CVE2112 HDB Resale Price Analysis Assignment: Data Visualization & Summary Stats in R Program Project, Singapore

Project Description

This is an individual project focused on descriptive statistics and data visualization techniques to analyse public housing HDB resale flat prices in Singapore from 2017 to 2025. Students will use real-world data sourced from data.gov.sg (Singapore’s official public data repository).

The objective is to develop proficiency in preparing, processing, analyzing, and visualizing data using RStudio. Students will also be encouraged to use Generative AI (GenAI) tools (such as ChatGPT, Copilot, Gemini) to aid in R programming tasks, including data wrangling and plotting.

Learning Outcome

By completing this continuous assessment (CA), students will be able to:

• Perform data cleaning and transformation on real-world datasets using R.
• Apply descriptive statistics to summarize housing market data.
• Generate meaningful visual representations of data trends and distributions.
• Interpret data analysis results and draw relevant conclusions.
• Utilize GenAI tools to assist in the development of data analysis code and visualizations.

Dataset Overview

The dataset used in this project was downloaded from data.gov.sg, which is Singapore’s official open data repository. The data set contains records of HDB resale flat prices based on the registration date of the transactions, covering the period from January 2017 to April 2025.

The dataset “ResaleHdbFlat2017to2025.csv” is provided in CSV (Comma-Separated Values) format. It can be opened using spreadsheet software such as Microsoft Excel, which allows you to view the tabular structure of the data before importing it into RStudio for analysis. An example of how the dataset appears in Excel is shown below.

The dataset contains 11 columns, each representing a specific attribute of the HDB resale transaction:

1. Month – The month and year of the transaction (formatted as YYYY-MM).
2. Town – The town or estate where the flat is located.
3. Flat type – The classification of the flat based on size and layout (e.g., 3 ROOM, 4 ROOM).
4. Block – The block number of the HDB flat.
5. Street name – The name of the street where the flat is located.
6. Storey range – The floor level range of the unit (e.g., 10 TO 12).
7. Floor area (sqm) – The size of the flat in square metres.
8. Flat model – The design model of the flat (e.g., New Generation, Model A).
9. Lease commence date – The year the 99-year lease started.
10. Remaining lease – The number of years left on the lease at the time of resale.
11. Resale price – The transaction price in Singapore dollars.

For this project, students are required to analyze only the transactions from the town where they currently reside. This ensures that each student conducts a focused and personalized analysis based on a familiar location.

Students will load the dataset into RStudio, filter it to include only their selected town, and then carry out data cleaning, statistical analysis, and visualization tasks as outlined in the assignment instructions.

SEE ANNEX A FOR THE LIST OF TOWN AND FLAT TYPE

To begin your analysis in RStudio, follow these simple steps to read and filter the dataset with the name of your town.

# Load the necessary libraries
library(tidyverse)

# Read the CSV file into R (ensure the file is in your working directory)
hdb_data <- read_csv("ResaleHdbFlat2017to2025.csv")

# Replace "YOUR TOWN" with the name of your town in CAPITAL LETTERS (e.g., "TAMPINES")
selected_town <- "YOUR TOWN"

# Filter the dataset to include only transactions from your town
town_data <- hdb_data %>%
 filter(town == selected_town)

# View the first few rows of the data
head(town_data)

Tasks

Students are to focus their analysis on only one town—specifically, the town where they currently reside. They are expected to perform the tasks below and submit a structured data analysis report.

Tasks Descriptions

1. DATA PREPARATION
   • Import and inspect the dataset.
   • Convert date fields to proper date/time format.
   • Filter the data to include only records from your residential town.
   • Handle any missing values or inconsistencies.
2. SUMMARY STATISTICSProvide descriptive statistics for resale prices in your town filtered by flat type (i.e., 1 ROOM, 2 ROOM, 3 ROOM, 4 ROOM, 5 ROOM, EXECUTIVE, MULTI-GENERATION)
   • Mean, median, minimum, maximum, Q1 and Q3
   • Standard deviation, interquartile range (IQR), coefficient of variation (cov)
   • Number of transactions per flat type

   Tabulate the above summary statistics in the following table format

   Town	Flat type*	Number of transactions	Mean	Median	SD	Min	Q1	Q3	Max	IQR	cov
   “YOUR TOWN”	1 ROOM
   	2 ROOM
   	3 ROOM
   	4 ROOM
   	5 ROOM
   	EXEC…
   	MULTI…

   * if applicable
   

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3. DATA VISUALISATIONSCreate the following plots for your town filtered by flat type using ggplot2 package:
   1. Histogram with KDE (Kernel Density Estimate): Visualize the distribution of resale prices, with a smooth density curve overlay.
   2. Cumulative Distribution Plot: Show the cumulative proportion of resale prices to understand pricing thresholds
   3. Time Series Plot: Analyze how the average resale price changes over year.
   4. Pie Chart: Show the proportion of total transactions by flat type.
   5. Box-plot with Jittered data points: Compare the spread and central tendency of resale prices across flat types
   6. Any other plots that you may think useful (e.g. further filtered by remaining lease or storey range)

   Ensure all visualizations are clearly labelled and interpreted.

