Group08 proposal

Wolf of HDB Street

Proposal

Poster

Application

Research Paper

PROBLEM & MOTIVATION

Problem
As a buyer looking for Resale HDB flats, it can be difficult to make a purchase decision due to the lack of information in the market. Information such as increasing or decreasing price trends over the years for each estate (e.g. Tampines) or submarket (e.g. 4-ROOM flats) could be essential in the decision making process. Current tools available in the market are insufficient to supplement this decision making process as they can be unnecessarily detailed resulting in the inability to conduct a high level analysis (e.g. Price trends for each submarket and/or estate).

Motivation
According to Ms. Christine Sun, head of research and consultancy at OrangeTee, She commented in November last year (2019) that demand for HDB resale flats has been strengthening in the recent months. However, our group felt that the statement was too generalised as there are several submarkets in the resale of HDB flats such as 3-ROOM flats and 5-ROOM flats just to name a few. Each submarket could have a different trend. Additionally, trends could also vary across different estates such as Bukit Merah and Tampines. The information online would not be useful for people looking at specific submarkets in certain estates.

OBJECTIVES

Target Group: Resale flat buyers
Our goal in this project is to design and create an interactive one-stop visualization tool that could provide Resale flat buyers with information by:

Displaying distribution of flat prices for each submarket (flat type) and estate
Allowing comparison of flat price changes over time for each submarket and estate (e.g. 4-ROOM flats price changes over the past 5 years for Ang Mo Kio)
Highlighting price and volume patterns by flat size for each submarket and estate (e.g. Are 3-ROOM flats in AMK generally bigger than 3-ROOM flats in Tampines?)

These information would help buyers make better purchase decision(s).

DATASET

Data/Source	Variables/Description	Rationale & Methodology
Resale Flat Prices (January 1, 2017 to January 31, 2020) Taken from: Data.gov.sg Link to Data Source	month: Transacted Year & Month Town: Town the flat is situated in Flat Type: Type of Housing Block: Identifier for each Housing Street Name: Identifier for each Street Storey Range: Range of Storey the flat is situated in Floor Area Sqm: Size of flat Flat Model: Flat Model Lease Commence Date: Start Date of Lease Remaining Lease: Duration remaining for Lease Resale Price: Price the Flat is sold for	This is a transactional dataset which provides us with some key information such as flat details and transaction details which we are using for our analysis. We would mainly be exploring how some variables such as price, volume, floor area, storey range etc. affect one another. We would also be highlighting interesting trends and findings. We could obtain information on flat prices against variables such as: Town Flat Type Town & Flat Type (e.g. 4-ROOM in Tampines vs. 4-ROOM in Bedok) Town & Block (e.g. 105 TAMPINES vs. 115 TAMPINES) Storey Range Floor Area Town & Floor Area Remaining Lease The list is non exhaustive, more could be added in the future.

BACKGROUND SURVEY OF RELATED WORK

In order for our group to design a new visualisation, it was important to us that we understand the current work out there in the field. This will enable us to make informed decisions on developing our own visualisations. We can also learn from the current visualisations to ensure that our own work adds value and to not repeat any mistakes made. Listed below are screenshots of visualisations and their learning points respectively.

Reference of Other Interactive Visualization	Learning Points
Title: Official HDB Map Services Image5 Source: https://services2.hdb.gov.sg/web/fi10/emap.html	This is an interactive visualisation by HDB that we can search and filter different regions This visualisation is quite messy as icons of all current HDB in a particular is shown, and the user might be confused to which house to pick from. Furthermore we are not able to understand the price changes across time The advantage of this visualisation is being able to visualise the clustering of HDB flats in a particular region
Title: Average HDB resale prices by town treemap Image3 Source: http://sgyounginvestment.blogspot.com/2018/03/visualisation-of-hdb-resale-prices-in.html	This is a heatmap that shows the relationship of average resale prices by towns One further improvement that we can do to this visualisation is to add in subcategories of the different resale prices. These subcategories could be type of HDB resale flats and which storeys they are on
Title: Distribution of Past HDB Transactions Source: https://hdbviz.shinyapps.io/hdbviz/	This is a highlight table that can be used to depict the distribution of price compared to region and flat type. Furthermore there is a more detailed box plot at the side that visualises the range of price One improvement that can be made to this visualisation is labelling the highlight table to include the prices of each cell, this is to give clarity by showing the magnitude of the price One other improvement that can be made to this visualisation is to allow the user to have an option to include volume of sales as well.
Title: Distribution of 4-Room HDB Resale Prices By Town Source: https://medium.com/@wojiefu/hdb-pusle-visualization-of-singapore-hdb-flat-resale-records-2e2fbedbee91	This is a animated visualisation of HDB resale prices on the map of Singapore, it is very effective in showing us the changes in number of occurrence of transactions being made at what frequency A disadvantage of this visualisation is that there is no legend or information to relate the colours of the points and the actual resale price A disadvantage of this visualisation is that there is no clear indication of which region belongs to which section in the geography map of Singapore. This leaves the user with the onus to understand the location in Singapore

