Data Analysis Relayed To House Prices In Kingfisher Bay
This report is aimed to develop understanding of data analysis relayed to house prices in Kingfisher bay. From analysis, it is determined that house prices in the market depend on condition and other factors like rooms, lot size in square metres, area in sqm, street, stories, bedrooms and bay views. Apart from this, it can also be identified from analysis that suburbs have a bigger impact on house prices rather than condition. Moreover, Kingfisher Bay is at least $600, and hence rated as the most unaffordable area to live in Melbourne metropolitan and surrounding suburbs. Apart from this, there was a lack of development across the Kingfisher Bay area as at least 75% of houses are 10 years or older.
It is the required to analyse the housing prices in an economy as the real estate industry plays a significant role in the economic development. The housing data includes house prices, affordability, investment returns, rental availability and rental returns that are required to make investment decisions. The housing prices in Melbourne are increasing that is major concern for the buyers to enter the market. The recent study reflects that there were more Melbourne suburbs with a median house price of over $1 million evidenced from recent auction and private sales figures. In media, it is reported that there is lack of available rental properties across Melbourne that is causing an increase in the house and rental prices. In order to determine authenticity of such analysis, a pilot survey is conducted from the City of Kingfisher Bay concerning house prices, rental costs and returns, and other related data. The below survey analysis informs the Real Estate Institute of Victoria (REIV) about the position of the City of Kingfisher Bay and more specifically its suburbs in relation to house prices and affordability. This analysis also determines whether the claims related to expansiveness of the City of Kingfisher Bay as areas in Melbourne to purchase or rent. So, this study is focused on the analysis of both the house prices and the rental prices across the different suburbs that make up Kingfisher Bay.
From data analysis, it can be determined that the average house prices in Kingfisher bay is $886575. There are availability of houses range between $192000 and $1761000. It shows that houses with different prices are available in the market in this region for people to buy. Total value of houses within the sample (120) is $106,389,000. The value of standard deviation and sample variance is so high showing there is large deviation of data points to the mean of the sample. It means the data of house prices are spread out over a wide range of values. Apart from this, the negative value of Kurtosis and less positive value of Skewness show that the data distribution of house prices is symmetrical due to little Skewness. The distribution of this data set has lighter tails and flatter peak than the normal distribution.
(b) The media articles focus on median house prices and not the mean. I have never been able to understand why this is so; surely the “average” is the “average”. Can you provide a straightforward answer for me?
It is better to use the median price to report in the evolution of property prices rather than mean prices. The mean shows the average of house prices for the given data set while the median is the middle point that divides the data set in to 2 equal parts. The median measurement shows that the half of all values occurs at a price that is lower than the median price and the remaining half occur at the price higher than the median price. In the given case of the Kingfisher Bay, it can be observed that the median is 852 means half of the houses are priced below $852,000 and remaining houses are priced above $852,000. The basic advantage of using median as a measure of central tendency is that this measure cannot be affected by the outliers, while the mean or average measure can be adversely influenced by outliers leading to major distortions due to bias data interpretation. Selling of a house at higher prices (more than $1761, 000) may pull average higher and results in an over-estimate of price growth in the region. But, median value is not affected by this selling at higher prices. It means the median measure provides a better and realistic measurement of the price growth in housing market. Based on results, the median seems to be a better descriptive measure of the data since the outlier of 1761 affects the mean.
(c) Provide an accurate estimate of the average house price for all houses in Kingfisher Bay.
Standard error of average = standard deviation /
= 29.66 (calculated in excel)
So, accurate estimate of the average house price for all houses in Kingfisher Bay will be as follows:
=886.575 ± 1.96(29.66) at a 95% confidence interval
=886.575 ± 58.139
=828.44 to 944.71
=$828440 to $944710
Based on the analysis, it can be stated that the number of all houses in Kingfisher Bay that are $1million and more is determined between 27.25% and 44.41%.
From the descriptive statistics, it can be determined that mean value for the condition of houses in Kingfisher Bay is 2.60. This value is more than 2 showing that the most of houses are in good condition. Apart from this, the median value of 3 indicates that half of the houses are in good or excellent conditions while remaining are in poor and very poor conditions.
