1.As a Business Intelligence consultant you have been contracted by Rachel Clare, a wealthy Melbourne property investor to investigate and report on opportunities for
investment into the AirBNB market in the Greater Melbourne area. Rachel has approximately $2 million of capital she wishes to invest.
You are to prepare a in depth professional report for Rachel
2.In extension tasks, you run many different models for crowdfunding project. Please pick up only ONE model based on SPSS analysis and write a research report on it.
I am a business consultant and Clare from Melbourne has contacted me. Clare would like to see a report of an investigation on investment in the Airbnb market in the Greater Melbourne area. Clare has a total of $2 million of worth capital. In this report, Clare would like to have answers for a number of questions. These questions are well answered from consultant’s perspective. The major base of reasoning while answering these questions will be the project evaluation and planning (Carl, 2011).
The questions that Clare want answered are; whether Airbnb a worthwhile market place, the best suburbs in Melbourne to purchase properties for Airbnb to guarantee the greatest return on investment, the type of properties that will generate the greatest return on investment (whether it is apartment or Houses) and finally, the types of rooms that will generate the greatest return on investment (whether it is shared or private or whole House). The answers to these questions will determine the decision that will be provided on second hand basis (Yuriy, 2013).
The first question is whether AirBNB is a worthwhile market place to invest. To investigate whether this is a worthwhile market, we can check a number of factors from pricing to portfolio diversification possibilities. It is very clear that this is a worthwhile market to invest in since it has a variety of products (Fisher Investments & Austin, 2009). This implies that for a risk averse person, the market can be favorable since it will allow room for the diversification of their investments. This will ensure a proper and proper means to questions. This market allows room for diversification of investments. This is because there are a variety of investments that on can make in the market.
The second question is to determine the best suburbs in Melbourne to purchase properties for AirB to guarantee the greatest return on investment. This question is about the best location that Clare should consider investing her money. From the dataset provided, the best suburbs or areas to invest are the metropolis area. This is because; the prices of houses around this area are relatively high. This implies that when Clare invests in this area, then it will take her a shorter period of time or duration to reach the break- even point and start realizing income.
The next question is about investigation to find out the types of properties that will generate the greatest return on investment between the apartments and the houses. From the data provided, it is clear that the houses have a higher return than the apartments. Therefore, I would advise Clare to major on investing on the tomorrow. Investing in houses will also ensure that she gets back her invested capital.
Lastly, the concern is to find out the types of rooms that will generate the highest return on investments. This comparison is done in terms of the whether the room is private, shared or whole house. From the data provided, we can check the best type of house with the greatest return by looking at the prices of the three types of houses. From the SPSS analysis, it is clear that the type of house with prospects of high returns is the whole House. The various summary figures for this type of house are shown below. This is a sample of five hundred observations taken from the extremely large population. This sample of 500 is sufficiently large enough to be used for making inferences about the population.
Conclusion
The purpose of any investment is making a profit. Therefore, all the relevant things that an investor needs to do must be towards improving on their profits. All the cases considered above are centered on the net future value of the investment. This is because, to find out whether the project will be making profits or not, it is very much crucial to check on their future performance. A net future value of an investment represents the financial projections at different points or time in an investment. These cash flows involve both negative and positive cash flows and they determine the level of return of an investment.
Introduction
Internet has made the whole world to be as a village with everyone else interconnected. These people are free to interact with one another through various social and professional platforms (Burstedde, 2012). Therefore, this makes it even more convenient for these interconnected people to come together to fund a project (Fisher Investments & Austin, 2009). The project must be deemed viable in order to appear more appealing enough to attract investors from all over the world. This aspect of pulling together to raise funds for different products over the internet is known as the crowd funding (David & Tim, 2011).
This study is an extension of a previous study on crowd funding. The previous study looked at several statistical aspects using several statistical tools. The aspects such as the graphical representation of the variables, hypothesis testing and inferential statistics are here (Falkowski & Wisniewski, 2013). Therefore, this study is concerned with advancing the previous research study. Specifically, this study is focused on establishing whether there is any relationship between the amount of money pledged (Carl, 2011)
Literature Review
Crowd funding can be described a social way or means of collecting funds for a project or projects. With the increasing social interactions between among the people all over the world, it is possible to reach out and collect funds from people for any project or projects (Bergaoui , 2013). These projects are voluntarily funded. Crowd funding is majorly involved in collecting funds over the internet from different people to fund different projects (Oyinbo & Damanisa , 2013)
This study is a development of a previous study. Several observations were made. The previous involved a number of observations that were done using different soft wares. Some of the analyses done are descriptive analysis, inferential analysis and hypothesis testing (Frank, 2008). The descriptive analysis done involved the use of graphical representations, the development of frequency tables and summary statistics (Frank, 2008).
The graphical representation used was the histogram. There were two histograms developed and observations made from its output. Similarly, several frequency tables were developed from the data set given (Fisher Investments & Austin, 2009). The summary statistics that well came out include the measure of central tendency, the measure of dispersion or spread.
Therefore, the summary statistics included the mean, the standard deviation, the variance, the maximum, the minimum, the standard error and the maximum and minimum. These summaries were produced from several statistical packages such as Statistical Packages for Social Sciences (SPSS), the R-Studio.
The previous study on crowd funding involved several important variables. Some of the variables included in the study are; the project name, the date of launch the duration in days, the goal amount, the percentage raised, the project state, amount pledged, major category, minor category, project update count, city, region, number of pledgers, comments count, average amount per pledger, whether the project has a video, whether the project has a Facebook page, Facebook friends count, whether the project has pledge rewards, lowest pledge level, highest pledge level, the total count of pledge levels and the success of the project.
