Homviz Homies: Proposal Version 1

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Research Paper

PROBLEM & MOTIVATION

Problem In the recent years, housing prices have been on the rise making it difficult for Singaporeans to own a house. In the National Day Rally 2018, Prime Minister Mr. Lee Hsien Loong addressed to the issue of housing to Singaporean. He stated the importance of the 99-year lease of Home Ownership as compared to Singaporean renting a house. However, several older estates were built within short periods such as Marine Parade, Ang Mo Kio and Bedok when their 99-year lease has expired they will all expire at the same time and the flats will be returned to the state. The government has also been progressively taking back flats over the years for redevelopment.

Motivation There is a need to relocate residents for redevelopment such as getting another flat to live in. In response to growing demand for housing, there is a need to analyze housing supply and housing prices to gain insights on how much is enough supply of housing and how much is enough payback price for citizens to afford a new housing.

OBJECTIVES

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

1. Identify the lease term of the HDB and highlight those that are going to reach their due year.
2. Identify the number of units and the number of households in the HDB that will be affected by the relocation
3. Identify how much housing should be supplied based on the household’s affected by buyback

SELECTED DATASET

The dataset for analysis will be retrieved from multiple databases, as elaborated below:

Dataset/Source	Data Attributes	Rationale Of Usage
HDB Property Information (https://data.gov.sg/dataset/hdb-property-information)	Block Number Street Max Floor Year_Completed Residential Commercial Town Contract Total Dwellings Unit 1 Room Sold 2 Room Sold 3 Room Sold 4 Room Sold 5 Room Sold Exec Sold Multigen Sold Studio Sold 1 Room Rental 2 Room Rental 3 Room Rental Other Room Rental FY(1 April - 31 March)	We want to see the number of Units Sold across Singapore that are sold or rented. This information are for public HDB housing estate. The dataset contains information on how long the HDB lease left and other information about a HDB property We want to filter out the residential area applicable to our analysis
HDB Resale Flat Prices (https://data.gov.sg/dataset/resale-flat-prices)	Month (YYYY-MM) Town Flat Type Block Street Name Storey Range Floor Area Sqm Flat Model Lease Commence Date Remaining Lease Resale Price	We can break down the resale price of each flat and look at the average price for each model. From the breakdown, we can also identify which flat type or storey range would produce a higher resale price compared to the original selling
HDB Median Resale Prices by Town and Flat Type (https://www.hdb.gov.sg/cs/infoweb/residential/buying-a-flat/resale/resale-statistics)	Town Year Quarter Room Type Median Resale Price	For this dataset, we have to transform the data and crawl the data so that we can use it to compare with the min and max selling price by HDB.
HDB PRICE RANGE OFFERED (https://data.gov.sg/dataset/price-range-of-hdb-flats-offered)	Financial Year Town Room Type Min Selling Price Max Selling Price Min Selling Price Less AHG SHG Max Selling Price Less AHG SHG	For this data set, we can look at the Selling Price Range from HDB. We can then compare how the selling price fair from the resell price
Completion Status of HDB Developments (https://data.gov.sg/dataset/number-of-units-of-hdb-developments-by-status?view_id=2f0e2460-4d87-464d-bd1f-aafa17fac75a&resource_id=ff97dd96-6db5-4eb7-ba79-ad8d4840a3aa)	Financial Year Property Type Completion Status No of Unit	We can look at the supply of new HDB flats that the government is building every year and the number of flats completed. From this dataset, we can then later identify if the government is supplying enough for the demand if they were to reallocate flat owners
Estimated Singapore Resident Population in HDB Flats (https://data.gov.sg/dataset/estimated-resident-population-living-in-hdb-flats?resource_id=b29c1af8-e11a-4e61-b813-933db9f69633)	Financial Year Town Population	From this dataset, we can calculate the population trend for each town to identify if population move in or out of town over the years. Later, we will be using it to compare with the HDB constructed over the years.

BACKGROUND SURVEY OF RELATED WORKS

Some of these visualizations that we draw inspiration from, are as follows:

