Exploring Relationships between Real Estate Variables

The assignment content discusses the relationship between listed cost, final sale, and all rooms in real estate business. The results show that there is a strong positive correlation between these variables, indicating that higher listed costs and more rooms are associated with higher final sales. The study also finds that Domaine city has the highest real estate prices, making it an attractive destination for investment. Hills city, on the other hand, had the lowest performance and is advised to be given low priority. Terratae showed a sharp increase in prices, indicating potential for future growth. The study concludes that all rooms have a significant influence on final sale, with near perfect positive correlation.

Cover page

Introduction
Real estate business is one of the most lucrative businesses in the world market today. Investors
tend to change from temporary/ short term investment to a permanent investment (Spertino et al,
2013). As compared to other businesses, real estate business in most of the cases experience
continuous increase in the value. The business involves both buying of a bare piece of land and
selling, putting up a house structure on a piece of land then sell and also just buying and selling
of already built houses. The value in the real estate business tends to be appreciating all the time
due to value increase in the pieces of land (Tiwari & White, 2010). Before venturing into real
estate business, there are various factors that the business people need to consider just like in
other normal business opportunities. Negotiation skills are some of the required attributes for
those aspiring to engage in real estate business or those who already are running the business
(Rothaermel, 2015). These skills are deemed important in this kind of business especially when
negotiating prices with the willing buyers and the willing sellers. On top of that, prior knowledge
about the trending price is as well helpful for they act as the reference point at the time of
negotiating prices with the customers so that prices are not overstated or understated (Bewayo,
2015).
Another key factor to consider is the population of the location of the business. Population is
always allied with good business performance (Graham et al, 2011). As a result therefore, highly
dense populated areas tend to have businesses thriving well than those in less dense populated
areas (Hahan et al, 2011). The business was then chosen to be situated and operate in five cities
such as Belton, Terratae, Mount, Domaine and Hills. The services that will be rendered by the
real estate agency in these cities will be buying pieces of land, constructing houses, renting out
houses, leasing houses and selling the built houses to the willing buyers. Since the business is

from new investors and the business is still new, thorough market research incorporated with
advertisements should be held viable to make the business have wider coverage in the cities.
Televisions, radios, newspapers, magazines, social media and other platforms should be engaged
in carrying out the advertisement to create awareness to the public to expand and win the trust of
the customers. Advertisement will also ensure that wider market is covered and that the business
stands a better chance of competing with the competitors favorably (Wagner et al, 2013). In
connection to that, advertisement will wipe off the middlemen from the channel. As a result
therefore, to have all of these done, more man power have to be engaged in the business and the
more the number of workers, the larger the size of the business. In this case, the business size
will be relatively big since more professional services will need to be hired. The prospected
customers for this business are the residence and the willing investors who would be looking for
apartments for hire in the cities.
Research questions
In order to identify and know what happened in the previous market in the previous years, data
was collected covering the location of business (cities), the number of bathrooms houses in the
cities were having, the number of bedrooms, the total number of rooms houses had in the cities,
the advertisement cost and the prices that were labeled against the house in different cities. Data
was to be used to construct research questions that would be helpful in identifying the behavior
of the market and to find the strategies to venture into the market in the defined cities. Some of
the objectives that were identified in this report were to determine if there was relationship
between the listed cost and the final sale of the houses. Secondly comes to determine the
relationship between all rooms the houses had and the listed cost and lastly, to determine the
relationship between all the rooms a house has and the final sale. All these stated objectives will

only be met if the research questions are answered. The first question then becomes; “Which is
the best city to invest in among the five sampled cities?”
Cost labeled against every product is one of the determinants of the value the product hold.
Depending on the persuasive and the bargaining power of the involved parties in the buying and
selling process, the listed cost can at sometimes be higher than the final sale. In the case where
the listed cost is lower than the final sale, then it is either the product has been sold at a discount
or the buyer had higher bargaining power than the seller. And if the real estate product had
higher final sale than the listed cost, then it is either that the value of the real estate product had
appreciated due to some added values to the product or the seller had higher bargaining power
than the buyer. As a result to that therefore, we are interest to have a question that “Is there
relationship between listed cost and the final sale?” Responding to this question will help the
owner of the business know how to treat the two or consider them at any time they are making
any transaction.
It is in the minds of the many without proof that a house with the highest number of rooms will
have the highest listed cost as compared to others that were having less number of rooms. To
ascertain the change and the effect, this report is therefore bound to answering the question “Is
there a relationship between (All rooms) and the listed cost of the houses in the cities?” Any sort
of relationship that is found existing between these two variables will mean that any change in
the number of rooms will definitely have effect on the listed cost.
Lastly, we have also sparked interest in determining the relationship that exist between all rooms
a house has and the final sale of the house. As a result therefore, we pose a question: “Is there
relationship between all rooms a house has and the final sale of the house?” response to this
question will also be viable to the decision makers of the business in that they will be required to

