Business Finance: Analysis of Historical Housing Prices and Income Data in Melbourne

Running head: BUSINESS FINANCE
Business finance
Name of the student
Name of the University
Author Note

1BUSINESS FINANCE
Table of Contents
1. Detection and justification of the historical prices of houses of Melbourne that indicate
assumptions taken regarding future prices:................................................................................2
2. Evaluation of the historical data of Melbourne to justify the assumptions for the income
data:............................................................................................................................................4
3. Detection of the net income through using ATO calculator to detect the home loan rate and
maximum borrowing amount that a client could receive:..........................................................6
4. Calculation of stamp duty related with property purchase at the time of detecting the
affordability of house prices.......................................................................................................8
5. Calculation and presentation of a financial plan with 20% upfront payment and 5% upfront
payment for the loan:.................................................................................................................9
6. Calculation regarding increment of interest rate payment that can destroy lender’s
capability regarding interest rate payment:..............................................................................10
7. Providing the relevant plan along with detailed risk that the assumption regarding financial
plan has entailed.......................................................................................................................11
References:...............................................................................................................................12

2BUSINESS FINANCE
1. Detection and justification of the historical prices of houses of Melbourne that
indicate assumptions taken regarding future prices:
Increasing housing prices has become a serious issue among Australians and this price
related to house has remained comparatively high in both Melbourne and Sydney. Hence, it is
essential to detect and justify the historical housing prices of any of these two cities with the
help of which some assumptions can be made for future prices of houses in Australia. This
report has selected the housing price of Melbourne for conducting forecast further. To
recognize the entire historical growth of housing price in this city, sufficient calculations have
been done. This is because the analysis on housing price growth of this city is required for
adequate purposes related to analysis. Based on data, it can be seen that the housing price in
this city has grown relatively from 2002 to 2017 and this in turn has helped to understand the
future trend of housing prices of Melbourne (Bayer, Ferreira & Ross, 2017). As the housing
price is at inflation in Melbourne, the average of Median Price of Established House
Transfers (Unstratified) of this city is used. This measurement has helped to identify the
relative price change for the next 20 years regarding housing property that is required to
evaluate for understanding the implications that it has on the client, who are going to
purchase houses. The data is collected from the ABS website for calculation and sufficient
dada are given to evaluate and understand the price action regarding housing property
(Abs.gov.au., 2018).
Prices in Next 20 Years
Year Quarter Price
Year 0 $ 713,000.0000
Year 1 Q1 $ 726,685.9519
Q2 $ 740,634.6041
Q3 $ 754,850.9991
Q4 $ 769,340.2760
Year 2 Q1 $ 784,107.6730
Q2 $ 799,158.5284
Q3 $ 814,498.2833

3BUSINESS FINANCE
Q4 $ 830,132.4829
Year 3 Q1 $ 846,066.7792
Q2 $ 862,306.9325
Q3 $ 878,858.8136
Q4 $ 895,728.4061
Year 4 Q1 $ 912,921.8085
Q2 $ 930,445.2363
Q3 $ 948,305.0242
Q4 $ 966,507.6286
Year 5 Q1 $ 985,059.6300
Q2 $ 1,003,967.7348
Q3 $ 1,023,238.7785
Q4 $ 1,042,879.7276
Year 6 Q1 $ 1,062,897.6825
Q2 $ 1,083,299.8797
Q3 $ 1,104,093.6947
Q4 $ 1,125,286.6445
Year 7 Q1 $ 1,146,886.3905
Q2 $ 1,168,900.7412
Q3 $ 1,191,337.6547
Q4 $ 1,214,205.2422
Year 8 Q1 $ 1,237,511.7704
Q2 $ 1,261,265.6646
Q3 $ 1,285,475.5121
Q4 $ 1,310,150.0648
Year 9 Q1 $ 1,335,298.2427
Q2 $ 1,360,929.1369
Q3 $ 1,387,052.0131
Q4 $ 1,413,676.3149
Year 10 Q1 $ 1,440,811.6670
Q2 $ 1,468,467.8791
Q3 $ 1,496,654.9490
Q4 $ 1,525,383.0663
Year 11 Q1 $ 1,554,662.6166
Q2 $ 1,584,504.1844
Q3 $ 1,614,918.5576
Q4 $ 1,645,916.7312
Year 12 Q1 $ 1,677,509.9111
Q2 $ 1,709,709.5184
Q3 $ 1,742,527.1934
Q4 $ 1,775,974.7998
Year 13 Q1 $ 1,810,064.4292
Q2 $ 1,844,808.4050
Q3 $ 1,880,219.2874