Factors Contributing To House Price Changes In Sydney Real Estate Business

Research Design

Discuss about the Contextualization of Entrepreneurship Research.

Marketing is the major point of focus for the success of any business in the current world. Some business ventures are more lucrative than the others, but with no proper marketing of the business irrespective of their lucrativeness, it would be on the verge of posting dismal performance in the long run Malhotra (2015). Due to appreciation in the prices of some commodities like pieces of land have made investors to diverge from other businesses to the real estate business (Babin & Zikmund, 2015). It is seen as the long term permanent investment and in the preference of investors. In the real estate business, its involved commodities have been experiencing value increase with time. Transactions in this business venture involves buying and selling of the piece of land, building houses of different types and sizes and either renting them to the tenants, leasing them or permanently selling the houses to the customers in need (Schneider & Kearney, 2013). Continuous increase in the value of pieces of land make the value of the real estate be on the hike and this is preferred by the business people all around the world. Various factors are supposed to be considered before venturing into any kind of business (Zahra, Wright & Abdelgawad, 2014). Specifically, for the real estate business, the business operators are supposed to ensure that they have high negotiation skills. Required negotiation skills are not limited to this type of business alone but to other business opportunities too Conrad & Newberry, (2012). These skills are important in running of this business to improve the bargaining power beyond that of the buyers since if the buyer overpowers the seller in the price negotiation process, the property is most likely to be sold at the lowest price possible in the favor of the buyer (Siewiorek, Saarinen, Lainema, & Lehtinen, 2012); Schneider (2012). Another major factor that would be important in the real estate business is the prior knowledge of the existing price in the market. This would help to set the quoting price of either the piece of land and house that you plan to rent out, lease, sell or even buy. Houses being one of the basic necessities in life of human beings, their prices have been appreciating year in year out depending on some of the factors such as the security of the place where the house is constructed, the area coverage of the house, the age of the house and many other factors that can be brought on the negotiation table Du & Zhang (2015); Hammond, Keeney, & Raiffa, (2015). This report is therefore aimed at identifying some of the factors that could be playing part in the price determination of the houses in Sydney city. Houses constructed in the remote areas and at distant places from the city centers tend to have relatively lower prices compared to those at the nearby or at the heart of the cities.

Data Collection Procedures

The key goal for conducting this research was to determine the factors contributing to the price changes of a house in real estate business. In order to the meet that general objective, specific objectives were as stated below;

1. To determine the relationship between selling price of a house and the area coverage of a house.
2. To determine the correlation between age of a house and the price for the house
3. To assess if there is a mean difference between selling price for houses in the city and those in remote areas
4. To generate the multiple linear regression model that would be used in determining the selling price of houses in Sydney city.

1. Is there any relationship between selling price and the area coverage of a house?
2. Is there any correlation between the age of a house and the selling price of a house?
3. Is there significant mean difference between selling price for houses in the city and those in the remote areas?
4. What is the multiple linear regression generated to predict the selling price of houses in the city?

In this section of the project, we shall cover subsections such as the research design, data collection procedures, targeted and sampled population, reliability and validity of project instruments and finally, the data analysis that will specify the used statistical techniques in the manipulation of the collected data management.

This is the procedure followed in correlating the research questions, data collection, data analysis and interpretations (Myers, Well, & Lorch, 2013); Ritchie, Lewis, Nicholls, & Ormston, (Eds.). (2013). In this project, researcher adopted the case study of the rising prices of the houses in the cities siting an example of Sydney city. Taking this into consideration, the researcher was capable of using the phenomenon and collect data for examination. The collected data were both qualitative and quantitative. Qualitative data are the information obtained which are non-numeric Lewis (2015); Taylor, Bogdan & DeVault, (2015). Such information are particularly obtained through the interviews and can be in the form of audio recordings, texts etc. for instance, in this project, data that were collected resulting to the variables of sex of the participants and their levels of education were the categorical variables that held the qualitative data. On the other hand, quantitative data are the numerical data obtained from the participants in the data collection process and the resulted variables contain numerical cases (Bryman, & Cramer, 2012; Criddle, & Stanley, 2012; Cox, & Mann, 2012). In this project, a number of questions resulted to the numerical variables that were used in the data analysis i.e. area coverage of the house, age of the house, the number of bedrooms a house has etc. descriptive survey design was employed in this research aimed at evaluating the factors leading to house price changes in the real estate business.

Real estate business people, house owners in Sydney, house owners in different parts in Australia and the residents in Sydney formed the targeted population. A population is a set of elements under study (Anderson, Sweeney, Williams, Camm, & Cochran, 2016). A sample is a subset or a fraction of the population under study and the sample size is the finite number of elements a researcher had decided to engage in the study Button et al (2013); Charan, & Biswas, (2013). In this case, the researcher engaged 20 people to respond to the questions structured in the questionnaires. The sample used was believed to provide the values that represented the true picture of what was happening in the real estate business in Sydney Australia.

