Group11 proposal V2

Proposal

Poster

Application

Research Paper

Roles

Version 1

Version 2

Problem

The above is an example of information online in helping Singaporeans decide the type of house they could choose from. In the context of this project, Resale HDBs is the focal point of our project. Choosing a resale HDB has never been easy as there are many factors to consider such as location, HDB type, number of remaining lease years, resale value, etc. On top of that, thousands of Resale HDBs transactions are happening each month, making it almost impossible for an owner to get a view of every transaction. Therefore, the majority of buyers and/or sellers have to consult property agents for their services.

However, there are times where the service of a property agent may not be satisfactory to the buyer and/or seller. It is still advisable for the buyer and/or seller to have a better understanding of the resale market before making an informed decision. In most cases, people tend to rely on multiple platforms to help them in understanding and predicting the market trend. On that note, our team would like to know whether the information provided to them is truly insightful or usable. Thus, WeHouse comes into play to provide greater insights for people to better understand the HDB resale market which can provide better aid in making more informed decisions.

Motivation

Given the abundance of information available on the internet in terms of resale prices, there are online resources provided to map out HDB locations and prices. However, such visualisations tend to be overwhelming due to vast data and non-specialised focus on visualisations. Throughout the course, we have learned how to discern between a good and bad visualised graph. This has allowed us to decide the usefulness of the charts displayed on various platforms. Therefore, instead of leaving people unsettled about the kind of housing choice to make, we are here to aid with our proposed visualisations! Coupled with the hands-on in-class sessions, we are able to reinforce what we learnt to play and explore re-organisation of the data to present an interactive dashboard, specifically pertaining to HDB dwellings, to empower users to make better-informed choices of their HDB location.

Objectives

In this project, we aim to deliver a focused and compact visualisation to allow Singaporeans to be well-informed of the average HDBs resale prices around their desired location.

1. Overall changes in the flat prices over time for each subzone by estates.
2. Price differences for each estate and subzone depending on the remaining lease of the flat.
3. Find out which month sells at the highest and lowest resale price and transactions
4. Specifically identify the most expensive street within each town area

Data Set(s) Used

Background Survey

Example	Takeaways
	New Insights / Comparisons and what to do better
	New Insights / Comparisons and what to do better
	New Insights / Comparisons and what to do better
	New Insights / Comparisons and what to do better

List of References

No.	Reference Link
1	Source: https://www.hdb.gov.sg/cs/infoweb/residential/buying-a-flat/resale/resale-statistics
2.	Source: https://services2.hdb.gov.sg/web/fi10/emap.html
3.	Source: https://www.srx.com.sg/heat-map
4.	Source: https://www.teoalida.com/singapore/hdbmap.htm
5.	Source: https://medium.com/@tianjie1112/singapore-hdb-resale-price-prediction-data-exploration-ac9e23de146
6.	Source: http://sgyounginvestment.blogspot.com/2018/03/visualisation-of-hdb-resale-prices-in.html

Proposed Storyboard

Dashboards	Rationale
Dashboard 1: Overview of Resale Trend in Singapore Volume VS Price	With the 1st dashboard, we will go with a dual axis bar and line chart to provide an overview on the trending HDB resale pricing over a series of 8 years. Each of the bars will depict the average resale prices per year coupled with a trending line that will depict if the resale prices are increasing or decreasing over the years. We have also included a side-bar that will enable users to filter their view of the trending resale prices of a specific town area in singapore.
Dashboard 2: Tree Map of Room Cost Vs Area	The 2nd dashboard aims at providing insight on how the HBD densities are segregated by the various towns within Singapore. With the use of a Treemap, we can depict the volume of HDBs by the size of each rectangular portion on the Treemap, upon clicking onto a specific town area, a box plot chart pop-up provides a detailed understanding of the averaging resale prices across the various HDB room types.
Dashboard 3: Animated Visualisation Heat Map of Singapore's HDB PSF Cost	We are intending to use an animated version with a static map that illustrates the variations in the changing cost Per Square Feet (PSF) in generalised land marks With the animated version, we are able to note the trend of changing cost PSF in each area Together with the static version, any Singaporeans would be asble to understand the cost of HDB in each respective area

Technologies Used

Key Technical Challenges

Challenges	Actions Taken
Unfamiliarity with R Shiny and little knowledge of R	Recap on R through past slides and exercises Learning of R shiny will be done through class lessons and weekly use of Datacamp to further reinforce the learning.
Limited knowledge in storyboard designing	Explore online resource through Tableau Public / Viz CookBook and Datacamp
Lack of postal code provided in the data	Use of OneMap API, together with python script, to retrieve Postal Codes of the Area and Street level. However, Postal Codes are insufficient for Tableau to zoom into street level visualization. Hence, we will use QGIS learnt in SMT 201 for better visualisation.

Team Milestones

Comments

Do Leave a Comment on how we can improve :)

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