MERCADO LOCAL

Quantitative research uses defined analysis techniques to address specific research questions. The research questions in the quantitative study are directional because they state either a relationship between two or more independent variables with the dependant variables or a comparison between the two variable groups. Quantitative analysis can accommodate a single or multiple combination of descriptive, correlational, quasi-experimental and experimental research design.

For this research, quantitative analysis is conducted first, analysing house price performance at different levels (country level, city level and local level) to establish if there is a difference in house price performance at different levels. Then, it analyses the relationship between local house price performance and identified macroeconomic factors to investigate if there is a relationship between local house price performance and macroeconomic factors. In other words, can localised house price differences be explained by macroeconomic factors? The second aim of quantitative analysis for this research is to use quantitative results to select representative case studies for qualitative analysis.

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3.4.1 Data Collection

The quantitative analysis phase in this research is based on secondary data. Secondary data refers to data which has been collected and collated and such data can be extracted for the purpose of the research. Some of the secondary sources can be found from government or semi- government publications, earlier research, personal records and mass media (Kumar 2005). This research sources secondary data from public records or archived databases to enable an objective modelling of Australia’s residential house price performance at different levels and its relationship with macroeconomic factors.

When obtaining secondary data, it is important ensure the validity and reliability of the data and understand the availability, format and context of the data (Kumar 2005). The subsequent sections involve discussion on various public records and achieved databases used to form the key data sources utilised in this research. Based on the objective of quantitative analysis discussed earlier, this section involves two sources of data – residential property performance data and various macroeconomic variables.

The Residential Property Performance Data

House price data at country level

House price data at a country level are collected from the Australian Bureau of Statistics (ABS). ABS collects and publishes various data including house price data, macroeconomic data and demographic data. ABS also conducts the Australian census every five years. The ABS Data Quality Framework is internationally recognised and is based on the Statistics Canada Quality Assurance Framework and European Statistics Code of Practice (ABS 2013).

House price data at city and local level

House price data at city and local level both are collected by the Real Estate Institute of Victoria (REIV). REIV is the peak professional body for the Victorian real estate agency industry with a current membership of over 7,000 in Victoria including corporate members and real estate professionals. Members specialise in residential/commercial sales and property management. The REIV gathers most of its data online from agents submitting their sales results

111 | P a g e electronically plus a dedicated call centre to collect property sales results at the time of contract. REIV data used for this research include median house price for Melbourne as a whole and median house prices for each individual Melbourne suburb. All REIV data are presented on a quarterly basis for the research time span of 20 years from 1996 to 2016.

REIV data was the only database available for the research. In order to avoid skewed sale data and the challenges with data input error, local locations with limited sales evidence were discarded using standard deviation modelling and medium house prices measured. This is further discussed in Chapter 4 (Quantitative Analysis).

House price data collected for the residential property market is used to examine the house price performance at different locational levels and compare the results between each level to determine if there is a house price difference between levels over time.

Macroeconomic Variables

The type of macroeconomic variables for this research were identified from the literature review on Chapter 2. Table 3.3 summarises the types and sources of the data collected for this research.

Table 3.3: Types and Sources of the Data

Types of Data Sources

GDP Australian Bureau of Statistics

Consumer Price Index (CPI all groups) Australian Bureau of Statistics

Housing Loan Rate Reserve Bank of Australia

Population Growth Rate Australian Bureau of Statistics

Unemployment Rate Australian Bureau of Statistics

Household income Australian Bureau of Statistics

Housing Supply Australian Bureau of Statistics

According to Table 3.3, macroeconomic data is collected from two major sources: ABS and RBA. ABS data has been discussed in a previous section. The other data is collected from the RBA which is Australia’s central bank and derives its functions and powers from the Reserve Bank Act (1959). The RBA determines the cash rate on a monthly basis (except January) and

112 | P a g e publishes the outcome on the RBA website (RBA 2016). All ABS and RBA data are presented on either a monthly or quarterly basis and for this research all macroeconomic variables are collected over the 20 year period from 1996 to 2016.

The data collected for macroeconomic variables are used to examine the relationship between local house prices and national factors to determine if local house price performance can be explained by macroeconomic variables.

3.4.2 Data Analysis (Descriptive Analysis)

Quantitative data analysis comprises mainly the analysis of numerical data using a variety of statistical methods with specific reference to descriptive and inferential techniques. Burns (1997) and Bryman (2006) explained that descriptive statistics allows researchers to summarise large quantities of data with the intention of discovering trends and patterns. Microsoft Excel Software is adopted for this research to analyse quantitative data. Excel functions such as standard deviation and correlation coefficient are applied to house prices and macroeconomic data. Outcomes of descriptive analysis mainly comprise performance results and correlations that are generally used to confirm or disconfirm the results obtained from the descriptive results. The outcomes of quantitative analysis for this research are used as a basis for selecting case studies - the foundation of the research theory. A measurement cannot be valid unless it is reliable; it must be both valid and reliable if it is to be depended upon as an accurate representation of a concept (Wan 2002). Therefore, it is important to ensure the data analysis process is validated, so that results and findings for this research are reliable. Based on the research objectives, the data analysis process for the quantitative stage of the research is formed in two sections which are listed below.