1.0: Introduction

Property management has become a development area in the recent past. It has been seen to evolve over the

years with radical changes in its structuring and management. Most property management agents are engaged in

residential, commercial, and real estate’s including apartments and detached houses (Lees, 2001).

In management, property manager are able to run business from management of large amount of businesses and

apartments. Therefore, creating a need for firms to evaluate most crucial factors that they should ensure are

looked into for increased profitability. It has been stipulated, that possible factors could include; the lot size,

number of rooms, number of bedrooms, and type of parking and architectural style. However, there has not

been a single literature that has looked into the question carefully to help property managers locate their

business in areas of business growth (Lees, 2001). In addition, little has been done in assessment of the most

critical factors which lay bases to the current study. Therefore, the current study will be undertaken to fulfil the

following objectives;

1. To compare the value of property in East Meadow and Farmingdale.

2. To find out whether there is an association between architectural style of property and geographical

location.

3. To find out whether there is a relationship between lot size of property and number of rooms.

To achieve the first objective, the analysis will be conducted through descriptive statistics. First histogram for

both geographical locations will be presented, after which an independent t-test will be conducted to assess

whether there exists any significant difference in value between the two geographical regions.

Objective 2 will be captured through a first simple bar graph which will present the distribution of architectural

styles. Then a cluster bar which cross tabulations will be presented to assess the associations between

geography and architecture. Finally, a chi-square test will be assessed to assess significant difference in the

associations.

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To achieve the third objective correlations will be run to assess association between lot size and the number of

rooms. Further a regression will be run to assess the effect of the lot size on the number of room an apartment

would have.

2.0: Objective 1: comparing value in East Meadow and Farmingdale.

2.1: Descriptive statistics for appraisal value in East Meadow and Farmingdale

Variable Number of

observations

Mean Standard

deviation

Min Max

East Meadow 59 213.00 37.62 155 310

Farmingdale 50 191.8 30.29 115 305

Looking at the means, it is possible to tell that East Meadow has a higher value compared to Farmingdale. The

mean vale for property in East Meadow is $213,000 while the mean vale of property in Farmingdale is

$191,800. From the analysis, out of the 300 responses, 59 of properties came from East Meadow, and 50 from

Farmingdale. This composed of 19.67% and 50% of the total sample respectively. The minimum value for

property in East Meadow is $155,000 and the maximum value is $310,000. On the other hand, the minimum

value of property in Farmingdale is $115,000 and the maximum value is $305,000. This is an indication that on

average people in East Meadow afford to pay for apartments worth $213,000, while those in Farmingdale

afford to pay for apartments worth $191,800.

Making use of a distributional plot, the histogram below shows that the value of property in different regions

varies. Looking at the plot, value in East Meadow is highly concentrated at around 150,000-200,000, which the

highest frequency of 12 being below $200,000. On the other hand, Farmingdale has most of its values lie

between 150,000-200,000, with the highest frequency of 19, being below $200,000 and close to the mean value.

This is an indication that most people in East Meadow, prefer paying a value less than the mean of $213,000.

On the other hand, people in Farmingdale prefer to pay a price around the mean vale of property in the area.

People in East Meadow do not prefer paying for apartment’s worth below $150,000 as they are considered of

low quality. This is a similar observation of people in Farmingdale area. The lowest frequency equals 1 for East

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Meadow which occurs at $155,000, while the lowest for Farmingdale is 1 which occurs at $115,000. This price

is not taken by many as is considered a very low price thus associated with low quality apartments. On the other

hand East Meadow has a low frequency for property valued above $300,000, with 1 at $310,000. At

Farmingdale property worth above $250,000 is not highly chosen with a frequency of 2. This values are seen as

extremely expensive for people and thus they are not taken up. They are seen to exceed the average value

people in both places can afford. But though it should be noted that even though only a few take the low price

apartments and the very high price apartments, the high price one will get the best as they are able to pay the

price, and the low people get the low quality as they are not able to pay the average price.

12

12

669

6

44

13

12

111

5

11

19

4

33

22

1

13

24

28

8

4

12

8

4

20

18

6

5

3

22

1124

34

88

7

2

11

100150200250300

100150200250300100150200250300

East MeadowFarmingdaleLevittown

IslipIslip Terrace

Frequency

Frequency

appraisal value (thousand dollars)

Graphs by geographic location

histogram for appraised vlaues by geography

It should be well noted that only a few people are able to afford the high price apartments but they go for them

as they are able to pay the price. Though generally, property in East Meadow is highly valued compared to

Farmingdale.

