TWA4941 (1) (2).docx

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Project Name: Future Outlook of Real Estate Investment Trust
after introducing REIT Modernization Act
Student Name: Krupa Nitin Mehta
Student Id:

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Acknowledgement
I am thankful to my professor and my friends who supported me to understand the research
topic and conduct the study in a systematic way.

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Abstract
The study of research focused upon the concept of REIT Modernisation Act and its traits by
exploring its different dimensions. The first chapter discussed about the background elements
of REIT Modernization Act and also framed the research related aim and object gives to
establish the relevant outcomes. The literature review chapter threw light upon the different
concepts associated with shaping up REIT Modernisation Act and also established the
efficacy of the recommender system with clarity. The outcomes of applying recommender
system were also evaluated. The third chapter of Research methodology incorporated the
specific research design and approach that enabled the researcher to conduct the study
perfectly. The exploratory data analysis was prescribed as the suitable way to analyse the data
set. The data analysis chapter focused upon designing the model output and developed the
relevant outcomes. The data partitioning and the 6 model regressors were worked upon.
Finally, the last chapter focused upon establishing the conclusive evidence and also designed
the quality outcomes linked to the study of research based on REIT Modernization Act and
its platforms being implemented.

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Table of Contents
Acknowledgement .....................................................................................................................2
Abstract......................................................................................................................................3
Chapter 1-Introduction...............................................................................................................6
1.1 Background of the study ..................................................................................................6
1.2 Objectives of REIT...........................................................................................................7
1.3 Problem Statement .........................................................................................................10
1.4 Aim and Objectives........................................................................................................11
1.5 Scope of the study ..........................................................................................................11
1.6 Significance of the study................................................................................................11
1.7 Summary ........................................................................................................................12
Chapter 2-Literature Review....................................................................................................13
2.1 Introduction....................................................................................................................13
2.2 Merits of REIT ...............................................................................................................15
2.3 Demerits of REIT...........................................................................................................17
2.4 Requirements of REIT for a Company ..........................................................................18
2.5 Ways of Investment In REIT .........................................................................................19
2.6 History of Recommender System ..................................................................................20
2.7 Recommended system application.................................................................................21
2.8 Types of Recommender System.....................................................................................21
2.9 Benefits of the Recommender system............................................................................22
2.10 Implementation.............................................................................................................22
2.11 Dataset..........................................................................................................................23
2.12 Exploratory Data Analysis ...........................................................................................25
2.12 Data Cleaning...............................................................................................................30
2.13 Data partitioning...........................................................................................................33
2.15 Data Staging.................................................................................................................35

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2.16 Model building.............................................................................................................35
2. 17 Summary .....................................................................................................................38
Chapter 3: Research Methodology...........................................................................................38
3.1 Research Philosophy ......................................................................................................38
3.2 Research Design.............................................................................................................39
3.3 Research Strategy...........................................................................................................39
3.4 Summary ........................................................................................................................39
Chapter 4: Results and Evaluation...........................................................................................40
4.1 Introduction....................................................................................................................40
4.2 Model Output Findings ..................................................................................................40
4.3 Summary ........................................................................................................................42
Chapter 5-Conclusions and Recommendations .......................................................................43
5.1 Introduction....................................................................................................................43
5.2 Discussion and Conclusion ............................................................................................43
5.3 Recommendations..........................................................................................................43
5.4 Summary ........................................................................................................................44
Bibliography ............................................................................................................................45

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Chapter 1-Introduction
1.1 Background of the study
Real Estate Investment Trust”, is a real estate business established in 1960 that gained
popularity since 1991. It has a large number of real estate companies that went public by
gathering relevant facts and figures with the underscored thesis that have been used. The
whole of the research went through by a few companies that started to appear after the REIT
modernization act. The act has been passed by companies like Meditrust, Patriot American
and First Union features, which have been traded together with one symbol stock market.
REIT structures that have paired value to be used as the corporation to be combined by any of
such markets that remain extremely popular among shareholders.
The need of the study of REIT is being made to deliver historically by the dividend with
long-term capital appreciation. They have been made to deliver by the correlation with
comparatively low correlation by the assets to make them an excellent portfolio. It is being
used to have diversifier facts that can help to reduce the overall portfolio by the increase in
returns.
“Real Estate Investment Trust” refers to a legal entity which is established for the need of
directing the funds which can be invested to run, have ownership, or fund the real estate
which are generating income. They are similar to mutual funds & they offer investors a high
liquidity to acquire the interests in real estate. It is a kind of security which offers income
stability, diversification of portfolios, & long-term capital growth to every type of investor.
They are generally listed on exchanges, just like other insecurities.
Machine learning is an important part of today's financial decisions. Using various machine
learning algorithms & models are improving the ability to make decisions. Various
mathematical models using data, also called training data. This data makes decisions based
on the past trends without being explicitly programmed. As per the modernization act “Real
Estate Investment Trust” have been controlled with the business gaining popularity with the
facts of any real estate business. REITs originated in 1960, but have gained popularity since
1991 (Keneley et al., 2018). The Congress leaders have passed the REIT modernization act to
be complete thus being used more fairly for business investments to carry out such business
plans. In the Modernization act of 1999 by REIT, it was an intermediary between the
investors to be allowed in diversified managerial portfolios. It is being used as the income
generated by the REITs that would be required to invest like mutual funds that require

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investment by non-taxable REITs by investors while withdrawing to get such investment
(Piao et al., 2021). REITs prove to have unique identity that only need to be invested in
profiling in the REIT management to have certain acts passed by 1999. It allows certain
efficient use management with the investments that needs to be completed fairly by the use of
such type of investment used in public. REITs invest with all such types of real estate usage
and modern amenities to rent out space by the tenants in generating income for the leases.
The well known use of most REITs has taxable income based on the REIT subsidiaries used.
The purpose of this act from early days was to broaden the use of taxable REIT subsidiaries
that could compete more fairly used by others. The act used to provide a limit provided to
have certain specific provisions with limited amounts of assets in REIT assets. Patriot
American is being closely related to the close of Bankruptcy (Highfield et al., 2021).
Starwood managed to define survival by converting with cooperation to maintain balance. It
is equally important to expand by the usage and limitations of company limits.
1.2 Objectives of REIT
Real Estate Investment Trusts, commonly called the REITs, was created by the Congress and
was included in a bill passed by the Congress in 1960. The REIT Modernization Act was
vetoed by President Clinton in September 1999 and was passed by the Congress in the year
1999. The main aim of the act was to allow the REITs to compete more fairly with other
investments and implement their business plans. This in turn, would let the REITs compete
more fairly with others providing similar services, and also limit any particular service from
dominating the market and overtake the services. This act permits the investors from all
genres to invest in the real estate based on their diversified and professionally-managed
portfolios. The REITs offer an opportunity to buy real estate as a financial security and it is
much cheaper and less tedious for the transaction of the assets or properties. Another merit of
the REIT is that it offers a new asset class to the investors, to diversify the risk in investing,
who invest in traditional equity, debt, cash and gold.
According to Baral and Mei (2022), the REITs are trusts registered to the SEBI and they
carry out the activities based on the Real Estate Investment Trusts Regulations of 2014
prescribed under SEBI. The SEBI regulations require the REITs to payout 90% of the
distributable cash flows to the unit holders. The REIT must have a minimum of 80% assets to
be used for the generation of income to reduce the execution risk. The Sponsor holds a

