attractive views, etc) encourage homeowners to invest more in the physical

expansion and maintenance of their properties- in building additions, modifications,

renovations, gardening etc. With the passage of time, these investments improve

neighbourhood appearance and as a result the property prices increases. However, if the

environmental conditions are not appealing, the local homeowners see little value in

investing in the maintenance of their properties, assuming that such investment is

unlikely to pay off in the future. Consequently the physical conditions of individual

properties in the neighbourhood deteriorate leading to low housing prices.

There are various factors affecting the property investment decisions. Investors generally

consider a large number of factors before forming their property investment decisions:

• Building characteristics such as number of floors, number of apartments per floor,

overall physical condition of the building and relative condition of the building

relative to the adjacent properties.

• Apartment characteristics such as balcony, parking, storage shed and private

garden.

• Location characteristics such as elevation of the building above the sea level,

landscape views, proximity to open areas, commercial areas and other location

specific amenities and disamenities.

Tom Kauko, 2003 has identified location as a composite effect of a set of locational

attributes in property value formation and property investment behaviour. He identifies

that the negative locational externalities such as social, physical or visual ones and the

positive locational externalities such as services, greenry, status and taxes influence the

investment behaviour and property value formation. Locational externalities such as

proximity to public transport, proximity to city, shops, parks, schools and major roads

gives rise to demand which in turn have a positive effect on property values. Various

studies in past have emphasized the importance of positive externalities of parks, water

parks, greenbelts, access to recreational facilities on the property values and the demand


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for the property. Public services and all the benefits provided helps in improving the

image of the place and hence positive effect on value whereas the higher taxes imposed

have a negative effect on value (Miller, 1982). Kauko,2003 gave the concept of

capitalization theory, which suggests that an environmental improvement or a public

good provided by local government leads to higher house prices in the vicinity, unless

the good causes significant negative externalities and becomes bad.

The study done by Richard Reed and Mills, 2007 came up with an interesting factor that

influences the decision to buy a residential property. Reed’s study highlighted the

importance of Lifecycle factors such as the family formation, marriage or the size of

existing house in the decision to buy a new house. The study also concluded that other

than financial factors, Lifestyle and Socio-economic factors play an important role and

are some of the central drivers behind the decision of first time owner’s purchase.

There are various studies done to investigate the effects of housing rehabilitation

programs on property values (Ding et al., 2000; Simons et al., 1998) which indicate that

residential investment in a new construction and rehabilitation has positive effect on

property values, specifically in low-income neighbourhoods. Sungur and Cgdas (2003)

list such elements as housing system, greenry, cleanliness, quality of construction,

landscape view, location and low traffic level as the components of environmental quality

that influence the user’s satisfaction. For instance, the proximity to arterial roads and

other sources of noise and air pollution may lower the apartments’s price, while beautiful

scenery and the high socio-economic status of the neighbourhood may raise apartment

price (Massey,1985;Yizhak,2003). Recent studies confirm that specific local

externalities, such as proximity to industry and refineries affect property values

negatively (Lentz and Wang, 1998). The neighbourhood environment both social and

physical factors affects the demand for the apartment and its price. Unfavourable

environmental conditions in a neighbourhood may lead to a higher turnover of its housing

stock, and to a gradual drop in the socio-economic status of its residents. Favourable

environmental conditions are likely to lead to a lower turnover and to gradual


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27

strengthening of the neighbourhood’s population (Boris A. Portnov, Yakov Odish and

Larissa Fleishman, 2003).

In most empirical studies, the Hedonic Price Modeling (HPM) is used to identify and

measure the effect of environmental valuables and building characteristics on property

values. This modeling approach assumes that the monetary value of a dwelling unit or a

residential property depends on the attributes a particular house or apartment may

possess. The attributes are namely physical size the dwelling and environmental

characteristics such as the number of rooms, age, location etc (Becker and Lavee, 1999

and Rosiers, 2002).

2.5.1 Renting Vs Buying:

Investment in housing is a considerable source of wealth for many individuals (Hilland &

Detersen, 1994; Reed, 2001). The actual level of such investment is reflected by both the

price initially paid for the property, and investment in post-occupancy changes and

modifications, such as additional rooms, shaded patios and balconies etc (Etzion,

2001).Making the incorrect housing decision can have an adverse effect on the long-term

wealth of the household and many first-time buyers are fully aware of the inflationary

characteristics associated with the property (Hennessey, 2003).

The decision to purchase a house is no longer an automatic and progressive step in a

household’s lifecycle – house purchasers are now faced with an increasingly complex

“rent Vs buy” decision. Benefits of housing ownership can again be summarized as two

fold, first being to provide shelter for the entire household and second as an investment

for the owner occupier (Hutchinson, 1994). Both buying and renting a house provides

shelter. Investment in house can provide different returns in different regions while in

case of renting a house, the funds saved can be redirected to other investment vehicles.

Both renting and buying have their own pros and cons.


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28

Interest in private rental market has been revived, this renewed interest can be seen from

a number of perspectives. A decrease in the level of support extended by government for

social rented housing and limiting it to lower-income groups people and reduced demand

for owner occupation has reinforced interest in private rental sector (Boelhower and van

der Heijden, 1994). Increased emphasis on labour flexibility has affected the variability

of incomes. The need for mobility has reduced the demand for home ownership and high

interest rates have restrained access to home ownership (Doling, 1988; Haurin and Gill,

1987). All these factors partly contribute to rise in the rental market.

The demand for rental housing also arose from difficulties and uncertainties of low and

variable wages and from the inability of households to access owner occupation.

Investment in rental housing provides investment opportunities for investors (Hamnett &

Randolph, 1988 p.47). The changing economic and social climate contributes to demand

for rental housing such as people unwilling to take on responsibilities for home

ownership because of long term commitments or because of affordability problems and

people wishing to leave existing households for better employment opportunities. All

these factors contribute to increased demand for rental housing.

There are various factors and advantages associated with homeownership and renting

which influence the purchasing or renting decision. All these factors must be accounted

for to estimate the demand for purchasing or renting. The benefits associated with home

ownership are:

• Eventual debt-free ownership after all mortgages have been paid.

