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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.
- Overall changes in the flat prices over time for each subzone by estates.
- Price differences for each estate and subzone depending on the remaining lease of the flat.
- Find out which month sells at the highest and lowest resale price and transactions
- Specifically identify the most expensive street within each town area
Data Set(s) Used
| DataSource
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Data Attribute
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Data Description
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Rationale of Usage
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HDB Resale Flat Prices
[2012 - 2020]
https://data.gov.sg/dataset/resale-flat-prices
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Month
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Year-Month label
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To attain valuable insights on Resale HDB Real estate prices which can be classified by the following
- Resale Prices trend by location
- Cost Per Square Metre (PSM)
- Maturity of estate
- Area size and floor level vs Price
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| Town
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The various Singapore estates that have HDBs
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| Flat Type
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The types of HDB Flats in terms of number of rooms in the flat
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| Block
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The block numbers of each HDB flat
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| Street Name
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The various street names where the HDB flats are located
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| Floor Area sqm (SQM)
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The overall floor size of the HDB flat
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| Flat Model
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The phase in which the HDB flat is categorized under
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| Lease Commence Date
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Date lease begins for the HDB flat
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| Remaining Lease
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The number of years left for the HDB flat is reclaimed back by the government
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| Resale Price
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The price after the HDB is sold
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Background Survey
| Example
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Takeaways
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- The time-series chart used provides a detailed overview of the trending price index of HDB resale flats as each plotted point follows a quarter/year timeline. However, showing each year’s quarter does make the x-axis legend hard to process upon a quick glance.
- This chart could be further improved by adding a filtered selection to view the quarters for a specific year rather than all quarters for the 25 years displayed.
- Also, an average reference line could be added to provide a benchmark for the various price
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- Although the use of HDB flat icons on the map does help in depicting the cluster of HDB flats in the specified area, however, it makes the visualisation too cluttered which impacts the map visuals as the street names / landmarks might be blocked.
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- The heatmap provided good sectioning of HDB densities across singapore, however a lighter grey shade of the planning areas that are not highlighted could be included.
- With this addition it could depict a better idea on where the densities of HDBs are close to which other areas for route planning or even workplace mapping.
- The various colours used might not be a clear representation of exactly how dense the HDB planning area is.
- A gradual gradient of single colour could be used:
- darkest tone → lightest tone (Most dense town → Least dense town respectively)
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- The use of pin-drop allows the user to get a visual representation of the landmarks in the area and the details provided in the visualisation are plentiful.
- However, the most important detail, pricing is not included which may defer users from utilising this platform to compare prices.
- The cluttered pins and multi-colour pins may be confusing to the user and the differences are not clear at first glance for a HDB buyer.
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- The use of legends helps to tell which lines belong to which estate but the use of too many colours might detract viewers from the main purpose of the visualisation
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- Here, the use of the treemap is good in terms of the colour-shading as appropriate colours are used depict the varying resale prices
- (Red → High Resale Prices)
- (Green → Average to Low Resale Prices)
- What’s lacking in this treemap is a little more in-depth information on the type of HDBs within the planning areas stated.
- The colour of the font used could be of a better choice to allow easy viewing
- It would be good to let viewers gain insight on which type of HDBs tend toward higher resale prices for future planning if home dwellers decide to sell the house at a later time
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List of References
Proposed Storyboard
| Dashboards
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Rationale
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Dashboard 1: Overview of Resale Trend in Singapore Volume VS Price
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- 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.
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Dashboard 2: Tree Map of Room Cost Vs Area
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- 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.
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Dashboard 3: Animated Visualisation Heat Map of Singapore's HDB PSF Cost
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- 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
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Technologies Used
Key Technical Challenges
| Challenges
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Actions Taken
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Unfamiliarity with R Shiny and little knowledge of R
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- 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.
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Limited knowledge in storyboard designing
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Explore online resource through Tableau Public / Viz CookBook and Datacamp
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Lack of postal code provided in the data
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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.
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Team Milestones
Do Leave a Comment on how we can improve :)
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