The given case is related to a major Australian housing construction firm which desires to execute an excellent housing development project in coastal New South Wales. Before undertaking the housing development project by the firm, a thourough market analysis of various houses and units is required to be done in the targeted areas so that a good business plan can be prepared. The areas targeted are Sydney, Wollongong and Newcastle. The houses and units considered for the analysis are taken on the basis of ocean and non ocean view. Basically, the prices of different houses and units need to be compared for observation purpose.
The data given in the problem contains prices of houses and units with ocean and non ocean view in three targeted areas i.e. Sydney, Wollongong and Newcastle. In order to analyze the given data,we are using mean as it is based on all the given observations. Further, it is simple to compute,easy to comprehend for the observations in the given set of data or sample. Mean always gives definite value as it is rigidly defined and not affected by personal bias. It does not require any specific arrangement of data to calculate the value alike other measures of central tendency. It is least affected by fluctuations in sampling and so ensures stability in calculations and consequently, provides good base for comparison. Therefore, we are calculating average price or mean of prices of houses and unit in the targeted area. In order to draw proper observation, we are also calculating the average price of house with ocean view, the average price of house with non ocean view, the average price of unit with ocean view and the average price of unit with non ocean view. Moreover, we have compared the different average values on tables and graphs so that proper and appropriate conclusions and observations can be drawn to solve the given problem.
By comparing the average price of house in each of the three targeted areas i.e. Sydney, Wollongong and Newcastle, it has been found out that houses in Sydney are the most priced and the houses in Newcastle is the least priced. Similarly, by comparing the average price of unit in each of the three targeted areas it has been observed that the units in Sydney are the most priced and the units in Newcastle is the least priced. Further, by comparing the average price of house of each of the three targeted areas with the average price of units, it has been found out that the price of house is greater than that of unit in each of the three targeted areas (clearly depicted from the graphs and tabular presentation provided above).
Although the conclusions drawn by using mean of given set of observations are considered the most appropriate but the mean or average is affected by the extreme values present in the given set of observations as the mean or average is calculated by using all the values present in the given set of data. Further, it can not be calculated in the absence of any of the values of given set of data. It assigns more significance to the higher values and less significance to the lower values present in the given set of observations.
By observing the different average prices calculated in the report, it has been found out that these average prices have provided a good base to compare the market prices of houses and units in the three targeted area and consequently facilitates execution of an excellent housing development project in the desired area by the firm. Although, in the given case, the average prices are calculated by using different number of observation in the three different targeted areas so this makes comparison quite absurd.
The ideal comparison tool in the given problem should be the weighted average mean of the observations in the given set of data as it assigns weights according to the importance of each value and therefore provides more accurate results.
· Investopedia, Available online http://www.investopedia.com/terms/d/descriptive_statistics.asp [Acessed on 08/10/2017]
· www.australia.gov.au, available online http://www.australia.gov.au/about-australia/facts-and-figures/statistics [Acessed on 08/10/2017]
· Schaum’s outlines statistics by Murray R Speigel and Larry J Stephens, Available Offline [Acessed on 08/10/2017]
· Singhal D K , 2017, Financial Management, Available at Offline, [Accessed on 08/10/2017]
· Academia.edu, Measures of Central Tendency, Available online,
[Acessed on 08/10/2017]
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