Chapter 1 Introduction 1.1 Introduction to Real

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Chapter 1

Introduction

1.1 Introduction to real state sector:

Real estate or immovable property is a legal term (in some jurisdictions) that encompasses land

along with anything permanently affixed to the land, such as buildings. Real estate is often

considered anonymous with real property (also sometimes called reality), in contrast with personal

property (also called personality). However, in technical terms, real estate refers to the land and

fixtures themselves and real property are used primarily in over real estate. The term real estate

and real property are used primarily in common law, while civil law jurisdiction refers instead to

immovable property. In law, the word real means relating to a thing as distinguished from a

person. Thus the law broadly distinguishes between real property (land and anything affixed to it)

and personal property (everything else e.g. clothing, furniture, money).

(a) Real Estate Business Includes:

With the development of private property ownership, real estate has become a major area of

business. Purchasing real estate requires a significant investment and each parcel of land has

unique characteristics, so real estate industry has evolved into several distinct fields.

Some kind of real estate businesses include-

Appraisal Professional valuation services

Brokerage Assisting buyers and sellers in transactions

Development Improving land for use by adding or replacing buildings

Property Management Managing a property for its owner(s)

Real Estate Marketing Managing the sale side of the property business

Relocation Services Relocating people or business to difficult country

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Eighty percent share of the real estate market is garnered by residential sector and the rest is

comprised of offices, shopping malls, hotels and hospitals. The sustained demand from the

Information Technology (IT) sector has fuelled the growth of real estate sector. It has been

estimated that the demand for IT space would be 66 million square feet over the next five years.

Several multinational companies are shifting their operations to India to take advantage of the

relatively low costs. With human resources being the key element in this industry, hiring people

and housing them assume great importance. The need to create space for people to work and live

triggers the development of other related infrastructure.

Traditionally, the government's support to housing had been centralized and directed through the

State Housing Boards and development authorities. In 1970, the Government of India set up the

Housing and Urban Development Corporation (HUDCO) to finance housing and urban

infrastructure activities. In 2002, the government permitted 100 per cent foreign direct investment

(FDI) in housing through integrated township development. However, FDI rules at the moment are

quite stringent. For FDI in real estate prior approval of the Foreign Investment Promotion Board is

required, which, can be rather tedious and there is a lock-in period for repatriation of the original

capital invested for a period of three years. On the top of it the rules stipulate a minimum land

holding of 100 acres. Getting 100 acres of free land in an urban area is almost impossible. Hence

the permission of FDI in real estate hasn't had the desired effect.

The boom in retail industry has also spurred the growth in real estate sector. India at the moment is

witnessing a spurt in extremely large retail spaces. Shopping malls with over 1 million sq ft of

space have become the order of the day. As the competition in the market intensifies, builders are

going out of their way to be different. Specialized malls, designer brands and multi-movie options

are the order of the day. With the big players like Reliance, Big Bazaar, and Bharti entering retail

market, real estate sector would be the big beneficiary.

The prospects for real estate industry in India looks buoyant. All the factors which contributed tothe growth of real estate sector-high disposable incomes, sharp increase in global liquidity,

selective capital account liberalization, looser credit policies, a greater availability of leverage due

to financial liberalization and a consequent increase in mortgage lending and price increases-look

set to continue.Indian real sector has seen an unprecedented boom in the last few years. This was

ignited and fueled by two main forces. First, the expanding industrial sector has created a surge in

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According to Housing Skyline of India 2007-08, a study by research firm, Indicus Analytics,

there will be demand for over 24.3 million new dwellings for self-living in urban India alone by

2015. As a result of this, this real estate sector is likely to throw huge investment opportunities. In

fact, an estimated US$ 25 billion investment will be required over the next five years in urban

housing, says a report by Merrill Lynch.

As far as the commercial property is concerned, the fast growing Indian economy has a cascading

effect. The growth will propel the demand for commercial spaces and space for modern offices,

warehouses, hotels and retail shopping centers. More over the demand for commercial office

space is led by the information technology (IT) industry and organized retail. For example, it is

estimated that the IT and ITes alone is estimated to require 180 million sqft. by 2010. Similarly,

the organized retail industry is likely to require an additional 220 million sqft. by 2010. This hugedemand will spill over to all parts of urban India. Lease rentals have been picking up steadily and

there is a strong demand for quality infrastructure. A significant demand is also likely to be

generated as the outsourcing boom moves into the manufacturing sector.

With the significant investment opportunities emerging in this industry, a large number of

international real estate players have entered the country. Currently, foreign direct investment

(FDI) inflows into the sector are estimated to be between US$ 5 billion and US$ 5.50 billion.

According to Cushman & Wakefield, foreign investors have raised nearly US$ 30 billion since

March 2005 for investing in Indian real estate. 100% FDI is allowed under automatic route in

townships, housing, built-up infrastructure and construction development projects (which would

include, but not be restricted to, housing, commercial premises, hotels, resorts, hospitals,

educational institutions, recreational facilities, city and regional level infrastructure) subject to

certain guidelines. Leading companies like Carlyle, Blackstone, Morgan Stanley, Trikona, Warbus

Pincus, HSBC Financial Services, Americorp Ventures, Barclays and Citigroup are some of the

international players who have entered into Indian reality market. Real estate accounted for 26 per

cent of total value of private equity investments, with 32 deals valued at US$ 2.6 billion. And

according to industry estimates, another US$ 10-20 billion would pour into the sector in the next

three years.

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The Government has introduced many progressive reform measures to unlock the potential of the

sector and also meet increasing demand levels.

100 per cent FDI allowed in realty projects through the automatic route.

In case of integrated townships, the minimum area to be developed has been brought down to 25

acres from 100 acres.

Urban Land (Ceiling and Regulation) Act, 1976 (ULCRA) repealed by increasingly larger

number of states.

Enactment of Special Economic Zones Act.

Minimum capital investment for wholly-owned subsidiaries and joint ventures stands at US$ 10million and US$ 5 million, respectively.

Full repatriation of original investment after three years.

51 per cent FDI allowed in single brand retail outlets and 100 per cent in cash and carry through

the automatic route.

1.3 Tax and the Regulatory Environment

Over a period of time the Tax and the Regulatory Environment in the Real Estate Sector have

become very important. The Construction Industry is already subject to a number of taxes and is

considered as one of the overburdened tax segments. The corporate involved in this segment are of

the general opinion that there should not be any further imposition of any levy in any form in this

particular sector of the economy. Any further tax burden to this sector would affect the orderly

growth and development of the Real Estate Sector.