• Ability to alter residence in any manner at the discretion of home owner for home

improvement.

• Intangible sense of pride in home ownership (Heikkila, 2000).

• Hedge against inflation with gradual but overall increase in overall value of the

property (Waxman, 2000).

• Capital appreciation.


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29

The benefits associated with renting are:

• Minimum capital outlay either weekly or monthly or in whatever agreed form.

• Fixed rent for an agreed period which is not subject to fluctuations in interest

rates.

• No Entry/ Exit fees such as stamp duty, solicitor’s fees, loan application and

processing fees, building inspection fees associated with home ownership.

• Higher level of mobility.

• No Capital maintenance cost.

• No possible negative equity with downturn in housing market.

All the above mentioned factors need to be considered for depending on the need and

desirability of the individual. If one needs the sense of pride of home ownership or capital

appreciation, then one would prefer purchasing the property. If one wants higher level of

mobility and cannot pay huge monetary sum or wants to pay only minimum agreed

amount per month, then one would prefer renting a property.

Conclusions of Literature Review:

It can be inferred from this section that an individual makes decisions which are bounded

by limited access to the information and behavioral differences. The literature discussed

about various macro and micro factors that underpin the behavioral decision making

process and affect the decision to invest in a residential property. The importance of

locational characteristics, building characteristics and social and physical environment

were also discussed. We understood that factors driving demand preferences for housing

are constantly changing and are a complex bundle of attributes.


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Chapter 3: Research Methodology

“Ask a question and you’re a fool for three minutes; do not ask a

question and you’re a fool for the rest of your life”

Chinese Proverb

Methodology

Qualitative

ResearchQuantitative

Research

“Management service

questionnaire”

“Purchasing

Behavior

questionnaire”

Factor Analyses

Paired sample t test

Linear Regression


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31

3. Research Methodology:

The research in this project is based on Qualitative and Quantitative research. Qualitative

research is done by administering two questionnaires and Quantitative research is done

by conducting factor analyses and Paired Sample t test on the extracted factors. Two

questionnaires were prepared for the group research; few questions were specifically

included in the questionnaires to identify the factors affecting the investment decision in

a residential property

3.1 Questionnaire:

Survey research has become a staple for policy analysts over past several decades and has

always served as a critical data source in the study of issues of interest to sociologists,

demographers, and political scientists (Jagannathan, 2001).

The group prepared two questionnaires to understand the customer perception about the

management service of current developments and mixed tenure developments. We also

looked at the impact of management service on the buying behaviour of people. One

questionnaire focusing on the management service (Appendix1) and the other focusing

on the mixed tenure developments (Appendix2). The group did postal survey for the

management service questionnaire and online survey for the mixed tenure developments

questionnaire.

While designing questionnaires, some questions were specifically designed focusing on

the individual project. These questions were then included in the questionnaires for group

project. The first question was “What are the important matters when considering your

next move or purchase” (Question 8 of Management Service questionnaire and question 5

of purchasing behaviour questionnaire), this question was asked after question 7 “Please

rate the probability of you purchasing a city centre apartment as your next move” which

set the tone for question 8. Question8 was asked to determine the weight of various micro


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and macro factors influencing customer’s decision to purchase a residential apartment.

Question 3 & 4 of “Management service questionnaire” (Appendix1) were asked to

determine the importance of On-site management in a development and its influence on

the purchaser’s decision to invest in a residential property.

Another question “Please rate the probability of you purchasing a city centre apartment as

your next move if interest rates move up by 0.5% to 6.25% from the current rate”

(Question 18 of Management Service questionnaire) was asked to determine the effect of

rise in interest rates on respondents decision to purchase a residential apartment

(Appendix1). Yet another question was asked to determine annual household income of

the respondents (Question 19 of Management service questionnaire and Question 12 of

purchasing behaviour questionnaire). Both these questions were then analysed to research

the effect of rise in interest rates have on the customer’s decision of different income

profiles in purchasing a residential apartment. The researcher combined the data from

both the questionnaires for question on factors affecting the purchase decision. Data was

collected for the factors affecting the purchase of a property from both the questionnaires

Question 8 of Management service questionnaire (Appendix 1) and Question 5 of

Purchasing Behaviour questionnaire (Appendix 2).

3.2 Management service Questionnaire:

A postal survey of residents who live in apartments within the NG1 postcode was

conducted (Appendix 1). Questionnaires were sent to about 800 city centre residents by

the way of post with an enclosed pre-paid envelope addressed to the University. A

covering letter was posted along with survey questionnaire requesting the respondents to

submit the questionnaire no later than 12 noon on Friday, 31 August 2007. To encourage

the respondents to submit the questionnaire on time Southreef Properties announced a

draw in which respondents will have the opportunity to win £250. It was mentioned that

the draw will take place on 10th of September.


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33

We got 58 responses for this questionnaire a response rate of 8.5%. Targeting city centre

residents was essential to understand as these are the people who are currently staying in

apartments in the city centre developments .These people are of same profile and have

similar characteristics to the target market of Southreef Properties Ltd. These are the

people who might be considering purchasing or renting another apartment so it was

essential to know their view on the management service and what are the factors that

influence their decision in purchasing an apartment in any development.

3.3 Purchasing Behaviour questionnaire:

An online survey was done to understand how mixed tenure residential developments are

perceived and what influences customers decision to purchase or rent. Online survey was

conducted to understand the effects of affordable housing on the development. Question

5 of this questionnaire focused on identifying the factors affecting the decision to invest

in a residential property (Appendix 2).

Online survey was conducted by posting the questionnaire on the web server and an e-

mail was sent out to all the professors and faculties of University of Nottingham

requesting them to fill it. This could be a good sample as these people have similar

profile and characteristics of the target market. The questionnaire was sent to about

approximately 600 staff members from different departments of University of

Nottingham. Online survey was also done by sending the questionnaires to the MBA

cohort as these could be people who might be living in an apartment and would consider

in future to buy or rent an apartment in a mixed use or mixed tenure development.