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certain number of units as the assets and the rest are issued to the investors in the form of
IPOs. After the assets are listed, the IPOs have to be used to raise debt and equity in capital
markets to acquire new assets and grow to make this REIT serve as a permanent vehicle. Any
REIT that doesn’t follow the standard guidelines set by the SEBI is subject to legislative
actions and the money pooled in such REITs may be cleared off. The assets of the REITs are
normally secured by long term leases posing little to no risk for the REIT investor. The
income flow to the REIT will be more predictable and continuous. The REIT aims to provide
professional management to the real estate assets and it permits them to bargain for better
lease rentals and get a better price on trading real estate properties.
The REIT is a tax-efficient vehicle that owns the portfolio of income-generating real estate
assets and is used by the Sponsor to transfer the ownership of assets to the trusts in exchange
for the units (Nwogugu, 2018). The REIT units represent the ownership of Real Estate assets
like properties. REITs are companies that buy, sell, operate, or finance real estate stocks to
the public. The REITs can be either privately traded, that is, making the resources available to
the accredited investors who meet certain income and net worth requirements, or publicly
traded, that is, the shares can be bought and sold on major stock exchanges by anyone with a
brokerage account. Investors prefer the publicly traded REITs owing to the high liquidity,
opportunities for diversification, and steady dividend income.
The income generated by the REIT such as the Mutual Funds is not taxable by the REITs.
These may be taxable if the investors withdraw their investment from the REITs. The
legislative act allows any REIT organization to form and own any Taxable REIT Subsidiary
or TRS. The services provided by the REIT parents and subsidiaries are taxable. This means
that, the services provided by the REIT and their taxable REIT Subsidiaries (TRS) provide
various types of services to both tenants of their properties and to third parties. The taxable
subsidiaries of the company should comply with this act to make sure that the Business is
ethical. The income generated from the services are taxable and there are certain limitations
to the charges imposed on the services by the taxable affiliates (Hanlon et al., 2019). The
services that are taxable in accordance with the REIT Modernization Act of 1999 include the
following:
· Real Estate related services- Property Management, Architectural and Engineering
Services, Land Development, Real Estate Brokerage, etc.

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· Technology Services- Internet Service Providers, Computer Software Development, and
Computer Hardware.
· Business Services- Education and Job training, Telecommunication services, media and
Communication services, Management, Marketing and research services and electrical power
and utility services.
Financial Services- Banking, Insurance, Mortgage and Brokerage Services.
Personal and Retail Services- Purchasing and Distribution services, Transportation, Dry
Cleaning and Office Cleaning, Online Retail services, and entertainment and recreational
services.
The 100 percent ownership of the TRS by the REIT can allow them to develop and sell
properties quickly. It also allows them to provide substantial services to its property tenants
and other third-parties. The REIT Modernization Act of 1999 allows the REITs to manage
their investments efficiently and compete with other types of investments available to the
public fairly. REITs invest in all types of real estate, inclusive of the buildings that are rented
out or leased to tenants. In other words, any real estate property that can be used to generate
income are taxable by the REIT Modernization Act. The only income that is exempted from
the Act is the rental income of the REIT. Any activity performed to shield the income from
taxes such as transfer pricing can be easily identified using this Act. The Act also allows
provision of specific provisions to limit the assets the REIT invested in the TRSs, allowing
the TRSs to act independently of REITs operations.
The Taxable income was given some deductions in the initial years before its existence. The
REITs were advised to pay 95 percent of the taxable income in the years 1960 to 1980, and
90 percent of their taxable income in the years 1980 to 1999. The REIT Modernization Act of
1999 is a federal law that requires the REITs to own up to 100 percent of the stock of a
taxable REIT subsidiary.
One of the benefits that can be replenished by the REIT Modernization Act is the Paired
share structure. The paired share structure of the REIT was used by the companies like
Starwood, Patriot American, Meditrust, and First Union to acquire corporations of the Real
Estate sector. The paired share structure of the REIT refers to the combined trading of the

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REIT and C-Corporation under one symbol in the stock market. The structure has become
extremely popular owing to the fact that the investors obtain the profits from both the
companies. This structure was, however, abolished in the year 1984, as this structure
permitted the abuse of the tax system. Yet, five pre-established paired share REITs are
operational and are permitted by the Government.
The Real Estate Investment Trust is similar to the fractional ownership option, Delaware
Statutory Trust or DST with the only difference being that the DST is set up with the sole
purpose of conducting business. The DSTs are formed with private agreements and they hold,
manage, administer, invest, and/ or operate the real, tangible, or intangible properties of the
Real Estate sector. The factors that make the REIT better than the DSTs is the minimum
investment fees starting with the range of 10000 to 15000 INR and minor transaction fees
that include brokerage and other small fees paid at regular intervals. The other affirmative
features of the REIT are the investment objectives that let unit holders own a stock in the
Real Estate sector and allow them to make investment decisions based on the amount of cash
flow that a property generates, and tax benefits, inclusive of the deduction in tax payment, to
the unit holders. Another important aspect is the time horizon which allows the unit holders
to obtain the generated income on a regular basis. The shares of the REIT can be bought and
sold at their will and could be held as long as the investor desires.
At present there are three operational REITs in India. With the REIT being acknowledged as
an investment option and the significant popularity among the institutions and retail
investors, the first REIT came into establishment in the year 2019. Two years later, two other
REITs came into practice, making the Mindspace REIT, Brookfield REIT, and Embassy
REIT as the three listed REITs in India.
1.3 Problem Statement
REITs are committed to a data-driven approach to property valuation.
We now have a record of unused transaction prices for previous properties on the market.
Data was collected in 2016. Our task is to use this dataset to create a property pricing model
with a mean error of less than $70,000. Then the client will be very happy with the resulting
model.
In this we have to deliver a trained machine learning

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1.4 Aim and Objectives
The aim of this project is to predict efficient prices for real estate clients given their budget
and priorities. Future prices are predicted by analyzing past market trends and price ranges, as
well as future developments.
It helps the authorities to find the potential houses after the introduction of the monetization
act so that they can invest in worthy outings.
Objectives:
● To offer investments by REITs in the form of diversification taken in the similar form
of investment to be made on several real estates.
● To list out by REITs to have such shares that need to trade to have an exchange, so
they can be made readily which is being readily marketable.
● To analyze with the chance for the real estate business that REITs offer by giving in
using as such retailers to be easy to access in high-value properties.
To evaluate in delivering on the steadily dividend income of REITs to suppress with the
excellent use of portfolio by the portfolio risk and with increased returns
In this we have to deliver a trained machine learning model which satisfies the requirement in
order to make a better decision on the problem.
1.5 Scope of the study
The scope of the study is quite significant as it refers to the study of machine learning models
which are used to predict the prices as per the requirements. Nowadays, Machine learning is
playing an important part in solving various business problems therefore this approach will
be very helpful for those who are looking for the expected prices of the real estate
(Nature.com, 2019). It is based on the industry trends and help the stakeholder to make better
decisions before investing into any real estate.
1.6 Significance of the study
In this century, real estate is the essential part for all of us. It holds a special position in our
daily lives. It is important not only for those who need to buy this but also for the company
who deals with it. Therefore it is very necessary to buy real estate as it represents one’s
prosperity, status and prestige in the society.

