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Project Background

In Singapore, as well as many other nations of the world, housing markets are characterised by the co-existence of a freely priced part of the market with a part that is subject to varying degrees and forms of government intervention and regulation. The Singapore housing market has an especially complex institutional structure with its large regulated public housing sub-sectors. The private housing price are affected by the standard determinants of supply and demand as well as by many government policies, it varies from locations, years, property type, type of sales and influenced by factors like SIBOR. In the last 20+ years, the 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. But this policy seems did not affect Property market much Budget 2018-Overview tax changes. 6th July, the Additional Buyers' Stamp Duty (ABSD) increases drastically. ABSD-6th July In this February, officials stated that the new announcement for increasing BSD rate is not a cooling measure. Higher stamp duty not a cooling measure, say analysts. However, last month when ABSD rate increases even more, Channel News Asia reported that, Higher ABSD rate, the Coolijng measure

What is it all about? What does Singapore IRAS try to manipulate? How the Property price will change now?

Project Objectives

Private housing prices in Singapore cannot be easily analysed by recourse either to analysis of private-sector supply and demand or simple trend-line forecasting. Therefore, it is imperative to analyse from a thorough perspective. However, there is a deficiency of Market Watch tool for analysing the real estate market data as well as the trends properly, these data are still in table formats and the visualizations are quite basic, most of trends are illustrated 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 gain insight how SIBOR influences total unit sold. 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.

The objective for this project, we would guide the user from high level

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.

Deliverables

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

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

Schedule