Place your order

Fill in the order form and provide all details of your assignment.

Proceed with the payment

Choose the payment system that suits you most.

Receive the final file

Once your paper is ready, we will email it to you

I’m working on a management writing question and need an explanation to help me

AMAIZING OFFER GET 25% OFF YOUR FIRST ORDER CODE FIRST25

I’m working on a management writing question and need an explanation to help me learn.CHAPTER FOUR FINDINGS & DISCUSSION4.1INTRODUCTION This chapter will discuss the findings from analysis of the data and information collected. This study used SPSS to test the research hypotheses. The findings of this study are divided into three sections. First, descriptive analysis on the data. Second, an independent sample t- test was run to test which independent variables have an impact on the occupancy rate. Third analysis using Multiple Regression Analysis, to determine which independent variableshave positive impact on the occupancy rate . 4.2DESCRIPTIVE ANALYSISDescriptive analysis are summary a given data set, which representation of the entire population or a sample of it. The data was taken from the primary and secondary data collection in Chapter 3. Below is the summarize on the data. Table 4.1 : Summary of the data on all LTH office buildings in Kuala Lumpur city as year 2017 Building Occupancy (%) ( Y) Rental (RM psf) (b) Location ( c) Age (year) (d) Facilities and amenities (e) PLATINUM PARK 25 6 1 3 3 TH SELBORN 83 3.5 1 19 2 TH PERDANA 88 3 1 19 1 TH GLOMAC 100 4 2 4 2 TH TOWER 83 6 2 16 1 LOT N 100 4 2 10 1 HORIZON 2 83 4 2 4 2 HORIZON 3 91 4 2 4 2 TH UPTOWN 79 4 2 22 2 Source : Author Note :Location 1- Kuala Lumpur City (WCC, GT, CBD)
Facilities 1- (Excellent) refer to malls, amenities nearby and variables
The figure on the occupancy rate based on the percentage occupied on the building . For example if the building occupancy at 83 % it will consider 0.83.
Thefigure on the column rental is an average rental price per sq ft (RM psf) of each building.
The figure on age is based on the number of year the office building is completed .
Location 2- KL Fringe(Refer Table 4.1.1)Facilities 2- (Good) refer to distance from mall and amenitiesis less than 500 metre and quite enough Facilities 3- (Average) refer to malls and amenities is more than 1000 metre(Refer Table 4.1.2) Table 4.2 : Location classification code Location Classification code Kuala Lumpur City 1 KL Fringe 2 Source : Author Table 4.3 :Classification code on facilities by factor Facilities Classification code Excellenct There are retail shops, mall, foods and beverage outlets, cinema nearby and variables 1 Good Distance from retail shops, malls, foods and beverage outlets is less than 500 metre and the number is enough 2 Average More than 1000 metre from malls and others amenities 3 Source : Author 4.3OUTPUT OF REGRESSION ANALYSISTable 4.4: Regression analysis of rental on location, age and facilitiesRegressionequation : =+++ Equation Coeffcients Intercept 3.494 (1.111) Location 0.214 (0.218) Age -0.020 (-0.291) Facilities R- square Adj R –sq F stat 0.366 (0.450) 0.118 -0.409 0.224 ( 0.875) Notes : Significant at 0.1(*), 0.05(**), 0.01(***) and 0.001(****)levels. Table 4.5: Regression analysis of occupancy on rental , location, age and facilities. Regression equation : =++++ Equation Coefficients Intercept 1.303 (4.512 *) Rental -0.120 (-3.282*) Location 0.199 (2.445*) Age -0.002 (-0.407) Facilities R square Adj R-sq F-stat -0.157 (-2.298*) 0.898 0.796 8.839 (0.028) Note : Significantat 0.05(*) level For regression 2, the RENTAL independent variable is added into the regression. The R- squared value for the regression has improved. An independent sample t- test was used to comparedthe result between each independent variables with occupancy.The t value for rental variable is3.282 was of ≥ 1.68. The result showthe rental at 3.282 , location 2.445 and facilities 2.29 . The this three independent variables wereabove than 1.68 . It meansrental, location and facilities are significant factors that affect office occupancy While age of building is below than 1.68, the result show that the age is not significantto office occupancy. A multiple regression analysis was used to determine whether rental, location and facilities have positive impact on office occupancy. The regression equation appeared as follow : =++++ The regression has significant F- value (sign F = 0.028, F = 8.839). The R square= 0.898, meaning 89.8% variances in office occupancy was due to the combination of rental, location and facilities. In term of effect size, the magnitude of the relationship from multiple correlation coefficient (R=0.947) shown a strong relationship of rental. location and facilities on every building with 94.7% variances. To conduct the analysis on the data given in accuracy, the proses to replace the occupancy as independent variable to rental is been conducted by using the same method. 4.4FINDING ON REGRESSION ANALYSIS Correlation dependentvariable andindependence
Public system transportation
Economic Location (Amenities)
Congested, Urban Sprawl and Integrated Government Policies
variable The main factor relate to higher occupancy (dependent variable) in this research is location with accessibility , rental and facilities (independent variable). The correlation and the strength of causal effects from three independent variable will increase the occupancy of the buildings as from the structure equation methods show that the three independent can influence dependence variablealmost 90%.. The most impact on the occupiers satisfaction by tenants are office building itself, location and amenities and also relation with property manager (Claire Sanderson & Mary Edwards, 2016) Accessibility is an important factor affecting occupancy. The building far from the public infrastructure such as LRT, MRT or Monorail will have low occupancy rate. It does not attract by tenants to occupied the building. TH Platinum Park office has low occupancy rate due to far from the public infrastructure. It is more than 1000 metrefrom LRT Ampang Park station. TH Glomac and Lot N accessibility to the public infrastructure is 100-200 metre. Platinum Park is more than 1000 metre to LRT. For Horizon 2 and Horizon 3 the accessibility to the public infrastructure is about 700- 800 metres. Physical location refer as accessibilities must be near to economic locationwhich refer to facilitiessuch as retail outlet, mall. The productivity of real estate influenced by economic and physical location. The economic location must be together with physical location. The activities surrounding TH Platinum is office and residence which refer to condominium such as Stonor Condominium. There are no element of economic location. It is important to identification of associates nearby. There must be mix development either office market with mall or residence with mall at the area. The combination of residence and office market will lead to low on occupancy. Convenient access to amenities is vital for office developments. A life style integrated office development should include components like retail lots, beverage outlets and bank branches. The increasing on urban population in Malaysia, as urbanisationrate at 73% and Kuala Lumpur city centre need to address the road congestion. Due to decentralization and urban sprawl, the traditional business centre are losing their dominances to new centre concept of the central business district. Edge cities, business space and office parks have entered the vocabulary as offices have also decentralised (Jones, 2013). The accessibility through public infrastructure, economic location, facilitiesis been put together and the weakness of town planning by government, The well plan KL Fringedevelopmentcompare to the Kuala Lumpur city centre especially with LRT and MRT. The insufficiently integrated institution on policies. Coordination and the efficiency betweengovernment institution issue across various of department can’t be solved . For example land and use planning. Low Density
Report onthe cities on grow and createopportunities2015show that Kuala Lumpur still relatively low on density as the density been measured by jobs per km or GDP by km. The density is low than other cities such as Singapura 5 times, Seoul 2.5 times. It show that the limit the benefits of agglomeration on the cities.please rephrase.
.doc file