Targeted and Sampled Population

This is the process of gathering data from the participants with respect to the subject of study Englander (2012). In the data collection process, data collection methods that were used by the researcher were interviews and questionnaires. The questionnaires were taken to a number of people where some of them were house owners in Sydney, others were house owners in other parts and others were Sydney residents. Questionnaires were preferred since they are known to providing privacy to the participants thus making them express themselves in free manner McCusker & Gunaydin (2015). Further they provide time to the respondents to have deeper understanding of the subject questions unlike when interviews are used (Ainsworth, Cahalin, Buman, & Ross, 2015).

Various research instruments can be employed by the researcher in the research process to help in the collection of data. The instruments used in any given research project are tested for their reliability and validity since they are what result to valid data Csikszentmihalyi & Larson (2014); Richardson, Iezzi, Khan & Maxwell (2014). Choice of the instrument to be employed in research process always depend on the objectives and the targeted sample population Flynn et al (2015). In this case therefore, questionnaires were used in the entire process of data collection. Reliability is the way of evaluating the value of the used measurement procedure in data collection in a dissertation. By measuring reliability, it showed the consistency of the used instrument management.

Table 1: Reliability test of instrument
Cronbach’s Alpha	Cronbach’s Alpha Based on Standardized Items	N of Items
.589	.537	7

Table 2: Item Statistics
	Mean	Std. Deviation	N
Selling price in city	109050.0000	30101.97581	20
Selling price in remote	105407.5000	29923.65406	20
Area	2309.0000	407.69997	20
Bedroom	3.1000	.55251	20
Age	13.1000	11.95562	20
Sex	1.4000	.50262	20
Education	3.0000	.64889	20

As in table 1 above, reliability of the questionnaires used in this research was tested using the Cronbach’s alpha. From the test, the Cronbach’s alpha (i.e. 0.589) was less than 0.7 thus showed lower internal consistency of the instrument hence not reliable. The instrument had seven items with varied means and standard deviations (e.g. selling price in city M=109050 and SD=30101.98 for area M=2309 and SD=407.7) as in the item statistic table 2 above. The more the means and standard deviations of the variables are varied from one another the more inconsistent the instrument was hence unreliable. A reliable instrument will show variables’ means and standard deviations being too close or onto one another Maden & Köker (2013). With the sample size of 20 respondents, they were believed to have provided valid data that truly depicted what happened in the real estate business in the cities in Australia i.e. Sydney.

Data that used in this research were primary data since they were collected by the researcher using the questionnaires from the respondents with the aim of fulfilling the objectives of subject under study. Data was fed in excel spreadsheet then later transferred into the SPSS (version 20) and checked to ensure that there were no missing values in the data for data analysis. Qualitative analysis such as the frequency of the categorical variables will be calculated and determined. The statistical software will be used to run the descriptive statistics i.e. (mean, standard deviation, minimum and maximum) of the numerical variables from the data set. Pearson correlation will be employed by the researcher to test for the existence of correlation between variables in the data set. Paired sample t-test will as well be used in the research to test for the mean difference between the selling prices of the houses in the city and in remote areas. With selling prices as the dependent variables, age, area and bedroom will be used as independent variables in the linear regression model to give the prediction of the selling price of the houses in the city. Linear regression equation used was;

Reliability and Validity of Project Instruments

Where b₀ is a constant in the equation and b₁ , b₂ and b₃ are the coefficients of area, bedroom and age respectively. Y is the dependent variable which in this case represents selling price of the houses in the city. Ε_i is the corrective error value. For better representation of data, the collected data will be represented in the tables and graphs for easy understanding and interpretation.

Demographic information are one of the most important information as they tell the researcher the characteristics of the people they would wish to engage in the research process. The demographic information collected about the participants in this research were sex of the participants and their level of education. These information are in most cases important for business and business people when they are outlining the business plan and also carrying out business research.

Table 3: Sex of the respondents
	Frequency	Percent	Valid Percent	Cumulative Percent
	Male	12	60.0	60.0	60.0
Female	8	40.0	40.0	100.0
Total	20	100.0	100.0

In the sample size used by the researcher, 60% of the respondents were male while the other 40% were female participants. These formed some of the observable features of the respondents that the researcher could tell without asking questions. The number of male participants could be seen from the percentage representatives outnumbered that of the female representatives. The variation and unequal representation of the sexes of the respondents was believed to have not any effect on the obtained results.