Some of our recommendations for this industry are as follows:

Imposition of Service Tax: Service tax in relation to construction of residential complexes

having more than 12 houses has been imposed. However, no rational has been provided for

exclusion of services in relation to construction of residential bungalows which may not form

part of a residential complex. There seems to be no plausible rationale for taxing residential

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complex and not construction of a bungalow which may entail a higher cost of construction in

many cases. Further no rational has been provided for the threshold of 12 dwelling units in a

residential complex. FICCI is strongly of the view that Service Tax should not be imposed in

the case of construction industry as the said industry is already paying a number of taxes on

different inputs purchased for constructing the houses in addition to taxes such as Works

Contract Tax (WCT).

Value Added Tax (VAT): VAT has been introduced in 20States. FICCI has advocated

that the other states should also put the system in place as soon as possible. This would help in

the free movement of goods across all the states. It is a well-known fact that the system is

beneficial to all stakeholders- Consumers, Manufacturers, the State Government and Central

Government. Moreover, it is important that the concerns of traders and the Corporate Sector

should be resolved. For the successful implementation of VAT, it is important that there should

be uniformity in rates, rules and regulations across all the States. Not only do the rules vary

from State to State but so do the regulations and the procedures. There is an urgent need to

abolish CST, as VAT and CST cannot go hand in hand. It is important that local levies be

completely abolished from all States. Allowing of Credit for interstate transfer should be

brought in at the earliest.

Free Trade Agreement (FTA): The Government may consider signing up of more FTAs

with other countries in the interest of the Real Estate Segment. However, while doing so the

interest of domestic players in that particular segment should also be kept in mind.

Form C: Uniformity regarding permission to issue a Form-C for the purpose of purchasing

the goods to be use din the works contracts. The State Governments should abide by the

Central laws regarding the issuance of Form-C.

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Central Sales Tax (CST): According to the CST norms, the sale definition includes works

contract. Hence, any goods moving from one State to another for the purpose of usage in

execution of works contract now falls under the ambit of inter-State works contract and the

State from which goods move is liable to impose a tax. Incidentally the trader ends up paying

tax in the State from which the goods moved, on the same item the tax has already been paid in

the place where the execution of the contract has taken place. This portrays a dichotomy in

taxation. The actual legal position is that the trader need not pay the tax in testate where the

work contract is executed on the inter-State purchases.

Excise Duty on Immovable Property: Excise duty should not be levied in the case of

Immovable property like in the case of Installations such as lifts among others. The present

norm that the goods cleared under CKD would have to pay excise duty should be done away

with.

1.4 Future of Real Estate In India

With the economy surging ahead, the demand for all segments of the real estate sector is likely to

continue to grow. The Indian real estate industry is likely to grow to US$ 90 billion in by 2015.

The future of the real estate sector in India is going to be guided by two important

factors, namely suitable amendments in the Foreign Direct Investment (FDI) guidelines in

townships, housing, built-up infrastructure and construction development projects as well as

abolition of Service Tax on the construction industry especially the housing sector. Conversely,

if the abolition per se is not possible then drastic modifications in the existing Service Tax

norms is the need of the hour. This Sector is already overburdened with taxes; any further

imposition of taxes in any form would adversely affect the growth of this sector of the

economy.

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Foreign direct investment in India in real estate sector recent regulatory

changes

Press Note No. 2 (2005 Series) Indian FDI Policy 2006

With a view to catalyzing investment in townships, housing, built-up infrastructure and

construction development projects as an instrument to generate economic activity, create new

employment opportunities and add to the available housing stock and built-up infrastructure, the

Government of India has decided to allow FDI up to 100% under the automatic route in townships,

housing, built-up infrastructure and construction development projects (which would include, but

not be restricted to, housing, commercial premises, hotels, resorts, hospitals, educational

institutions, recreational facilities, city and regional level infrastructure), subject to the following

guidelines:

100% FDI is allowed under automatic route in townships, housing, built-up infrastructure and

construction development projects (which would include, but not be restricted to, housing,

commercial premises, hotels, resorts, hospitals, educational institutions, recreational facilities, city

and regional level infrastructure) subject to certain guidelines

1.5 Risk and Return:

1.5.1 Risk

Risk is an important consideration in holding any portfolio. The risk in holding securities is

generally associated with the possibility that realized returns will be less than the returns expected

Risks can be classified as Systematic risks and Unsystematic risks.

Unsystematic risks:

These are risks that are unique to a firm or industry. Factors such as management capability,

consumer preferences, labor, etc. contribute to unsystematic risks. Unsystematic risks are

controllable by nature and can be considerably reduced by sufficiently diversifying one's portfolio.

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Systematic risks:

These are risks associated with the economic, political, sociological and other macro-level

changes. They affect the entire market as a whole and cannot be controlled or eliminated merely by

diversifying one's portfolio.

The three main risk associated with investing in a share are

1. The value of your investment could fall.

2. The amount of income you receive can fall, or stop altogether.

3. Your investment may increase at a lower rate than the rate of inflation, thus eroding the

purchasing power of your investment.

How to minimize the risks?

The company specific risks (unsystematic risks) can be reduced by diversifying into a few

companies belonging to various industry groups, asset groups or different types of instruments

like equity shares, bonds, debentures etc. thus, asset classes are bank deposits, company deposits,

gold, silver, land real estate, equity share, computer software etc. Each of them has different risk-

return characteristics and investments are to be made, based on individuals risk preferences. The

second category of risk (systematic risk) is managed by the use of beta of different company shares

1.5.2 Return

The gain or loss of a security in a particular period is called return. The return consists of

the income and the capital gains relative on an investment. It is usually quoted as a percentage. The

general rule is that the more risk you take, the greater the potential for higher return - and loss.

Return can come from two sources, capital growth and income. Capital growth occurs when the

market value of the share increases. Income is the cash flow paid by a share such as dividends.

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1.5.4 Methods to Calculate the Risk

Standard Deviation:

Volatility is a direct indicator of the risk of the fund. The standard deviation of a fund

measures this risk by measuring the degree to which the fund fluctuates in relation to its

average return of a fund over a period of time. A security that is volatile is also

considered higher risk because its performance may change quickly in either direction at

any moment.

Beta

Beta is a measure of the volatility, or systematic risk, of a security or a portfolio in

comparison to the market as a whole. Beta is fairly a commonly used measure of risk. It

basically indicates the level of volatility associated with the fund as compared to the

benchmark and is also known as "beta coefficient". So quite naturally the success of Beta

is heavily dependent on the correlation between a fund and its benchmark. Thus if the

funds portfolio doesnt have a relevant benchmark index then a beta would be grossly

inadequate.

Beta can be calculated using regression analysis, and beta is the tendency of a security's

returns to respond to swings in the market. A beta that is greater than one means that the

fund is more volatile than the benchmark, while a beta of less than one means that the

fund is less volatile than the index. A fund with beta very close to 1 means the funds

performance closely matches the index or benchmark.

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1.6 Objectives of the Study

To analyze the risk and return pattern of selected securities of real estate

companies for Year 2008-09

To compare the performance of the real estate stocks with that of the Nifty index.

To analyze the performance of the real estate companies before and after

announcement of the interim budget 2009.

1.7 Need for the Study

Real state sector in considered one of the most booming sector but it is also the most

affected sector during last year sub prime crisis. Despite of effect of sub prime crisisaccording to Asschom real estate sector will receive huge FDI in India. The real estate

sector is second only to agriculture in terms of employment generation and Five per cent

of the country's GDP is contributed to by the housing sector. In the next five years, this

contribution to the GDP is expected to rise to 6 per cent So this necessitates the need for

analyzing the risk and return relationship of the selected stocks of real estate sector.

1.8 Scope of Study

Study will be conducted to analyze the performance of the ten selected companies of real

estate sector for the Year 2008-2009 and compare the same with the performance of the

NIFTY in the Year 2008-2009. In addition to this the effect of interim budget 2009 on

real estate is also taken for study.

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Chapter 2

Review of Literature

Barry, Rodriguez and Lipscomb (1996) examined real estate in emerging markets as an

asset class. Based on data from 1989 to 1995 for a composite index, they report than real

estate in emerging markets provided diversification opportunities for common stock

portfolios and for real estate portfolios in developed markets.5 Due to limitations in their

data, they did not examine real estate diversification opportunities for investors within

any individual emerging market or how any individual emerging markets real estate

investments may provide diversification opportunities to global investors.

Christopher B. Barry and Mauricio Rodriguez1 (1996) examined the investment

performance and diversification benefits of real estate investments in emerging capital

markets using property indices, and we contrast the risk and return characteristics of

those property indices with the broader equity markets in those countries and with real

estate and broader equity investments in developed markets. Real estate indices

experienced relatively high total risk and low returns, but only a few of theseindices

underperformed on a risk-adjusted basis. Real estate investments underperformed equity

investments in both emerging anddeveloped markets during the period examined.

However, only a few real estate indices significantly underperformed their BMI

counterparts on a risk-adjusted basis.

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Barry, et al. (1997) document that the market capitalization of equity for emerging

markets tracked by the International Finance Corporation (IFC) grew from $167 billion in

1985 to $603.5 billion in 1990. The market capitalization of all emerging markets

(including some not previously tracked by the IFC) grew to more than $3 trillion by the

end of 1999. The market capitalization of developed markets experienced a little over a

three and three quarters-fold increase over the same time interval. Hence, the market

capitalization of emerging markets as a percentage of world market capitalization

increased during the past decade.

Lai and Wong (1998) examined and claimed that the root causes for understated

volatility is real estate market inefficiency. The link between market inefficiency and the

distortion in real estate risk measures, however, is yet to be demonstrated. Private real

estate returns are appraisal-based and appraisal practices bias against timely and adequate

updating of real estate valuation in response to new information . The effect of these

biases is called appraisal smoothing. The adequacy of this explanation is also studied by

them

Bulchandani (1998) andShrivakumar (1999). Kallberg, et al. (2002) in particular

examine real estate markets in Asia during 1992-1998 and conclude that the 1997-1998

period reflected a regime shift in which real estate contributed much to the chaos in

those markets. They argue that increased risk and decreased diversification opportunities

for real estate resulted within the markets.

Quan and Titman (1999) examine real estate prices versus equity market values and

find relatively low contemporaneous correlations between equity market values and real

estate prices, but they find higher correlations across time and on a pooled basis.

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Seiler, Webb and Myer (1999) reviewed literature related to real estate diversification

within mixed-asset portfolios and diversification within real estate portfolios. They

reviewed various real estate portfolio diversification mixtures and diff option adopted by

different counties real estate portfolio. Various issues were also discussed in their study

related to real estate investment. Also found that international real estate securities

provide some incremental diversification benefits over common stocks even if currency

risks are hedged.

Corgel and deRoos (1999) summarize the extraordinary risk and return relationships

found in private real estate markets that have long been uneasy to real estate researchers.

Private real estate returns have abnormally low coefficient of variation relative to other

risky assets, including real estate securities, and exhibit little correlation with stock-

market returns and returns on real estate securities

Hamelink et al. (2000) argue that the classification of property markets defines the

dimensions of market risk. This implies that the drivers of property-market performance

are influenced differentially by office, retail, and industrial markets. By diversifying

efficiently across those property types, commonality in returns is achieved. The same

applies for geographical or regional diversification and for combined property type and

area classifications.

Yuming Fu and Lilian K Ng (2000) investigated the price adjustment process in real

estate and stock markets. They showed that the speed of price adjustment to news affects

the volatility and correlation statistics of excess returns. We find that quarterly real estate

price captures only slightly more than half the effect of news, but quarterly stock price

captures the full effect of news. Slow price adjustment not only induces highly positive

auto correlation in real estate excess returns, but also dampens their volatility and

correlation with stock market returns. Their analysis identified a cumulative price

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adjustment that is most informative of news in the real estate market. While preserving

the sample mean of real estate return series, the cumulative price adjustment recovers a

greater volatility and a higher correlation with stock market returns that would be

observed if real estate pricing were more efficient.

Brooks and Tsolacos(2001) employ a number of time series techniques to assess the

Predictability of securitized real estate returns in the U.K. They find that a VAR model

which incorporates financial spreads exhibits a better short-term out-of-sample

forecasting performance than unvaried time series models. However, after establishing

trading rules with the forecasts, no excess returns are found over a buy-and-hold strategy

once transaction costs are accounted for. In a follow-up paper, they compare the

Predictability of ARMA, VAR, and neural networks models in five European countries.

They conclude that whilst no single technique is universally superior, the neural networks

model generally makes the most accurate predictions for one-month horizons.

Ravi Bansal and Magnus Dahlquist (2002) showed that the cross-sectional differences

in the equity returns across sovereign economies is determined by two featuressystematic risk and a selectivity premium. We show that the selectivity premium captures

more than 1/2 of the average risk premium in emerging markets. The equity risk premia

in developed markets seems to be driven solely by systematic risk. The main economic

implication of this result is that after taking account of selectivity premium all

international equity returns reflect systematic risk, as predicted by theory. Empirical work

also shows that sovereigns that have better financial market reputations and trade more

actively have to pay a smaller selectivity premium. This empirical evidence lends support

to the view that both reputations and fear of trade sanctions are important in determining

the cost of equity borrowing for a sovereign nation.

