BUSINESS RESEARCH DATA ANALYSIS REPORT

Executive summary

The aim of this report is to collect primary data regarding housing and real estate deals and find out the purchase frequency of certain types of houses with the necessary amenities and facilities, in Victoria, Australia. This study has also considered collecting and analysing the objectives based on the number of bedrooms, bathrooms, and garages for the houses, with respect to their prices and the number of houses available for purchase. Furthermore, the research has used the method in which both MS Excel and SPSS to manage data, organise them. Use them for establishing inferential and descriptive statistics, so as to consider the outputs for addressing the identified problems. Additionally, a set of recommendations have also been provided to address the business issue that has been identified.

In an analysis of the result section, a positive significant statistical relationship has been found among the variables of this analysis process. Property buying rate among the customers depends upon the postcodes, bedroom, bathrooms, and garage related attributes in this analytical structure. Therefore, variable dependency rate in the Australian real estate industrial sectors was high level in number.

Introduction/Background

Appropriate data collection methods, followed by proper systems of synthesising information and knowledge so as to make the required assumptions are essential for applying data analysis techniques to gather results. Analysis of the data of the three postcodes of Victoria Australia such as 3064, 3329 and 3221 and their house-purchasing ratio are the part of this analysis process. In this case, it cannot be possible for one to collect, synthesis, analyse and establish results of certain research for addressing business issues, and should be done in collaboration with people having different areas of expertise. This report aims to collect primary data regarding housing and real estate deals and find out the purchase frequency of certain types of houses with the necessary amenities and facilities, in Victoria, Australia. This report will collect and analyse the objective based on the number of bedrooms, bathrooms, and garages for the houses, with respect to their prices and the number of houses available for purchase.

Research objective & Questions

Main Objectives;

• To collect and synthesise primary data gathered from the provided source and apply proper techniques of research and analysis.
• To ensure appropriate group collaboration and division of tasks and facilitate interpretation of data insights.
• To solve certain business issues based on the proper decision-making process based on the data and results.

Sub-Objectives;

• To establish the statistics for purchase frequency in the case of 3BHK houses in Victoria, with respect to their price.
• To relate both parametric and non-parametric tests for the chosen dataset in order to find the relations between house price and buying frequency.

Research Questions;

1. How can the primary data be collected and synthesized, and further analysed with proper research and analysis techniques?
2. What is to be done for ensuring group collaboration, data interpretation, and decision-making, for addressing business problems?
3. In what ways can the gathered data be statistically established with proper reference to the primary, independent, and dependent variables related to the houses in Victoria?

Hypothesis statements for testing

H0: There is no statistical and significant relationship between the independent variable (Buying Frequency) and the dependent variables (postcodes, number of bedrooms, bathrooms, garages, and price), for the collected data.

H1: There is a significant statistical relationship between the independent variable (Buying Frequency) and the dependent variables (postcodes, number of bedrooms, bathrooms, garages, and price), for the provided dataset. 

Methodology

This report has been solely based on the data gathered from the housing systems and the "realestate.com.au" website to search for the number of available 3BHK properties available in the 3064 postcode area of Victoria. Furthermore, this research and analysis report is also based on the chosen primary variables of the number of bedrooms, bathrooms, and garages, while relating it with the dependent variables of the property price and the frequency of buying. In this case, the hypotheses of the research have been supported with the help of secondary data gathered by the researchers, while establishing all the required statistical analyses. The report used MS Excel and SPSS for data collection and analysis, where only the above-mentioned variables have been considered for the analysis and statistics (Islam, 2020). Considering the use of MS Excel, it has mainly been used for data management, which included the merging, and organising of data, that have been gathered from secondary databases, prior to exporting them to the SPSS software. This has helped in storing and analysing the qualitative data, which have been further used for the formation of the quantitative data. After data screening, cleaning, coding, and transformation, the data have been further put into the software for data analysis, in order to retrieve descriptive statistics and establish data visualisation (Lu, 2020). SPSS has also been used for the derivation of inferential statistics, which include parametric and non-parametric (Wilkoxon Test), "One Way Annova" vs "Kruscal Wallis Test", along with one-tailed or two-tailed tests for all the samples. Furthermore, this report has considered a simple random sampling technique to consider all 3BHK housing properties (a subset of the total samples) in the chosen area of Victoria while considering a total of 61 samples (Pandey and Pandey, 2021). This amount is much easier for manual handling and is appropriate for the research to be done as per the main objectives to fulfil the research hypotheses.