We got in total 66 responses to this questionnaire, a response rate of 11%. Here in this

questionnaire, we could not include the question that “Please rate the probability of you

buying or renting an apartment if the interest rates increase by 0.5% from the current

rates” because this would have effect on the focus of the questionnaire. So to not to affect

the quality of the questionnaire, this question was not included.


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34

3.4 Pilot Survey:

A pilot survey of the ‘Management service questionnaire’ was conducted within the

MBA cohort at The University of Nottingham. The purpose of the survey was to identify

any difficulties in understanding the questions posed in the questionnaire. The pilot

survey was done by e-mailing the questionnaire to the MBA cohort and asking for their

comments on the interpretation of the questions. Emphasis was given on the feedback

given by the MBA cohort coming from varied experience and backgrounds. The

questionnaire was also shown to Bob Berry, my supervisor, for his comments on the

questionnaire. His comments on the questionnaire were very helpful in designing the

questions for my individual project. The ‘Management service questionnaire’ was

amended quiet a number of times which improved the quality of the questionnaire.

.


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35

Chapter 4: Findings and Discussion

“The person who sends out positive thoughts activates the

world around him positively and draws back to himself

positive results”

Norman Vincent Peale


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36

4. Findings and Discussion:

4.1 Introduction:

The questionnaire aimed to investigate the factors affecting the purchase or investment

decision in a Residential Development. It also focused on identifying the rise of interest

rates on the purchase decision. The researcher used factor analysis to compress the

factors affecting the purchase decision and has related it to different income group

respondents. The researcher has conducted regression analysis to find the effect of rise in

interest rates on the purchase decision and also conducted T test the importance of On-

site management.

4.2 Results of the survey:

Question 8 of Management Service questionnaire and question 5 of Purchasing behaviour

questionnaire was asked to determine that what are the significant factors influencing the

purchasing or renting decision of customers in general. Scores were given to each factor

from 5 to 1 depending on the importance of the factor to the customer in making their

purchase decision. About 129 respondents rated these factors on a scale of 1 to 5. Then

these total scores were divided by the number of respondents to report average scores for

each factor. Here the maximum score and minimum score for any factor is 5 and 1

respectively. The findings suggest that Location (4.60) was cited as the most influential

factor, followed by value for money (4.38), Size of Living Accommodation (4.22).

Access to car parking (4.13) and Number of bedrooms (4.04) were other important

influencing factors. On-site management was rated with the score of 3.95 implying that it

was an important factor but only after the primary factors such as location and value for

money. So it can be inferred that On-site management is important but only after basic

criteria of the location and size of the dwelling are fulfilled. (See figure 3).


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37

Access to public transport (3.53) and On-site security (3.64) were other important factors.

Developers should consider all these factors into account to improve the perception and

services offered in their development to distuinguish themselves from other

developments.

Figure 3: Factors influencing purchase decision

Important factors influencing purchase decission

3.323.53

4.60

3.30

4.044.22

3.95

3.29

4.38

3.64

4.13

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Developers reputation

Access to public

Location

Close to recreation

Number of bed

Size of living

On-site manage

Sense of com

Value for

On-site security

Access to car

Criterion

Avera

ge s

core

out of 5

Question 19 of Management Service questionnaire and question 13 of Purchasing

behaviour questionnaire was asked to determine the annual household income of the

respondents to relate that what factors affect the people of different income groups.

Findings suggest that majority of respondents (35%) were from higher income group.

(See figure 4).


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38

Figure 4: Income bracket of the respondents.

4.3 Income group classification:

The researcher looked at the Annual Survey of Hours and Earnings to determine the

average UK salary and for the purpose of classifying respondents in different income

groups. According to ASHE 2006 survey results, the median weekly pay for full time

employees in UK grew by 3.7% in April 2006 to reach 447 pounds. So we could classify

low income group in £0-£20,000, middle income group within £20,000-£40,000 and

£40,000 and over within higher income group (Source: Annual Survey of Hours and

Earnings, 2006).

4.4 Factor analysis:

Once we collected responses from Question 8 (Factors) &19 (Household Income) of

Management service questionnaire (Appendix 1) and Question 5 (Factors) & 12

(Household Income) of Purchasing Behaviour questionnaire (Appendix 2). We used

factor analysis to compress the data and entangle the complex linear relationships

What is the income bracket of repondent

10.34%

17.24%

18.97%10.34%

8.62%

34.48%

10,000-19,000

20,000-29,999

30,000-39,999

40,000-49,999

50,000-59,999

60,000+


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39

between the data, since the questionnaire had 11 criterions that could affect the purchase

decision of respondents therefore in order to facilitate the analysis of data, the study uses

factor analysis to classify data based on Co- variance and Correlation. This approach

would facilitate prospective developers to better understand the relationships between set

of variables and develop applicable strategies to fulfill the needs of the purchasers. The

researcher understands that different income group people have different needs therefore

the study uses factor analysis for three income groups:

• Low income group (£0 - £20,000)

• Middle income group (£20,000 - £40,000)

• High income group (£40,000 and above)

The researcher used factor analysis because of following:

• Parsimony or Data Reduction: Factor analysis is used to reduce mass data into

condensed and more comprehendible formant.

• Structure in data: Factor analysis is used to uncover the basic structure in data

analysis. It condenses complex data into more comprehensible factors on the basis

on underlying structure of data.

• Hypothesis testing: Factor analysis helps test hypothesis as it classifies clusters of

data under one factor it becomes easier for the researcher to identify factors that

are more important than others.

• Data Transformation: Factor analysis is used to transform data especially in case

of financial research, as a lot of information is highly co related which gives rise

to the problems of multicolinearity in Data. Factor analysis technique eradicates

this problem and transforms data without loosing relevant information.

Besides this factor analysis is also used for mapping i.e. systematic attempt to chart major

empirical concepts, sources of variation. The researcher used factor analysis to help the

developer target and position the project rightly, as mentioned that earlier different

income groups have different needs. This would help developers to identify the target


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40

segment and for the success of such project the developer would need to concentrate on

the factors that have a highest score when compared to other factors.