From correlation analysis, it can be stated that the correlation between suburbs and condition is 0.0109 showing a weak linear relationship between both variables. It means there is no significant difference among the suburbs in terms of condition. From this, it can be interpreted that all suburbs including A, B and C live in the houses with different conditions including very poor, poor, good and excellent.
ii. in house prices by condition in Kingfisher Bay?
Based on correlation analysis in excel, it can be interpreted that correlation between house prices and condition is 0.3544 indicating small positive relationship between both variables. It means there is slight difference in house prices by condition. So, differences appear in house prices by condition in Kingfisher Bay. From this, it can be interpreted that the house prices slightly vary with the changes in conditions of the house. The houses with excellent conditions are highly costly while, houses with poor conditions are less costly at small extent.
The correlation analysis in excel indicates that the correlation between house prices and suburbs is 0.3737showing small positive relationship between both variables. It means there is slight difference in house prices between the suburbs.
On the basis of the above analysis, it can be stated that suburb has a bigger impact on house prices rather than suburbs as the correlation between suburb and house prices (0.3737) is greater than the correlation between condition and house prices (0.3544).
In order to define whether the cause of increasing housing prices is driven by good rental returns, for that excel will be analysed to find out correlation between the variables.
Cov (house prices, weekly return) = 48565.925 EXCEL: COVARIANCE (B2:B121, V2:V121)
r = 0.6655 EXCEL: CORREL :(B2:B121, V2:V121)
Based on correlation coefficient, it can be stated that there is fairly positive relationship between house prices and good rental investments. The reason of positive correlation of variables would be less availability of housing in the Kingfisher Bay. The expectation of good rental investments causes increase in house rentals. Therefore, the people are more likely to buy the houses rather than renting. The net impact on the effective demand for house purchases may be increased that may increase the prices of houses. This statement clearly defines the influences of rental return over the housing price.
There are various key indicators that cause higher house prices. On the basis of excel, the factors would be rooms, the lot size in square metres, area in sqm, street, stories, bedrooms and bay views. Likewise, lot in square indicates the area of the block of land. It means the area of land and it is the important factor as land value is decided with area only. Other than that, area is also considered as an important factor when it comes to housing price. It is because increase in area of house increases the prices of house also. Besides that, bayviews is another factor which is the cause of high housing price. The house which installed with more number of beds tend to have high housing cost as compare to other that house which have less number of beds.
On the other hand, there are common factor that could also reason of making high value property. The heath of economy is the major influence in the value of house as GDP, household expenditure and people purchasing capacity is the factor which frames the price of house. Population growth and movement is another factor that increases the value of house. This is because growth of population is the driver of demand and supply so in that case, higher demands of house among the large population makes the house price high. Thus, these are the factors that maximise the price of house.
(a) They stated that the weekly rent in Kingfisher Bay is at least $600, and hence rated as the most unaffordable area to live in Melbourne metropolitan and surrounding suburbs. Is that true for all suburbs in Kingfisher Bay? Can you check this claim for each of the three suburbs in Kingfisher Bay?
From the data analysis, it can be stated that null hypothesis cannot be rejected means the value of the weekly rent in Kingfisher Bay is equal to or greater than $600. At the same time, data analysis in excel shows that p-value is greater than 0.05 (Fcritical<Fcal.). It indicates that there are no significant differences in all suburbs. It implies this claim can be done for each of the three suburbs in Kingfisher Bay. From the data analysis, it is obvious that there is no difference exists in the data set of all suburbs means the rental rates are high in all suburbs in Kingfisher Bay that makes Melbourne metropolitan and surrounding suburbs as the most unaffordable areas to live.
Based on data analysis, it can be concluded that null hypothesis cannot be rejected. It means there was a lack of development across the Kingfisher Bay area as at least 75% of houses are 10 years or older. The null hypothesis sates that at least 75% of houses are 10 years or older means it equals or more than 75%. Data analysis shows that the null hypothesis cannot be rejected that implies the lacking of development across the Kingfisher Bay area. So, you cannot claim and say that it is below 75%.
In order to satisfy these criteria, you will need to focus on sample size of 1118 to estimate the average house price to within $50,000 and the true proportion of vacant houses in the market to within 3%.
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