Determinants of House Prices in Sidney, Australia

Statistics for financial decisions
Statistics for Financial Decisions
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Statistics for financial decisions
Executive summary
The main objective of this research was to identify the major determinants of prices of
houses in the city of Sidney in Australia. The size of land in square meters, house price
index, annual price change and age of the house were identified as the major
determinants of the house prices in the city. This research employed simple random
sampling method where a sample of 15 years was selected. Analysis was carried out
through the use of excel after which the findings were presented in form of tables and
graphs. It was found that the price of houses in Sidney was affected differently by the
house price determinants. Housing price index was found to have the strongest
influence on the house market price while the size of land in square meters was found
to have the least influence on the price of houses. To add on, the research discovered
that all the variables had a positive relationship with the house price except the age of
the house in years which depicted a negative relationship.
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Statistics for financial decisions
Introduction
Housing prices in the real estate industry have been seen to fluctuate in the recent
decade globally (Dongsheng & Zhong, 2010). This has been confirmed by the house
price trend in many cities of the world. Australia’s housing prices have not been any
different. To be specific, the city of Sidney has witnessed increased house prices. This
has been caused by proximity to the city, forces of demand and supply among others
(Hua, 2008) and (Shisong & Hongmei, 2009). Less research has been conducted on the
factors affecting housing prices in Sidney therefore it is difficult to attribute this price
increase to any factor. However, some of the evident factors affecting house prices in
Sidney include the size of land the house is lying on, housing price index and annual
percentage change
This research study is focused in creating a model that will be used to predict the house
prices in Sidney using the above named variables. For example, the age of the house
would be a major component of the model since new houses are likely to fetch high
prices due to high demand while old houses are likely to fetch lower prices due to low
demand (Nellis, 2011). Another major component of the model will be the size of land
on which the house is built (Abelson & Chung, 2005). It is normal that a house sitting on
a large area will go at a higher price while a house sitting on a smaller size of land will
fetch a lower price in a given area (Quigley , 2009).
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Statistics for financial decisions
LITERATURE REVIEW
The last decade has witnessed a soar in the house prices in Sidney. According to a
research done by (Haurin & Gill, 2012), in 2003, the median house price in Sidney was
about 473 dollars. About 11 years later in mid-2014, the median price had shot to
811,000 dollars. This is an increase of 70%. According Real Estate Institute of Australia
(REIA), the data indicated stability between the year 1990 and 1999. However, an
increase was witnessed in the year 2000. The scenario was even worse during the
global financial crisis of 2007/2008 as there was a steady increase. In 2014, a research
conducted by demographics 10th annual survey compared the ratio of family income to
median house prices in Australia. The research found that Australia had a very high
mean house income price in comparison to developed economies of the world such as
japan, UK and US (Karantonis & Ge, 2007).
METHODOLOGY
Data collection
The data used in this study was shared by the Australian Bureau of Statistics. This was
under residential property prices. The information gathered included the house values in
thousand dollars, the number of years that the houses had stayed the size of land, price
index of the city and yearly percentage change in real estate values. The sampling
method used by the study to select the years from the year 2002 to 2017 was
judgmental sampling where a convenient sample was used. The recent last 15 years
were picked for the study. The variables involved in this research study were all
numerical variables thereby prompting the use of quantitative analysis. There were both
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