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In this, the price for each real estate matters a lot as it affects the buyer , shareholder ,
company and the others which are involved in it. As opined by Mjörnell, Femenías, &
Annadotter, (2019), doing the Investment in the real estate is a profitable option as it doesn’t
change its value frequently as it holds its importance through recommending system. But
choosing the right one at the right time is very important, so the investment is to be made
very carefully while taking care of various factors. Hence, Machine learning algorithms help
us to find the best prices as they are not biased and predict the result according to the trend of
the data.
1.7 Summary
In the above chapter, we have discussed the nature and details of the dataset with the
objectives of this project. We have given detailed explanation about REIT and we have
discussed the scope as well as significance of REIT.

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Chapter 2-Literature Review
2.1 Introduction
The dataset is related to REITs that is Real Estate Investment Test. For the implementation
and for getting the result, we have performed different steps which include: Exploratory Data
Analysis, Data Cleaning, Data Partitioning and final step that is Model Building which
includes subsidiary steps neighbour Regression, Random Forest Regressor, Support Vector
Regressor, Lasso Regression, Decision Tree Regressor and lastly Ridge Regressor. The
Recommender system plays a significant role serving as a user providing as a personalised
recommendations for the real estate platform testing the scope of the Future outlook of Real
Estate Investment Trust based on the profile user introducing REIT Modernization Act. Real
Estate Funds are the sector funds invested by investors as securities of companies from the
real estate sector. These funds are used as an investment to provide the capital to the real
estate companies following the REIT Modernization Act of 1999 to develop a property. The
fund is secured as the sector grows and makes good returns. These investments are managed
by professionals with the investments as stocks. Based on the investment objective, you can
either invest the fund in the Real Estate that has the prospective chance of developing in the
near future or in the REITs. Investors without sufficient funds to purchase a property opt for
the Real Estate Mutual funds and the risk in the option is minimal. The notable feature that
makes the REIT better than the Mutual funds is the benefit of monetizing the assets, which
makes the investing company to focus on executing projects rather than owning real estate
assets. This in turn, reduces the asset hold and enhances the ROI or the Return of Interest.
Another distinguishing advantage of REIT over the Mutual funds, is the greater transparency
offered due to the SEBI guidelines.
REITs are similar to mutual funds. Mutual funds provide an opportunity to invest in equity
stocks whereas REITs are allowed to invest in one real estate asset. The REITs are collective
investments that are operated and managed property portfolios and give returns in form of
dividends to the investors (John and Ravi 2004).
According to SEBI's Circular dated April 23, 2019, the minimum investment guidelines are
as follows:
● Each allotment lot must be worth at least Rs.50000.

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● Each lot should contain 100 units.
Real estate funds can either actively or passively manage those that are positively managed
and can track the performance of a benchmark index.
Three types of Real estate funds
● Real estate exchange traded funds (ETFs)
● Real estate mutual funds
● Private real estate investment funds
These three are different types of mutual funds which are described as follows:
Real estate exchange-traded funds (ETFs) the funds which own the shares of real estate
corporations and REITs. Like other ETFs, these funds can be traded like stocks on major
exchanges. According to Nwogugu (2018), real estate mutual funds are those funds which
can be open or closed and either actively or passively managed.
Private real estate investment funds are those funds which can be professionally managed
directly in real estate properties. These are available to accredited, high-net-worth investors
and it requires a large minimum investment to be done for the benefit of the investor.
The basics of investing in REITs and Real Estate Mutual Funds are alike, that is, offer
diversification and an easy and affordable method for individuals to invest in various
segments of the real estate market (Nwogugu, 2018). The type of investment in the Real
Estate funds is more liquefied, which means that there is a slight difference between the
owning and investing in the Real Estate sector directly. This method can be used in cases
where the investors do not wish to be a direct part of the investment process in the Real
Estate sector. REITs have delivered competitive total returns, based on high and steady
dividend income and long-term capital appreciation for a comparatively longer time duration.
The overall portfolio risk can be reduced and it will help in increasing the returns by making
the REIT assets have a comparatively low correlation with the other assets in the market.

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2.2 Merits of REIT
There are several benefits to the investor by investing in Real estate investment trust which
includes: -
Appreciation of Capital When investors invest their capital in real estate investment trust the
capital invested by him steadily appreciates over the long term. Investing in REITs provides
substantial dividend income to the investor over a period of time in an efficient manner which
is a benefit or a merit of investing in REITs. REITs are also known for their total return
investments which provide high dividends plus the potential for the long term capital
appreciation. Capital abbreviation means a rise in an investment's market price. So, in this
way the capital is appreciated in the long run which is a benefit enjoyed by the investor.
High Yielding funds REITs mainly distribute their funds in the form of dividends, their 90%
of earnings are distributed in the form of dividends to the REITs investors. Due to this
distribution of earnings in the form of dividends to the investors, the yielding capacity of
funds enhances with time. In this way the benefits of high yielding funds are provided to the
investors.
Tax efficient Real estate investment trust is tax efficient in nature. REITs are having ' pass
through' status. The government has approved this pass-through status in which REITs rentals
will be treated as pass through flow and will not be taxed.
Transparency The Real Estate Investment trust is regulated by SEBI, REITs are required to
submit their financial reports, it gives an investor an opportunity to get access to relevant
information on various aspects like taxation, dividend. Hence making the process transparent
in nature.
Diversification REITs provide an opportunity to invest in different types of assets. Risk is
minimized once investment is done in different types of assets because REITs own a multi-
property portfolio. In this way the risk is minimized due to diversification in REITs.
Cost efficient Real estate investment trust is affordable and cost efficient in nature, the
investor of REITs enjoys the benefit of capital in the long run. The investor who invested in

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REITs acquires larger interest as compared to others. In this way REITs become affordable in
nature for the investor.
Benefit of liquidity In REITs, there is liquidity that means investors can convert their assets
in cash at any time. Investors can sell their assets anytime and convert them into cash. In this
way the investor enjoys the benefit of liquidity in REITs.
Flexibility Real estate investment trust is flexible in nature. REIT investors can easily get
access to information related to REIT prices and get involved in trading throughout the day
according to their flexible timings. So, this is another merit or an advantage which is enjoyed
by the investor.
Management of property The property in which investors invested are managed by the
property managers which is another merit which is enjoyed by the investor. The investor
enjoys the benefits of having experienced property managers work to make money for them
without .A carefully selected Property management team handles marketing, rent collection,
tenant management, and facilities maintenance. In this way the investor is free from the
management responsibility, all the work related to Management of property has been done by
the Property Managers itself.
Low volatility of shares REIT share prices enjoy lower volatility than equity stocks. The
reason behind this is that rental income and management expenses are predictable over the
period of time whether short term or long term. Analysts can predict the performance of
REITs more easily in equity stocks because rental income is usually very predictable.
Analysts can be very accurate in their predictions which is helpful in reducing share price
volatility.
Easy to buy and sell Real estate investment trusts can be bought and sold in a very efficient
way because there are clear rules and regulations as well as clear established procedures
which makes it easy for the investor to invest in REITs easily. Therefore, it is easy to buy and
sell the REITs in an efficient and effective manner which will benefit the investor.
Stable cash flow In real estate investment trusts there is an advantage of stable cash flow due
to its feature of 90% distribution of dividends. It provides dividends-based income to the