Figure 5: Graphical Representation of the model

Graphical representation of the model

Divide the respondents

based on 3 income

groups: Low income,

Middle income and

high income group.

Conduct individual t

tests to evaluate the

importance of the

factors indivisually

Conduct factor

analysis based on the

responses given by

each income group

Conduct paired

sample t test to

ensure that the

important factors are

statistically different

from others

4.5 Results from factor analysis:

The study conducts factor analysis based on income groups; we would first look at the

results of low-income group that consists of respondents in the income bracket of £0-

20,000. The Kaiser-Meyer-Olkin (KMO) measure of sample adequacy is 0.23, and any

value between 0.3-0.7 says that the sample is adequate to run a factor analysis. The

Bartlett’s test of Sphericity, that measures for the adequacy of sample and looks at the

correlation among the components to analyze if the data set needs factor analysis. The

Bartlett’s test of Sphericity is measured in terms of Chi-square, in our case the Chi-square

statistics is 84.178 at 55 degrees of freedom this says that there was significant co relation


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41

in the data and the data warrants factor analysis. The determinant for the co variance

matrix is .0065 that is higher than the significant value of 0.0001. In case of lower

income groups factor analysis compressed the components into 3 factors:

Figure 6: Factor analysis for low income group

4.6 Low Income group:

Once factor analysis compressed the data into 3 factors we ran t tests (the scores of which

are reported in the diagram above) to determine which of these factor proves to be the

most significant factor (See Figure 6). The researcher reckons that factor 1 that has a t

score of 30.63 is the most important factor for this income group. For lower income

group Access to public transport, Number of bedrooms, Location and Value for Money

cluster are the important factors. In order to find the importance of the factor in relation

with the other factors the researcher also conducted a paired sample t test for factor 1 and


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42

factor 3 in this case, as both these factors are relatively important factors. The results of

the paired sample t tests are listed below:

Figure 7: Paired sample t test for factors for low income group

Name Factor 1 Factor 3

Mean 4.15 3.38

Standard deviation 0.97 1.12

Paired sample score 2.56 2.56

T score 30.63 21.08

We observe here that the paired sample t score when comparing factor 1 and factor 3 is

2.56 which is higher than the cut off level of +/-1.96 this means that factor 1 is

statistically more significant than factor 3 and can act as a Unique selling proposition if a

developer wants to target low income groups.

4.7 Middle-income group:

The middle-income group in our sample consists of respondents that have income

between £20,000-40,000. The Kaiser-Meyer-Olkin (KMO) measure of sample adequacy

is .575 that is much higher than what the study has in lower income group but still above

the level of 0.50 and says that the sample is adequate to conduct a factor analysis. The

Bartlett’s test of Sphericity that measures for the adequacy of sample and looks at the

correlation among the components to see the need for factor analysis. As said earlier this

test is measured in terms of Chi-square in this case Chi-square is 72.08 at 55 degrees of

freedom. This says that our test is significant at .05 level of significance, the determinants

for Co-variance is 0.080 that is higher than the .0001 cut off level. The compressed

factors and its components are presented below:


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43

Figure 8: Factor analysis for Middle income group.

The test compressed the data into 4 factors (which are listed above with their respective t

scores) it is evident from above chart that factor 1 i.e. Location, Number of beds, Size of

living and Value for money are the factors that are most important to middle income

group people (See figure 8). In order to make sure that factor 1 can act as a differentiating

factor if one wants to target the middle income group the study conducts paired sample t

test (between factor 1 and factor 2 the next strongest factor in terms of t scores). The

statistics for the paired sample t test are reported below:

Figure 9: Paired sample T test for factors for middle income group.


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44


Mean 4.39 3.58

Standard deviation .88 1.26


T score 57.71 32.92

The results of the paired sample t tests suggest that factor 1 is statistically more

significant than factor 2 as the t score is 6.36, that is above the critical t level of +/-1.96.

The researcher feels that factor 1 can act, as a unique selling proposition if one wants to

target the middle income group, laying emphasis on the services that fall under factor 1

can help a developer better position their houses/apartments in the market.

4.8 Higher income group:

The higher income group in our sample consists of respondents that have income of

£40,000 and over. The Kaiser-Meyer-Olkin (KMO) measure of sample adequacy is .569

that says that the sample size is adequate to conduct a factor analysis. The Bartlett’s test

of Sphericity is 150.96 at 55 degrees of freedom that says that the test is statistically

significant at 0.05 level of significance. The covariance matrix determinant is .060, which

is higher than the critical level of .0001. This means that the sample size is adequate and

the sample warrants a factor analysis. The compressed factors and its components are

represented below in figure 10.

Figure 10: Factor analysis for Higher income group


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45

The test compressed the components into 4 factors that are listed above with their

respective t scores. We find that factor 1 that comprises of Sense of community, location,

Number of bedrooms and value for money is relatively more important than the other 3

factors. In order to see that factor 1 can act as a differentiating factor the researcher also

looks at paired sample t test for factor 1 and factor 3 to ensure that factor 1 is statistically

more significant than factor 3. The results of the paired sample t test are given in figure

11 below:

Figure 11: Paired sample t test for factors for High income group.


Mean 4.10 3.54

Standard deviation .99 1.13


T score 63.70 47.86


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46

We can see that factor 1 is statistically more significant than factor 3 with a t score of

5.39 that is higher than +/-1.96. The researcher feels that factor 1 can act, as a unique

selling proportion in terms of satisfaction level if one wants to target the higher income

group.

4.9 Onsite and off site management as an influencing factor in

purchase decision:

The study also looks at the comparison between onsite and offsite management as an

influencing factor in purchase decision (Question 3&4 of Management service

questionnaire). The questionnaires asked the respondents to rate their apartment

management based on 4 criterions depending on how satisfied or dissatisfied they were

with those aspects of their management. The respondents were also asked to state

whether their apartment management was onsite or offsite (Question 2 of Questionnaire

1).The figure 12 below shows the responses as rated by the respondents.

