Group07 Proposal

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Project Background

The Singapore Real Estate market has always been a hot topic for years ever since 1995. Government, local banks, developers and investors from all over the world has been closely watching Singapore property market. In the last 20+ years, the Singapore property price witnessed a roller coaster of change. URA-Media Releases Appendix. In the most recent 10 years, since Singapore economy rebounded back after 2008 financial crisis. As a result, Singapore Real Estate market price has been heating up significantly from 2009 onwards, until Singapore Government announced that the property tax rates will be made more progressive over two years from Jan 2014, the price had been cooled down.2014-2015 Singapore property tax changes

After a four-year slump until the end of 2017, the property market started to bounce back, and it has the trend that will go up even more. On the other hand, “the US Federal Reserve’s interest-rate has been increased 5 times since President Trump took office in Jan 2017”, says from Bloomberg news, “and there will be 2 more increase happen in 2018”. Trump Blasts Powell’s Rate Hikes, Trespassing on Fed’s Independence This foreseeing news will have impact on Singapore economy in near future. How does this news will affect Singapore residential property price? As Singapore IRAS (Inland Revenue Authority of Singapore) made an announcement this February, says that the property tax will increase to 4% for property house price over 1 million SGD. Budget 2018-Overview tax changes Officials state that this new announcement is not a cooling measure. Higher stamp duty not a cooling measure, say analysts.

But is it really not a cooling measure? Or is it to protect domestic banks from the developers that might potentially being unable to pay back loans after the Federal Reserve’s interest-rate go up?

Project Objectives

There is a deficiency of Market Watch tool for analysing the real estate market data as well as the trends properly. As it is very important to show the trendline cross different years and time, however, these data are still in table formats and the visualizations are quite basic, most of trends are illustrated statically, static graphs are not explanatory enough to show a full picture of changes, which sets barriers for readers from getting any useful insights and findings. To better understand how the Real Estate market price moves cross different type of sales, property types and planning areas in the last 20+ years. And to answer the questions, for instances: How does Singapore Property Market pattern changes overtime after 2008-2012 heat-up? Does the government make action towards the market heat-up effectively? How does market respond when the policies released? A tool that is capable to compare the market trends in different time period would be desirable. Our team aims to build such a web visual analytics-driven application as a Market Watch tool for readers to understand and compare the Property Market pattern changing overtime.

Project Scope

Our project consists of the following components :

NEED TO CHANGE

1. Data cleaning and preparation: We need to remove invalid records from the dataset. We also need to prepare the dataset in a way that allows the plotting of network graphs. This enables users to see the flow of goods from warehouses to consumers, and could be helpful for distribution management.
2. Time series analysis: Using historical data, we will identify if there is any seasonality in customer orders. Then, we will apply time-series forecasting methods to provide users an estimated demand figure for future periods, which could be helpful for inventory management and procurement planning.
3. Calendar plot: We will visualize the number of customer orders using a calendar plot. This enables users to see which months and days of week have higher demand, and this information could help them in manpower management. For example, if I know there are more demands coming in on weekends close to year-end, I can hire more temporary workers to process and deliver the orders.
4. Warehouse performance analysis: We will calculate and pinpoint the warehouses which have high proportions of delivery delays, and uncover possible reasons behind the delay. (e.g. certain product categories have more delayed deliveries; long distances between warehouse and consumer)
5. Distribution analysis: We will visualize if there are any geographical gaps between warehouse supply and consumer demand, which could help users decide if they should rearrange their stocks to more strategic locations.
6. Geospatial analysis: We may uncover spatial patterns in consumer demand, which can be helpful for distribution optimization by stocking spatially-correlated product categories or SKUs in warehouses of the same area.

Dataset Overview

Datasets are extracted from http://ibit.temple.edu/analytics/can-small-independent-pharmacies-compete-with-the-big-chains/
All 6 data sets with 36 columns were in CSV format, total number of rows after joining data sets - 4,680,635 rows. The final dataset covers 6 months time period [July 2016 - Dec 2016] and geographically represents U.S. territory.

Metadata

Variable	Description
No. of Units	Number of units sold for each transaction
Area (sqm)	Spacial measure of the unit sold
Type of Area	Type of area is either Strata or Land
Transacted Price ($)	Amount of each transaction ($)
Unit Price ($ psm)	Price per Square Meter
Unit Price ($ psf)	Price per Square Feet
Sale Date	Date of Sales settled
Property Type	Types can be: Apartment/Condominium/Executive Condominium/Detached House/Semi-Detached House/Terrace House
Tenure	A duration of holding an interest in land or other such real property
Completion Date	Property completed date
Type of Sale	New Sale/Resale/Sub Sale
Purchaser Address Indicator	HDB / Private
Postal District	Two digits of Singapore postal district
Postal Sector	1st two digits of 6-digit postal codes.
Postal Code	Detail location of the property
Planning Region	To facilitate urban planning, the Urban Redevelopment Authority (URA) divides Singapore into regions, planning areas and subzones.
Planning Area	To facilitate urban planning, the Urban Redevelopment Authority (URA) divides Singapore into regions, planning areas and subzones.

Deliverables

At the end of the project, we will submit the following deliverables:

• R Shiny web application
• Research paper
• Poster
• Project artifacts

Schedule