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Conover, et al. (2002) report that foreign real estate had a lower correlation with U.S.

stocks than did foreign stocks in a period encompassing the stock market crash of1987.

They conclude that the lower correlation from real estate (unlike the correlation Benefits

of foreign equities) was relatively stable through time, suggesting diversification Benefits

even during times of crisis.

Ling and Naranjo (2002) found international diversification benefits from commercial

real estate securities across several developed markets. Although their data included a

few emerging markets, they did not examine how these markets performed relative to

developed markets. They provided a calculation of the cut-off correlation in each

market. The cut-off correlation is the correlation above which real estate would have a

weight of zero in the countrys minimum variance combination of real estate property

and equity market index. To examine diversification opportunities available for global

equity portfolios, they provided the same metrics based on correlations between each

property index and the global BMI

Bond, Karolyi and Sanders (2003) examined risk and return characteristics of publicly

traded real estate indices in 14 developed markets and find evidence of a strong global

market factor in the returns of those markets. They took multiple factors and multiple

analysis method to find most suitable factor.

Ooi and Liow (2004)examine the risk-adjusted returns of real estate securities traded in

seven Asian markets. Panel regressions were conducted to shed light on how the firm-

specific attributes and time-varying factors affected the risk-adjusted returns of the real

estate securities across different markets and over time. They find that securitized real

estate in five of the East Asian economies underperformed the general stocks between

1992 and 2002.

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Monteiro, Mohan & Rai, Rahul (2005) studies the Indian real estate sector and various

investment opportunities in the Indian real estate sector and the capital market

instruments that are presently available to local and foreign investors to enter this

emerging asset class from an international investors perspective

Ellis and Wilson (2005) find that portfolios constructed with neural networks techniques

consistently out-perform the market on both a nominal and risk-adjusted basis. In the

direct real estate literature, the performance of neural networks is less conclusive. The

quality of the house price predictions obtained with this technique is supported by some

researchers but criticized by others. Their results indicate that the best predictions are

obtained with neural networks models and especially when the model includes stock,

bond, real estate, size, and book-to-market factors.

Liow and Sim (2006)examine the risk and return profile of Asian property securities

from an American investor's point of view. Their results indicate that Asian property

securities markets had not produced high levels of compound returns relative to the US

REIT and UK real estate securities markets In addition, Asian property securities

experienced a higher level of volatility compared to their USA and UK counterparts.

Camilo Serrano & Martin Hoesli (2007) examined whether the predictability of

securitized real estate returns differs from that of stock returns. It also provides a cross-

country comparison of securitized real estate return predictability. Empirical distributions

of the prediction errors reveal differences in predictability between securitized real estate

and stock returns. So is the case when excess returns of an active strategy over a passiveinvestment are used in the analysis. The latter results, however, allow for the economic

significance of the results to be assessed. These analyses show that the maturity of the

securitized real estate market plays an important role in the predictability of its returns. In

particular, we find that in countries with established REIT regimes, securitized real estate

returns are more predictable than stock returns as the rental focus of REITs generates

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more predictable income returns. Stated differently, tax transparency is likely to lead real

estate securities to behave more like the underlying real estate assets. This finding is

confirmed with the cross-country comparisons of securitized real estate return

predictability.

Kim Hiang Liow(2008) examined that Asian real estate and equity maxima and minima

return series are characterized by a fat-tailed Frchet distribution. The frequency and

severity of extreme Asian real estate returns are greater than their European and North

American counterparts. Securitized real estate markets are riskier than the broader stock

markets before and during the Asian financial turmoil. In contrast, many stock markets

become riskier after the financial crisis with their VaRs higher than the equivalent VaR

estimates for the real estate series. International real estate portfolio risk management

should include both extreme risks and standard deviations. Accordingly, global investors

should be even more cautious in formulating their diversification strategies since gains

from diversification can be reduced significantly by the severity of extreme return levels.

Vandana &Komal (2009) studied theissues concerned with real estate investment sector

in India. Investment on real estate in India and the trends in the concerned industry. They

studied the fundamental factors affecting the real value like demand, supply, property,

restrictions to use and site characteristics. Also explained the causes and the constraints to

the present real estate boom respectively in India and future prospects of real estate in the

country. They found As the GDP increases the real estate prices also increases because

there is a high degree of positive correlation between the real estate prices and GDP. Real

estate prices also increases with increase in the per capita income as there is high degree

of positive correlation between these two also.The infrastructure of India is also growing

day by day so it adds to the better facility to different sectors which affect the real estate

prices.

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Kim Hiang Liow & Alastair Adair (2009) studied the risk-return performance for all 13

Asian as well as the US and UK real estate securities markets and compared them. They

found prior inferior performance for many Asian real estate securities markets might

unlikely to repeat in future performance, it has provided some interesting and important

insights into the dynamics and performance of Asian real estate securities. A clear picture

emerges from study is that over the last ten years, Asian real estate securities have failed

to contribute to mixed-asset portfolios of Asian shares, bonds and cash in terms of

improved risk-return performance and enhanced portfolio diversification benefits. This

was mainly due to the real estate securities' inferior investment performance affected by

the Asian financial crisis and reasonably high real estate securities/common stock

correlations in several Asian countries.

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Chapter 3

Research Methodology

3.1 Research methodology:

The study will be carried out to compare the selected real estate securities with the Nifty index

using their returns, and to analyze the risk involved in each company in the sectors and risk

involved in the sector for investment. Thus the study undertaken is Descriptive study.

3.2 Sample Size

Real estate sector is selected for the study and ten companies from sector are selected for study

