DATA ANALYTICS.

DATA ANALYTICS
STUDENT ID:
[Pick the date]

Introduction and Background
During the last decade or so, property prices in Australia have soared which is particularly
true for certain cities such as Sydney and Melbourne. As a result, the investment in real
estate has seen a significant increase in the last two decades or so. It is essential to determine
a intrinsic value of various residential properties so that the clients can be advised with
regards to a fair price for a given property. In this backdrop, the objective of the given report
is to predict the fair value of the residential property prices in non-capital cities and towns
corresponding to State A. In this regards, data has been provided from the relevant cities and
towns which corresponds to data about the houses which have been recently sold.
Data and Empirical Strategy
Sample data has been presented in order to facilitate the summary of data and also estimation
of best multiple regression model to estimate the prices for the various residential properties
(i.e. house and unit) based on their relevant attributes. The starting point is the data which
has been segregated for the coastal city, coastal town along with regional city. The underlying
objective is to produce the best possible multiple regression model for the estimation of
residential property. The data sample includes information about various variables such as
price, internal area, number of bedrooms, number of garages, number of bathrooms. Further,
there are certain variables such as Land area for which data is available only in particular
cities and not available for all properties. Most of the variables are quantitative in nature and
measures based on ratio scale since there is an absolute zero which can be defined as
variables such as price, number of bedrooms, bathrooms, garages cannot assume negative
values. A key example of categorical variable is type which has been measured using
nominal scale since two possible labels namely unit and house are possible (Flick, 2015).
The first step in data analysis is to compute the descriptive statistics for the various
quantitative variables which provides a summary of these based on regional city, coastal city
and town. In order to understand any differences in the sample with regard to price of other
attributes, a comparison of the descriptive statistics in accordance with the location has been
carried. With regards to the estimation of the regression model, the cumulative data for state
A has been considered. Some variables are available for only properties located in a
particular city or town. These variables have not been taken into consideration for the
multiple regression thereby limiting the participation of only those variables which are
available for all properties included in the sample.
2

The first step is to run the multiple regression model with the aid of MS-Excel where price
would act as the dependent variable and all the various variables for which data on all
properties is available would serve as independent variables. The tweaking of this model
would be performed based on the statistical significance of the respective slopes of the
independent variables used for the regression model. The independent variables which are
found to be statistically insignificant would be ignored and the multiple regression would be
run without these variables so as to enhance the statistical significance of the regression
model (Hair et. al., 2015).
Results and Discussion
The descriptive statistics for the residential properties to regional city in state A are
summarised below.
The descriptive statistics for the residential properties to coastal city in state A are
summarised below.
3