MICRO ECONOMICS.

Same as MAH_260619_263733_9_1020656 company name should be different. your choice eveyrthing is explained in the assignment it should be multi national or something you choose any company i m not ristricting you you choose we dont mind chosing any company the criteria is written in the assignment description

Running head: MICRO ECONOMICS
MICRO ECONOMICS
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Table of Contents
Introduction................................................................................................................................2
Methodology..............................................................................................................................2
Diagnostics.................................................................................................................................2
Model fit.....................................................................................................................................2
Accuracy of projection...............................................................................................................2
Results........................................................................................................................................3
a) Executive summary of outcomes...........................................................................................3
b) Information supporting the choice of the variables, including the socio-economic variables
(model specification)..................................................................................................................3
C) Detail concerning the model development and data acquired..............................................4
d) A sensitivity analysis for alternative scenarios, and interpretation of the elasticity and the
implication to pricing strategy...................................................................................................5
e) Interpretation of the results with implications to business policy..........................................5
Conclusion..................................................................................................................................8
Reference list..............................................................................................................................9

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Introduction
The introduction of regression model for the housing market of UAE are going to help
the study in identifying the process and economic variables that will help in improving the
business policies within the future. The incorporation of linear regression is mainly going to
help the study in identifying the business policy that are mainly helpful for the prediction of
future policies. The model specification is going to increase opportunity of policy that will
not only bring in high quality of statistical modelling but will also increase the business
development.
Methodology
The model has taken the help of mostly secondary data from various journals and
websites that are highly beneficial for the development of regression analysis. Now in order
to increase the accuracy of model, the linear regression techniques has been used for this
purpose. The study has taken the housing market of UAE in order to determine the demand of
the people living within the country.
Diagnostics
While making the diagnosis the study took the statistics of correlation coefficient and
regression analysis with p-value being one of the significant variable that is actually
determining whether to reject or accept the null or alternative hypothesis.
Model fit
The fitness of model is maintained by the close value of R-squared and adjusted R-
squared. Both the variables are having values around 0.97 and it is close to 1.

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Accuracy of projection
The accuracy of projection is mainly depending on the variation among dependent
and independent variable. Now this can be highlighted from the regression analysis that has
been done. P-value of total value of rents is less than 0.05 and that signifies the rejection of
nll hypothesis.
Results
a) Executive summary of outcomes
The value of R-squared is around 0.985 and the adjusted R-squared is around 0.976.
Both the values are close to one and it is showing the goodness of fit and the model is well
fitted. However, so close the value of both R-squared and adjusted R-squared is showing the
fact that the data set is not having huge amount of outliers within the model. The regression
equation that can be formed from the given data is Y= 1125+0.014X1+29.311X2. In the
model, the total rent value is clubbed under X1 and inflation rate has been clubbed under X2.
b) Information supporting the choice of the variables, including the socio-economic
variables (model specification)
The choice of the variables are going to modify the opportunity of statistical
modelling that will definitely help in improving the future prediction that are going to help in
future. In this modelling, the value of completed building in UAE housing market, inflation
rate for housing industry, housing units by type of buildings, rent that is paid annually in both
rural and urban regions are the set of variables that will be useful in developing the model.
These set of variables has been chosen in the sense that through the use of such variables the
formation of statistical modelling will help in the determination of better linear relationship.
The dependent variable Y is number of households in UAE that are in demand among the

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people of UAE. The model is mainly taking two main independent variables in the form of
inflation rate and total values of the rents that are being provided to the customers of UAE1.
C) Detail concerning the model development and data acquired
For the purpose of this study, data on the independent variables has been collected
from various authentic government websites and journals2. Through the detailed study of
variables, strong relationship has been obtained. The model took the help of linear regression
that is mainly aimed in making correlation with the set of dependent and independent
variables3. Moreover, the study will be able to identify the presence of any kind of correlation
that the independent variables. The identification of the autocorrelation will help the model to
eliminate the insignificant variables. Moreover, the improvement in the better piece of
prediction is mainly helpful for the development of highly improved variables that will help
in understanding the preference of common people that are living in UAE4.
1 Ahmed, Khaled. "From ‘Rigid’to ‘Resilient’: A Proposed Self-Build Relocatable SIP Construction Mechanism
for Sustainable Social Housing Models in UAE." Buildings 8.4 (2018): 58.
2 www.dsc.gov.ae. "Home". Dsc.Gov.Ae, 2019, http://www.dsc.gov.ae/en-us.
3 Al-Mohana, Safa, and Abdulnasser Hatemi-J. "The Impact of Recent Crisis on the Real Estate Market on the
UAE: Evidence from Asymmetric Methods." Economia Internazionale/International Economics 69.4 (2016):
389-428.
4 Elkaftangui, M. O. H. A. M. E. D., and B. Mohamed. "A Methodology for Successful Retrofitting in the UAE
Old Residential Sector towards Sustainable Measures." Proceedings of the Obsolescence and Renovation—20th
Century Housing in The New Millennium, Universidad de Sevilla, Spain, December (2015): 14-15.