DLF Limited

Unitech Limited

Indiabulls Real Estate Limited

Parsvnath Developers Limited

Ansal Properties & Infrastructure Limited

Mahindra Gesco Developers Limited

Sobha Developers Limited

Phoenix Mills Limited

Peninsula Land Limited

Akruti Nirman Limited

3.3 Data Collection

The data for study is mainly secondary data and it is to be collected form capital line

database and internet

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3.4Tools for Analysis

Beta

Correlation

Regression

Paired sample t test

Descriptive statistics

o Mean

o Standard deviation

Return

Return will be calculated using the formula

Return = Yesterdays share price Todays share price

Yesterdays share price

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Chapter 4

Analysis and Data Interpretation

4.1 Analysis of Risk & Return

Table 4.1a Monthly Return of the selected securities in the Real estate sector

Month India

bulls

DLF Mahindra Shobh

a

Akruti Phoenix Ansal Unitec

h

Penin Perve

Apr-08-0.62 -0.65 -1.32 -.0.102 -1.82 -0.18 -0.1 -0.79 -0.82 -0.62

May-080.6 0.89 -0.87 1.02 0.56 0.69 0.57 0.66 1.22 0.6

Jun-082.58 1.78 1.78 2.67 1.62 3.56 1.67 0.805 2.07 2.56

Jul-080.44 -1.29 -0.5 0.3 -0.51 -1.14 0.019 0.104 -1.25 -0.44

Aug-08-0.24 0.11 -0.06 -0.26 -0.887 -0.03 -0.43 0.255 -0.23 -0.24

Sep-082.29 1.5 1.45 2.62 0.106 1.26 0.83 1.87 1.68 2.29

Oct-080.99 1.99 2.63 1.87 0.756 4.87 3.79 1.93 3.69 0.99

Nov-081.024 0.32 0.87 0.984 0.03 -1.39 1.86 0.031 0.86 1.02

Dec-08-1.86 -1.94 -0.62 -0.68 0.18 -0.95 -1.07 -0.56 1.82 -1.86

Jan-090.83 2.11 1.52 0.83 -1.26 0.49 2.11 0.19 0.44 0.28

Feb-091.12 0.65 1.57 0.422 -0.49 1.06 0.131 -2.17 0.62 1.12

Mar-09-0.47 0.058 -1.83 0.051 -1.23 -1.11 -0.51 -0.48 -0.6 -0.47

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Returns of real state companies

-3

-2

-1

0

1

2

3

4

5

6

Apr-0

8

May

-08

Jun-08

Jul-0

8

Aug-08

Sep-08

Oct-0

8

Nov-0

8

Dec-0

8

Jan-09

Feb-

09

Mar

-09

Date

Reurn

India bu

DLF

Mahind

Shobha

Akruti

Phoeni

Ansal

Unitech

Penin

Perve

Chart 4.1 a Real estate sector company returns

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Table4.1 b1: Risk analysis of Real estate sector Companies

Month DLF India Bulls Mahindra Unitech Akruti

SD Beta SD Beta SD Beta SD Beta SD Beta

Apr-082.11 0.43 2.47 0.14 2.19 0.02 2.39 0.1 3.93 0.12

May-081.61 0.64 2 0.11 2.62 0.16 1.56 0.43 1.74 0.6

Jun-082.94 0.56 3.65 0.3 3.2 0.3 2.36 0.71 2.86 0.43

Jul-084.48 0.65 6.64 0.37 4.36 0.55 3.44 0.33 4.19 0.54

Aug-082.77 0.61 4.7 0.32 2.23 0.43 1.5 0.3 2.6 -0.1

Sep-083.28 0.76 4.95 0.48 2.78 0.47 3.7 0.1 5.15 0.36

Oct-08 7.04 0.71 9.59 0.45 6.68 0.59 5.34 0.62 4.66 0.62

Nov-085.75 0.58 6 0.43 3.78 0.41 3.04 0.41 3.1 0.35

Dec-085.47 0.4 5.51 0.45 3 0.52 2.52 0.16 2.29 0.6

Jan-094.62 0.51 6.01 0.44 3 0.52 3.19 0.6 3.66 0.25

Feb-094.2 0.22 3.62 0.45 2.47 0.44 11.47 0 3.6 0.11

Mar-093.68 0.43 3.2 0.46 4.7 0.34 0.67 0.67 10.8 0.007

Table4.1 b2 : Risk analysis of Real estate sector Companies

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Month Phoenix Ansal Shobha Penins Parvesh

SD Beta SD Beta SD Beta SD Beta SD Beta

Apr-082.03 0.44 0.17 0.13 1.35 0.45 2.14 0.21 2.13 0.46

May-08 1.91 0.38 1.64 0.43 143 0.49 1.98 0.49 2 0.45

Jun-083.24 0.3 2.12 0.5 2.65 0.58 3.25 0.46 3.65 0.45

Jul-085.08 0.29 3.57 0.63 3.91 0.68 5.02 0.53 5.32 0.47

Aug-083.38 0.29 3.37 0.35 2.38 0.49 2.73 0.56 4.7 0.45

Sep-086.26 0.38 3.12 0.54 2.92 0.49 3.23 0.63 4.95 0.43

Oct-084.43 0.42 5.46 0.65 5.75 0.53 7.11 0.66 9.5 0.48

Nov-08

3.3 0.27 4.91 0.48 3.94 0.67 5.39 0.42 6.07 0.32

Dec-083.18 0.25 2.84 0.11 3.2 0.6 3.34 0.47 5.52 0.57

Jan-092.24 0.42 4 0.46 3.65 0.61 3.39 0.56 6.01 0.3

Feb-092.34 0.61 3.73 0.09 1.9 0.29 2.46 0.16 3.62 0.1

Mar-092.9 0.25 2.4 0.56 2.3 0.64 2.68 0.43 3.21 0.23

4.2 The Relationship of the Nifty with the Selected Securities in the Real Estate

Sector

Hypothesis

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Ho: There is no significant relationship between the selected securities return and Nifty return.

Ha: There is a significant relationship between the selected company securities return and Nifty

return.

Table 4.2:- Correlation between the selected company securities return in the power sector and

Nifty return.

Comp. India

bulls

DLF Mahind

ra

Shobh

a

Akrut

i

Phoen

i

Ans

a

Unitec Peni Perv

Nifty 0.67 0.74 0.56 0.65 0.28 0.42 0.54 0.24 0.68 0.67

Sig yes yes yes yes yes yes yes yes yes yes

p-level 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

4.3 The impact of selected securities return in real estate sector on the nifty return

Hypothesis

Ho: There is no significant impact of the return of the selected securities on the nifty

return

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Ha: There is a significant impact of the nifty return of the selected securities on the Nifty

return.

Table 4.3 AnANOVA table for Nifty return and real sector companies returns

ModelSum of

Squaresdf

Mean

SquareF Sig.

India bulls

Regression 781.661 1 781.661

204.97

8.000

aResidual 919.029 241 3.813

Total 1700.691 242

DLF

Regression 952.381 1 952.381

306.72

3.000

aResidual 748.309 241 3.105

Total 1700.691 242

Mahindra

Regression 551.027 1 551.027

115.51 .000aResidual 1149.663 241 4.77

Total 1700.691 242

Shobha

Regression 734.7 1 734.7

183.29

7.000

aResidual 965.99 241 4.008

Total 1700.691 242

Akruti

Regression 135.146 1 135.146

20.804 .000aResidual 1565.545 241 6.496

Total 1700.691 242

Pheonix

Regression 310.371 1 310.371

53.8 . 000a

Residual 1390.32 241 5.769

Total 1700.691 242

Ansal

Regression 501.58 1 501.58

100.80

9.000

aResidual 1199.111 241 4.976

Total 1700.691 242

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unitech

Regression 106.046 1 106.046

16.027 .000aResidual 1594.645 241 6.617

Total 1700.691 242

Peninsuela

Regression 789.313 1 789.313

208.72

2.000

aResidual 911.378 241 3.782

Total 1700.691 242

Preversher Regression 781.661 1 781.661204.97

8.000

a

Residual 919.029 241 3.813

Total 1700.691 242

Table4.3 b - R square table of NIFTY return

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

Indiabulls 0.678 0.46 0.457 1.9527

DLF 0.748 0.56 0.558 1.7621

Mahindra 0.569 0.324 0.321 2.1841

Shobha 0.657 0.432 0.43 2.002

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Akruti 0.282 0.079 0.076 2.5487

Phoenix 0.427 0.182 0.179 2.4018

Ansal 0.543 0.295 0.292 2.2305

Unitech 0.25 0.062 0.058 2.5723

Penin 0.681 0.464 0.462 1.9446

Total 0.678 0.46 0.457 1.9527

Table 4.3 c Coefficient table for Nifty return and Real sector companies return

Model

Unstandardized

Coefficients

Standardized

Coefficients t

Sig

B Std. Error Beta

Indiabulls

0.265 0.019 0.678 14.317 0

DLF

0.362 0.021 0.748 17.514 0

Mahindra

0.338 0.031 0.569 10.748 0

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2007 this shows that the returns where highly volatile in those months. SD for the Ansal

in the month of May and October are critically higher than nifty returns overall SD, this

shows that the investors cannot expect the same return as of nifty return. The volatility

was very high for the Ansal securities in those months; also the securities had a high beta

value in the month of March 2009. Day trading would be suitable in the months other

than May, September, February. The volatility was high in the months of October. The

overall beta value of the Ansal properties share return for the year is at a moderate risk

level.

For the Mahindra , The overall beta was 0.8 which shows that the company mahindra

developers industries was a moderately risky company for investors to invest. As beta

value increases risk increases. The beta values show a moderate risk in the months of

May, July, September. In these months of the year the beta value is above 0.7 near to 1

where beta value of one declares high risk position. This shows at those months the

volatility of the shares was high compared to Nifty. High beta value is also due to

volatility. Volatility in share price occurs due to news or results. The main reason for the

share value to have high volatility was the news about fall in wall street and global

markets equity shares. The volatility in the month of September may have been due to

the release of second quarterly result. In the month of August JP Hydro declares the

interim dividend at 7.50%.The volatility may be due to the result, there was a decrease in

the EPS. Taking the overall beta value of the company the company was a moderately

risky company for the investors to invest.

For Ansal Properties and Infra, The overall beta was 0.8997, which shows that Ansal

Infra was a highly risky company to invest in. The beta values were in the range of

moderate risk in the months of , August, September, and October. And the beta values

were in the range of high risk in the months of Oct 2008 and January 2009. As beta

increases towards one the volatility of the security is correlating to the volatility of the

market which is very risky for an investor. The SD was high in the months of October

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2008and January 2009, where in the month of October 2008 and January 2009 the beta

was also high. As SD increases the volatility of the return increases, which is the cause of

volatility in the share price. In the months of October 2008 and January 2009 the

volatility would have been higher than any other month of the year. The reasons for

volatility in the market are news and results. The volatility in the months of October

should have been the effect of the news about Ansal Properties gives Final dividend @

10% for the year. There was no significant news other than regular AGM proceedings

submission. Still the volatility was high in January.

For Shobha , The overall beta was o.378, That shows that Shobha Infra has risk of

investment. The SD of all the months of the year are not significantly higher than the SD

of overall nifty return for the year, this also supports the fact that GMR Infra was of

medium risk for investors to invest. The beta value was higher than 1.0 in the months of

May, June, July, August, September, October, January, February, and March . Those

months were of highly risk. The reasons for volatility in the market are news and results.

The high volatility in the month of January should have been due to the announcement of

approval for allotment of equity shares to the employees of the GMR Infra under the

Employees Stock Option Scheme (ESOS)". Also in the month of July the company

declared split, through split the companies equity face value is changed by Rs 2. In the

month of October the SEBI had announce to ban on P Notes, due to announcement the

market was gone down.

For Pheonix , The overall beta was 0.5623, which shows that pheonix Infra was a

moderately risky security to invest in. The beta values were higher than 0.6 in the months

of May, June, July, January and March. Those months are considered as moderately

risked for investment. The volatility and down fall would have been high in those months

and in the month of June the security return would have been almost equal to nifty

return. The SD for all the months was not significantly higher than the overall SD of the

nifty return. The SD was quite high in the months of July. The volatility would have been

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decreases. The returns earned by an investor in Unitech would have been less than the

returns he could have earned if invested in any of the other securities we have selected for

the study in the Infrastructure sector. The SD was significantly high in the month of

August.

For the Pershavnath developers the overall beta was 0.470 which shows that the company

was a highly risky company for investors to invest.. The SD for all the months was not

too higher than the overall SD of the Nifty return. The value of the returns varies as the

Nifty return varies for the year. The beta value was high in the months of October,

November, December, January and February. In those months the beta value is higher

than 1.1 it would have been of highly risk to invest in the months of October, November,

December, January and Feburary. The volatility would have been higher than the

volatility of the Nifty. The volatility in the share price in the month of September may be

due to the MOU with BHEL. The fluctuation in the share prices in the month of

December due to government decision and quarterly result of the company. In third

quarterly result of the company net profit is 1,779.90 crore .It is around 200 crore higher

than previous result.

For the The overall beta value shows that the company was a Highly risk company for

investors to invest. The SD for every month was very high than the overall SD of nifty

return. The SD was high only in the months of October and January. The beta value is

high in the months of October, and January. Those are the months with high risk for the

year. Months of April had a low beta value so those months should have been of low risk.

4.4 Dependance of Nifty Retun on the Real Estate Sector Company Returns

Table4.4a Regression between nifty return and highly correlated company returns in real

estate sector

Model Unstandardized Standardized Beta t sig

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Coefficients Coefficients

Indiabulls 0.265 0.019 0.678 14.317 0

DLF 0.362 0.021 0.748 17.514 0

Penin 0.37 0.026 0.681 14.447 0

Presv 0.265 0.019 0.678 14.317 0

a. Dependent Variable: nifty

Table 4.4.b R square table for highly correlated companies with Nifty return

Model R R SquareAdjusted RSquare

Std. Error of theEstimate

Indiabulls0.678 0.46 0.457 1.9527

DLF0.748 0.56 0.558 1.7621

Penin0.681 0.464 0.462 1.9446

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Presv0.678 0.46 0.457 1.9527

Interpretation

The dependence of the Nifty return to highly correlated company returns can be given by

above tables

The R square value of more then 0.4 proves that the India bulls, DLF, Peninsula and

Pervesrnath return have a good impact on the Nifty return.