2.2: Difference between mean appraisal in East Meadow and Farmingdale

From, the analysis, we have been able to spot that prices in different geographical regions differ. It is however,

important to make a hypothetical test to assert, that the value difference is statistical. And thus areas with a

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higher mean are statistically highly valued compared to those with lower mean. This engaged an independent t-

test. As shown in the table below, the mean value of property in East Meadow is $213,010,000, and that in

Farmingdale is $191,810,00.

Group Statistics

geographic

location

N Mean Std.

Deviation

Std. Error

Mean

apparised value in

thusands

East Meadow 59 213.01 37.628 4.899

Farmingdale 50 191.81 30.295 4.284

Hypothesis test

Ho: µ E = µ F

Ha: µ E ≠ µ F

ɑ= 0.1, p-value (from test) = 0.002

Decision: reject H o

The independent sample test as shown below indicate that there is a significant difference in the mean value of

apartments in East Meadow and Farmingdale. We are 95% confident that the variance value in the two area are

statistically different.

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Independent Samples Test

Levene's

Test for

Equality of

Variances

t-test for Equality of Means

F Sig. T df Sig.

(2-

tailed)

Mean

Differe

nce

Std.

Error

Differen

ce

95% Confidence

Interval of the

Difference

Lower Upper

aApparise

dd value

in

tThousa

nds

Equal

variances

assumed

5.919 .017 3.2

00

107 .002 21.201 6.625 8.068 34.333

Equal

variances not

assumed

3.2

58

106.

741

.002 21.201 6.508 8.299 34.102

The results showed, that there existed a statistical difference in the mean appraisal value between and within the

geographical locations. As thus we can conclude, that East Meadow, has a statistically high appraisal value

compared to Farmingdale. The test indicates that there is also a statistical difference in the variance. This means

that the value deviations are different in the different regions. As thus the variance in East Meadow is

significantly higher than that of Farmingdale.

From the analysis, we can be able to conclude that there is enough statistical evidence to say that the value of

property in East Meadow is not the same as that in Farmingdale. In fact it higher than the property value at

Farmingdale. This is also an indication that income levels in East Meadow is higher than in Farmingdale.

Therefore, property management agencies would benefit as more property will be raised in East Meadow to

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capture the high income earner, thus have a positive impact on their profits. On the other hand, in Farmingdale

that appraisal value is lower and thus less demand for property which will lead to less property management

firms. Therefore, the firm should focus on Farmingdale which is less attractive to most firms and use a low

price high volume strategy thus gain profits.

3: Objective 2: To find out whether there is an association between architectural style of property and

geographical location

3.1: Graphical presentation of architectural styles

It was important to first look at the distribution of architectural styles in the area. People have different

preferences for different designs. The figure below indicates that Ranch has the highest count with 103,

followed by Cape with93, Colonial with38, Spliot level with37, and last is expanded ranch with 29.

3.2: Graphical presentation of architectural design in different geographical locations

To assess the association between geography and architectural style there is need for a cluster bar chart

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The results from the figure above indicate that expanded Ranch is the least preferred architectural style in

different geographical locations. The cape style and ranch style are highly preferred, which cape highly

preferred in Levittown with a frequency of 44 as shown in the table below. On the other hand ranch is highly

preferred in Iaslip with a frequency of 33. In East Meadow, the most preferred style with a frequency of 14 is

Spliot level. And in Farmingdale, they highly prefer use of Ranch style.

geographic location * architectural style for the house Crosstabulation

Count

architectural style for the house Total

Cape Expanded

Ranch

Colonia

l

Ranch Spliot

level

geographic

location

East

Meadow

24 1 6 14 14 59

Farmingdal

e

13 1 6 17 13 50

Levittown 44 8 5 22 2 81

Islip 10 11 14 33 4 72

Islip

Terrace

2 8 7 17 4 38

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Total 93 29 38 103 37 300

3.3: Chi-square test of association between architectural style and geographical location

The association show that geography and style are different. However, it is important to test this hypothesis

thus necessitating the use of a chi-square test. From the table below, the person chi-square value is 82.37 which

is significant at 1%. This is an indication that there is significant difference in style applied in different

geographical regions.

Hypothesis test

H 0 : no association of geography and architectural style

H a : there is association of geography and architectural style

ɑ= 0.05, p= 0.000

Decision: reject H 0

Chi-Square Tests

Value df Asymp.

Sig. (2-

sided)

Pearson Chi-Square 82.366 a 16 .000

Likelihood Ratio 88.395 16 .000

Linear-by-Linear

Association

.899 1 .343

N of Valid Cases 300

a. 4 cells (16.0%) have expected count less than 5. The

minimum expected count is 3.67.

