ISSS608 2016-17 T1 Assign1 CHIA Yong Jian

Problem and Motivation

The public housing resale market is a closely watched market. A quick search on news revealed that the public housing resale market movement is watched closely almost on a monthly basis, especially for indicators hinting the stability or trend of the prices. These are crucial information not just for real estate companies, but also buyers and sellers of resale flats. This analysis is focused on providing relevant information that can aid buyers and sellers of resale flats to make informed decisions when deciding to buy or sell a flat.

• For buyers, they include newly-wed couples that may urgently need a house for their family needs. Knowing the prices of resale flats by areas can allow them to decide on a suitable area for house-hunting depending on their budget and preferences for amenities in the area
• For sellers, they are existing house owners that might be watching the market to understand the general market trends to decide on a good price to sell their houses.

We assume that the buyers are sellers are the general population where they might be untrained in statistics. Hence, charts would be generally designed from an approach that are visually attractive without unnecessary fluff, and requires little time for them to understand without requiring statistical knowledge.

Approaches

Data Acquisition

From Data.gov.sg

The dataset used to answer the above questions can be found from https://data.gov.sg/dataset/resale-flat-prices . There are two datasets available on the webpage. We will be using the resale flat prices data that includes data for the year 2015 to first half 2016, to answer the questions required.

From HDB Website

Supplementary data from HDB website is used to understand better the resale flat supply by the 23 towns and 3 estates planned by HDB, by normalizing the resale flats sold by the total flat supply in the area. The data is retrieved from http://www.hdb.gov.sg/cs/infoweb/about-us/history/hdb-towns-your-home .

Variable Exploration

In the data.gov.sg dataset, a quick review of the variables reveals no missing data.

The dataset includes the following variables:

Variable Name	Description	Example Levels/Values
Month	Example	Example
Town	Example	Example
Flat Type	Example	Example
Block	Example	Example
Street Name	Example	Example
Storey Range	Example	Example
Floor Area Sqm	Example	Example
Flat Model	Example	Example
Lease Commencement	Example	Example
Resale Price	Example	Example