Reference of Other Interactive Visualization	What We Can Learn
Title : Property prices and household income over the years Source: https://sbr.com.sg/residential-property/exclusive/10-charts-prove-singapore-propertys-down-in-doldrums	The use of time series chart allows users to view the rise and fall of prices and prevents users from getting overwhelmed by too much cluttered data as compared to using bar charts.
Title : An Animated Time-Lapse Visualization Source: https://medium.com/@wojiefu/hdb-pusle-visualization-of-singapore-hdb-flat-resale-records-2e2fbedbee91	We can learn from this animation the temporal transition of the data points. We can see the evolution of the data points for example in our case we can show the time transition for the lease end date. User will be able to see the change of the node from green to red if the lease is ending soon.
Title : Interactive Map Source: https://wiki.smu.edu.sg/is480/IS480_Team_wiki%3A_2015T2_REALIS_Project_Overview	What we can learn on this project is the use of cross filtering to provide an interactive filtering of data. The charts on the map will zoom into the details based on the user’s filter preference.
Title : Area Shading Map Source:http://www.viz.sg/viz/map_housing/	Area shading map allows us to quickly see which area has more HDB flats of which type. We can also understand that there are new areas and drawing of boundary changes across the years. There is also the information at a glance at the side for the users to view.
Title : Bar chart and Box plot Source: https://public.tableau.com/profile/priyadarsan.shankar#!/vizhome/Singapore4-ROOMHDBresalepricesvisualization/HDBdashboard	Data is sorted in descending order, making sure that the viewer will be able to have quick inference.
Title : Interactive Zoom Map Source: https://lhuasheng.carto.com/builder/b897e86d-e394-4fb5-ac8a-b00d179b1c67/embed	Interactive map allows viewers to see the data at a quick glance, the viewer is able to zoom into the data that they are interested in. This allows the creator to convey a lot of geospatial data without overloading the user.

PROPOSED STORYBOARD

Our group has proposed the following storyboard to assist analysts in the use of our visual application:

Proposed Layout	What can we Analyse
Analyze the lease due by subzones by year	Firstly, we present an overview of Singapore Map to display the “age” of HDB in Singapore By using different intensity, we can identify the remaining Lease Years all over Singapore by the different dimensions, by Town, Sector and Area.
A line graph of the number of houses supplied vs the number of houses reclaimed over the years	From the number of HDBbuild and the number of HDB expiring, we can then look at the trend if we have enough supply to catch up on our demand.
Box plot of housing price by flat types	Using a Box Plot, we can show that the Min, Max and Median price of Resale Flat across Flat Type. This would give us a better estimate of how we should expect for a buyback price if government would to buyback the flat before the lease ends We could break it down to look at the transactional resale price by their remaining lease year
Bar Chart of Number of Room types by dwelling	Using a bar chart, we can show the number of each room types across the dwelling. This would give us a better estimate of how many households of each room type will be affected

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 Challenge	Proposed Solution
Unfamiliarity of Visualization Tool Usage	Independent learning on visualization tools Peer Learning
Data Cleaning and Transformation	Need to crawl data like postal codes and areas codes missing from dataset Clean, transform and analyze data together
Unfamiliar with Javascript and Rshiny Libraries	Attend R shiny Workshop Independent learning via online tutorial

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:

REFERENCES

• National Day Rally 2018, https://graphics.straitstimes.com/STI/STIMEDIA/Interactives/2018/08/ndr-speech-analysis-2018/
• ggplot2, https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf, https://ggplot2.tidyverse.org/reference/
• vslashr, http://www.vslashr.com/category/visualization/
• Singpost, https://www.singpost.com/find-postal-code

OUR BRAINSTORMING SESSIONS

Version 1

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

• [1] Map of Singapore that the users are able to choose to filter by subzones or by individual dwellings. There will also be a timeline slider at the bottom of the Map. This will enable the users to pick a particular year. The map will change to show which dwelling's lease year is going to hit it's expiry mark

• [2] This will contain information at a glance of the dwellings of the user's choice such as number of households, number of units, the subzone etc.

• [3] The box plot will contain the transactional price of resale flats of each room type (1 room, 2 room, 3 room etc) in the dwelling. This will be based on what the user has clicked in the map.

• [4] A line graph of the number of houses supplied vs the number of houses reclaimed over the years.

• [5] A radar chart to show the distribution of the number of room types by the chosen area sector.

• [6] A bar chart of the number of dwelling by room type and area number.

Version 2

After meeting with professor, we have decided to brainstorm again on what kind of charts should we be plotting and have come out with the following:

• [1] Map of Singapore that the users are able to choose to filter by newtown or by individual dwellings. There will also be a timeline slider at the bottom of the Map. This will enable the users to pick a particular year. The map will change to show which dwelling's lease year is going to hit it's expiry mark

• [2] This will contain information at a glance of the dwellings of the user's choice such as number of households, number of units, the newtown etc.

• [3] The box plot will contain the transactional price of resale flats of each room type (1 room, 2 room, 3 room etc) in the dwelling. This will be based on what the user has clicked in the map.

• [4] A line graph of the number of houses supplied vs the number of houses reclaimed over the years.

• [5] A bar chart to show the distribution of the number of room types by the chosen new town as well as the number of household living in each room type.

• [6] A bar chart of the number of dwelling by room type and area number.

COMMENTS

Feel free to comment on our project