Result and Interpretation

Descriptive statistics analysis

Table 1: Descriptive statistics analysis of buying rate of real estate sectors of Australia

(Source: SPSS)

As per the analysis of the descriptive statistics of the variables such as a postcode, bathroom, bedroom, garage, prices and property purchasing frequency of the real estate statement from the Australian sectors have provided an in-depth idea. The value of standard deviation (SD) for those variables was 0.818, 0, 0.582, 0.720, 101934.561 and 144.609 respectively. On the other hand, the value of the standard Error (SE) in analysis process has depicted the number of 0.469. Descriptive statistics have provided the frequency and distribution rate of the following variables in the real estate purchasing ratio of Melbourne in the postcode of 3064.

Parametric Tests

One sample T-test analysis

Table 2: One Sample T-test analysis among variables of buying rates 

(Source: SPSS)

In the above table of one sample T-test has found that the values of the Mean Differences of those variables were 2.010, 1.7221, 1.423, 585645.394 and 383.760 respectively. In addition, the "t" values of those variables were 25.041, 30.148, 20.158, 58.591 and 27.063 accordingly. Therefore, the highest "t" value has been observed in the data over bathroom and price values of the housing estate according to the Australian Industry condition. In the 95% confidence level of this analysis process, the highest values in upper and lower limits have been observed over the variables of the prices. Therefore, product pricing and management rate constructively improved the scope of the supportive market conditions according to the analysis process.

ANOVA analysis

Table 3: Model Summary analysis

(Source: SPSS)

In the model summary analysis table, it has been found that the value of "R square" was 0.056. On the other hand, the value of SE of the estimation process has indicated the number 143.290. 

Table 4: ANOVA analysis

(Source: SPSS)

In the above table of ANOVA analysis, the value of Significance (p-value) has indicated the number 0.215. In addition, the F value of this analysis process has indicated the number of 1.476 respectively. The positive value of the significance ANOVA analysis has reflected a positive significant relationship among the variables of this analysis process. Significance values have been estimated as per the condition of p>0.05. It has been indicated that dependent and independent variables were interrelated with each other. 

Table 5: Coefficients analysis of variables

(Source: SPSS)

In the Coefficient analysis table, values of the significance of the variables bathrooms, garage and prices were 0.039, 0.535, 0.443 and 0.418 accordingly. Values of standardized coefficients analysis of the three variables are 0.249, 0.069, 0.085 and 0.083 accordingly. In the coefficient analysis process, the SE values of these three variables were 21.012, 27.509, 22.116 and 0.

Non-parametric test 

Kruskal Wallis analysis 

Table 6: Kruskal Wallis Test 

(Source: SPSS)

The above table has indicated the Chi-square value of the bathroom, garages and prices of Melbourne according to the property purchasing frequency rates were 21.520, 0, 18.142, 20.631 and 22.844 accordingly. The values of the significance of the above three variables in the Kruskal Wallis test analysis were 0.803, 1.00, 0.923, 0.840 and 0.741 accordingly.

Wilcoxon Test analysis

Table 7: Descriptive statistics analysis in the Wilcoxon test

(Source: SPSS)

The mean values of the descriptive statistics of the Wilcoxon test were 2.01, 1.72, 1.42, 585645.39 and 383.76 accordingly.

Table 8: Ranks analysis of variables

(Source: SPSS)

In the Rank analysis table, a positive relationship among the variables of the property purchasing frequencies with bathrooms and garages has been indicated. Therefore, variations in bathrooms and garages are considered as the parameters of product purchasing according to real estate business-standard.

Table 9: Test Statistics analysis

(Source: SPSS)

The values of the test statistics table have indicated a positive response with a value of significance 0. It has indicated a positive significance level among the variables of this analysis process. Frequency analysis of the house purchasing rates has indicated the customer engagement level in the industrial sectors. 

