The Prices the Houses in Blue Lakes - Paper Example

Distance of the house to the nearest train station (kilometres) 265.74328 -26.874 119 .000

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The prices of houses depend upon various factors. According to the analysis, the prices depend on the size of the house, the type of material the house is built with, the age of the house, the number of renovations, the size of land the house sits, the number of bathrooms and bedrooms, and distance to the nearest bus stop, train station, and shopping centre. In addition, the number of levels (storeys), the number of main rooms in the house, the type of heating system, the availability of air conditioning, street appeal, and the view of the lake all determine the prices of houses in Blue lakes as indicated by the measures of statistical significance (see Table 1). The prices appear to be influenced by accessibility to public services such as shopping centres, bus stop, and train station. The size of the house and the size of land influence their prices. In addition, the type of house such as stand-alone units and multiple storey buildings influence the price of houses. Key installations that make the house liveable such as air conditioning and heating increase the value of the house. For example, installation of a heating system and an air conditioning system increases the value of the house. Lastly, the locality of the house raises the value of houses. Houses which are located within localities with better street appeal and the view of the lake seem to have higher prices than those that are in other locations that are not so appealing.

Summary of the number of main rooms houses have

According to Figure 1, the overwhelming majority of rooms of houses in Blue lakes range are 6 (21.67%) and 7 (27.5%) rooms. Five rooms (17.5%), 8 rooms (20%), and 9 rooms (10%) are also significantly high. Ten room houses are the fewest in Blue Lakes.

Figure 1: Number of main rooms in the house

There are 56 stand-alone houses in Blue Lakes whose combined value is $27,885,700.

Summary of different cladding material for houses built or renovated in the last 10 years

Table 2. Cladding of houses in Blue Lakes

Cladding Frequency Percentage

Timber 33 27.5

Veneer 21 17.5

Brick 39 32.5

Render 27 22.5

Total 120 100.0

The houses in Blue Lakes are cladded differently. The predominant form of cladding is the brick followed by timber, render, and veneer in decreasing manner. With regard to the four different suburbs, analysis shows that brick is mostly used in the suburbs of Gobova and Arlington (see table 3). Timber was used to clad houses mostly in Timmastown and Gubova which represents more than half of all timber cladded houses in the four suburbs. On the other hand, render is used mostly in houses found in Nineva Gardens, Timmastown and Arlington and only one found in Gubova. Lastly, veneer appears to be distinctively used in Timmastown.

Table 3: summary of material used in suburban houses

The suburb of Nineva Gardens The Suburb of Arlington The Suburb of Gubova The Suburb of Timmastown

Material Timber 5 5 10 13

Veneer 3 5 3 10

Brick 6 12 16 5

Render 11 7 1 8

Total 25 29 30 36

The predominance of one style of cladding in different suburbs shows that one can tell the distinction of the various suburbs by their predominant cladding materials.

The effects of new and renovated houses on house prices

The effects of new and renovated houses on house prices

Table 4: The effects of new and renovated houses on house prices

New and renovated houses Pearson Correlation .397**

Sig. (2-tailed) .000

Sum of Squares and Cross-products 6190.332

Covariance 52.020

N 120

**. Correlation is significant at the 0.01 level (2-tailed).

Statistics showing the effects of renovation or newly built houses on selling prices shows that renovated and newly built houses might been worth more than those that have not been renovated.

The effects of the number of rooms on price of a house

Table 5: Analysis of Variance

Model Sum of Squares df Mean Square F Sig.

1 Regression 2807183.984 1 2807183.984 59.187 .000a

Residual 5596669.756 118 47429.405 Total 8403853.740 119 a. Predictors: (Constant), Number of main rooms in the house b. Dependent Variable: Selling price of house in $'000 Coefficients

Model Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta 1 (Constant) -143.411 105.393 -1.361 .176

Number of main rooms in the house 114.983 14.946 .578 7.693 .000

a. Dependent Variable: Selling price of house in $'000 When the number of main rooms are used to predict the selling price of the houses within Blue Lakes, it is found to be highly significant (p=0.00).

Figure 2: Prediction of house prices by the number of rooms

Figure 2 provides further evidence that the number of rooms affects the prices of houses. The prices of houses increase exponentially with the increasing number of rooms in the house.