4.5 Analysis of the Performance of the Power Companies before and After the

Announcement of Budget:

Paired sample t test

Hypothesis

Ho: there is no significant impact of the announcement of budget on the returns of

Indiabulls, DLF, Mahindra, Ansal, Shobha, Phoenix, Ansal, Peninsula, Perveshernath,

Unitech before and after the budget.

Ha: there is a significant impact of the announcement of budget on the returns of

Indiabulls, DLF, Mahindra, Ansal, Shobha, Phoenix, Ansal, Peninsula, Perveshernath,

Unitech before and after the budget.

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Table 4.5 a- SD and mean before and after the budget for the power sector companies

return

COMPANY

Before After

Mean SD Mean SD

Indiabulls 3.8731 8.1178 0.2803 -0.5325

DLF 4.7533 5.8664 -2.1169 -0.8362

Mahindra 6.093 3.1818 -1.5255 -0.7878

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Shobha 2.011 4.446 -0.8316 0.1045

Akruti 5.0979 4.3877 -1.2636 -0.9423

Phoenix 3.636 3.8686 0.495 1.4366

Ansal 2.6861 4.3746 -2.1164 0.5577

Unitech 6.7948 3.68843 0.1981 0.4865

Penin 3.5018 4.0203 0.4422 -1.0117

Total 3.8731 8.1778 0.2803 0.53262

Table 4.5 bpaired t test table for real estate companies before and after Budget

Paired Samples Test

Paired Differences

T dfSigtaile

MeanStd.Deviation

Std.ErrorMean

95%ConfidenceInterval of theDifference

Lower Upper

Pair 1 india bulls 1 - Indiaaf 0.9567 9.838 2.199 -3.647 5.5613 0.435 19 0

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Pair 2 DLF be - DLf af 3.0936 7.847 1.7547 -5.79 6.7664 1.763 19 0

Pair 3 Mahidra BE -Mahindra af 3.55 6.578 1.471 0.4716 6.629 2.414 19 0

Pair 4 Akruti Be - Akrutiaf -1.614 1.8157 4.0601

-8.6593 8.3366 -0.04 19 0

Pair 5 Shobabe - Shobhaaf 9.7256 5.0203 1.1225 -1.377 3.3221 0.866 19 0

Pair 6 Phenoix Be -Phoenix af 2.2628 4.446 9.9416 1.8203 4.3436 2.276 19 0

Pair 7 unitech be -Unitec af 7.8824 8.0466 1.7992

-2.9777 4.5542 0.438 19 0

Pair 8 Ansal be - Ansal af 2.7108 5.5305 1.2366 1.2243 5.2991 2.192 19 0

Pair 9 Penin Be - Penin af 1.4785 5.8318 1.304

-1.2508 4.2079 1.134 19 0

Pair 10 Preseve be -Preves af 9.5671 9.8385 2.1999

-3.6479 5.5612 0.435 19 0

Interpretation

In all the cases the SD before the budget is lower than the SD after the budget. This was

due to the investors perception towards the budget. Due to the perception towards the tax

reforms and other aspects of the budget the buying and selling of shares increases

increasing volatility, thereby increasing the SD of the returns. Later after the budget there

is no as much volatility as before budget. In real estate sector securities Akruti had

effected through budget. This shows that the securities of akruti had impact due to the

budget. The mean is positive for Akruti before the budget, but the mean after budget is

negative. Through the budget there is no any significant on Ansal, unitech, India bulls,

DLF. The Paired sample T test result shows there was no significant impact of budget on

the returns of the securities of Ansal, unitech, India bulls, DlF companies. So null

hypothesis is accepted. But there are some changes after and before budget.

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Chapter- 5

Findings and Recommendation

5.1 Findings

From studying the performance of the 10 companies in the two sectors for the

year 2008-2009. The Power sector was found to be the most risky sector to

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invest in. As the risk was high the returns gained by the investors would have

been huge.

. Most of the companies selected for the study in the Infrastructure sector had

a beta in the less risky range, a beta less then 1. Some companies have their

beta very low making the investment in the company a unrisky one.as

individual companies are affected less as impact is not seen to fast.

The effect of announcement of Budget on the company securities was studied

by comparing the means of the returns of the company securities before and

after the announcement of Budget using Paired sample t test.

From comparing the mean and standard deviation for the twelve companies in

two sectors before and after the announcement of budget in the financial year

2008-2009, it was found that the announcement of Budget had no immediate

effect on the company securities in sector. The data used to analyze the effect

of Budget was the share price of the companies every day for one month

before the Budget and one month after the budget. The budget would have had

an effect for sure on the share price. But the analysis proves that there was no

effect. The study considered only the fluctuation in the share price for one

month after the Budget. The effect on share price due to the announcement of

the Budget was not influence at that stage. It would have taken some time to

have a significant impact on the share price of the companies return.

From the analysis we can see that some companies had increase in the

standard deviation of their returns, some companies had decrease in the

standard deviation of their returns. But most of the companies did not have

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significant decrease or increase in their standard deviation. this supports the

results of the paired sample t test results.

From the study we can see that the beta value is high for most of the

companies real estate sector, month of October when effect of crisis is seen

In the Real estate sector the standard deviation was high for most of the

companies in the month of October. Day traders could have made good profit

due to the fluctuation in the price in the month of October. But the standard

deviation was highest in the month of September for India bulls and DLF. The

SD was between7-9.

Unitech was the company under study which had the highest beta of 0.7. This

makes company the most risky company to invest in, among the companies

which were taken for the study.

We can infer from the study that the Nifty index was dependent on real estate

companies collectively then individually.

The performance of the selected companies in the Real Estate sectors were

studied and compared with the performance of Nifty index. The returns for the

selected companies were analyzed. The risk involved in investing in the

sectors was studied. The risk factor beta was calculated for all the companies

selected for study and the calculated beta was used to differentiate risky and

non risky companies. The impact of the company securities on the Nifty Index

was assessed. The effect of price fluctuation on the returns of the company

securities was also studied. The performance of the selected company

securities before and after the announcement of budget was also analyzed.

