ISSS608 2016-17 T1 Assign1 Goh Kar Hock Raymond

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== Abstract ==

  • A quick and dirty visual analysis of public housing resale market for 2015, using data extracted from data.goc.sg to understand the prevailing trend in today’s resale market. The analysis was focused on the share of resale public housing supply and distribution of resale public housing prices, focusing on 2015. A comparison of trends between first-half of 2016 and 2015 was also included in this study.


== Problem and Motivation ==

  • Singapore’s public housing today has housed over 80% of Singapore's resident population, and of which 90% of these households owned their home (Housing & Development Board 2016) (Housing & Development Board 2016). The movement and changes of the public housing’s prices, especially the resale housing, will have a significant impact on a Singapore resident’s ability to own the house. Hence the main motivation of this project is to understand the behaviours of the resale public housing market and its prevailing trend, in order to identify any unusual trends that could influence the prices of the resale public housing.


== Approaches ==

  • Number of Resale Applications Registered by Flat Type (https://data.gov.sg/dataset/number-of-resale-applications-registered-by-flat-type) and Resale Flat Prices (https://data.gov.sg/dataset/resale-flat-prices) were the 2 sets of data extracted from data.gov.sg website, for use in this analysis. The dataset Number of Resale Applications Registered by Flat Type was used to understand the share of the resale public housing supply, focusing on 2015 data. The dataset Resale Flat Prices was used to analysis the distribution of resale public housing prices, also focusing on 2015 data. Lastly, a comparison was done to identify notable trends between first-half of 2016 and 2015.
  • During data preparation, a total of 4 variables were computed in Tableau, 1 variable in the Resale Applications dataset and 3 variables in the Resale Flat Prices dataset. For the Resale Applications dataset, the variable “Quarter” which recorded the year and quarter of the data per row, in string format, was converted into date and time format, using the “DATEADD” function. The formula was “DATEADD('quarter',((INT(LEFT([Quarter],4))-2007)*4)+ (INT(RIGHT([Quarter],1))-1),#2007-01#)”. For the Resale Flat Prices dataset, the variable “Month” which recorded the year and month of the data per row, in string format, was converted into date and time format, using the “DATEADD” function. The formula was “DATEADD('month',((INT(LEFT([Month],4))-2012)*12)+(INT(RIGHT([Month],2))-3),#2012-03#)”. Next the “Age” variable was computed using the formula “2016-[Lease Commence Date]” to calculate the age of the resale housing. Lastly, the variable “Price Per SQM” was computed using the formula “[Resale Price]/[Floor Area Sqm]”, to calculate the price per square metre of the property.


== Tools Utilized ==

  • Tableau was used to prepare the data and analysis the data. These included creating the required visual aids and charts for reporting and presentation. MS Words was used to create the report before uploading into Wiki, and MS PowerPoint was used to create the analytical infographic.


== Results ==

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