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investor which is further a merit of real estate investment trust which is enjoyed by the
investor. The dividends are often higher than the investments done by the investor in REITs.
So, these all are the benefits of REITs which are enjoyed by the investors in the long run.
2.3 Demerits of REIT
REITs also have some drawbacks or demerits, including:
Demand sensitive Real estate investment funds are demand sensitive in nature which means
that rising interest rates will make securities more attractive and when an investor withdraws
their investment from a particular asset, withdrawing funds away from REITs can lower the
share price. When the price gets reduced due to its demand the loss is borne by the investor
which is a drawback or demerit of investing in REITs.
Less tax benefit When an investor invests in REITs, the dividends which are earned by the
investor are not tax free completely, the investor is liable to pay some amount of money in
the form of tax. Due to this reason, the REITs are not tax efficient completely in nature. This
is another demerit of investing in REITs.
Fluctuations Real estate investment trusts are at major risk due to fluctuations in market in an
economy. It is susceptible in nature due to market risk . Due to fluctuations in the market the
investor can suffer loss and bear it due to uncertainty of the market. This is another demerit of
investing in REITs
Slow growth Investors can face a situation in which their investment is not appreciating with
time due to the slow growth aspect of REITs. It is mainly due to only 10 % reinvestment into
the venture of capital and the rest 90% distributed as dividend. So this is another drawback
which is faced by the investor.
High maintenance fees for management of property REITs provide Property managers for the
maintenance of property, investor has to bear the high maintenance fees for that which is also
another drawback of investing in real estate investment trusts
No control over performance Direct real estate investors has a great control over their returns
and performance But REIT investors can only sell their shares if they don’t like the
performance. In the case of some private REITs, they can’t even do that which is another

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drawback of REIT’s investors of not having control over performance, only a single selling
option is available to the investors.
Risk oriented Real estate investment trusts are more risk oriented in nature because the
market fluctuations are there, less options for investors, high maintenance fees, high tax rates
on REITs These are some factors due to which REITs become more risk oriented in nature.
Lack of liquidity There is lack of liquidity in real estate investment trusts, the non-traded
REITs are also illiquid which means that these non-traded REITs are not tradable for a
minimum seven years. So, in this way the non-tradable investments are facing an issue of
liquidity which means that they can't be converted into liquid form that is cash instantly. This
is another drawback of investing in REITs which is faced by the investor. So, these all are the
demerits or disadvantages of Real estate investment trust which is faced by the investor.
2.4 Requirements of REIT for a Company
If any company wants to invest in real estate investment trusts, to qualify as a REIT, a
company has to meet some specific requirements as mentioned below.
● The company must be registered as a business trust or a corporation.
● The company must extend fully transferable shares.
● The company must be managed by the team of trustees or a board of directors as
mentioned in the procedure of the company.
● The company must have a minimum of 100 shareholders to qualify as a REITs.
● In a company if there is less than 5 people then they should not have 50% of its share
during each taxable year.
● The company is required to pay at least 90% of the taxable income as a dividend.
● The company must accrue a minimum 75% of its gross income from mortgage
interests or any type of rents.
● In a company a maximum of 20% of the corporation assets is taxable under the REITs
subsidiaries.
● A minimum 95% of REITs total income should be invested.
● A minimum 75% of the investment assets must be in real estate.
So, these all are the necessary requirements for a company to qualify, in real estate
investment trust.

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2.5 Ways of Investment In REIT
Real Estate Investment Trusts have delivered competitive total returns, based on high, steady
dividend income and long-term capital appreciation. Their comparatively low correlation
with other assets also makes them an excellent portfolio diversifier that can help reduce
overall portfolio risk and increase returns. Because of the strong dividend income REITs
provide, they are important investments both for retirement savers and for retirees who
require a continuing income stream to meet their living expenses.
Individuals can invest in REITs in a variety of different ways, including purchasing shares of
publicly traded REIT stocks, mutual funds and exchange-traded funds (Bradley and James
2005). REITs also play a growing role in defined benefit and defined contribution investment
plans. Some of the investment ways are listed below: -
1) Private REITs: They are generally sold only to institutional investors, such as large
pension funds and accredited investors. Private REITs may have an investment
minimum.
Risk involved: They are often very liquid. It means that it can be difficult to access your
money when you need it. Secondly, because they are not registered, they are not required to
have any corporate governance policies. That means the management team can do things that
show a conflict of interest without much oversight.
2) Non-traded REITs: They occupy middle ground, like publicly traded companies, they
are registered with SEC, but like private REITs, they do not trade on major
exchanges. Because they are registered, this kind of REIT must make quarterly and
year-end financial disclosures, and the filings are available to anyone.
Risk involved: Non-traded REITs can change hefty management fees and like private REITs,
they are often externally managed, creating potential conflicts of interest with your
investment.
3) Publicly traded REIT stocks: They are considered superior to private and non-traded
REITs because public companies usually offer low management costs and better
corporate governance.
Risk involved: The price of REIT stock may decline, especially if their specific sub-sector
goes out of favor, and sometimes for no discernible reason at all.

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4) Publicly traded REIT funds: This offers the advantages of publicly traded REITs with
some additional safety. These funds comprise all equity REIT sub sectors, such as,
residential, commercial, lodging, towers and many more.
Risk involved: If investors decide that REITs are risky and won’t pay such high prices for
them, many of the stocks in the sector could go down.
5) REIT preferred stock: It is an unusual kind of stock, and it functions much more like a
bond than a stock. Like a bond, a preferred stock pays out regular cash dividend and
has fixed par value at which it can be redeemed. Also, like bonds, preferred stock will
move in response to interest rates, with higher rates leading to a lower price, and vice
versa.
Risk involved: Preferred stock tends to be less volatile than regular common stock. However,
if interest rates rise substantially, preferred stock would likely hurt, much as bonds would be.
2.6 History of Recommender System
The Recommender system plays a significant role serving as a user providing personalised
Estate Investment Trust. The fundamentals of the recommender system provide its
background information about the originality of the recommender system which were
founded by expert researchers into Cognition Science with proper retrieval of information
(Singh et al., 2021). Its primary manifestations started with the idea of exploiting the
computer system and its software operation through dataset inputs recommending the best
idea for the real estate user since the initial beginnings of the computing system.
Real Estate Investment Trusts (REIT) was first initiated in the market in 1960 and it could be
implemented in a planned way by the real estate tycoons (Keneley et al., 2018). Since the
1960s, the regulations concerning REITs have undergone various changes. Numerous
modifications to REIT legislation have had a significant impact on the industry's growth,
character, and makeup. The Tax Reform Act of 1986 (TRA), the IRS private letter ruling on
the initial public offering (IPO) of Taubman Centers Inc. in 1992, the Omnibus Budget and
Reconciliation Act of 1993 (OBRA), and the REIT Modernization Act of 1999 (RMA) are
some of the most notable amendments made to the REIT. The Tax Reform Act, in specific
could eliminate the beneficial taxation treatment linked to limited partnerships of real estates