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d) A sensitivity analysis for alternative scenarios, and interpretation of the elasticity and
the implication to pricing strategy
A sensitivity analysis is mainly going to identify the crux that are mainly associated
with the suitable modelling. From the obtained data it is clear that total rent value is having
more significance in the formation of mode. It has been seen that the inflation rate is having
no significance at all in making the model realistic in nature.
Though the involvement of concept of elasticity is mainly not working in this
regression analysis because of the fact that there has been no data on the elasticity of the
economy. However, from the regression result that has been obtained it is clearly evident in
the sense that the demanding factors of the housing industry is average rents that are being
given to the property, the wage of the employees, employment rate that is prevailing within
the economy. In most of the model, it has been seen that inflation rate is not heavily
impacting on the development of model and demand of housing industry.
e) Interpretation of the results with implications to business policy
Figure 1: Descriptive statistics of various housing types
(Source: Created by Author)

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In the given scenario, the above table is mainly giving the results that mean of value
of villas and residential complex is around 2348 and on the other hand, the mean of the value
of multi storey residential and commercial buildings is around 3853. From the revealed
preference of the people of UAE regarding the housing pattern they prefer to live, it is clear
that people are preferring more to stay in multi storey commercial building and in high rise
buildings compared to normal villas.
Figure 2: Correlation coefficient among the variables
(Source: Created by Author)
The actual rents is having a correlation of 0.98 with the factor housing. This means,
the actual rent is having significant impact on the demand of housing. This is true in the sense
that most of residents are demanding houses based on the actual rents and inputed rents as
inputed rents is also having high significance in the demand of housing. Materials for
maintence is also having negative correlation with the variable housing.

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Figure 3: Model formation for the regression analysis
(Source: Created by Author)
The given regression analysis output is mainly going to help in the determination of
regression equation that will definitely help in predicting the future that will definitely help in
improving the business strategies5. The general regression model is Y=α+β1X1+β2X2+c.
where α is the base intercept and β1 & β2 are the coefficients of the independent term X1 and
X2. In the table above the R-squared and adjusted R-squared are more or less same. The
value of R-squared is around 0.985 and the adjusted R-squared is around 0.976. Both the
values are close to one and it is showing the goodness of fit and the model is well fitted.
However, so close the value of both R-squared and adjusted R-squared is showing the fact
that the data set is not having huge amount of outliers within the model. The regression
5 Huston, Simon, Ebraheim Lahbash, and Ali Parsa. "Investigating the UAE Residential Valuation System: A
Framework for Analysis." Available at SSRN 2553738 (2015).

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equation that can be formed from the given data is Y= 1125+0.014X1+29.311X2. In the
model, the total rent value is clubbed under X1 and inflation rate has been clubbed under X2.
The P-value for the factor of total value is around 0.0009. The variable total rent value is
important and highly significant in nature.
Conclusion
The study has come to conclusion that model has been taken into consideration
variables in the form of inflation rate, total rent value, types of housing that is in demand
while making the model of regression analysis. It is highly important in the sense that through
the improvement in the business policies, the results of the regression analysis is important.
Through the sensitivity analysis, the study is mainly aiming to bring down main factors that
will be able to highlight the growth of predictive modelling.

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Reference list
Ahmed, Khaled. "From ‘Rigid’to ‘Resilient’: A Proposed Self-Build Relocatable SIP
Construction Mechanism for Sustainable Social Housing Models in
UAE." Buildings 8.4 (2018): 58.
Al-Mohana, Safa, and Abdulnasser Hatemi-J. "The Impact of Recent Crisis on the Real
Estate Market on the UAE: Evidence from Asymmetric Methods." Economia
Internazionale/International Economics 69.4 (2016): 389-428.
Elkaftangui, M. O. H. A. M. E. D., and B. Mohamed. "A Methodology for Successful
Retrofitting in the UAE Old Residential Sector towards Sustainable
Measures." Proceedings of the Obsolescence and Renovation—20th Century Housing
in The New Millennium, Universidad de Sevilla, Spain, December (2015): 14-15.
Huston, Simon, Ebraheim Lahbash, and Ali Parsa. "Investigating the UAE Residential
Valuation System: A Framework for Analysis." Available at SSRN 2553738 (2015).
Ibrahim, Hatem, et al. "A comparative assessment of housing dynamics In Abu Dhabi and
Doha." ArchNet-IJAR: International Journal of Architectural Research 10.3 (2016):
152-169.
Khalfan, Malik, and Irfan Ul Haq. "Tenants’ Satisfaction in Abu Dhabi (UAE): A
survey." Middle East Journal of Business 14.1 (2019): 20-27.
Mohamed, B., M. O. H. A. M. E. D. Elkaftangui, and R. A. N. A. Zureikat. "Towards
Rethinking the Precast Concrete Industry in the UAE." Learning prototyping and
Adapting(2018): 287-296.
www.dsc.gov.ae. "Home". Dsc.Gov.Ae, 2019, http://www.dsc.gov.ae/en-us.