Figure 12: Location of management company.

Where is your Management Company located

19%

14%

7%

21%

34%

5%

On-site

Nottingham

Outskirts

East Midlands

IN UK

Don’t know


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47

As one can see from the above graph that 19% of the respondents reported that they have

on site management in their apartments, the rest of the respondents had offsite

management of which 14% reported that their management company was located in UK,

7% said that their management company was located in the outskirts of the city, 5%

reported that they had no information on where there management company was located

while 21% said that their management company was located in east-midlands.

The researcher feels that the respondents would rate on site management higher than off

site management on all the 4 criterions. The study aims to test the following hypothesis

about onsite and off site management:

H0: There is no difference between the satisfaction levels of onsite and offsite

management and hence no effect on the purchase decision.

H1: The satisfaction levels of onsite and offsite management differ

significantly, hence have a significant effect on the purchase decision.

Figure 13: Breakdown of Factors for On-site management.

The respondents were asked to rate their satisfaction level on the following criterion:


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48

Management

company

Satisfaction

Cleanliness

General

maintenance

Handling of

request and

complains

Value for

money

The respondents were asked to rate their satisfaction on the following scale:

• Satisfied

• Neither satisfied nor dissatisfied

• Dissatisfied

In order to quantify these responses we use the following scale:

Satisfied Neither satisfied nor Dissatisfied Dissatisfied

10 5 2


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49

Figure 14: Scores given for onsite and offsite management.

The matrix below shows the average scores given by onsite and off site management:

According to our sample study, the respondents rated On-site management higher on

almost all the 4 criterions especially on Handling of request and complaints and Value for

money (See figure 14 and 15). The study now aims to explore that are these differences

statistically different the researcher therefore conducts paired sample t tests between all

the 4 criterions of onsite and off site management. The graph below depicts the

satisfaction levels of onsite and offsite management.

Figure 15: Satisfaction levels for Onsite and Offsite management.

Satisfaction levels in case of Onsite and off site

management

6.93

5.866.21 6.29

4.774.30

3.553.02

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Cleanliness Genral

maintenance

Handling of

request and

compains

Value for

money

Criterions

Average satifaction score out of

10

Onsite

management

Offsite

management

Cleanliness Genral maintenance Handling of request and compainsValue for money

Onsite management 6.93 5.86 6.21 6.29

Offsite management 4.77 4.30 3.55 3.02


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50

The study now aims to explore that are these differences statistically different. The

researcher therefore conducts paired sample t tests between all the 4 criterions of onsite

and off site management. The results of the paired sample t tests are presented in the table

below: (See figure16).

Figure 16: Paired sample t test for Onsite and Offsite management.

Cleanliness General

Maintenance

Handling of

requests and

complains

Value for

Money

Onsite Management 9.04 8.02 10.82 6.64

Off site

Management

6.48 5.99 8.41 4.90

Paired (onsite and

offsite

Management)

1.69 2.11 4.06 3.44

The t values for all the criterions in case of onsite management are higher than those of

off site management. In order to see that the satisfaction level in case of onsite

management is higher than off site management the researcher conducts paired sample t

tests, the results of which are also attached in the above table (See figure16). We can

observe although the satisfaction levels in cleanliness for onsite management are higher

than those for off site management, the difference however is not statistically significant

as the t values are lower than the 1.96 cut off level. The satisfaction levels in case of

General maintenance, Handling of requests and complains and value for money are

higher than those of offsite management and the difference is statistically significant with

t statistics of 2.11, 4.06 and 3.44 respectively and all of these are higher than the critical t

value of 1.96. Therefore we reject the null hypothesis at 0.5 level of significance (See

figure 16).


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The researcher feels that On-site management can act as a significant differentiating

factor and given enough awareness can also affect the purchase decision of people

looking for apartments.

4.10 Likelihood of purchase in case of rise in interest rates:

One of the questionnaires aimed to gather opinion of people about the affect of purchase

decision if there were a rise in interest rates (Question 18 & 19 of Management service

questionnaire, Appendix1). This was to know that to what effect the rise of interest rates

have on their purchase decision. The researcher has divided the respondents into 3

categories based on annual income:

• Low income group (£0-20,000)

• Middle income group (£20,000-£40,000)

• High income group (£40,000 and above)

The researcher feels that respondents in the high-income group would be willing to

purchase a property despite rise in interest rates. Looking at the current scenario and the

effortless increase in the real estate market the high-income group respondents would still

be willing to purchase properties.

The response to this question was consistent with the hypothesis laid above. The

respondents were asked to rate their willingness to purchase on a scale of 1-5 where:

• 1= Highly Unlikely

• 2= Unlikely

• 3= Neither likely not unlikely

• 4= Likely

• 5= Highly likely

The average ratings of the respondents are presented in the figure 17 below:


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52

Figure 17: Number of respondents and average score given by respondents.

Low Income Middle Income High Income

Number of

respondents

6 21 30

Average score given

by Respondents

2.17 2.29 2.97

The average scores given by respondents in the Low-income group is 2.17 which is

skewed towards unlikely, even the middle income groups decision seems to be affected

by the rising interest rates. However as predicted the higher income groups willingness to

purchase seems to be unaffected by rising interest rates and the score of 2.97 is skewed

towards 3 which, this says that their decision will be unaffected by the rising interest

rates.

The matrix below shows the responses given by each of the group:

Figure 18: Rating given by respondents of different income profiles.

Highly likelyLikely Neither likely nor unlikelyUnlikely Highly unlikely

Low income 0 0 3 1 2

Middle income 0 2 8 5 6

High income 7 6 2 9 6

There were 7 respondents in case of high-income group who expressed their willingness

to purchase as highly likely despite of rising interest rates and 6 of the respondents said

that they would be likely to purchase despite the rising interest rates. In case of lower

income group 3 of the 6 respondents were undecided on their purchase decision if the

interest rates rose. Due to these 3 respondents the average score of low-income group

rose significantly, if we exclude these 3 cases the average score of low-income group as

rated by respondents would fall to 1.33 that says that they would be unlikely to purchase


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53

in case of rising interest rates. Similarly in case of middle income group 8 respondents

are undecided about their purchase decision if we remove these respondents the average

score of middle income group respondents would fall to as low as 1.84 in case of high

income group intensity of the affect on score is less and the score would be reduced

marginally to 2.96.

The figure 19 below shows a graphical representation of the number of respondents and

their willingness:

Figure 19: Likelihood of purchase in case of rise in Interest rates.

Income wise likelihood of purchase

0 0

50

17

33

0

10

38

24

29

2320

7

30

20

0

10

20

30

40

50

60

Highly

likely

Likely Neither

likely nor

Unlikely Highly

unlikely

Likelihood of purchase

Percentage of respondents

Low income

Middle income

High income

One can observe that high-income group respondents have expressed their willingness to

purchase despite the rising interest rates. The researcher also wanted to conduct a t test to

find the significance of responses but due to the inadequacy in sample size such a test

could not be conducted. However the study infers from the graphs and analysis above

that purchase decisions of high-income group are less likely to be affected by rising


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54

interest rates. In order to identify the effect of interest rates the researcher conducted

regression analysis.

4.11 Regression analysis

In order to test the hypothesis of impact of rise in interest rates on different income

groups we conduct a linear regression analysis. The idea here is that if rise in interest

rates have a differential impact on different income groups the intercept (alpha) of the

regression will be higher for high income group than middle income group. The study

uses linear regression for the following reasons:

• To establish a relationship between dependent variable (likelihood of purchase)

and independent variable (dummy variable).

• The dependent variable is random in nature hence the regression would aim to

establish a relation between income group and the likelihood (willingness to

purchase and income groups).

• The study uses the following regression equation to predict the dependent

variable:

Yi = α+ β (D) +i

Where:

Yi= Dependent variable

α = Intercept

β= Beta

D= Dummy variable

i= Error term.

• The dummy variable is set to 0 if the income group in question is a low income

group and it is set to 1 if the income group is middle or high income group.

• Dummy variables are useful as they enable us to use a single regression equation

for multiple groups.


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55

• In order to make the result of regression comparable the researcher conducts two

such regressions based on middle income and high income group. The study then

compares the intercepts of both the regressions.

4.12 Assumptions:

The study makes following assumptions about the data in order to conduct linear

regression:

• The study assumes a linear relation between the dependent and the independent

variables.

• The independent variable is categorical or quantifiable in nature.

• There is no perfect co linearity between predictor variables.

• The residual terms are uncorrelated that is they are independent terms and has no

auto correlation.

• The residuals in the model are random and normally distributed with a mean of

zero (that is they are random).

The figure 20 below shows graphical representation of the model:


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56

Figure 20: Graphical Representation of the model

Income group classification:

Based on the questionnaires the researcher has classified the income groups into 3

categories:

1. Low income group

2. Middle income group

3. High income group

The Low income group comprises of respondents in income group of 0-£20,000. The

middle income group comprises of respondents in the income group of £20,000-£40,000

Graphical representation of model

Distribute income

groups as high

income, middle

income and low

income

Conduct regression

1 for middle and low

income group and

set the dummy 1 for

middle income

Conduct regression

2 for low income and

high income group

set dummy 1 for high

income

Compare the

intercepts of both

regressions

If intercept of high

income group is

higher than middle

income group then

alter. Is true


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57

this group inculcated the average UK salary that is £ 25,000. The high income group

consists of respondents in the income bracket of £40,000 and above.

4.13 Results of Regression analyses:

The study looks at testing the following hypothesis:

H0 = Rise in interest rates do not affect different income groups differently

H1 = Rise in interest rates will affect different income groups differently.

The results of regression show that there is a deferential impact of interest rates on

different income groups. The following table depicts the results of regression analysis:

Figure 21: Results of Linear Regression:

Type R2 Alpha (α) T values

Middle income group .026 2.0 4.44

High income group .088 2.167 3.90

The table(See figure 21) above summarises the statistics of regressions carried out in

order to test the hypothesis. It is evident from the table that high income group

respondents are less likely to change their purchase decision in case of rising interest

rates. The intercept in case of high income group is 2.167 and in case of middle income

group is 2.00 the t value for intercept of middle income group is higher that the t vale for

high income group however both the values are statistically significant and above the cut

off level of 1.96. The table also shows that R2 in both the cases is low, but this doesn’t

affect the result of the studies as the method deployed in this case is more concerned with

looking at the values of α more than any thing else.


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58

Chapter 5: Limitations


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59

5. Limitations:

The research in this project specifically aimed at the city centre residents of Nottingham

for Management Service questionnaire and staff of University of Nottingham for the

Purchasing Behavior questionnaire. The research aimed at identifying the factors

affecting the purchase decisions and the effect of rise in interest rates on such decisions.

5.1 Limitations of the Postal survey:

In this research there were certain limitations which could have affected the outcome of

certain results. The group did postal survey of city centre apartment residents living in

Nottingham. The survey didn’t include people living elsewhere in Nottingham and people

living in houses, who might also be considering purchasing an apartment in future. The

postal survey was conducted during summers, when most of the people were on vacations

which could have affected the response rate.

5.2 Limitations of Online Survey:

The limitation of this questionnaire was that it focused more on the effects of affordable

housing on the development. The group received 66 responses for this questionnaire, a

response rate of 11% for about 600 respondents. To not to affect the focus of the

questionnaire the question on interest rate wasn’t included in this questionnaire. So the

data and responses from this questionnaire could not be used to analyze the effect of rise

in interest rates on purchase decision of people of different income profiles. The

questionnaire was complex and lengthy which may have altered the customers’ responses

by finding them difficult if not impossible to understand. The online survey was

conducted during summers, when most of the people are on vacations and this might

have affected the response rate. Another limitation was that we had a moderate response

rate and subsequently a small data set for our analysis due to the fact that mixed income


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60

housing is a sensitive matter and not many people would like to express their comments

on such a sensitive issue.

