House Prices in Kingfisher Bay
Discuss about The confidence intervals and statistical guidelines.
Date: 20th December 2017
To: Hannah Zhou, Director, Housing Affordability Division, Real Estate Institute
From: Sandy Stedwell, Manager, Research and Analysis, Real Estate Institute
Subject: Analysis of Kingfisher Bay’s housing and rental data
Dear Hannah,
I want through your letter and came across the reason of your concern. The city of Kingfisher Bay in being considered to be one of the most expensive areas of the country. The houses rents and the house prices are announced to be rarely low and this thing is creating a hinder regarding the attraction of this area. Lots of people are really not being able to afford the houses of the said area. These reports needs to be analyses proper on the basis of the a few data sets so as the situation can be controlled with proper measures.
I have received the data set as well and I have gone through the required analysis and provide the required results that are the answers of the mentioned questions.
- Overall house price summary of the region of Kingfisher Bay can be stated as:
The price of the houses is $886.575 in an average, that is, $886.575 can be taken as the standard price level for every house (Lowry 2014). The house prices can vary within a range of $324.94 from the average price. Half of the house prices can be taken to be in a range lower than $852 and the rest of the house prices are more than $852. The highest price in the area is $811.
- Mean or the average is a generally used measure but median is also preferred sometimes. The reasons can be sited as: A dataset generally contains data that vary within a limit within themselves (Pituch Whittaker and Stevens 2015). However sometimes, a single data may seem to vary in an abnormal way and may seem to lie distant from the general line that can be observed with bare eyes. It can be said alternatively that a single data point can be seem to be detached or situated away from the main stream. This type of data points is called outliers. Dataset that contain outliers can be handled more easily through median than the mean because an average means the addition of all the available values and a division of the sum by the total frequency. This sum can get affected a lot with even a single outlier. The average may get deviated a lot (Mayers 2013). Whereas median signifies the middle most value of the whole dataset and this doesn’t get effected much with the outliers because a middle most value will remain in the middle position or may vary only for a single point only. The same explanation goes for extremely high values or extremely low values. Here comes another thing that is extremely high or low values. A value may not be seemed to be that detached from the streamline but may be extremely high or low within the line. These type of extreme values can affect the total a lot regarding the mean calculation and estimation of the exact average may become tough. Datasets that contains more than a limit of extreme values can be handled well through median. Again a dataset may consist of data points that vary symmetrically within themselves. Plotting of the whole dataset can make this point more vivid. However if a dataset shows somewhat a non symmetrical or asymmetrical graph, then it can be handled in a better way through median since the asymmetry in the dataset can affect the total that is needed for an average calculation and hence can deviate the average. Situations can be seen like a dataset does not have a clear more. There may not be any particular highest value in the set and there may be more that one nearby high values(Konietschke and Pauly 2014). Cases like this will experience an average which will nearly evolve around those highest values and hence may not show a proper estimate of the average.
- Estimate of the average house prices for the houses in Kingfisher bay can be given like:
An estimate of the average house price is $887 and this estimate may vary within a limit of $935.75 in the highest level and $837.40 in the lowest level (Kanda 2013). These are estimates and can vary within a limit.
- Number of houses in the said area with prices more than $1million is 43. Estimate of the proportion of houses with prices more than $1 million is 0.3583 (Altman et al. 2013). This proportion can also vary within a limit. It can vary in the highest till 43.03% and in the lowest till 28.63%.
(a). The house conditions as can be seen from the dataset is:
Kingfisher Bay is known to be one of the posh area of Melbourne and it is expected to be the most costly area of the country (Bates et al. 2014). The city consists of houses of different ages as it is not considered to be one of the newest city of Melbourne. The houses range from very old ones to newest ones. It has very old houses and very new houses as well. As is known by everyone that houses needs to get maintenance in a proper level or otherwise it may become dangerous for the staying purpose as well. A well maintained house will generate higher revenue and that will have higher resale values as well. 15 houses are in a very poor condition. They highly lacks maintenance and it may be dangerous to stay there since they are really old. 40 houses are in poor condition means in a condition which is slightly better than the first 15 houses. They have got a little better maintenance and they have to be get sorted a bit more with construction and all. 42 houses are in really good conditions. They are highly maintained with modern resources and they can be said to actually be the modern houses. They also generate higher rates regarding the rent case and also regarding the resale value. The rest of the 23 houses are in moderate condition (Anderson et al. 2014). They are comparatively better than the old ones or the very old ones but they still need a little bit of attention like a bit of construction, cleaning and more maintenance. They are the one who can generate higher rents and resale values. This conditional factor plays an important role in deciding the price level of the houses. The houses which are in a very poor condition need immediate attention or which may decrease the price for these houses.
