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Research Paper

PROBLEM & MOTIVATION

In the year 1960, Singapore was facing a huge crisis. Many people were living in unhygienic slums and crowded squatters with only a meager 9% of Singaporeans lived in government flats, while everyone else yearned for a place to call home sweet home.To counter this crisis,, the Housing & Development Board (HDB) was incorporated on 1 February, 1960 and tasked with the critical mission of solving the crisis ar hand. In a mere span of 10 years, HDB had attained its goal and solved the housing crisis.

However, in 1993, HDB stopped deciding the prices of new apartments based on construction costs, instead they decided based on market prices. Prices of resale flats and new flats entered in a vicious circle, rising 50% in just 6 months of 1993 and tripled to 1996. This move closed the price gap between small and large flat types and hub pricing have never been he same again.

Thus, as graduates to be who will most likely enter the job market soon and start looking for a place to call home, we felt that it would be interesting to look into the historical flat data so that we can see which flats in Singapore would be the most value for money so that we can actually get a home which is worth its investment. We also felt that it would be fun to explore trends in the resale flat prices and see what factors really affect the prices of HDBs and see how much of a premium people attach to amenities such as proximity to public transport, schools and etc...

OBJECTIVES

In this project, we are interested to create a visualisation that helps users perform the following:

1. View the trend in the resale prices over time with respect to major events that happened in the year (Example: 1993 Change in Pricing Model,1997 Recession
2. Identify which areas are more expensive and possible reasons for the high value (Proximity to public transport, Schools, Shopping Malls, Park)
3. To find out if getting a specific HDB is a good investment based on the number of year left on the lease and which locations may potentially be more profitable based on the age of the HDB.

By using our visualisation, we will be able to give users a better idea of the pricing situation of the resale HDBs so that people can make better decisions in the HDB which they want to choose to call their home. Such as when is the best time to buy as HDB; where are the most profitable / cheapest locations; whether a HDB is expensive

BACKGROUND SURVEY OF RELATED WORKS

There are many charts and visualisations available which illustrates the various trends of house prices and index. We have selected a few of these to study and learn before we begin developing our own visualizations.

Related Works	What We Can Learn
An Analysis of the trend and correlation between resale prices and flat production Source: http://www.teoalida.com/singapore/hdbprices/	The use of 2 different chart types with a secondary axis is effective in illustrating the correlation between resale prices and flat production. The colours used are striking and contrast well with each other. There are dips in both variables which are not explained in the infographic itself (E.g. 1997 Asian crisis, 2003 SARs outbreak). This events could be incorporated into the charts to make it more informative.
An interactive heatmap of Singapore’s house prices in various districts Source: https://www.srx.com.sg/heat-map	This heatmap uses colours appropriately so that the house prices of each district can be identified intuitively (Red means expensive, blue means cheap, orange means mid-range) The use of filters allows user to find out more about the price distribution of each house type easily. When user mouseover a district on the heatmap, the corresponding district on the legend is highlighted. This improves usability as users do not have to match district numbers manually.
An interactive visualization of house prices along MRT stations Source: https://www.srx.com.sg/mrt-home-prices/property-listings-near-east-west-line	This visualization makes use of unique ways to illustrate the relation between nearby facilities and house prices. The separating of the various MRT lines using filters at the top prevent too much information from being shown in one page

ADDRESSING KEY TECHNICAL CHALLENGES

The following are some of the key technical challenges that we may face throughout the course of the project:

Key Technical Challenges	How We Propose To Resolve
Unfamiliarity of Visualization Tool Usage	Independent Learning on Visualization Tools Peer Learning
Data Cleaning & Transformation	Work together to clean, transform and analyze the data
Unfamiliarity in Programming using Javascript & D3 Libraries	Attend D3 Programming Workshop Independent Learning on D3 Libraries & Technical Tools Peer Learning
Unfamiliarity in Implementing Interactivity and Animation Tools/Techniques in Visualization App	Develop a Storyboard/Design Flow Assign members to specialize on Interactivity/Animation Techniques

PROJECT TIMELINE

The following shows our project timeline for the completion of this project:

TOOLS/TECHNOLOGIES

The following are some of the tools/technologies that we will be utilizing during the project:

• D3.js
• Chart.js
• Google Charts
• Google Search API
• Github
• Netbeans

REFERENCES

• Marina Bay Attack Plot from Batam ‘Not to be Taken Lightly’ (http://www.straitstimes.com/singapore/rocket-attack-plot-not-to-be-taken-lightly)
• National Consortium for the Study of Terrorism and Responses to Terrorism (START). (2016). Global Terrorism Database [Data file]. Retrieved from http://www.start.umd.edu/gtd
• UN Datasets (http://data.un.org/)
• Big Allied and Dangerous Database (https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl%3A1902.1/16062)
• World Bank Database (http://databank.worldbank.org/data/home.aspx)
• D3.js (https://d3js.org/)
• Examples By Mike Bostock (https://bost.ocks.org/mike/example/)

OUR BRAINSTORMING SESSIONS

The following are some of the proposed storyboard that we designed during our brainstorming sessions:

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COMMENTS

Feel free to comment to help us improve our project! (: