Statistics and Business Research Methods Housing Market

Question: Discuss about the Statistics and Business Research Methods for Housing Market. Answer: Introduction: The housing market in Australia is a strong source of money. There has been a great change in the housing market in Australia from the beginning of the 21st century. Dwelling values are higher in cities like Sydney, Melbourne while Perth, Darwin, Brisbane and Canberra has shown a downward trend in the dwelling values(Buys Miller, 2012). There are several factors that effect the dwelling values. The Reserve Bank interest rate, the Core logic mortgage index, increase in price of property market are some of them(Chhetri et al. ,2013). The objective of this assignment is to study how the housing market in Australia has changed over the few years and what factors are affecting this change. An outline idea about the data collection and the methods used for interpretation and analysis of the data are given in this assignment. Problem statement The problem statement of this research is to determine the change in values of housing market in Australia in the current year and to find out the major factors that are accountable for the change. Aim of the Research The aim of the research is to determine to what extent the dwelling values has been changed in Australia and what are the reasons behind this change. Research Objective The objective of the research is given below: To determine the change in the dwelling value. To determine the major factors (like interest rate of Reserve Bank, Property price of the country) that are the cause behind the change Research questions The study is concerned with answering the following research questions: 1.How much the dwelling value has changed in the recent period? 2.Are there any fluctuations in the values from season to season? 3.How have these values changed across different cities? 4.Are the change in values due to the change in factors like property price, interest rate of Reserve Bank, core logic Mortgage index, etc.? 5.What is the effect on the economy due to this change? Literature review: The interest rate of the Reserve Bank has been lowered. The Core Logic has reported that there is a delay in the housing prices, and the values of dwelling have also increased. Among the top eight Australian cities there has been a decrease in the dwelling values of four cities while four cities have seen a rising in the dwelling values (Muellbauer, 2012). Also, the number of days required to sell the dwelling has become longer as compared to the previous years. The average number o days required to sell dwelling in previous year was 42 days which has become 47 days in the current year. This average value is longest during the period of Christmas and New Year. The dwelling values have been increased in Melbourne by 7.5% and in Sydney by 9.1%. In Sydney, there has been a large number of stock available than the number of buyers. Adelaide and Perth, however, has seen a decrease in the values. Brisbane, Hobart, and Darwin has also seen an increase in the dwelling values. Adelaide and Hobart are the cities where the average number of days required to sell the property has been increased. Methodology: Data collection: The data collection in this problem is to be done by sampling procedure. The cluster sampling method can be used to collect data on dwelling prices of the houses. At first the different cities of the country can be divided into several clusters based on the financial condition. Then the data on dwelling prices can be collected from this cluster. This is a first-hand data or primary data(Yang et al.,2014). There are also some other data that has to be collected for determining the effect of other factors on the dwelling values. These other factors include interest rates of Reserve Bank, Core value mortgage index, property index, etc. The data on these variables can be collected from the sites of the Reserve Bank, official site of Core Logic, etc. These data are secondary data. Data Analysis: The collected data could be analyzed using various descriptive statistics methods. The data used in this case are mainly time series data. Time series analysis could be done to determine the effect of time on the dwelling prices(Box et al., 2015). The time series analysis includes: Trend analysis: To check whether there is any trend(increasing or decreasing) in the welling values. Seasonality: To see if there are any seasonal fluctuations in the values. Cyclical fluctuations: To determine whether there is any oscillatory movement in the time series. Irregularity: To see if there is any irregular movement in the data(Chatfield, 2016). Along with the time series analysis, a regression model has to be fit to see how the factors like interest rate of Reserve Bank, Core Logic index values affects the dwelling prices(Draper and Smith, 2014). The regression that would be used here would be linear regression analysis. The value of the regression coefficients has to be checked whether they are significant or not (Montgomery, Peck and Vining, 2015). A cluster analysis study can also be done to determine if the values of different cities can be grouped into some specific clusters (Anderberg, 2014). For example, the business capital cities of the country will fall in the same cluster as compared to other cities. Conclusion: In this assignment, the change in dwelling prices of the country and the factors affecting the change would be analyzed. A detailed idea about the collection of data, analysis of the data and interpretation of the data has been given. The effect on the Australian economy due to the change of the dwelling values has been analyzed in this data. Also the effect of change of the factors like Core logic index values, Interest rates has been analyzed in this data. This report would help the Australians to take a deeper look on the change in dwelling values. This would also help to increase the state of economy of the country. References: Anderberg, M. R. (2014).Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks(Vol. 19). Academic press. Box, G. E., Jenkins, G. M., Reinsel, G. C., Ljung, G. M. (2015).Time series analysis: forecasting and control. John Wiley Sons. Buys, L., Miller, E. (2012). Residential satisfaction in inner urban higher-density Brisbane, Australia: role of dwelling design, neighbourhood and neighbours.Journal of Environmental Planning and Management,55(3), 319-338. Chatfield, C. (2016).The analysis of time series: an introduction. CRC press. Chhetri, P., Han, J. H., Chandra, S., Corcoran, J. (2013). Mapping urban residential density patterns: Compact city model in Melbourne, Australia.City, Culture and Society,4(2), 77-85. Draper, N. R., Smith, H. (2014).Applied regression analysis. John Wiley Sons. Montgomery, D. C., Peck, E. A., Vining, G. G. (2015).Introduction to linear regression analysis. John Wiley Sons. Muellbauer, J. (2012, September). When is a housing market overheated enough to threaten stability?. InProperty Markets and Financial Stability, RBA Annual Conference Volume. Reserve Bank of Australia. Yang, S., Chen, K., Chen, X., Shi, J., Li, W., Du, Q., ... Gao, G. (2014, March). Reliability and validity evaluation based on Monte Carlo simulations by computer in three-stage cluster sampling on multinomial sensitive question survey. InAdvanced Engineering and Technology: Proceedings of the 2014 Annual Congress on Advanced Engineering and Technology (CAET 2014), Hong Kong, 19-20 April 2014(p. 271). CRC Press.