Impact of Social Media on the Housing Market in UAE

This study examines the impact of social media on the housing market in UAE, specifically focusing on the mid-income segment. Regression analysis is used to analyze the relationship between various variables, such as wages, GDP, and rent. The results highlight the importance of social media in improving brand awareness and suggest implications for business policy.

Running head: MICRO ECONOMICS
MICRO ECONOMICS
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Table of Contents
Introduction................................................................................................................................3
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..........................................6
Conclusion..................................................................................................................................8
Reference list..............................................................................................................................9

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Introduction
Looking into the overall housing market of UAE, the impact of the social media on
the millennial is highly large and significant in improving the brand awareness. The
residential market of United Arab Emirates is getting cooled down and more investments are
coming for the mid income people who are earning a range of DH 15000-DH 20000. Cost of
getting a studio apartment in Gate tower at about $220,000 and a two-bedroom sea facing
apartment would have costs about $475,000. On the other hand, most of the houses in prime
locations of UAE are being rented away by individuals and investors. The study is important
in the sense that through the incorporation of regression analysis, it is important to understand
the development of policies.
Methodology
The study is using the methodology of inductive method. Through this method the
study will highly incorporate existing theories into the collected data. Incorporation of
regression analysis will help in identifying the gaps in the set of independent variables. Use
of correlation coefficient is definitely going to predict degree of interdependence that the
independent variables that are having within the model.
Diagnostics
Using linear regression the model aimed at predicting high preference over choice of
independent variables.
Model fit
The goodness of fit of the model is mainly identified from the R-squared value and
Adjusted R-squared value. Both of them are having close value and the value is about 0.72.

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Accuracy of projection
Accuracy of projection is mainly depending on the P value. The p-value is mainly
responsible for the rejection or acceptance of null or alternative hypothesis. This is important
in the sense that through the development of regression better amount of prediction is
possible.
Results
a) Executive summary of outcomes
The mean distribution is more or less same in all the quarters except in Q3. In Q3 the
mean is around 0.825. On the other hand, the median in the fourth quarter is showing
negative value of -0.535. The correlation among rent of 1 bed apartment and average rent for
studio is around is 0.94 that close to 1. The adjusted R-squared is 0.72 and the R-squared is
0.74. The absence of redundancy is clearly evident from the above data set. However, the
regression model that can be formed using the above values are as follows. Y= 5798+0.27X1.
b) Information supporting the choice of the variables, including the socio-economic
variables (model specification)
In order to carry out the linear regression method it is important to determine the set
of independent variables and dependent variables that will definitely helpful in getting
insightful knowledge. For this study, the no of real estate transactions to be done within UAE
is the dependent variable (Y) and set of independent variables (Xi’s) are wages in UAE (AED
mm), GDP, PPP of UAE (billion), inflation rate and yearly rent of various properties as a set
of independent variables. The study will incorporate the linear regression that will identify

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the level of correlation that are going to have between each Xi’s. Presence of high correlation
among the independent variables will lead to the problems of autocorrelation1.
C) Detail concerning the model development and data acquired
The model will be following linear regression theory in order to determine the R
squared and adjusted R-Squared that will automatically help in the identification of outliers.
The gaps among R-squared value and adjusted R-squared value will automatically highlight
presence of any kind of redundancy within the data set2. The negative and positive
coefficients are looking to help the direction of the relationship that are going to help the
business in understanding the impact of the variables that are having high significance. The
intercept will be the value of α and the coefficients will be the value of β’s that will develop a
proper linear regression model3.
Now with the incorporation of inflation and purchasing power parity will identify
whether there is relationship between microeconomic principles and business implication.
The data has been acquired from various authentic sources in the form of World Bank,
websites of UAE government. The regression model has been delivered using excel platform.
1 Bel, Germà, and Mildred E. Warner. "Factors explaining inter-municipal cooperation in service delivery: a
meta-regression analysis." Journal of Economic Policy Reform 19.2 (2016): 91-115.
2 Carroll, Raymond J. Transformation and weighting in regression. Routledge, 2017.
3 Gabitto, Mariano I., et al. "Bayesian sparse regression analysis documents the diversity of spinal inhibitory
interneurons." Cell 165.1 (2016): 220-233.