21
and also allowed for carrying out internal control of REITs, thereby laying down the
foundation for the expansion of enormous sector that was followed up.
Each company that satisfies the REIT tax qualification requirements is tracked and
categorized. Every year, the industry's returns performance and betas are presented. They
show that REIT returns are less risky and lower than the market returns. The amount of
investor capital dedicated to the asset class has been relatively low throughout the majority of
the fifty-year history of the REIT business. However, a surge of IPOs brought a fresh inflow
of capital to the business as it entered the contemporary era.
2.7 Recommended system application
The Recommended system application promotes customises as well as personalised
recommending datasets implication within the real estate market developing survey based
informative engine systems. As opined by (Mehrotra, McInerney, Bouchard, Lalmas, & Diaz,
2018), it promotes guidance and suggestion of real estate products and services for the real
estate personalised profile user based on different data sources. The recommender system
possesses the abilities of quality predictions about a particular real estate user measuring the
preference criteria about solving the data evaluation purpose regarding selection of the
accurate services and products from the real estate ensuring personalisation recommendation.
2.8 Types of Recommender System
There are two main types of Recommenders System-Personalised and Non-personalised. The
Non-personalized recommendation systems. Both these recommendation systems promote
popularity-based recommenders, recommending the most popular real estate items to the real
estate profile users (Kouki, Schaffer, Pujara, O'Donovan, & Getoor, 2019). For instance, top
selling books, top-10 movies and the most frequently purchased products. Personalised
recommendation system analyses the user data, their purchases, developing rating system and
the real estate user relationship with other users get evaluated in detail offering customised
recommendations. Non-personalised recommendation systems suggest accurate real estate
products and services to the particular profile user based on their purchase history. A non-
personalised recommender system exhibits products which are popular products among real
estate profile users used in real estate investment within the time frame.

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2.9 Benefits of the Recommender system
The Recommender system plays a significant role serving as a user providing personalised
Estate Investment Trust based on the profile user introducing REIT Modernization Act.
Recommender system serves the primary manifestations started with the idea of exploiting
the computer system and its software operation through dataset inputs recommending the best
idea for the real estate user since the initial beginnings of the computing system. It promotes
guidance and suggestion of real estate products and services for the real estate personalised
profile user based on different data sources developing data analytical interaction better the
real-estate profile users (Munawar, Qayyum, Ullah, & Sepasgozar, 2020).
2.10 Implementation
For python, importing of modules takes place as the modules in python get the direct access
for the code of the module which is required for the program using import. Here we have
imported NumPy, pandas, seaborn, matplotlib initially then warnings module. Numpy library
is used basically to work with array functions including matrices too. Pandas’ library is
imported here for the analysation of data. Seaborn libraries is imported for the graphical
representation. Here from matplotlib pyplot libraries is imported for making the required
plots based on the dataset. Warning module is used to express specifically the warning
messages. For the programs related to Machine Learning which we have performed in our
project for building recommender system so for that all the related tools are being imported
using Scikit-learn which helps in regression, classification and other required modelling
implementation. The below attached parts are included in literature review to explain the
analytical process of reviewing this project based on the taken procedures.

23
Figure 1: Machine Learning Dataset
(Sourced: Maseer, Yusof, Bahaman, Mostafa, & Foozy, 2021)
Inclusion of Imported libraries proved to be significant as Python is dynamic in its
application. Imported libraries would guide the individuals in a proper way since sometimes,
the name associated wit the function is very confusing. The literature review can focus and
explore the different features and aspects of machine learning dataset used by the application
of imported libraries.
2.11 Dataset
The given dataset 'real_estate.csv' consists of 1883 surveys in the places where the REIT
performs its operations. Each survey is for the transaction of only one property
Each transaction was between US $200,000 & $800,000.
'tx_price' is the target variable in the dataset
Other features
'tx_year' - Year of the transactions
'property_tax' - property tax per month

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'insurance' - insurance cost per month
'beds' - bedrooms
'baths' - bathrooms
'sqft' - Total Area of floor in Square feet
'lot_size' - Area outside in Square feet
'year_built' - Year
'active_life' - Number of shops nearby
'basement' - basement
'exterior_walls' - materials used for walls
'roof' - materials used for roof
'restaurants' - restaurants nearby
'groceries' - groceries nearby
'nightlife' - nightlife stay nearby
'cafes' - cafe nearby
'shopping' - shopping store nearby
'arts_entertainment' - art venue nearby
'beauty_spas' - spa nearby
'active_life' - gym etc nearby
'median_age' - median age of area people
'married' - percentage married people in the area
'college_grad' - percentage college graduates in the area
'num_schools' - Schools in the area
'median_school' - Median Score of school
Importing Dataset
We have saved the dataset in the file location of our own computer in csv format and dataset
is related to the House Price Prediction where csv file helps to read the rows of the dataset
file and delimiter is assigned here for importing the dataset where we use the file location
using panda’s library in below format as we know panda’s library helps in reading the csv
type files.

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2.12 Exploratory Data Analysis
This is basically known as EDA process and in python, it is used for the graphical
representation of dataset including the basic information regarding dataset in a descriptive
manner, visualization of the specific values of data, filtering of data if there include any null
values and further representing correlation plot.
Histogram of Features
For the distribution of data, we have taken here the numerical values from the dataset. Below
histogram is implemented to show the frequency for each of the variables.
Figure 2: Histogram of Dataset Features
Sourced: (Nazir, Ashraf, Hamdani, & Ali, 2018)

26
Statistics Description
Since we have worked upon the dataset related to different prices of houses so using the
dataset analysation, below we have represented different values of requirements for the
houses. The factors which are being covered here: total count, mean, standard mean,
minimum,25%,75% and maximum for the factors: tax price, beds, square feet, bathrooms,
year, size and basement. The below image represents the test data summary.
Figure3: (Rozemberczki, 2020)
Displaying Categorical Features
Here we have used two types of methods and the methods are defined for the distribution of
exterior walls and roof depending on the values of assigned variables for the category for
which the categorisation is possible.