consider the number of rooms a house has in labeling the final sale of the house irrespective of
whether the buyer has higher bargaining power than him/her.
Selected statistical methods
Some of the selected statistical methods for use in this research were Pearson’s correlation
coefficient (r). This statistical method will help to identify the relationships that exist between
variables to be tested as stated in the research questions. The value of r will show whether or not
the relationship was strong positive if the value of r is positive and close to 1 or strong negative
if the value of r is negative and close to -1 (Keith, 2014). The hand calculation of Pearson’s
coefficient will be as follows;
r =n∑ xy−¿ ¿ ¿
Descriptive statistics such as the mean, variance and standard deviation of the collected
quantitative data will be calculated as shown below;
Mean = ∑ Xi
n where i= 1, 2, 3,… where n is sample size
Mean will be calculated for listed cost and final sale for the sampled cities. Mean is important in
determining the characteristic of the dataset (Schielzeth, 2010). To calculate the variance denoted
by (S2) for the sample of the data from the dataset and it was done as follows;
Variance (S2) = ∑ x2−¿ ¿ ¿ ¿ where from the formula, standard deviation is now the square root
of variance and it is denoted by letter S when it is calculated for a sample.
S = √∑ x2−¿ ¿ ¿ ¿ ¿

The dataset will also be represented in figures such as the scatter plot and box and whisker for
the variables of interest i.e. listed cost, final sale and all rooms. The scatter plot will help us
visualize the trend line between the plotted variables whereas the shape of the data represented in
the variables will be shown in box and whisker plot.
Technical analysis
Belton
Belton
Belton
Belton
Belton
Domaine
Domaine
Domaine
Domaine
Domaine
Domaine
Hills
Hills
Hills
Hills
Hills
Mount
Mount
Mount
Terratae
Terratae
Terratae
Terratae
Terratae
0
500
1000
1500
2000
2500
A Graph of listed cost and final cost against cities
Listed ($000) Final Sale ($000)
In response to question one, the line graph was used to compare how the listed cost and the final
sale was changing with the sampled cities. As a result, final sale was slightly above the listed
cost in most of the cities. The cost were seen appreciating in Belton city and the trend even
continued to shoot higher in Domaine city. A sharp drop was recorded through to Hills city and
remained almost constant at all the time when data was being collected. In Mount city, the listed
cost and final sale were slightly increasing and higher than that in Hills where lastly, both the

final sale were still on the rise in Terratae city with listed cost taking a sharper increase than
Final sale.
Bedroom
s
Bathroom
s
All
rooms
Listed
($000)
Final
Sale
($000)
Advertising
expenditur
e ($000)
Bedrooms 1
Bathrooms 0.816468 1
All rooms 0.947519 0.930913 1
Listed ($000) 0.898309 0.891372 0.954659 1
Final Sale ($000) 0.880324 0.9083 0.950101 0.980465 1
Advertising
expenditure ($000) 0.696894 0.62631 0.726355 0.76513 0.757146 1
The correlation coefficient (r) for the test of relationship between listed cost and final sale was
0.980465 showing that there almost perfect positive correlation between the two variables. This
means that the higher the listed cost the higher the final sale, this responds to the second
question. Similar test was conducted to test for the relationship between all rooms and the listed
cost. The value of r was 0.954659 indicated near perfect positive correlation and that increase in
the number of all rooms leads to an increase in the listed cost. This responds to the third
question. In response to the forth question, correlation was tested between all rooms and final
sale and the value of r was found to be 0.950101 that showed near perfect correlation. This can
as well be interpreted to mean that higher number of rooms attracted higher final sale.

All rooms Listed ($000) Final Sale ($000)
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Q3
Median
Q1
All rooms, listed cost and final sale showed positive skewness and that higher were more than
lower costs.
0 200 400 600 800 1000 1200 1400 1600 1800 2000
0
500
1000
1500
2000
2500
R² = 0.961311853664003
Scatter plot forFinal Sale ($000) Against listed
($000)
Listed ($000)
Final Sale ($000)

The scatter plot above was to confirm for the strength of the relationship between final sale and
listed cost that was shown to be at 96.13% and that most of the plotted points were concentrated
around the trend line.
0 5 10 15 20 25
0
500
1000
1500
2000
2500
R² = 0.902691181812353
Scatter plot for Final sale against All rooms
All rooms
Final Sale ($000)
All rooms showed to have influence on the final sale. From the collected data, relationship
between the two variables was found to be at 90.27% as indicated by coefficient of determination
(R2).
Results and discussions
From the research questions in response to question one indicated that it was crystal clear that in
the previous years the business performed particularly in Domaine city. The prices of the real
estate products were the highest and stood high as compared to other cities. It was therefore an

advice to John Hikins and George Main real estate agency to prioritize Domaine city as the best
destination for venturing real estate business. As opposed to that, Hills city recorded the lowest
real estate business performance with no significant signs of showing increase in the trend as
from figure 1. As a result therefore, the real estate agency is advised consider other cities over it
and if at all it is to be considered, then it should be based on other factors otherwise it should be
given low priority. Terratae is another city that had shown sharp price increase of the real estate
products and the graph still shows that the trend was to continue for some time in the future. This
makes the city one of the best investment places for real estate business. The number of rooms
(All rooms) in the cities were found to have influence in both the listed prices and the final sale
for the houses. Due to that, near perfect positive correlation was recorded in the test of
relationship between all rooms, listed cost and final sale. The coefficient of determinant showed
all the strengths at over 90% to mean that all the tested relationships had near perfect positive
correlation.

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