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From the analysis we thus conclude that there is association between architectural style and geographical

location. The architectural design helps look at space, environment, and design. The geographical position

looks into the position of the apartment. Therefore, it is clear that in choice of an apartment one will look at its

position, continue to looking at its size, design, outlook, and the environment which are important consideration

to the value of an apartment.

4: Objective 3: to find out whether there is a relationship between lot size of property and number of

rooms

4.1: Correlation analysis

To assess the association, the correlation measure the strength of the association between variables. The

analysis as shown in the table below show existence of strong relationship

Hypothesis testing

H 0 : person R= 0

H a : Pearson R≠ 0

ɑ = 0.05, p=0.344

Decision: fail to reject H0. Thus conclude that there is weak linear association between the two variables.

Symmetric Measures

Value Asymp.

Std. Error a

Approx.

T b

Approx.

Sig.

Interval by

Interval

Pearson's R

.055 .057 .948 .344 c

Ordinal by

Ordinal

Spearman

Correlation

.046 .060 .792 .429 c

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N of Valid Cases 300

a. Not assuming the null hypothesis.

b. Using the asymptotic standard error assuming the null hypothesis.

c. Based on normal approximation.

4.2: Linear relationship between lot size and number of rooms

It is thus important to assess the relationship. Hence necessitating the use of a linear regression model.

Variables Entered/Removed a

Mod

el

Variables

Entered

Variables

Removed

Method

1

lot size in

square

feets b

. Enter

a. Dependent Variable: number of room

b. All requested variables entered.

As shown in the table above, number of rooms becomes the dependent variable while the lot size becomes the

independent variable.

Model Summary

Mod

el

R R

Square

Adjusted R

Square

Std. Error

of the

Estimate

1 .122 a .015 .011 1.314

a. Predictors: (Constant), lot size in square feets

As shown in the table above, the mode is able to explain 1.1% variability in number of rooms. And thus lot size

can be able to explain 1.1% of the variability in number of rooms.

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ANOVA a

Model Sum of

Squares

df Mean

Square

F Sig.

1

Regressio

n

7.720 1 7.720 4.473 .035 b

Residual 514.267 298 1.726

Total 521.987 299

a. Dependent Variable: number of room

b. Predictors: (Constant), lot size in square feets

The ANOVA table shows that the overall model fitness is good at 5% level of significance. And thus the model

is bale to fit the data and can be used to explain the relationship between lot size and number of rooms.

Coefficients a

Model Unstandardized

Coefficients

Standardize

d

Coefficient

s

t Sig.

B Std. Error Beta

1

(Constant) 6.712 .159 42.343 .000

lot size in square

feets

.031 .015 .122 2.115 .035

a. Dependent Variable: number of room

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Finally, the coefficient is significant at 5%. This is an indication that lot size influence the number of rooms in

an apartment. The coefficient indicate that a unit feet increase in the lot size, increases the number of rooms by

3.1%. This is because the coefficient sign is positive indicating a positive relationship between the variables.

Constant = 6.712

B= 0.031

Y= a + bx

Y= 6.712 + 0.031x

R= beta= 0.122

An association that is below 50% indicates a weak relationship. In this case we have a weak positive

relationship between lot size and number of rooms. This means that there is need to add more variable to the

model to be able to predict factors influencing the number of rooms more appropriately.

5: Conclusions

The study was conducted with a random sample of 300. The results indicated that the average value of property

in East Meadow is $213,000, and the value in Farmingdale is $191,800. The values were seen to be

significantly different form each other and that of East Meadow was significantly higher. It was realised that

most people preferred the Ranch style, followed by case, and least preferred the expanded style.

From the analysis it is clear that geography is an important part of determining the value of an apartment.

Different locations have significantly different values and thus management firms need to get to area that have

lesser competition. In addition, it is important to note that geography is highly associated to architectural style

and people will look at the position of an apartment as well as its environment to assess the value. In addition,

lot size influences the number of rooms in an apartment but portray a weak positive association.

6: Limitations and suggestions

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Thus the current analysis recommend for further analysis to the issues to assess other factors that may have an

influence on the value of an apartment. There is need to get more factors into the model for factors influencing

number of room since lot size is not the only factor. In addition, the study was conducted with a sample of 300

which is small compared to the coverage. Thus recommended for further research making use of a bigger

sample to reduce bias.

7: References

Lees, L. (2001). Towards a critical geography of architecture: