M30289 - Subject Group of Accounting and Financial Management Assignment

Assignment Task

Section A – Multicriteria Decision Analysis

You are acting as a consultant for an estate agency in data analysis tasks that enhance decision making. A client of the agency would like to invest in a buy-to-let property in England. Five properties have been selected, based on filtering, that match both the profile of potential tenants and the needs of the client. The details are summarised in Table 1.

Table 1. Property investment alternatives

This investment decision will be based the following criteria.

• Price (x£1000): the overall price paid by the client, including any fees, land tax,
• Size (in square metres): the overall size of the interior living space
• Rooms: the number of rooms in the property
• The area’s projected price growth in the next 5-years (figures based on agency’s estimates)

• Nearby amenities: the number of amenities within a 100-yards radius
• Noise during peak time: Noise measured in the interior of the house (in db) during the peak hour window of 15:00-18:00

In line with the client’s preferences, price and noise should be minimised, whereas size, number of rooms, projected price growth and nearby amenities should be maximised.

In the modelling stage, assume a linear preference function for all criteria, with the indifference and preference thresholds set to 0 and maximum pairwise difference accordingly. This can be done by setting the two sliders entirely to the left and right in Smart Picker Pro.

Q1. After you have conducted a survey about your client’s preference, you find that her pairwise criteria preferences are as follows:

Table 2. The client’s pairwise criteria preferences are given below:

1. Find the weights that correspond to those preferences and report
2. Is the client consistent in her choices by each criterion? Provide your

Q2.   Using the weights assigned to each of the criteria, which you have obtained from Q1, and the data from Table 1, set up relevant parameters in the Smart Picker Pro software according to the objective (Maximise/Minimise), and produce your analytical results. You are required to answer the following questions:

1. Which property offers the best performance? Explain
2. Which property offers the least regret? Explain
3. Combining performance and regret aspects, which property performs the best overall, taking into account all conflicting criteria? Explain
4. If the construction company fits an extra layer of sound-proofing materials, the noise levels in the property in Salford Quays can be reduced to How would that change your results, and what can you infer from this change as to the robustness of your evaluation
5. Is there a significant difference compared to the second-best property? As a manager leading the team responsible for answering this question, what would be your approach if there were extreme differences of opinion among team members? In what circumstances might you decompose the overall performance back to the elementary criteria?

When submitting your coursework, you should also submit your result tables and graphs.

Section B – Statistics and Regressions

The task is to model, or analyse, the following case study:

The annual premiums charged for car insurance for 10 car drivers are shown in the following table. The age of the customer, the number of years that they have not made a claim and their annual mileage are also shown in the table below:

(So, for example, a value of 4 in the miles driven column would mean that the customer drives 4,000 miles per year)

You are required to analyse the data above using the Microsoft Excel software. For parts (c-e) below, do not use the standardised Excel functions – instead you should use Excel to apply the relevant formulas learnt in the course. Specifically, you should answer the following questions:

• Use Excel to construct a histogram to show the distribution of Insurance Premium (You will need to choose suitable class intervals).
• Discuss whether this distribution is
• Calculate the quartiles of this distribution, and its inter-quartile
• Calculate the values of both Kendall’s and Pearson’s correlation coefficients between Insurance Premium and Age of Driver. Explain what a correlation coefficient means in general terms.

• Calculate the standard deviation of Insurance Premium, Age of Driver, No. of No Claims Years and Annual Mileage Driven.

Now do a regression analysis using the Excel Regression Tool with Insurance Premium as the dependent variable, and Age of Driver, No. of “No Claims Years” and Annual Mileage Driven as the independent variables. You will produce estimation result tables and graphs (i.e., regression coefficients and model fit statistics) to support your answers based on data in the table above.

• Present the regression results in a table, together with regression coefficients and P-values. Explain the meaning of the figures in the P-value column.
• Discuss the results of your regressions. What conclusions can be drawn from the regression analysis with regards to the effect of Age of Driver, Number of ‘No Claims Year, and Annual Mileage Driven? You will need to discuss the results based on the regression coefficients, P-values and other useful information in the output table.
• Report model fit statistics. Discuss the significance of the R-Squared value in your estimation results and what it tells us about a regression
• Suppose a new driver is going to buy insurance. He is 40 years old and has 6 years’ no claims. What is the estimated premium that the insurance company is going to charge him?
• Discuss the limitations and strengths of your analysis and results. Consider what recommendations for further analysis you would make to enhance your

 Section C – Monte Carlo Simulation

Danske Haldor is a Dansh boatbuilder, based in Copenhagen. The company is currently evaluating a proposal to invest in producing a new type of inshore rescue boat. Two possible boat designs, Type A and Type B, have now been developed, each of which is expected to have a five-year sales life, before a radical redesign would be required. The Type A design would require an investment of one million krones (DKK), while the Type B would require an investment of two million krones. The expected cash flows of the boat types if there was no inflation (i.e. today’s values), are as follows:

The net cash flows for each boat type are estimated as revenues (sales) minus costs. These net cash flows can be assumed to arise at the year-ends of each of the five years. Specific annual inflation rates have been estimated for each of the cash flow elements:

Furthermore, the sales, materials, labour, and overheads for each boat type were estimated using various pieces of information. The sales for each boat type, for example, may not be precisely as estimated above because of various uncertainties. For this reason, they decided to use probability distributions to allow for a range of possible values for the above variables. The associated distribution for each variable and the parameters for each probability distribution are given in the following tables (in K000):

Finally, the cost of capital is 10 per cent per annum. However, there is a 20% probability that the company will take a loan to finance the development of the boat types. If that happens the cost of capital will drop to 8 per cent per annum.

• For the situation described above, develop first a deterministic model for the Net Present Value (NPV) of each boat type. Use then the information provided for the uncertain variables to run a Monte Carlo simulation and present the results in relevant diagrams (you should at least have two graphs, each showing the simulated distribution of the NPV for each boat type).

• Discuss the recommendations you would make to the management of Danske Haldor on the basis of these

• Discuss the advantages of using Monte Carlo simulation instead of a deterministic

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