REFERENCE LIST

References

KEY TECHNICAL CHALLENGES & MITIGATION

No.	Challenge	Description	Mitigation
1.	Lack of Familiarity with Tools	Everyone in the group do not know how to program in RShiny for visualisation	We will learn Rshiny during class, call for consultation and rely on Googling for any programming challenges. Alternatively, there is also Datacamp available for us.
2.	Viability of Ideas	We do not know if the current dataset is sufficient in providing all the information needed to conduct analysis and building of planned visualizations.	There are multiple dataset online to use and we can use Prof Kam's REALIS dataset provided to us to supplement our dataset if we are lacking of certain variables. We could also derive our own variables based on the current dataset if needed (e.g. Geocoding).
3.	Lack of Domain Knowledge	HDB resale prices are affected by a spectrum of different factors such as policy measures and redevelopment. It is hard for us to understand without domain knowledge.	Learn from informative websites such as from HDB and iteratively discover and learn insights into the dataset

STORYBOARD

Dashboards	Description
Dashboard 1: Overall Price Distribution by Submarket and Submarket Sizes	The first dashboard that we would like to envision is to have a filter that is visualized by the Singapore map. This will allow users to intuitively find an area that they are familiar with. Since this is only just a filter, it will not show the distribution of HDB Resale Flats in the map itself. By clicking on a specific town, the dashboard will dynamically change (a) and provide insights of its submarket price distribution. This will be visualized in the form of a box plot. Furthermore, by clicking on a specific box plot, it is used as a filter for graph (b), where there will be a histogram plot to visualize the distribution of flat sizes for each submarket, in each town.
Dashboard 2: Comparison of price changes and volume transacted across time periods	The second dashboard that we envision is to allow users to compare average price and volume changes across the years. Users would be able to compare average prices and volume transacted for specific estates and specific submarkets. Users would also be able to identify the high and low value estates based on the data. The charts on the left displays the overall picture of average price changes as well as average volume changes across each year (e.g. 2015 - 2020). While the top left chart has price as its Y-axis, the bottom left chart has volume as its Y-axis. The charts on the right allow Users to view average price and volume changes across each quarter. By selecting a point on the charts on the left (e.g. Year = 2015), the charts on the right is changed to display the quarterly data for the selected year. The charts provide Users with the flexibility to view the data from a high level view (Yearly) as well as from a funneled down view (Quarterly). The Multi-select Estate filter in the middle, which might be shifted elsewhere depending on our UI design, allows Users to view the data of 1 estate as well as compare across multiple estates. The Single-select drop-down menu allows Users to view data or make comparisons from the perspective of 1 submarket at a time or all submarkets. (E.g. Users can compare average price and volume changes for 3-ROOM flats in AMK vs. Pasir Ris or all submarkets in AMK vs. Pasir Ris) We are still exploring the possibility of adding more filters, features, and having a more flexible view. We could possibly add a benchmark line so that estates that have a value above the benchmark is classified as a high value estate.
Dashboard 3: Tree Map of HDB Storeys by Volume and Price	The final dashboard that we envision to complete is a Tree Map plot to visualize the volume and price of HDB Resale flats by their Storeys. The size of the boxes will be equivalent to the volume transacted value and the colours will represent their respective price. The dashboard will come with filters that enables users to select the particular towns that they will want to compare with. The maximum allowed number to select is up to 2 towns. The number of towns selected will generate the same number of graphs for visualization. Lastly, one other filter is the submarkets in which the user is able to select the type of submarket for deeper analysis.