   Creativity is encouraged — feel free to enhance your visuals with themes, colors, or annotations to improve clarity.

   SEE ANNEX B FOR EXAMPLE OF DATA VISUALISATION PLOTS

Report Submission Requirements

Students must submit a written report in PDF format that includes the following clearly labeled sections. The report should be concise, well-organized, and supported with tables, plots, and commentary. You are encouraged to apply creativity in your presentation and data visualization to effectively communicate your findings.

1. IntroductionBriefly introduce the project and your selected townState the objective of the analysis (e.g., to explore resale price trends, flat type distributions, and lease effects in your town)
2. Data PreparationDescribe the data cleaning and preprocessing steps you performed.Mention any assumptions made or filters applied (e.g., filtering by town, handling missing values, converting date formats).
3. Summary StatisticsPresent summary statistics in tableComment on price levels, variation, and trends
4. Data VisualisationsInclude and describe each of the required plotsHighlight key observations
5. Observations and InsightsDiscuss any interesting patterns or anomalies you discovered.Provide your interpretation of the data in the context of your town.

   Consider factors like flat type, lease duration, and price variation.

6. Use of GenAI in learningDescribe how you used Generative AI tools to: Write or debug R code, Understand statistical concepts, Generate visualizationsReflect on how these tools supported your learning
7. ConclusionSummarize your key findingsReflect on what you learned about your town’s HDB resale market

   Mention any limitations for further data exploration

Guiding questions. Use these to help structure your analysis and reflection:

1. What is the most common flat type in your town?
2. How have resale prices changed over time?
3. How does your town compare to national averages (if known or researched)?
4. Are there specific flat models or storey ranges that are more common in your town?
5. Does your town show signs of gentrification or aging infrastructure based on lease dates and prices?
6. Which flat type has the highest average resale price?
7. Are there any outliers or unusual trends in the data?
8. How does the remaining lease affect resale prices?
9. What challenges did you face during the analysis?
10. How did GenAI tools help you in this project?

SEE ANNEX C FOR THE ASSESSMENT RUBRIC

Other submission instructions

Please submit the softcopy of your written report in a pdf format together with your RStudio script file (.R) into LMS dropbox by Sunday, 1st June 2025, 23.59hrs.

Plagiarism is strictly prohibited.

• Submitting work that is copied from another student will result in a zero mark.

You are encouraged to discuss ideas and collaborate on learning, but:

• Do not copy code or text from others
• Your submission must reflect your own understanding and work

Use of Generative AI tools (e.g., ChatGPT, Copilot) is allowed, but you must:

• Use them responsibly
• Acknowledge how you used them in your report

ANNEX A – LIST OF TOWN AND FLAT TYPE (based on the csv file provided)

LIST OF TOWN

1. ANG MO KIO
2. BEDOK
3. BISHAN
4. BUKIT BATOK
5. BUKIT MERAH
6. BUKIT PANJANG
7. BUKIT TIMAH
8. CENTRAL AREA
9. CHOA CHU KANG
10. CLEMENTI
11. GEYLANG
12. HOUGANG
13. JURONG EAST
14. JURONG WEST
15. KALLANG/WHAMPOA
16. MARINE PARADE
17. PASIR RIS
18. PUNGGOL
19. QUEENSTOWN
20. SEMBAWANG
21. SENGKANG
22. SERANGOON
23. TAMPINES
24. TOA PAYOH
25. WOODLANDS
26. YISHUN

LIST OF FLAT TYPE

1. 1 ROOM
2. 2 ROOM
3. 3 ROOM
4. 4 ROOM
5. 5 ROOM
6. EXECUTIVE
7. MULTI-GENERATION

ANNEX B – EXAMPLE OF DATA VISULATION PLOTS

The following are example data visualisation plots created using dummy data to illustrate the expected outputs. Students are encouraged to be creative — feel free to enhance your visuals with themes, color palettes, annotations, or layout adjustments to improve clarity and presentation.

ANNEX C – ASSESSMENT RUBRIC