The effect of Lot Size on the Price of a House

Figure 3: the impact of the area of land on the selling price of the house

The area of the block on which the house stands also affects the price of houses. More specifically, the price of houses is seen to be increasing with the increasing area of land in square meters. Regression statistics also show that the area of the block of land is highly significant in determining the value of the house (see table 6)

Table 6: Analysis of Variance

Model Sum of Squares df Mean Square F Sig.

1 Regression 1881051.007 1 1881051.007 34.029 .000a

Residual 6522802.732 118 55277.989 Total 8403853.740 119 a. Predictors: (Constant), Area of the block of land (lot) in square metres b. Dependent Variable: Selling price of house in $'000 Model Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta 1 (Constant) 263.422 70.123 3.757 .000

Area of the block of land (lot) in square metres .327 .056 .473 5.833 .000

a. Dependent Variable: Selling price of house in $'000 The relationship between distance to the nearest train station and the price of a house

Distance of the house to the nearest train station (kilometres) Pearson Correlation .005

Sig. (2-tailed) .954

Sum of Squares and Cross-products 81.357

Covariance .684

N 120

Regarding the distance of to the nearest the statistics show that there is a strong relationship between the distance to the train station and the prices of the houses. However, this relationship is statistically insignificant as the level of significance is greater than 0.05.

The use of the median house price

The median house price is often used to report the prices of houses as a measure of central tendency because it is not influenced by extreme prices. This leads to minimal distortions that bias data interpretation. In the case of Blue Lakes for example, the minimum price is $138,000 and the maximum value is $1,750,000. The calculated average price is $652, 848 and the median price $613,000. A half of the houses are cheaper than $613,000 while the other half is more expensive than $613,000. As compared to median which gives us the more realistic idea of the house prices, the average price does not provide a representative price that reflects the reality (Bickel, 2003).

Summary

In summary, Blue Lakes has a rather diverse type of houses with different price values. The closer the house is to important amenities such as the bus stop, railway station, and shopping centre, the higher the price of the property. The type of material that is used to build the house also affects the value of the house. The more expensive the building material is the more expensive the house becomes. The price of houses built from bricks varies to that of veneer, timber, and render. The age of the house affects the price of properties negatively such that the older the house the cheaper it becomes. However, the value of the house will increase significantly if it is new or quite recent, or if major renovations are undertake to improve the condition of the house. Apparently, big things come with a big price tag. The size of the house, the number of major rooms, the number of bedrooms, the number of bathrooms, and the size of the area of land where the house is determines the price of the house. More sizes of major rooms, bedrooms, and bathrooms increase the price of the house. And is the size of land that holds the property. The price is also determined by the type of property; whether the property is a stand-alone house or is a multiple storey building. A multiple storey building is likely to be more expensive to buy than a stand-alone unit. Value added services will most likely increase the value of the house. For example, compared to those without, the installation of a heating system and an air conditioning system will increase the value of the house. Lastly, the general ambience of the location is associated with higher prices of houses. Unsurprisingly, houses which are located within localities with better street appeal and the view of the lake seem to have higher prices than those that are in other locations that are not so appealing (Bourassa and Hendershott, 1995; Black, Fraser, and Hoesli, 2006; Fitzpatrick and McQuinn, 2007; Glindro et al., 2008; Zietz, Zietz and Sirmans, 2008).

Reference

Bickel, D.R., 2003. Robust and efficient estimation of the mode of continuous data: The mode as a viable measure of central tendency. Journal of statistical computation and simulation, 73(12), pp.899-912.Black, A., Fraser, P. and Hoesli, M., 2006. House prices, fundamentals and bubbles. Journal of Business Finance & Accounting, 33(910), pp.1535-1555.Bourassa, S.C. and Hendershott, P.H., 1995. Australian capital city real house prices, 1979-1993. Australian Economic Review, 28(3), pp.16-26.Fitzpatrick, T. and McQuinn, K., 2007. House prices and mortgage credit: Empirical evidence for Ireland. the manchester school, 75(1), pp.82-103.

Glindro, E.T., Subhanij, T., Szeto, J. and Zhu, H., 2008. Determinants of house prices in nine Asia-Pacific economies.Zietz, J., Zietz, E.N. and Sirmans, G.S., 2008. Determinants of house prices: a quantile regression approach. The Journal of Real Estate Finance and Economics, 37(4), pp.317-333.