Introduction and Background During the last decade or so, property prices in Australia have soared which is particularly true for certain cities such as Sydney and Melbourne.As a result, the investment in real estate has seen a significant increase in the last two decades or so. It is essential to determine a intrinsic value of various residential properties so that the clients can be advised with regards to a fair price for a given property. In this backdrop, the objective of the given report is to predict the fair value of the residential property prices in non-capital cities and towns corresponding to State A. In this regards, data has been provided from the relevant cities and towns which corresponds to data about the houses which have been recently sold. Data and Empirical Strategy Sample data has been presented in order to facilitate the summary of data and also estimation of best multiple regression model to estimate the prices for the various residential properties (i.e. house and unit) based on their relevant attributes.The starting point is the data which has been segregated for the coastal city, coastal town along with regional city. The underlying objective is to produce the best possible multiple regression model for the estimation of residential property. The data sample includes information about various variables such as price, internal area, number of bedrooms, number of garages, number of bathrooms. Further, there are certain variables such as Land area for which data is available only in particular cities and not available for all properties. Most of the variables are quantitative in nature and measures based on ratio scale since there is an absolute zero which can be defined as variables such as price, number of bedrooms, bathrooms, garages cannot assume negative values.A key example of categorical variable is type which has been measured using nominal scale since two possible labels namely unit and house are possible (Flick, 2015). The first step in data analysis is to compute the descriptive statistics for the various quantitative variables which provides a summary of these based on regional city, coastal city and town. In order to understand any differences in the sample with regard to price of other attributes, a comparison of the descriptive statistics in accordance with the location has been carried. With regards to the estimation of the regression model, the cumulative data for state A has been considered. Some variables are available for only properties located in a particular city or town. These variables have not been taken into consideration for the multiple regression thereby limiting the participation of only those variables which are available for all properties included in the sample. 2
The first step is to run the multiple regression model with the aid of MS-Excel where price would act as the dependent variable and all the various variables for which data on all properties is available would serve as independent variables. The tweaking of this model would be performed based on the statistical significance of the respective slopes of the independent variables used for the regression model. The independent variables which are found to be statistically insignificant would be ignored and the multiple regression would be run without these variables so as to enhance the statistical significance of the regression model (Hair et. al., 2015). Results and Discussion The descriptive statistics for the residential properties to regional city in state A are summarised below. ThedescriptivestatisticsfortheresidentialpropertiestocoastalcityinstateAare summarised below. 3
The descriptive statistics for the residential properties to coastal town in state A are summarised below. The objective is to draw a comparison between the descriptive statistics in three different locations in state A. This is carried below. Price: A common feature for price across the three locations is the existence of high skew owing to which the average price would be best captured by median rather than mean (Eriksson and Kovalainen, 2015). Comparing the median prices across the three locations, it is evident that the prices are lowest for regional city while maximum for coastal town. Further, the extent of dispersion in the price also seems to be influenced by the median prices as more volatility is observed in locations exhibiting higher median prices. Internal Area:Owing to presence of skew, it would be preferable to compare the median value. There does not seem to be any significant difference in the median internal area across the three types of locations in State A. The median area is marginally lower for coastal city. Also, the extent of dispersion in this variables does not significant differ across the three locations in state A (Hair et. al., 2015). Bedrooms: The median value of bedrooms for the sample data across the three locations in state A is the same at 3 bedrooms. This implies that for all the three locations, there ar e 50% properties in the sample data where the bedrooms do not exceed 3. The extent of dispersion in the number of bedrooms for the three locations also does not show any meaning difference to be of any statistical significance. Bathrooms: The median bathrooms for properties located in coastal city and town is 2 while that for properties located in regional city, it is 1. This suggests that in regional city, half of the sample properties would not have more than 1 bathrooms (Hillier, 2016). In terms of 4
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variation, lesser variation is observed for this variable in the regional city as compared to the coastal city and town. Garages: Owing to highly skewed nature of this variable, the median is compared across locations in state A. it has been seen that median garages is 1 for sample properties in coastal city while the corresponding value is 2 for sample properties at other two locations namely coastal town and regional city. The extent of dispersion in the given variable does not differ significantly across the three locations. Estimation of multiple regression In order to frame a multiple linear regression model for the estimation for the residential properties, the dependent variable has been taken as price while the independent variables to begin with were internal area, bedroom, garage, bathroom along with type of unit. In order to capture the potential influence of type of residential property on the price, a 0/1 notation has been used whereby unit has been represented by 0 and a house by 1. The initial multiple regression with all the above variables as independent variables indicated that the influence of type on price was not statistically significant and hence this independent variables was removed from the regression model. Also, the slope corresponding to number of bedrooms was also not significant thereby implying that the bedroom count did not materially influence the price (Taylor and Cihon, 2017). As a result, the final multiple regression model has been proposed with three independent variables namely the internal area, bathroom and garages. The requisite output obtained from the regression analysis using MS-Excel is indicated as follows. 5
The requisite regression equation based on the above output is shown below. Price ($ 000) = 41.75 + 1.27*Internal Area m2+ 105.49*Bathrooms + 30.66*Garages As expected the slope coefficients of internal area, bathrooms and garages are all positive which would imply that an increase in any of these variables would lead to increase in price of residential property. The R2for the model is 0.5212 which implies that 52.12% of the variation in the residential property prices can be explained jointly on account of variation in internal area, bathrooms and garages (Lieberman et. al., 2013). Recommendations A key concern with regards to the above proposed regression model is that the R square value even for the best model continues to remain quite small. This implies that there is a significant amount of variation in the residential property prices which is not explained by the independent variables included in the regression models. In the wake of this, it is imperative that additional variables ought to be introduced so as to improve the predictive power of these regression models (Medhi,2016). Some of the likely candidates to be included are distance of home or unit from the bus stand or railway station, locality, distance from CBD. Inclusion of 6
more independent variables could improve the results. Also, it is seen that price of the preprty is independent of the underlying type (i.e. house and unit) and thereby the same may be ignored. More research ought to be performed regarding the same so as to enhance the understanding of property market in state A (Lind, Marchal and Wathen, 2014). 7
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