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Figure 4: Displaying Categorical Features
Sourced: (Shi, Naumov, & Yang, 2020)
Creating Boxplot for the Features
For the creation of boxplot, we can make the use of matplotlib for the representation. Here
the boxplot is implemented below using two factors where one is the type of properties
(Apartment,Condo,Townhouse) and the other is included the tax price. Here we can also see
the outliers along with the visualization of below boxplot.
Figure 5: Boxplot for the Features
Sourced: (Azeroual & Koltay, 2022)

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Finding Correlation between The Features
Finding correlation between the features relates to the representation of the categorical
features to that of numerical features. Here we have converted the categorical features of the
assigned variables to numerical features based on the categories on which we have worked
for our dataset.
Figure 6: Details of the Correlation
Sourced: (Batmaz, Yurekli, Bilge, & Kaleli, 2019)
Visualizing the Correlation using Seaborn
For the visualization of correlation, we need to use heatmap using seaborn. Our first step here
is to create a matrix named as correlation matrix for our dataset. The reason behind creating
the correlation matrix is to know the correlation between the features with each other. The
way to know the correlation using correlation matrix to look upon the values as we know here
all the values for this matrix lie between -1 to 1.The values which lie towards the direction of
1 that represent correlation of the features as higher whereas the values which lie towards the
direction of 0 that express the correlation of the features as poor. After the implementation of
correlation matrix procedures, we can go for using of seaborn heatmap for calling and for
passing of the correlation matrix. The below heatmap represents the basic heatmap being the
initial one. Here the colors are set automatically for the representation purposes.

29
Figure 7: Visualizing the Correlation Matrix using Seaborn
Sourced: (Jacob & Tappert, 2019)
To get the below image representation, we have used different colours such as: blue, pink,
orange, green by using the palette for argument of cmap. There different ranges of palettes
are available. The colour style that is being used here is dark grid. Here the colours represent
the correlation of features based on positive as well as negative values. Here pink colour
represents the positive correlation. Green colour represents to the high values of positive
correlation. Here orange colour represents both the negative and positive correlation where
most of the values indicate to the negative correlation and blue colour represents the
correlation of negative integers. Here to get the below image representation, we have added
the required numbers to the heatmap.
The reason of adding different colours to get the clear representation of the above image and
here we have added the numbers to get the exact reading for our representation. Here we have
not worked upon the text colour because the text colour changes accordingly the colour of
cell. We have used different properties for changing font size and font weight for different
annotations. Here we have called annot_kws for passing it through dictionary to make the
required changes for the font. The reason for adjusting the font is to make the assigned
numbers clearable.

30
Figure 8: Visualizing the Correlation Matrix using Seaborn
Sourced: (Jacob & Tappert, 2019)
2.12 Data Cleaning
Handling Mis-labeled Classes
The first step of data cleaning is to handle the mislabelled classes. The mislabelled classes
refer to the classes of data in which some of the levels are incorrect. We need to do the
labelling properly to avoid any kind of noise or incorrectness or error of the class. For the
mislabelling, we also cannot be able to get the accurate result. Below we have taken the
method called as noise tolerance which specifically known as handling of overfitting and we
have done the filtering of data so that it cannot give any impact on our resulting output.

31
Figure 9: Handling Mis-labeled Classes through data Cleaning
Sourced: (Pleiss, Zhang, Elenberg, & Weinberger, 2020)
Removing The Outliers
The outliers are referred to as errors in the time of visualization of the dataset. We have seen
the outliers when we have used the box plot method for the visualization. So to get the
accuracy of result, its needed to remove outliers which called as the process of outlier mining.
Though there are different processes for the detection of outliers, here we have used boxplot
method where we get the summarization of the data using percentiles.
For removing of outliers, we have been assigned the size as xlabel. We have represented the
lot_size for assigning the values as ascending=False which calls python for making changes
in the dataset which is original. We have used here the method known as insull() for checking

32
the cells if those have numeric value in that or if the data is missing. As here we are dealing
with larger dataset so we have used sum()method that usually helps in returning the values
which are missing per column basis. We have used fillna(‘Missing’) as we have checked the
cells with missing values so we need to include object here using sum()that helps for
returning the values which are missing.Then we have excluded the objects which return value
as 0 per column. We have represented the minimum age of property using
print(df.propery_age.min()) where we get the result as -8.Then we have used again the print
statement to print the sum of the properties which has age less than 0.
Finally we get the property which has the age greater than or equal to 0=1863 and the shape
of the same=28.Then we have dropped those columns which are not necessary.We have used
df.drop([‘basement’]) for dropping the basement column as main factor then we have further
used df.drop([‘nightlife’])for dropping the nightlife column.
Merging the sparse classes
From the operations which we have performed to label the categorical data which are
missing, we got some column values as 0. So here we are going to merge the sparse
classes/columns where the value of most of the elements is zero. We have used here replace
method for replacing the columns which have zero value with the other columns which have
non-zero values. After replacing action takes place, we have represented the obtained result
in a graphical manner using sns.countplot(y=’exterior_walls’, data=df).The below image is
the representation of this step.
Figure 10: Merging The Sparsity
Sourced: (Fu, Peng, Wang, Xu, & Li, 2019)

33
2.13 Data partitioning
Splitting the data into train and test data
The partitioning method is being used for splitting the data which we have into training and
testing data by using x and y labels where in the below image we have represented the shapes
of the train and test data with different labels and for splitting we have used train_test_split
whereas to get the values as shape of each, we have used print statement for
X_train.shape,X_test.shape,Y_train.shape and Y_test.shape.
2.14 Model Implementation
Implementing 6 models
The models which are being implemented here are-k neighbour regressor, random forest
regressor, support vector regressor, lasso regressor, ridge regressor and decision tree
regressor.
K neighbour regressor: KNN regression method is used for building this model where it
used the approach of the relation of the variables which are independent with the outcome
which is continuous through the observations by averaging it in the neighbourhood where the
relation is present. It is a simple model type based on machine learning. It has two
hyperparameters which include-K value and distance function. K value is assigned as per the
total number of neighbours and it is related to validation error.
Random Forest Regressor: We have used the sklearn module for the training purposes of
this model and we have precisely used the Random Forest Regressor function. For the
implementation of this model we have used n_estimators as one of the parameters which
represents the decision trees number which we have to run here. We have used min_samples
to get the lowest size for each of the samples.We have used min_samples_split to split the
values of each sample which contain lowest value.

34
Support vector regressor: Support vector regression method is used to build this model
whereas Support vector regression model is used for the prediction of the values which are in
discrete format. We have used different parameters and for implementing which we have
used tuned_params. We have set the estimator as SVR (Afsar, Crump, & Far, 2021).For
getting the scoring part, we have used ‘neg_mean_absolute_error’ that gives the absolute
errors.
Lasso Regressor: Lasso regression process is being performed to get this model. The
purpose of implementing this model is to get the subset of the predictions that helps in
minimizing the errors in prediction for a particular variable. In lasso regression, constraint on
the model parameters is imposed which leads the coefficients of regression to get the values
near to 0 for some specific variables. We have used different parameters and for
implementing which we have used tuned_params. For getting the scoring part, we have used
‘neg_mean_absolute_error’ that gives the absolute errors.
Decision Tree Regressor: This model is built using the Decision Tree Regression modeling
process in machine learning. This model is built for observing different features of any object
which is related to the data and it is a type of training model which helps to make the model
in a particular tree structure for prediction of data for producing relevant output. The output
which we get here does not contain any discrete value. We have used different parameters
and for implementing which we have used tuned_params. For getting the scoring part, we
have used ‘neg_mean_absolute_error’ that gives the absolute errors.
Ridge Regressor: As in our dataset the used variables are correlated with each other and we
have previously represented the correlation of the variables using both positive and negative
correlation so this model is specifically built here for the representation of correlation
between variables. Here we have used sklearn. linear model for loading the dataset
previously. Then we have used X,y variables for defining the model. Then we have used
model=Ridge(alpha=100) for fitting the model. Then for defining new data here we have
used model.fit(X,y).We have used one row where we have put the values of the variables for
making the required prediction.

