Basic Business Statistics Australia 2022

Running head: BASIC BUSINESS STATISTICS 1
Basic Business Statistics
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BASIC BUSINESS STATISTICS 2
Basic Business Statistics
Section 1
Introduction
The housing industry in Austria is facing an affordability crisis and as a result, the
government and other stakeholders are investing their time and resources to develop the
appropriate housing model that could possibly counter the crisis (McLaren, Yeo & Michael).
Empirical models of the Australian housing market have been developed to quantify the
relationship between construction, prices, rents and vacancies and how these factors can be put
into account in establishment of affordable housing (Saunders & Tulip, 2019). The objective of
the paper is to apply inferential statistics on both primary and secondary data to determine the
very basic factors that influence the housing price in Airbnb and to examine how statistically
significant the factors can be relied upon in examination of housing prices.
Description about Dataset
The datasets used are labelled dataset 1 and dataset 2 and their specific features are
discussed below.
Dataset 1
Dataset 1 is collected from an already existing website hence it is a secondary data. It
has got twenty-two variables of categorical and numeric type. Categorical variables are; listing
URL, name, city, property type and room type. On the other hand, numeric variables are; id,
latitude, longitude, accommodates, bathrooms, bedrooms, beds, price, guests included, extra
people, availability_30, availability_60, availability_90, availability_365, number of reviews and
review score rating. The dataset has got different cases highlighted as the distinct properties in
Airbnb from which the data is collected (Shao, 2010). The data consists of 10000 samples.

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Dataset 2
Dataset 2 is collected from one-on-one survey of thirty random international students in
my class hence it is primary data (Levie, 2012). It has got three variables. The variable random
code is discrete numeric, indicating the random id allocated to the student by the time of data
collection. The variable gender is categorical indicating the sex of the student while the variable
room type is categorical indicating the type of room occupied by the international student. The
cases in the dataset are the different students surveyed. The sample size chosen for data
collection is thirty. The limitation of the data is that it is collected from a sample of random
students only in my class and hence it may not be representative of the whole population of
international students (Linoff, 2012).
Literature Review
The housing industry in Austria is facing an affordability crisis thereby pressing policy
challenge for the government. Analysis from historical data indicates that by the year 2016, the
deficit of the gross social housing that was being provided had hit an optimum value at 140,000
houses and the country’s public housing system had also become highly rundown (Pawson,
Milligan, & Martin, 2018). Moreover, In Australia’s capital cities, the housing market indicated
that the prices of the houses that could be affordable to low and medium range earners had
become scarce.
To address affordable housing scarcity issue, the government, policy makers and other
stakeholders are investing their time and resources to develop the appropriate housing model that
could possibly counter the crisis (McLaren, Yeo & Michael). Specifically, huge emphasis has
been placed in the creation of private owned affordable housing industry, developed through
private financing and supported the government through public private partnership.

BASIC BUSINESS STATISTICS 4
Empirical models of the Australian housing market have been developed to quantify the
relationship between construction, prices, rents and vacancies and how these factors can be put
into account in establishment of affordable housing (Saunders & Tulip, 2019).
Section 2: Is 40% a Plausible Value for the Proportion of Private Room in Airbnb Room
Type?
The proportion for the distinct room types that are available in Sydney Airbnb for rent are
highlighted in the following summary statistic table.
The visualization of proportions can be done through the aid of a pie chart (Lock, 2013). The pie
chart is shown below.
From the pie chart above, the proportion of room type “entire home/apt” in Airbnb is 62%,
private room is 35%, hotel room is 1% and shared room is 2% of the total.