Education of the respondents also formed the center of focus in their demographic information. This was important since in the data collection process, the instrument used in data gathering were questionnaires which required the respondent to read the questions and respond to them appropriately as to the requirement of the questions. The level of education of the respondents and their ability to respond and give appropriate responses resulted to valid data that could be depended on. Out of the sampled participants, 20% had secondary level of education, 60% representing the majority of the respondents had bachelor degree and the last proportion represented by 20% had master degree as shown in figure 1 above. This showed that almost all he respondents had some level of education and that they could read and right comfortably and thus responding to the questions in the questionnaire was not a problem.

Table 4: Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
Selling price city	20	59000.00	147500.00	109050.0000	30101.97581
Selling price remote	20	58000.00	144100.00	105407.5000	29923.65406
Area	20	1339.00	3124.00	2309.0000	407.69997
Bedroom	20	2.00	4.00	3.1000	.55251
Age	20	.00	42.00	13.1000	11.95562
Valid N (listwise)	20

Descriptive statistics for numerical variables used in the data set was calculated and the calculated descriptive values recorded as in the table 4 above. The prices (in dollars) of the houses were recorded for both in the city and remote areas out of which, the minimum amount an individual could pay for a house in the city as in selling price city was 59,000 dollars and the maximum amount for the particular houses was 147,500 dollars, the mean values that could be paid for houses in the city was 109,050 dollars and standard deviation of 30,101.98 dollars. For the same houses, the prices in the remote areas a minimum of 58, 000 could be sold for those particular houses and a maximum of 144,100, the mean and standard deviation of the selling prices of the houses in remote areas was 105,407.5 and 29,923.65 dollars respectively. Out of the sample, the area coverage was estimated in square feet and the minimum measurement provided by the respondents in this study was 1,339 square feet and a maximum of 3,124 square feet. The mean square feet for the area of the houses was 2,309 and the standard deviation for the area of the houses was 407.7 square feet. The number of bedrooms was also another point of focus that the real estate business would use in quoting the prices of the house. In response to that, the minimum 2 bedroom houses had their prices estimated by the respondents and a maximum of 4 bedrooms. The mean of the number of bedrooms from the sample was 3.1 rooms and the standard deviation of the rooms was 0.55251. Age of the houses was also another key area of interest that the researcher tested where the respondents upon indicating the area coverage and estimating the selling prices of the houses, they were as well to estimate the ages of the houses out of which minimum age provided was 0.00 and the maximum age of the houses estimated was 42 years. The mean age of the houses in the sample was 13.1 years and the standard deviation of 11.96 years.

Data Analysis

In order to determine the factors that could be affecting the prices of the houses in the city, Pearson correlation was tested to determine whether or not there exist relationship among variables such as the spcity (selling price of the houses in the city), spremote (selling price of the houses in remote areas), area coverage of the houses, the number of bedrooms a house has and finally the age of the houses.

Hypothesis

H₀: There is no relationship between selling price of a house and the area coverage of a house.

H₀: There is relationship between selling price of a house and the area coverage of a house.

Table 5: Correlation test between variables
	Spcity	Spremote	Area	Bedroom	Age
Spcity	Pearson Correlation	1	.998**	.827**	.566**	-.912**
Sig. (2-tailed)		.000	.000	.009	.000
N	20	20	20	20	20
Spremote	Pearson Correlation	.998**	1	.836**	.580**	-.930**
Sig. (2-tailed)	.000		.000	.007	.000
N	20	20	20	20	20
Area	Pearson Correlation	.827**	.836**	1	.811**	-.780**
Sig. (2-tailed)	.000	.000		.000	.000
N	20	20	20	20	20
Bedroom	Pearson Correlation	.566**	.580**	.811**	1	-.464*
Sig. (2-tailed)	.009	.007	.000		.039
N	20	20	20	20	20
Age	Pearson Correlation	-.912**	-.930**	-.780**	-.464*	1
Sig. (2-tailed)	.000	.000	.000	.039
N	20	20	20	20	20
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

Pearson correlation coefficient of 0.827 was recorded for the correlation between the selling price of the houses in the city and the area coverage of the houses. Also, the Pearson correlation coefficient of 0.836 was recorded for the correlation between selling price of a house in remote areas and area coverage of the houses. These correlation coefficients showed that there was a strong positive correlation between the tested variables. Statistical significance test showed that the correlation was statistically significant for the correlation test between the variables. Since the p-value was less than 0.05, we would reject the null hypothesis and favor the alternative hypothesis and conclude that there was relationship between the selling price of houses and the area coverage of a houses.

For research objective 2: To determine the correlation between age of a house and the price for the house

Table 5 above gives the correlation tests for all the variables used in the data set to meet this research objective, Pearson correlation test was used to tests the hypothesis as below;

Hypothesis

H₀: There is no correlation between age of a house and the selling price of a house.

H₁: There is correlation between age of a house and the selling price of a house.