35
2.15 Data Staging
Standardisation of data
This process is used for data scaling to fit it into a distribution and the type of the same is
normal standard. It is the process where all the variables contribute in equal manner. This
method is required for the scale type variables for running of the Support Vector
Machine(SVM).
Figure 11: Data Staging
Sourced: (Karimi, Jannach, & Jugovac, 2018)
2.16 Model building
1. K Nearest Neighbor Regressor
KNN regression method is used for building this model where it used the approach of the
relation of the variables which are independent with the outcome which is continuous through

36
the observations by averaging it in the neighbourhood where the relation is present. It is a
simple model type based on machine learning. We have firstly created the features for the
variables. We have defined the range using neighbours = list(range(1,50,2)).We have
imported KNN algorithm from the module named as skcit-learn. Then we have splitted the
data into training data and test data and using the below code ,we have fitted the training data
into model.
2. Random Forest Regressor
We have used the sklearn module for the training purposes of this model and we have
precisely used the Random Forest Regressor function. For the implementation of this model,
we have used n_estimators as one of the parameters which represents the decision trees
number which we have to run here. We have used min_samples to get the lowest size for
each of the samples. We have used min_samples_split to split the values of each sample
which contain lowest value. We have set the variable number of decision tree for defining
the model. Using the below code ,we have fitted the training data into model.
3. Support Vector Regressor
Support vector regression method is used to build this model whereas Support vector
regression model is used for the prediction of the values which are in discrete format. We
have used different parameters and for implementing which we have used tuned_params. We
have set the estimator as SVR.For getting the scoring part,we have used

37
‘neg_mean_absolute_error’ that gives the absolute errors. Then we have used X,y variables
for defining the model and using the below code ,we have fitted the training data into model.
4. Lasso Regressor
We have used different parameters and for implementing which we have used tuned_params.
For getting the scoring part,we have used ‘neg_mean_absolute_error’ that gives the absolute
errors. For the implementation of this model we have used tuned_params to set the row for
the variables upon which we have worked using the below code. Then we have used the
below code for fitting training data as X_train and y_train to the model.
5. Decision Tree Regressor
It is a type of training model which helps to make the model in a particular tree structure for
prediction of data for producing relevant output. The output which we get here does not
contain any discrete value. We have used different parameters and for implementing which
we have used tuned_params. For getting the scoring part,we have used
‘neg_mean_absolute_error’ that gives the absolute errors. Then we have used the below code
for fitting training data as X_train and y_train to the model. Using the below code ,we have
fitted the training data into model.

38
6. Ridge Regressor
As in our dataset the used variables are correlated with each other and we have previously
represented the correlation of the variables using both positive and negative correlation so
this model is specifically built here for the representation of correlation between variables.
Here we have used sklearn.l inear model for loading the dataset previously. Then we have
used X,y variables for defining the model. Then we have used model=Ridge(alpha=100) for
fitting the model.Then for defining new data here we have used model.fit(X,y).We have used
one row where we have put the values of the variables for making the required prediction.
2. 17 Summary
In the above chapter, we have explained all the processes which we have taken for the
analysis of our dataset. We have explained about specific results which we have got during
the analysis process and in each step of the process.
Chapter 3: Research Methodology
3.1 Research Philosophy
The researchers understanding the properties of datasets while applying the recommender
system within the real estate platform and testing the desired aims, purpose and objectives
about the study applies Positivism research philosophy. The positivism research approach
will promote a better future outlook of Real Estate Investment Trust after introducing REIT
Modernization Act ensuring improved personalised recommender systems. The positivism
philosophy helps to define the operative dynamics of recommender system application within
the real estate platform and analysing the user profile evaluating and interpreting the results
about how the real estate products are services get utilised efficiently (Kruger, Verhoef3.3 &
Preiser, 2019).

39
3.2 Research Design
The framework of the research design helps to improve various research patterns by allowing
the researchers to choose the right research methods for demonstrating a confined research
outcome about the contribution of the recommender system within the real estate platform.
The researchers applied the Descriptive research designing where multiple datasets and
sources are being experimented and investigated the data analytical-based statistical
description and various technical data models implication. This Descriptive research design
Personalised recommendation system analyses the user data, their purchases, developing
rating system and the real estate user relationship with other users get evaluated in detail
offering customised recommendations.
3.3 Research Strategy
The application of the Descriptive research design research strategy comprises Qualitative,
Quantitative and Mixed methods practising meta-analysis helping the researchers to have
clear understanding about the concept recommender system and personalised
recommendations applied within the real estate platform. It will promote scientific
evaluation, numerical data analytical process and statistical description where all the relative
models are being demonstrated leading to improved model building, understanding data
merging, data cleaning and sourcing in future real estate platforms (Dou, et al., 2020). Both
the primary as well as Secondary data collection methods have been executed experimenting
and observing both offline and online data sources, understanding the impact of the
recommender system within the futuristic real estate platform applying the personalised
recommender system.
3.4 Summary
It has been discussed and analysed that research of the processes which we have performed
and we have explained different objectives for the research processes. We have explained
about our background thoughts for taking the specific processes with the visualisation.
Descriptive research design associated with quality features that were effective for framing
recommender systems thereby ensuring quality decision making to be implemented. Features
like imported libraries were significant to be used as they provided assistance to Python to be
executed in a planned process. The inclusion and logical application of secondary methods
made the research study more effective.

40
Chapter 4: Results and Evaluation
4.1 Introduction
We have got total 6 model as the implementation result of the same. Here for each of the
problems we have used same printing statements for except the one printing statement which
addresses different every time as it represents the test results of different models. We have
used printing statements for printing the values include: root mean square error, root square,
mean absolute error for each of the models. The process for each of the models shown below
are described for specific model with the diagrams.
4.2 Model Output Findings
1. K Nearest Neighbor Regressor
After performing the steps of implementation and model building, we have made the
prediction for X and y training model and to get the prediction for y training,we have used
y_train_pred = model. predict (X_train) and y_pred = model.predict(X_test).For printing the
statements for root mean squared error,we have used sqrt(mse(y_test, y_pred))) and for root
square values we have used rs(y_test, y_pred) and for mean error we have used mae(y_test,
y_pred) in the different printing statements .We have also print the values using different
printing statements to get the values shown below.
2.Random Forest Regressor
After performing the steps of implementation and model building,we have made the
y_train_pred = model.predict(X_train) and y_pred = model.predict(X_test).For printing the
y_pred) in the different printing statements .We have also printed the values using different

41
3.Support Vector Regressor
5.Decision Tree Regressor

42
6.Ridge Regressor
After performing the steps of implementation and model building, we have made the
prediction for X and y training model and to get the prediction for y training, we have used
statements for root mean squared error,we have used sqrt(mse(y_test, y_pred) and for root
4.3 Summary
To summarise, firstly we can look upon the advantages of using REIT’s which is a type of
unique approach as it makes the accessibilities for the equity type market better. The models
which are being implemented here because of the representation of the dataset regarding
house prices have specific advantages. The overall advantages include easy and simple
implementation processes, reducing of the problems related to overfitting, reducing of
variance, ensuring accuracy of prediction model, automatic method of choosing features,
comprehensive easy analysis methods, better prediction based on observations etc.
Considering the mentioned advantages into count, we have implemented the models in this
project.

