16238 Property Data Visualization and Analytics Assignment 1, 2025-26 | Sydney

16238 Assignment One:

This assignment aims to ensure that students understand the large variety of property data types and data sources. Property data visualisations and interaction tools are frequently used for property market research  and  property  analytics,  and  this  assignment  mimics  an  analyst’s  role  in  real  life.  This assignment also ensures that students understand the different visualisation techniques when dealing with other forms of property data. 

Students are required to select an appropriate property data source and data type and then collect any relevant economic and property data for their chosen suburb. Students have to store data in the format style  as  an  example  dataset  related  to  the  suburb’s  property  economics  and/or  built  environment contexts. Please note that data from the chosen suburb will also be relevant for group assignment three. Lectures will record the list, and students will be able to select from a shortlist of potential suburbs outlined below.

General Requirements: 

Students  are  required  to  select  one  suburb  from  the  list  of  Perth’s  suburbs,  and  then  select  the appropriate property data source and data type, and collect the necessary dataset. Please check the latest list in Canvas.

For this assignment, one student can only select one suburb. The collected dataset should be the same as the example dataset, which includes relevant economic and property data for that suburb between 2011 and 2021. Students using ABS Census data, which has four separate datasets for 2011, 2016, and 2021, should use suburb-level statistical dataset, which is compatible with PriceFinder data, RP data and  other  data  sources.

The  dataset  collated  by  students  should  contain  at  least  eight  dimensions, including but not limited to the following: 
1). The suburb’s property median price 
2). The suburb’s house finance status, personal and family finance status 
3). The suburb’s ownership and household information 
4). The suburb’s dwelling information 
5). Family information 
6). The suburb’s population and marriage status 
7). Unemployment and employment status 
8). The suburb’s distance to CBD and travel time by train/bus, and car 

The suburb Albion’s sample dataset: 

Please note that the data from this individual assignment will be used later for the group assignment (Assignment 3). Thus, make sure the data is correct without any mistakes. The dataset should be saved in an Excel file (named Student-ID-Ass1.xlsx).

After collecting the dataset, students must analyse and visualise the data for the relevant time series between 2011 and 2021. Statistical summaries of each dimensional data should be generated.

Students must select the particular visualisation technique that is most suited to their dataset based on the data type,  attributes  and  characteristics  of  the  dataset,  and  the  desired  form  of  visual  representation. Students should be aware that this step's viewing and interaction schemes are key concepts. Students should create several visualisation graphs that should involve at least two (i.e. two or more) dimensions with labelling techniques and trends analytics. The data's dimensions (excluding distance to CBD) should be compared across several visualisations and graphs. Students are also expected to provide  commentary  on  these  trends'  real-world  causes  and  implications.  All  visualisation  graphs should be generated and saved to an Excel file (named Student-ID-Ass1.xlsx). Storytelling is key when attempting property data visualisation in this assignment. 

The visualisation should  explore  the  total  dataset  in  a  sufficiently  specific  level  of  detail  to  enable  the  reader  to understand the attributes and trends of the particular data the student has found.  Students must cite all data sources and references used.

The weighting of this assignment is 30% of a student’s overall final grade.

Detailed Requirements:

I. Make sure all data have been collected and correct with standard format (a few data need to be converted in percentage). Each incorrect data will deduct ONE mark.

II. Exclusive summarises the suburb’s profile through each category’s data. Indicate where the dataset came from, and highlight the data’s attributes and characteristics. Data attributes should be adjusted to fit the visualisation techniques the student intends to use. The dataset has to be submitted with an assignment report. (5 marks)

III. Select the combo chart method that you believe is the most appropriate to represent the dataset, and illustrate the data graphically in time  series  by using  designed  a) layout techniques, b) rescaling  axes  techniques,  c)  labelling  techniques,  d)  trends  techniques;  e)  highlight  and commands inserting storytelling techniques. The visual trend graphs should combine at least two  dimensions,  such  as  the  ratio  between  price  and  finance.  All  dimensions  (excluding distance  to  CBD)  should  be  compared  throughout  the  visualisation  process.  Those combinations of multiple dimensions should be based on the concept of property economics, including

1. Affordability 

• Household’s affordability (ratio between property price and annual household income) status 

2. Supply and Demand 

• Property supply and demand status (ratio between dwelling supply and household need) 
• Property price index (ratio of property price change) vs supply-demand ratio 

3. Finance 

• Property price index vs finance status change (household, family and personal) 
• Property price index vs suburb’s finance status (income/mortgage/rent groups based on 30% income)

4. Population 

• Property price index vs population change and marriage status 
• Property price index vs workforce status

5. Ownership

• Property price index vs household ownership status 

6. Dwelling

• Property price index vs property dwelling status 
• Property price index vs household dwelling status 
• Property price index vs dwelling density status  

7. Family 

• Property price index vs family status  

8. Other comparisons that influence the suburb’s property behaviours All visualisation graphs should be saved in an Excel file (named as Student-ID-Ass1.xlsx). And those trend analysis graphs should also be used in the report. (15 markst). 

IV. Buy a house and a unit in the suburb analysed in 2011 with the most popular bedrooms and car park spaces.

1. For living purposes. Paying the mortgage with median payment using the householder’s median income. What are your property values in 2021? How much mortgages are paid and  how  much  capital  you  have  gained  in  10x  years.  Do  you  belong  to  the  mortgage pressure group that mortgage payment is more than 30% of income?

2. For investment purposes. Get the rental payment income to rent your property out at the median  rent  price.  Combining  the  mortgage  payment,  the  rental  income  and  household income, how much mortgage do you have to pay in 10x years? Do you still belong to the mortgage pressure group that mortgage payment is more than 30% of income? How much capital you have gained in 2021? 

There are no other financial considerations such as cash flow, interest rate, GST, stamp duty, management fees, expenditure, dwelling structure, etc. Visualise what you have found through those data analyses (5 marks)

V. Write a report explaining how you dealt with data node overlap/data edge-crossing / re-scaling in  your  data  visualisation,  particularly  in  combining  multi-dimensional  data.  Describe  the graphic attribute designs and labelling techniques used in your data visualisation and how they enhanced  the  readability  and  storytelling  of  the  visualisation.

Highlight  any  trends  and breakthrough analysis you have discovered through the data visualisation process, particularly the  price  movement  visual  comparison.  Concludes  overall,  you  would  give  any recommendations to a buyer and investor for this suburb. Summarise the advantages of the visualisation approaches you have used. (5 marks)