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d) A sensitivity analysis for alternative scenarios, and interpretation of the elasticity and
the implication to pricing strategy
From the above analysis it is clearly evident that the elasticity of price is not playing
any active role in determining the number of households that are to be used for transactions in
UAE. In the data set of types of apartments, the mean and standard deviation are having high
variation indicating the fact that there may be some redundancy that is present within the data
set. On the other hand, the ANOVA is showing that there is high percentage of
autocorrelation that are going to impact the overall model formation4. It is important for the
model to minimise the autocorrelation that are having within variables. The elasticity of
demand is playing huge role in determining the number of households that are to be used for
the transaction in UAE. However, if the pricing strategy is to be considered then the
government of UAE must consider the middle income groups of people who are earning
between DH15000-DH200005. It is important to lower the renting price of apartments so that
the renting market can improve also.
4 Galling, Britta, et al. "Antipsychotic augmentation vs. monotherapy in schizophrenia:
systematic review, meta‐analysis and meta‐regression analysis." World Psychiatry 16.1
(2017): 77-89.
5 Hayes, Andrew F. Introduction to mediation, moderation, and conditional process analysis:
A regression-based approach. Guilford Publications, 2017.

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e) Interpretation of the results with implications to business policy
Figure 1: Descriptive statistics of each quarter
(Source: Created by Author)
The mean distribution is more or less same in all the quarters except in Q3. In Q3 the
mean is around 0.825. On the other hand, the median in the fourth quarter is showing
negative value of -0.535. This means majority of the data set of this quarter is mainly going
towards the negative line of deviation from the mean values.
Figure 2: Correlation among types of apartments that are being sold in UAE
(Source: Created by Author)
The above table is showing the fact that the correlation among rent of 1 bed apartment
and average rent for studio is around is 0.94 that close to 1. This means that both these type is

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having a positive relationship regarding the price of rents. Increase in the price of rents for
studio type apartments is going to increase 0.94 times renting price for the 1 bedroom
apartments. Considering the business policy, it is important to aim the pricing of both studio
and 1 bedroom apartment.
Figure 3: Model formation
(Source: Created by Author)
The adjusted R-squared is 0.72 and the R-squared is 0.74. The absence of redundancy
is clearly evident from the above data set. However, the regression model that can be formed
using the above values are as follows. Y= 5798+0.27X1. In the given regression model, X1 is
the wages in UAE. The other variables has been rejected from considering them in the model

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because of the fact that they are having high p-value which is making them highly significant
in nature. From the above regression it is clear that the number of household units to be sold
in UAE is depending on 0.27times the wages the employees are getting from their jobs. To
some extent the government must aim to increase the wages of employees so that they can
enter the housing market more efficiently.
Conclusion
It can be concluded that wage rate is important in determining the number of
households that are needed to be used in transactions in UAE. From the above model, the
government must be aiming to bring down more level of employment so that the employees
will be able to increase the consumer purchasing parity in the housing industry. It is
important to increase the employment opportunities that are being present in UAE so that the
housing market can be smoothly operated.

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Reference list
Bel, Germà, and Mildred E. Warner. "Factors explaining inter-municipal cooperation in
service delivery: a meta-regression analysis." Journal of Economic Policy
Reform 19.2 (2016): 91-115.
Carroll, Raymond J. Transformation and weighting in regression. Routledge, 2017.
Gabitto, Mariano I., et al. "Bayesian sparse regression analysis documents the diversity of
spinal inhibitory interneurons." Cell 165.1 (2016): 220-233.
Galling, Britta, et al. "Antipsychotic augmentation vs. monotherapy in schizophrenia:
systematic review, meta‐analysis and meta‐regression analysis." World
Psychiatry 16.1 (2017): 77-89.
Harrell Jr, Frank E. Regression modeling strategies: with applications to linear models,
logistic and ordinal regression, and survival analysis. Springer, 2015.
Hayes, Andrew F. Introduction to mediation, moderation, and conditional process analysis:
A regression-based approach. Guilford Publications, 2017.
He, Yu-Lin, Xi-Zhao Wang, and Joshua Zhexue Huang. "Fuzzy nonlinear regression analysis
using a random weight network." Information Sciences 364 (2016): 222-240.
Hox, Joop J., Mirjam Moerbeek, and Rens Van de Schoot. Multilevel analysis: Techniques
and applications. Routledge, 2017.
Kaytez, Fazil, et al. "Forecasting electricity consumption: A comparison of regression
analysis, neural networks and least squares support vector machines." International
Journal of Electrical Power & Energy Systems 67 (2015): 431-438.

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Levine, Hagai, et al. "Temporal trends in sperm count: a systematic review and meta-
regression analysis." Human reproduction update 23.6 (2017): 646-659.
Silverman, Bernard W. Density estimation for statistics and data analysis. Routledge, 2018.
van Smeden, Maarten, et al. "No rationale for 1 variable per 10 events criterion for binary
logistic regression analysis." BMC medical research methodology 16.1 (2016): 163.