43
Chapter 5-Conclusions and Recommendations
5.1 Introduction
This project introduces the idea of “Real Estate Investment Trust” by using it in real time
project work. This idea is used for establishing needs of funds that can be invested further
related to real estate and used for generating income. The assigned project is based on
Machine Learning and different modelling approaches for the dataset for training and testing
data as well. The main purpose of using the models and moreover Machine Learning
algorithm is that it basically helps for taking decisions in the financial section through the
implementation of various models.
5.2 Discussion and Conclusion
This project is used for the prediction of prices of real estate clients based on their budgets
and priorities. This idea is used for establishing needs of funds that can be invested further
related to real estate and used for generating income. The dataset which we have used here
helps in making the decisions based on the trends. The modelling approaches are being used
for the clear understanding of our approaches using further implementation of different
graphical features with boxplots and through removing the outlines as errors using different
methods related to machine learning. Through this model, we also get to know about the
affordable prices for different house buildings. It helps the authorities for getting the required
houses with the help of the monetization act.
5.3 Recommendations
Use of the User-Based Collaborative Filtering Model
The real-estate platform applying the recommender system needs to implement the User-
Based Collaborative Filtering Model, improving the decision-making process and quality
practising personalised and customised decision recommender systems with the real estate
platform. This will help the identification and engagements of accurate data sets signifying
the appropriate user profiles connected with the real estate futuristic investment trust.
A Gold Standard Similarity Computation Technique
The recommending system will experience a supercharged performance developing new
methods of measuring the semantic similarity of the datasets developing data quality and
decision-making approach. The use of the Gold Standard Similarity Computation Technique

44
will promote Cognition Science and information retrieval. Developing the data computing
system within the real estate platform.
5.4 Summary
Conclusive evidence related to the concept of Real Estate Investment Trust and its features
could be incorporated by the efficient use of investment funds that are productive. The
research project focused upon specification of the standard price rates for the different
client’s wo exist in the society thereby brining about improvement within the recommender
system.

45
Bibliography
Afsar, M. M., Crump, T., & Far, B. (2021). Reinforcement learning based recommender
systems: A survey. . ACM Computing Surveys (CSUR).
Azeroual, O., & Koltay, T. (2022). RecSys Pertaining to Research Information with
Collaborative Filtering Methods: Characteristics and Challenges. Publications, 10(2),
17.
Batmaz, Z., Yurekli, A., Bilge, A., & Kaleli, C. (2019). A review on deep learning for
recommender systems: challenges and remedies. Artificial Intelligence Review, 52(1),
1-37.
Dou, J., Yunus, A. P., Bui, D. T., Merghadi, A., Sahana, M., Zhu, Z., & Pham, B. T. (2020).
Improved landslide assessment using support vector machine with bagging, boosting,
and stacking ensemble machine learning framework in a mountainous watershed.
Japan. Landslides, 17(3), 641-658.
Fu, W., Peng, Z., Wang, S., Xu, Y., & Li, J. (2019). Deeply fusing reviews and contents for
cold start users in cross-domain recommendation systems. In Proceedings of the AAAI
Conference on Artificial Intelligence, 33(1), 94-101.
Jacob, B., & Tappert, C. (2019). A Preliminary Machine Learning Model for Extended
Comparative Market Analysis. In The 30th Annual Conference of the International
Information Management Association, 35.
Karimi, M., Jannach, D., & Jugovac, M. (2018). News recommender systems–Survey and
roads ahead. Information Processing & Management, 54(6), 1203-1227.
Kouki, P., Schaffer, J., Pujara, J., O'Donovan, J., & Getoor, L. (2019). Personalized
explanations for hybrid recommender systems. In Proceedings of the 24th
International Conference on Intelligent User Interfaces, 379-390.
Kruger, H., Verhoef, A., & Preiser, R. (2019). The epistemological implications of critical
complexity thinking for operational research. Systems, 7(1), 5.
Maseer, Z. K., Yusof, R., Bahaman, N., Mostafa, S. A., & Foozy, C. F. (2021).
Benchmarking of machine learning for anomaly based intrusion detection systems in
the CICIDS2017 dataset. IEEE access, 22351-22370.
Mehrotra, R., McInerney, J., Bouchard, H., Lalmas, M., & Diaz, F. (2018). Towards a fair
marketplace: Counterfactual evaluation of the trade-off between relevance, fairness &
satisfaction in recommendation systems. In Proceedings of the 27th acm international
conference on information and knowledge management , 2243-2251.

46
Mjörnell, K., Femenías, & Annadotter, K. (2019). Renovation strategies for multi-residential
buildings from the record years in Sweden—Profit-driven or socioeconomically
responsible. Sustainability, 11(24),, 11(24), 6988.
Munawar, H. S., Qayyum, S., Ullah, F., & Sepasgozar, S. (2020). Big data and its
applications in smart real estate and the disaster management life cycle: A systematic
analysis. Big Data and Cognitive Computing, 4(2), 4.
Nature.com. (2019, 09 26). Recent advances and applications of machine learning in solid-
state materials science. Retrieved from Nature.com:
https://www.nature.com/articles/s41524-019-0221-0
Nazir, A., Ashraf, R., Hamdani, T., & Ali, N. (2018). Content based image retrieval system
by using HSV color histogram, discrete wavelet transform and edge histogram
descriptor. In 2018 international conference on computing, mathematics and
engineering technologies (iCoMET) IEEE, 1-6.
Pleiss, G., Zhang, T., Elenberg, E., & Weinberger, K. Q. (2020). Identifying mislabeled data
using the area under the margin ranking. Advances in Neural Information Processing
Systems, 33, 17044-17056.
Rozemberczki, B. R. (2020). Characteristic functions on graphs: Birds of a feather, from
statistical descriptors to parametric models. In Proceedings of the 29th ACM
international conference on information & knowledge management (pp. 1325-1334).,
1325-1334.
Shi, H. J., Naumov, M., & Yang, J. (2020). Compositional embeddings using complementary
partitions for memory-efficient recommendation systems. In Proceedings of the 26th
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,
165-175.
Singh, P. K., Pramanik, P. K. D., Dey, A. K., & Choudhury, P. (2021). Recommender
systems: an overview, research trends, and future directions. International Journal of
Business and Systems Research, 15(1), 14-52.

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