The Pearson correlation coefficient of (-0.912) was recorded between age and selling price of a house in the city. This showed that there was a strong negative correlation between age of a house and the selling price of a house in the city. For the test between selling price of a house in remote areas, the Pearson correlation coefficient of (-0.930) was recorded. This also showed that there was a strong negative correlation between age and the selling price of the house in remote areas. The tests were confirmed to be statistically significant between age and selling price in the city and between age and selling price in remote areas. Since in both cases the p-value was less than 0.05, we reject the null hypothesis and conclude that there was correlation between age of a house and the selling price of a house both in the city and in remote areas.

Results

Research objective 3: To assess if there is a mean difference between selling price for houses in the city and those in remote areas

In order to meet this objective and answer the research question connected to this objective, paired sample t-test was conducted between the selling price of the houses in the city and that of the houses in remote areas. The tested hypothesis was as follows;

Hypothesis

H₀: There is no mean difference between selling price of the houses in the city and those in the remote areas.

H₁: There is mean difference between selling price of the houses in the city and those in the remote areas

Table 6: Paired Samples Test between selling price in city and remote areas
	Paired Differences	t	df	Sig. (2-tailed)
Mean	Std. Deviation	Std. Error Mean	95% Confidence Interval of the Difference
Lower	Upper
Pair 1	Spcity – Spremote	3642.50000	2121.21334	474.31772	2649.74160	4635.25840	7.679	19	.000

From the table above, t(19) = 7.679 and p < 0.05 showed that the mean difference was statistically significant. Since the p-value was less than 0.05, we reject the null hypothesis and favor the alternative hypothesis and conclude that there was mean difference in the selling prices for the houses in the city and those in remote areas. 

Research objective 4: To generate the multiple linear regression model that would be used in determining the selling price of houses in the city.

Table 7: Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	83122.754	29769.257		2.792	.013
Area	18.229	18.340	.247	.994	.335
Bedroom	2240.245	9564.111	.041	.234	.818
Age	-1764.067	413.437	-.701	-4.267	.001
a. Dependent Variable: Spcity

In the construction of the model, multiple linear regression model was used with selling price of the houses in the city being the dependent variable and the other variables i.e. area coverage of the house, the number of bedrooms a house has and the age of the house formed the independent variables used to predict the dependent variable. Considering the coefficients from table 7 above, both area and bedroom had positive impact on the selling price of a house in the city but age of the house had negative impact on the selling price of a house since its coefficient was negative. As a result, the generated model for predicting the selling price of a house in the city was as below;

B₀ = 83122.754                       b₁ = 18.229     b₂ = 2240.245             b₃ = -1764.067

From the above equation, substituting the coefficient values we have the following equation model 

As ε_i approaches zero the modelled equation will be

Which is the generated model that could be used in determining the selling price of a house in the city (Spcity).

Sex of the respondents was not equally distributed since they were varied at 60% male and 40% female. The level of education of the respondents varied from secondary education level through bachelor degree to master degree. The bachelor degree respondents were dominant over secondary and master degree. This confirmed that the respondents fully understood the questions as in the questionnaires and responded to their full knowledge as per the requirement of the questions.

Conclusion

In response to research question one, Pearson correlation test was carried out between the area coverage of the house and the selling price of the houses. In regards to that, strong positive correlation was found to be existing between the selling price of the house and the area coverage of the house. From the findings in this research, it can then be seen from the positive correlation that the higher the area coverage the higher the selling of a house would be. This made the area coverage of the house to be one of the factor identified in this research study to be a factor determining the selling price of the house in the city.

Additionally, research question two asked for the existence of correlation between the age of the house and selling price of the house. In response to that question, Pearson correlation was carried out where a strong negative correlation (-0.912 and -0.93) existed. This implied that the increase in the number of years would result to a decrease in the price at which that house would be sold thus making the age of the house a factor that would be used to determine the selling price of the house. The paired sample t-test showed that there was a substantial difference between the mean selling prices in the city and those in remote areas.

Finally, the linear regression model was generated. From the coefficients, the number of bedrooms was seen to have greater positive effect on the selling price of a house than the area coverage i.e. the more number of bedrooms a house has would result to higher prices in return than when the number of bedrooms were less. Also, the area coverage had the effect that the wider the area coverage of a house the much one was likely to sell the house. From the model, age showed to have negative effect on the prices of the house. The older the house was, the less amount one was likely to sell the house.

This research study used questionnaire as an instrument for data collection. Reliability and validity of the instrument was tested using the Cronbach’s alpha. The test showed that the instrument was not reliable since alpha (0.589) was less than 0.7 hence was having lower internal consistency. This was further confirmed by the fact that the means and standard deviations of the items were far much apart hence not close or onto one another. In regards to that, the results obtained in this research study might not be reliable.

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