Therefore, findings analysis of the statistical data of this real estate business analysis in the Melbourne area of Australia has indicated positive growth attributes. It signifies that the dependent and independent variables of this analysis structure have found a strong and positive statistical relationship with each other. Therefore, in this study, the null hypothesis was rejected and the alternative hypothesis was accepted to understand the dependency level among the variables.

Findings and discussion

Quantitative analysis of the data that has been found from this analysis structure has indicated a significant positive relationship among the variables of this analysis process. As per the opinion of Ji et al. (2018), house purchasing frequency analysis based on the economic establishing process has indicated positive growth in the housing management process. The features of the housing complex that reflected a high possibility of pricing variations over this industry sector are the number of bedrooms, bathrooms and garages. Pricing rate variation also affected the property purchasing frequencies in the area of Melbourne. 

Figure 1: Post Code analysis of House buying process of Melbourne

(Source: SPSS)

Postcode 3329 and 3221 are the highest frequency of housing management and purchasing increasing process. The values of mean and SD are 2.01 and 0.818 accordingly. 

In the analysis of the descriptive statics of the variables in the result, sections have indicated an inferential analysis of the random samples that were analyses based on the survey data analysis process. As per the analysis of the hypothesis of the study, it has been found that the dependent and independent variables of this analysis process are statistically interdependent.

Figure 2: Frequency analysis of the number of garages in housing complexes

(Source: SPSS) 

As per the analysis of the above graph, it has been found that the frequency of garage 3 in number is highest in the frequency distribution tables. It has indicated a positive response from the survey analysis process that regulated property purchasing rates. The value of mean and SD values of this graphical analysis has been reflected in the value of 1.42 and 0.72 accordingly. 

Figure 3: Frequency analysis of the number of bathrooms in housing properties

(Source: SPSS)

The above graph has depicted that the frequency analysis of the bathroom from the survey analysis has integrated with the highest number of 2 from the survey analysis process. In the analysis of the frequency distribution of bathrooms, numbers indicated the SD and mean values of this analysis structure were 0.582 and 1.72 respectively.

Figure 4: Price rate analysis and frequency distribution process

(Source: SPSS)

Price frequency analysis of the real estate housing planning process has depicted the growth of the pricing level of the housing plans has mostly indicated a value of 600000. The mean value of the pricing frequencies from the above-mentioned graph has been indicating the number 585645.39. In addition, the value of SD of this analysis structure has found a value of 101934.561 respectively. According to Ge et al. (2021), constructive improvement of the real estate business structure potentially developed the scope of customer engagement and management process in a competitive market situation. On the other hand, technological specification is also associated with constructive growth in supportive business development and the emergence process that is associated with business structure. Service variation of networking analysis process constructively identified the growth of effective service regulation and customer engagement level over the competitive market conditions.

Figure 5: Product purchase frequency analysis of the real estate property level

(Source: SPSS)

The frequency analysis graph of the property purchasing frequencies based on the market condition has indicated a positive rate of sustainable service balances in competitive market sectors. The highest rate of purchase frequencies has been observed based on the ranges from 300 in the above histogram. The SD value of this analysis process has indicated the number 144.609. In addition, the mean value of this analytical structure has depicted the number of 383.76 accordingly. As per the opinion of Munawar et al. (2020), customer purchasing frequencies were constructively managed based on features and pricing strategies in competitive market situations. However, the real estate business in Australia has managed a high level of economic significance in the economic stabilities of the country's financial structure. Therefore, supportive growth over distributor management and analysis structure has constructively enhanced the scope of the supportive business improvement process. 

Figure 6: Histogram analysis of Property purchasing frequencies

(Source: SPSS)

The above histogram analysis of the dependent variables has indicated a positive significance growth over the variables of this analysis structure. It structurally indicated a positive statistical relationship among the variables of this analysis structure. However, the mean and SD values of this analysis structure were indicated the number of -7.98E-17 and 0.980 respectively. Positive dependency among the dependent and independent variables has indicated a positive growth rate in the competitive real estate business market developmental situations.