These are mixed types of variables. These variables contains both nominal and ratio variables. Some of the useful conclusions that were made out of the study are discussed in the paragraphs that follow.
An analysis was done on the duration of the projects. The durations were recorded in days. The variable duration was analyzed using the graphical representation technique (Bergaoui , 2013). A histogram of the number of days was produced. From the study, it was discovered that most products have duration of 30 days. Similarly, the median of the duration variable was observed from the histogram. It was observed that the median number of days taken by most of the projects is 30. However, this previous study on crowd funding did not reveal the number of projects with more than 30 days duration.
Similarly, the study did not reveal the number of those projects with less than 30 days duration. Therefore, a further study can be conducted to find out the number of those projects with less than or more than 30 days period (Hoover, 2010). Moreover, a study could be conducted to find out if there is any significance in the number of those projects with more than 30 days and those with duration of less than 30 days in terms of the percentage of the amount pledged.
The previous study on crowd funding also revealed that the frequency for the less project update count is greater (Crumby, et al., 2013). Furthermore, it was also discovered that the update counts for projects are increasing as the frequency is decreasing. The study also revealed that the variable is skewed to the right. This is an indication that the variable has no normal distribution properties (Gladka, 2013). However, the study did not reveal the kind of distribution follow. Moreover, the study did not tell even the family of distribution that the variable belongs. Hence, a further study could be conducted to find out the family of distribution that this variable follows.
The previous study on crowd funding also looked on the major categories of used by the people for their projects. According to this study, the major categories used in crowd funding projects by the people are music, film and video, publishing and arts (Jose’, 2015).
The study, however, did not reveal the preference levels of the target contributors. Therefore, a further research could be conducted to find out the opinions of the target contributors or clients about the various categories. This kind of research will be helpful to the project managers in knowing the right kind of tool to use for collecting funds. Therefore, it can be a
Another important observation from the previous research is that majority of the projects funded through crowd funding have to their own face book pages. Similarly, it was also observed that majority of the projects have their own Facebook accounts. Furthermore, it was revealed that it is very important to create a Facebook account for a project. However, the study did not reveal the performance of those projects that have video and Facebook pages against those that do not have. Therefore, a further study could be conducted to find out whether that project with videos and Facebook accounts actually collects much money than their counter part projects without such privileges (Anon., n.d.) (Jerzy & Eugenuisz, 2008).
The previous study also discovered that the number of projects that are classified as failed and those that are classified as success are almost equal. However, the numbers of projects that are classified as failed are more than those that are classified as success. This is an indication that most projects that are meant to get funding from crowd funding can equally fail when the target market is not well targeted (Jose’, 2015). A further research could be done to establish the various sources of failure cases. This can help in finding ways to improve on the funding strategies.
From the previous study, the average number of number of days to completion is 32.75. This is an indication that the projects take approximately this number of days to complete (Buchman, 2016). This is a very important figure for those prospective projects targeting crowd funding as a way of generating cash (Brian, 2015)
Hypothesis Development
Hypotheses are statements about a topic or phenomenon (Burstedde, 2012). The truth-value of hypotheses is tested using various methods such as the Analysis of Variance (ANOVA). Normally, hypothesis test is done on a sample and the findings are used for referring. In order to fully develop a hypothesis, one must state both the null and the alternative hypothesis.
A null hypothesis is statement about a given utopic or phenomenon that is stated negatively. Normally, it I the truth value of a null hypothesis that is tested against the truth-value of the alterative hypothesis. A null hypothesis is denoted by the symbol H0. An alternative hypothesis on the other hand is a statement about a topic or phenomenon that is stated positively. An alternative hypothesis is denoted by letter H1 or Ha. From the research problem at hand, we can develop the following hypotheses:
H0: There is no relationship between the average amount per pledger and the amount pledged.
H1: There is a relationship between the amount between the average amount per pledge and the amount pledged.
Data Analysis
This study is aimed at establishing any association between the amount of money pledged and the pledge per person (Zhukova, 2010). This is a correlation analysis task. Therefore, the analysis is completed by running a correlation analysis using SPSS (Financial Executives Research Foundation & Financial Executives Research Foundation, n.d.). The analysis was done using SPSS. The full analysis output is found in the appendix. From the analysis, the correlation coefficient is 0.081.
The result of the correlation analysis reveals that the correlation coefficient is 0.081. This is a strong positive correlation (Brian, 2015). This implies that as the amount of the amount pledged increases, the pledge per person also increases and vice versa (Financial Executives Research Foundation & Financial Executives Research Foundation, n.d.). A correlation coefficient is classified as strong positive if the value ranges between 0.5, 1, and weak if it falls in the range zero and 0.5. Similarly, a correlation coefficient is said to be negative weak if its value falls in the -0.5 to zero and strong negative if it falls in the range- 0.5 and -1.
Discussion and Conclusion
The result of the correlation analysis reveals that the correlation coefficient is 0.081. This is a strong positive correlation (Brian, 2015). This implies that as the amount of the amount pledged increases, the pledge per person also increases and vice versa (Financial Executives Research Foundation & Financial Executives Research Foundation, n.d.).
A correlation coefficient is classified as strong positive if the value ranges between 0.5 and 1 and weak if it falls in the range 0 and 0.5 (Donna, et al., 2010). Similarly, a correlation coefficient is said to be negative weak if its value falls in the -0.5 to 0 and strong negative if it falls in the range- 0.5 and -1 (Kramin & Young, 2009).
From the observations above, it is clear that we reject the null hypothesis that there is relationship between the per person payment and the amount (Donna, et al., 2010). Therefore, we conclude that there is no sufficient evidence to there is a relationship between that amount of money pledged for crowd funding a project and the per person pledge.
References
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