5.3 Assumptions:

The researcher has combined responses of both the questionnaires for the study to have a

large data set in order to produce better results. Data was collected for the factors

affecting the purchase of a property from both the questionnaires Question 8&19 of

Management service questionnaire (Appendix 1) and Question 5&12 of Purchasing

Behaviour questionnaire (Appendix 2). This was done to understand the factors that are

more important to people of Low, Middle and Higher Income group.

Another assumption is that purchasing behaviour of people in Nottingham is akin to the

purchasing behaviour of people all over UK. Factors affecting the decision to invest for

people in Nottingham are assumed to be similar to factors affecting the decision of people

all over UK.


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Chapter 6: Conclusions


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62

6. Conclusion:

This research identified, prioritized and categorized factors affecting the decision to

invest in a residential property of different income groups. To facilitate the analysis of

data, the study uses factor analysis to classify data based on Co- variance and Correlation

and compressed the number of factors into clusters. These clusters of factors were then

related to various income groups, as different income group people have different needs.

It can be inferred from the study that factors such as Access to public transport, Number

of bedrooms, Location and Value for Money were highly rated by lower income group

people. Location, Number of bedrooms, Size of living accommodation and Value for

money were the factors that were highly valued by middle income group people. Higher

income group people rated number of bedrooms, sense of community, location and value

for money as the most important factors. So for a developer like Southreef properties,

which is targeting at the higher end of the market should consider all the factors which

were rated highly by middle income and higher income group people. A developer should

keep in mind all these factors i.e. large number of bedrooms, location, value for money,

Size of living accommodation, Sense of community and Value for money and try to cater

these services better in order to target and position its development successfully.

The research also focused on to determine the satisfaction levels of On-site and Off-site

management with respect to services such as General maintenance, Handling of requests,

complaints and value for money. After conducting quantitative analyses this study

accepts the alternative hypotheses and concludes that satisfaction level in case of On-site

management are statistically higher than those of Offsite management. The researcher

feels that including On-site management with high levels of service commitments would

act as a differentiating factor and contribute to the future valued of the development. So a

developer, like Southreef Properties should include On-site management in their

development.


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63

The researcher also conducted a study to determine the extent of effect of 0.5% rise in

interest rates on the purchase decision of various income groups. According to the

research, Low-income group and middle income group people seem to be affected by the

rise in interest rates. The research confirmed with the help of regression analyses that

respondents in the high-income group would be generally be willing to purchase a

property despite a rise in interest rates. So the study accepts the alternative hypotheses,

that high income group people are less likely to be affected by 0.5% rise in interest rates

in their purchase decision.

The conclusions drawn from the study should be helpful to Southreef Properties Ltd or

for any other development. These factors should be considered by the developer for the

success of the development.

6.1 Further Research:

The researcher has implemented basic statistical tools and questionnaires to analyze the

important factors affecting the purchase decision. But there are avenues open for further

research in terms of using focused questionnaires for the primary research on

investigating the micro factors affecting the investment decision of the respondents.

There is scope for a focused study with a larger sample and more advanced techniques

could be used to produce better results.


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64

Chapter 7: References


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Chapter 8: Appendix


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Appendix1:

Management Service Questionnaire


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MANAGEMENT SERVICE 1. Is there a service charge for the upkeep of communal parts of the property? Yes No 2. Is your management company located …? On-site In the Nottingham City Centre In Nottingham, but outside the City Centre In the East Midlands Elsewhere in the UK Don’t know ANSWER THIS QUESTION IF YOU HAVE AN ON-SITE MANAGEMENT/CONCIERGE

SERVICE. 3. Thinking about the communal areas of your property, how satisfied are you with the

management in respect of: Very Fairly Not There are no Satisfied satisfied satisfied communal

areas Cleanliness General maintenance Handling of requests/complaints Value for money ANSWER THIS QUESTION IF YOU DO NOT HAVE AN ON-SITE MANAGEMENT/CONCIERGE

SERVICE. 4. Thinking about the communal areas of your property, how satisfied are you with the

management in respect of: Very Fairly Not There are

no Satisfied satisfied satisfied communal

areas Cleanliness General maintenance Handling of requests/complaints Value for money 5. How would you rate the importance of a management service within apartment blocks? High Importance Medium Importance Low Importance 6. Which area of the management service would you most like to see improved? Please tick one only

Cleanliness General maintenance Handling of requests complaints Value for money Other (please state) ___________________________________________________________


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YOUR FUTURE PLANS 7. Please rate the probability of you purchasing a city centre apartment as your next move? Please tick one only Highly likely Likely Neither likely Unlikely Highly Unlikely Nor unlikely 8. What are the important matters when considering your next move or purchase?

Please tick the appropriate box, where 1 = not at all important and 5 = extremely important. 1 2 3 4 5 Developer’s reputation Good access to public transport Location

Close to recreation and leisure facilities Number of bedrooms Size of living accommodation On-site-management facilities Strong sense of community among neighbours Value for money Good on-site security Access to car parking Other (Please state) _______________________________________________________

9. Which other services are you likely to use in an apartment complex, if made available at a reasonable charge?

Highly Likely Neither likely Unlikely Highly Likely or Unlikely Unlikely Apartment cleaning Car valeting Laundry Ironing Key holding** Personal Shopping Gym Contract parking Storage facilities Car Hire Other (please state) _______________________________________________________ **Taking delivery of groceries, watering plants whilst on holiday, etc. 10. Are you considering moving or buying another property within the next 2 years? Yes No Don’t know If your answer is yes go to question No 11. 11. If purchasing an apartment, will you purchase the property …? As an individual As an investor As a shared owner with a partner


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As a shared owner with friend(s) As a shared owner with a housing association or similar association

PERSONAL PROFILE 12. Please tick your relevant age band? 18 – 24 25 – 34 35 – 53 35 – 45 45 – 55 55 plus 13. Are you currently working either full-time or part-time? (This includes any part-time working by students and those on training courses.) No Yes 14. Which of the following best describes the type of work you have in your main job? Please tick one only Senior management or professional Intermediate management, administrative or professional Supervisor, clerical, junior management, administrative or professional Manual worker (with industry qualifications) Manual worker (without industry qualifications) Self-employed 15. How long have you lived at your current address?