Therefore, all the independent and dependent variables of this analysis process are statistically related to each other. They have a positive significant relationship among the variables of this analysis process. Therefore, the constructive business emergence process purposively rejected the null hypothesis in this analytical structure. However, the alternative hypothesis was accepted to understand and the property-buying intentions of people based on the number of bedrooms, garages, bathrooms and pricing rates of the real estate industry sectors. Therefore, an essential business strategy generation for constructive management of the business standard has improved the scope of supportive business development and management process over constructive market improvement situation. Strategic management of purchase intentions and analysis process has indicated a supportive growth over competitive market sectors.

Recommendations

Considering how the housing prices in Australia are always known to be very high and are also not appropriate for several mediocre families, there is a need to consider other alternatives for addressing this issue. Furthermore, considering how all the properties come with garages and more than two bedrooms and bathrooms, it can be more than necessary for small families with three to four members, compared to the price range of 4,00,000 to 8,00,000 AUD.

Accordingly, the following can be considered by the real estate companies, the government of Australia, or the local government of Victoria, to address the issue.

• It is recommended to consider creating house properties based on the type of families that are generally found in Victoria, and the number of people who require proper housing to suffice their family headcount. In this case, housing complexes can be made for smaller families, such as 2 BHK rooms with one garage, which can be personalised and expanded based on the choice and requirements of the client after the booking is done, so as to save investment costs, and avoid "informal housing" while sufficing the family needs (Gurranet al., 2022).
• As for the problem of high housing prices and large houses with more than 2 bathrooms and garages, it might as well be considered for social and rental housing systems, which can allow the costs of the property to be shared and equally distributed (Pawson et al. 2018). This can be helpful for the part of the population that does not have access to proper living spaces due to financial constraints, or family headcount being less than three. Large housing properties can also be used as shared houses for the elderly population who live alone, and whose pension is not enough to have a personal house.

Limitations of this study

This research does not include any primary data sources and is only dependent on the information gained from the Australian real estate website for the housing properties and the facilities that come with them. Furthermore, this research has been done to mainly consider the techniques and methods of gathering, synthesising, and analysing data, in order to support the solutions for a chosen business problem. Thus, this research did not consider any interpretation of the sufficiency and credibility of the price with respect to the number of rooms and garages. This research has only considered the inferences based on the relationships and analysis done from the statistical representations of the data, in order to fulfil the objectives of the study.

Conclusion

Concluding this report, it is evident how the research and data collection can be done for the analysis of certain statistics and calculations that can be gathered from the available sources. This report has also provided the method in which both MS Excel and SPSS can be used to manage data, organise them, and use them for establishing inferential and descriptive statistics, so as to consider the outputs for addressing the identified problems. It has also been found that the price ranges for the 104 3BHK housing properties have been an issue as it can be considered a range high above affordability, regardless of the amenities and facilities that come with it. After having done the required analysis of the data with regard to the purchasing frequency, it has been understood that the system is suitable for the real estate market condition in the country, but it is not very appropriate for the mediocre and elderly population who live alone. This report has thus provided a set of recommendations for the government and real estate companies to cater to the identified problem.

Reference list

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Gurran, N., Maalsen, S. and Shrestha, P., 2022. Is ‘informal’housing an affordability solution for expensive cities? Evidence from Sydney, Australia. International Journal of Housing Policy, 22(1), pp.10-33.

Islam, M., 2020. Data Analysis: Types, Process, Methods, Techniques and Tools. International Journal on Data Science and Technology, 6(10).

Ji, Q., Marfatia, H. and Gupta, R., 2018. Information spillover across international real estate investment trusts: Evidence from an entropy-based network analysis. The North American Journal of Economics and Finance, 46, pp.103-113.

Lu, J., 2020. Data analytics research-informed teaching in a digital technologies curriculum. INFORMS Transactions on Education, 20(2), pp.57-72.

Munawar, H.S., Qayyum, S., Ullah, F. and Sepasgozar, S., 2020. Big data and its applications in smart real estate and the disaster management life cycle: A systematic analysis. Big Data and Cognitive Computing, 4(2), p.4.

Pandey, P. and Pandey, M.M., 2021. Research methodology tools and techniques. Bridge Center, pp. 47-48.

Pawson, H., Parsell, C., Saunders, P., Hill, T. and Liu, E., 2018. Australian homelessness monitor 2018.