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5.2 Recommendations

Assuming that the market and the companies securities will perform in the

different way as they performed in the year 2008-2009 during financial crisis.

Risk adverse investors must not invest in the Real estate sectors at this moment.

These risk adverse investors have to wait for some time as market conditions are

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improving due to various steps taken by Govt. and market is responding

positively to it.

Risk willing investors can invest in the real estate sector and as the risk involved

is high the returns earned will also be high. But conditions are not according s

financial crisis affected global market.

Bibliography

A K Vashisht & RK Gupta,Investment Management and Stock Market(Deep

and Deep Publication Pvt Ltd,Delhi)

Bansal.R and Dahlquist .M (2002) , Expropriation Risk and Returnin GlobalEquity Markets.Journal of International Economics 51, 115144.

49


50/54

Barry, Christopher B., John W. Peavy, and Mauricio Rodriguez, Emerging Stock

Barry, Christopher B., Mauricio Rodriguez, and Joseph Lipscomb,

Diversification Potential from Real Estate Companies in Emerging Capital

Markets, Journal of RealEstate Portfolio Management, Volume 2, Number 2,

1996, 107-118.

Bond, Shaun A., G. Andrew Karolyi, and Anthony B. Sanders, International

Real Estate Returns: A Multifactor, Multicountry Approach, Real Estate

Economics, Volume 31, Number 3, 2003, 481-500.

Brooks, C., & Tsolacos, S. (2001). Forecasting real estate returns using financial

spreads. Journal of Property Research, 18(3), 235-248.

Bulchandani, Ravi, Asian Currencies: Crisis, Contagion, and Containment

EmergingMarkets Quarterly, Volume 2, Number 1, 1998, 8-13.

Conover, C. Mitchell, H. Swint Friday, and C. F. Sirmans, Diversification

Benefits from Journal of Real Estate Portfolio Management, Volume m 8,Number 1, 2002, 17-26.

Corgel, J.B. and J.A. De Roos. 1999. Recovery of Real Estate Returns for

PortfolioAllocation. The Journal of Real Estate Finance and Economics 18(3):

279-296.

Donald R Cooper and Pamela S Schindler, Business Research Methods,( Ninth

edition ; New Delhi : Tata McGraw-Hill ,2006)

50


51/54

Ellis, C., & Wilson, P.J. (2005). Can a neural network property portfolio selection

process outperform the property market? Journal of Real Estate Portfolio

Management, 11(2), 105-121

Foreign Real Estate Investments, Journal of Real Estate Portfolio Management,

Volume8, Number 1, 2002, 17-26.

Fu.y and Ng .L 2000.Market Efficiency and Return Statistics: Evidence from Real

Estate and Stock Markets Using a Present-Value Approach. Real Estate

Economics 27(2): 183-207.

Hamelink, F., Hoesli, M., Lizieri, C., MacGregor, B. (2000), "Homogeneous

commercial property market grouping and portfolio construction in the United

Kingdom", Environment and Planning A, Vol. 32 pp.323-44.

I M Pandey, Financial Management , ( Ninth edition; New Delhi: Vikas

Publishing House, 2007)

James C. Van Horne and John M. Wachowicz Jr, Fundamentals of Financial

Management , ( Eleventh edition ; New Delhi: Prentice-Hall of India ,2006)

Kim Hiang Liow Extreme returns and value at risk in international securitized

real estate markets Journal of Property Investment & Finance, Volume 26

,Number 5,2008, 418-446

Lai, T.Y. and K. Wong. 1998. Appraisal Smoothing: The Other Side of the Story.

Real Estate Economics 26(3): 511-535.

Ling, David and Andy Naranjo, Commercial Real Estate Return Performance A

Cross- Country Analysis, Journal of Real Estate Finance and Economics,

Volume 24, Numbers 1 and 2, 2002, 119-142.

51


52/54

Liow, K., Sim, M.C. (2006), "The risk and return profile of Asian property

markets", Pacific-Rim Property Research Journal, Vol. 12 No.3, pp.283-310.

Markets: Risk, Return and Performance, The Research Foundation of the Institute

Chartered Financial Analysts, Charlottesville, VA, 1997.

Liow.K. J , Adair A(2005) Do Asian real estate companies add value to

investment portfolio Real Estate Economics 26(3): 511-535.

Markets: Risk, Return and Performance, the Research Foundation of the Institute

of Chartered Financial Analysts, Charlottesville, VA, 1997

Monteiro, Mohan & Rai, Rahul (2005). The Indian Real Estate Sector:

Opportunities for Australia, September 2005.

Ooi, J., Liow, K.H. (2004), "Risk-adjusted performance of real estate stocks:

evidence from developing markets", Journal of Real Estate Research, Vol. 26

No.4, pp.371-95.

Quan, Daniel C. and Sheridan Titman, Do Real Estate Prices and Stock Prices

Move Together? An International Analysis, Real Estate Economics, Volume 27,

Number 2, 1999, 183-207.

R.Nandagopal, K.Arul rajan and N. Vivek , Research Methods in

Business, ( First edition; New Delhi : Excel books, 2007)

Seiler, Michael J., James R. Webb, and F.C. Neil Myer, Diversification Issues in

Real Estate Investment, Journal of Real Estate Literature, Volume 7, Number 2,

1999, 163-179.

Serrano.C , Hoesli M (2008) Are Securitized Real Estate Returns more

Predictable than Stock Returns JEL Classifications: C53, C22, G2

SP Gupta, Statistical Methods(34th edition; Sultan chand and sons)

Vandana & Komal Prospects & Problems of Real Estate in India International

Research Journal of Finance and Economics, Issue 24 (2009)

Websites

52


53/54

http://www.economywatch.com/sector-watch/realestatesectorindia

http://www.directories-today.com/estate.html

http://www.ficci.com/studies/RealEstate.pdf

www.indianground.com/real_estate_india.aspx -

www.nseindia.com

www.sethassociates.com/Real_estate_sector_in_india.php

http://www.sethassociates.com/Real_estate_sector_in_india.php

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Appendix

Abbreviations:

o B Beta

o df Degree of freedom

o

SD Standared deviation

o Sig Significanc

o Std error Standared error

o DLF DLF Limited

o Unitech Unitech Limited

o Indiabulls Indiabulls Real Estate Limited

o Pervesh Parsvnath Developers Limited

o Ansal Ansal Properties & Infrastructure Limited

o Mahindra Mahindra Gesco Developers Limited

o Shobha Shobha Developers Limited

o Phoenix Phoenix Mills Limited

o Akruti Akruti Nirman Limited

o Penin Peninsula Land Limited

Documents

Chapter 1 Introduction 1.1 Introduction to Real