House Prices Vs. Condition/Suburbs
(1) The houses can be classified according to the conditions in the different suburbs. The area is not a very new area only with the new houses. This is believed to be an area developed in the older times and hence it has older houses and newer houses as well (Dean et al. 2015). There are also moderately aged houses which are not that old and not that new. These house ages and the houses conditions impact the houses prices. They can be considered as indicators of prices. A very old house will be in a very bad condition and the one which is newly constructed will be in a good condition. The moderately aged houses are in moderate conditions. The classification of the houses says that there is less number of houses in a very good condition and in a very good condition in all the three suburbs. Most of the houses are poor or in good conditions.
(2) The house prices can be classified according to the conditions. The price factor is highly dependent on the conditions of the houses. Like the houses which are in better condition will draw higher revenue and the houses which are in the worse condition will generate lower revenue (Floudas et al. 2013). All the houses in the different suburbs are classified according to the conditions and the analysis says that the average prices of the houses in a very bad condition a are comparatively low that the house which are in great conditions generated great amount of revenues. The analysis hence completely supports the general theory.
(3) The prices of houses in the different suburbs can be compared. Any of the three suburbs can be considered to be more expensive than the other (Miller 2013). The analysis sates that the Suburb c has the highest average pieces and the suburb A has the maximum of the average prices. The Suburb B has a moderate average price record.
(c) Suburb and condition has an impact on the prices but lots of other factors are also responsible like: The distances from the bus stand, train stand, shops. What exactly is the style of kitchen and what is the bathroom style. The kitchen can be of old styles and can lack modern facilities like the presence of chimneys and other resources. Bathrooms can lack modern amenities and there can be less number of bathrooms. Prices also depends on storey number and the number of room is the house. People may have a bit of preferences for higher storey and a few may have lower preferences for the lower storey like aged people may have preference for lower storey. Again people may have a height phobia and this category may consist of the lower age group as well. Rooms in the houses should also be considered. People may have big families and the they will be always in need of more rooms. Again people can have small families and they will always need fewer rooms (Lazaridis et al. 2014.). More rooms also need more maintenance but people with more family member have to bear these propositions. The presence of air conditioners is also important as air conditioners are considered as one of the basic requirement. Another important factor can be being traditional in style and being non-traditional in style. Aged people may prefer traditional houses and younger people can prefer modern houses. Bay view is also a very important criterion as a clear and beautiful view can enhance the attraction of the house.
House Prices Vs. Factors Influencing House Prices
- As is a general belief, house prices are generally driven by people seeking for good rental investments. The data is to be tested for this purpose. It can clearly be seen from the analysis that the house prices are not completely dependent on the rental investment factor (Albert and Tullis 2013). Though they have a weak relation and a positive relation like one will increase with the increase of the other, but the prices are not completely dependent on this factor.
- There are various key indicators for higher house prices (Gravetter and Wallnau 2016). The factors can be listed like:
The total number of rooms in the house, The age of the house since the constructions are being done, The total area that the block of the land covers, The total house area, the house distance from the nearest railway station, nearest bus stand and may be from the nearest shop, The condition of the nearest street, Styles and stores of the house and lot more factors.
- Kingfisher bay is reported as one of the most expensive area in Melbourne. This area is divided into three suburbs. The dataset is to checked to see whether all the suburbs are equally expensive. The average price level can be set to $600 and it can be claimed that if the average monthly rent exceed the level, the suburb can be denoted as an expensive suburb (Kautonen, Van Gelderen and Tornikoski 2013). It can be said from the analysis that the average price for each and every suburb exceeds the decided level. The dataset claims that all the suburbs are equally expensive. Scatter plot of the prices and returns is given below:
Figure 1: Scatterplot for House Prices and Rental Returns
(Source: Created by Author)
(b)Number of reports claims that there is a lack of development in the Kingfisher Bay area. The area consists of more than 75% of the houses which ages 10 years or even more than that. This claim can be tested through the dataset. Data analysis claims that the area consists of more than of 75% old houses.
Future Survey:
Estimations are to be made regarding the sample sizes that are necessary for any future survey. These estimations are to be made on the context of future surveys and on the basis of few points. The future surveys are to be made for comparing the prices and also to keep a track of it (Kautonen Van Gelderen and Tornikoski 2013.). The average house price which is within $50000 will be estimated and the vacant house proportion in the market which is within 3% will be calculated. Data analysis claims that the sample size should be 120 regarding the average part and should be 342 regarding the proportion part.
References:
Albert, W. and Tullis, T., 2013. Measuring the user experience: collecting, analyzing, and presenting usability metrics. Newnes.
Altman, D., Machin, D., Bryant, T. and Gardner, M. eds., 2013. Statistics with confidence: confidence intervals and statistical guidelines. John Wiley & Sons.
Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D. and Cochran, J.J., 2014. Statistics for business & economics, revised. Cengage Learning.
Bates, D., Maechler, M., Bolker, B. and Walker, S., 2014. lme4: Linear mixed-effects models using Eigen and S4. R package version, 1(7), pp.1-23.
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