a) Less than a year b) Between 1 and 2 years c) Between 2 and 5 years d) 5 years plus

16. How many people live in your household? Number of adults Number of children 17. How many bedrooms do you have? 1 2 3 Other please state _______ 18. Please rate the probability of you purchasing a city centre apartment as your next move if interest rates move up to 6% from the current rate? Please tick one only

Highly Likely Neither likely Unlikely Highly Likely or Unlikely Unlikely 19. What is your household annual income (before tax deductions)? £10,000 - £19,999 £40,000 - £49,999 £20,000 - £29,999 £50,000 - £59,999 £30,000 - £39,999 £60,000 or over


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THE UNIVERSITY OF NOTTINGHAM WILL DETACH THIS PART OF THE FORM WHEN RECEIVED IN ORDER THAT THE QUESTIONNAIRES REMAIN ANONYMOUS. FULLY COMPLETED QUESTIONNAIRES WILL BE PLACED IN THE DRAW AND THE WINNER TO RECEIVE £250 CASH. NAME : ADDRESS : TELEPHONE NO. : E-MAIL :


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Appendix 2:

A sample response to:

“Purchasing Behaviour

Questionnaire”


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We are working with the Nottingham University Business School on a research project to understand how

mixed tenure residential developments are perceived and what influences your decision to purchase or rent.

Mixed tenure developments include residences that are available to purchase outright, with a mortgage or on a shared ownership basis and may also include residences available to rent privately or on an affordable basis (i.e. through a Housing Association or Local Council). We would ask that you complete the questionnaire and return it to us as soon as possible. We can assure you that all information received will be treated as strictly confidential. THANK YOU FOR TAKING THE TIME TO COMPLETE THIS SURVEY. Section A – Purchasing and Renting Profile & Preferences 1. Please rate the probability of you purchasing a property in the next 2 years. Please tick one only

Highly

Likely Likely

Neither unlikely

nor likely Unlikely

Highly

Unlikely

Go to question 3. 2. Please rate the probability of you renting a property in the next 2 years. Please tick one only

Highly

Likely Likely

Neither unlikely

nor likely Unlikely

Highly

Unlikely

Go to question 3 and then to question 5. 3. What type of property will you consider purchasing/renting? House Apartment Other ____________________ (please state) 4. Will you be purchasing as: An owner-occupier An investor Through the New Homebuy Scheme (i.e. shared ownership) 5. What are the important matters when considering your next property to rent or purchase?

Please tick the appropriate box, where 1 = not at all important and 5 = extremely important. 1 2 3 4 5 Developer’s reputation Good access to public transport Location

Close to recreation and leisure facilities Number of bedrooms Size of living accommodation On-site management services Development tenure, i.e. private or mixed Sense of community among neighbours Value for money On-site security Access to car parking


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Other (Please state) _______________________________________________________

5. Please rate the probability of you purchasing or renting a property within a development

that includes the following tenures … Please tick the most appropriate box for each statement

Ω See definitions below Highly

Likely Likely

Neither unlikely

nor likely Unlikely

Highly

Unlikely

Private purchase only

Private purchase & private rent

Private purchase & shared ownership

Private purchase & affordable rent

Private purchase, shared ownership &

private rent

Private purchase, shared ownership,

private rent & affordable rent

Shared ownership & private rent

Shared ownership & affordable rent

Private rent & affordable rent

Ω Private purchase – purchase all of the property with or without a mortgage Shared ownership – purchase a share of the property with the assistance from a Housing

Association or similar body Private rent – Renting directly from a private landlord Affordable rent – Renting from a Housing Association or Local Council 6. Would you consider a development that included properties for affordable rent if …

(Please tick the most appropriate box for each statement)

Highly

Likely Likely

Neither unlikely

nor likely Unlikely

Highly

Unlikely

All affordable tenants are employed

All affordable tenants are key workers**

There is a mix of affordable tenants, i.e.

employed and unemployed

All affordable tenants are subject to

rigorous vetting procedures prior to

allocation

24 hour CCTV & security is present

On-site management suite & concierge

service is present

An access control system is present

The development is designed to keep the

separate tenures in separate blocks.


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**A key worker is someone who works in a key public service, such as teachers, fire fighters, police officers, clinical NHS staff (excluding doctors and dentists) etc.


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7. Please tick the statement that you feel most relevant to each statement: Please tick one box for each statement

Strongly

Agree Agree

Neither Agree Nor

Disagree Disagree

Strongly

Disagree

Mixed tenure should be introduced into

residential apartment developments.

Mixed tenure is more suited in housing

developments rather than apartment

developments.

The inclusion of affordable tenants within

an apartment development will negatively

affect the future value of all the

apartments within that development.

The inclusion of affordable tenants within

an apartment development will positively

affect the future value of all the

apartments within that development.

I would be willing to rent in a mixed tenure

apartment development.

I would be willing to purchase in a mixed

tenure apartment development.

Mixed tenure apartment developments are

desirable and sustainable.

Section B: Personal Profile 8. Please tick your relevant age band? Please tick one 18 – 24 35 – 44 55 plus 25 – 34 45 – 54 9. Are you …? Male Female 10. Are you currently working? (This includes any part-time working by students and those on training courses.) No Yes Other ____________________


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11. Which of the following best describes the type of work you have in your main job? Please tick one Senior management or professional Intermediate management, administrative or professional Supervisor, clerical, junior management, administrative or professional Manual worker (with industry qualifications) Manual worker (without industry qualifications) Self-employed 12. What is your household annual income (before tax deductions)? Please tick one £10,000 - £19,999 £40,000 - £49,999 £20,000 - £29,999 £50,000 - £59,999 £30,000 - £39,999 £60,000 or over

PLEASE USE THE SPACE BELOW TO PROVIDE ANY OTHER COMMENTS: