IS428 2016-17 Term1 Assign1 Margot Marie T Stelleman

Abstract

Housing, business for ones, investment for others, but necessity for all. This project gives an analysis and understanding of the private properties supply and their prices during the last year, 2015. A major step to do it is to first determine and understand the problem and next collecting and cleaning, transforming the data required. Then, the trends will be enlightened and easier understood thanks to graphical images. This graphs are designed thanks to Tableau Software. Based on this and information discovered, policy recommendations are imagined and shared for 2016.

Problem and Motivation

According to Singapore statistics, in 1970, 2 013 600 persons were forming the resident population of Singapore. Thirty years later, the next billion was already reached, with 3 273 400 people. Last year in 2015, the forth billion was almost obtained with 3 902 700 billion for Resident Population. It means that the raise has been almost twice faster between 2000-2015 than between 1970-2000.This raise will probably continue and might be more aggressive than ever. All this people must have a roof, that is a necessity for all of us. That is why it is interesting to have a look at the housing situation in Singapore. Private properties supply is here studied. In this project it will be considered as the residential properties only, not commercial and industrial properties because the problem is centralized on the housing problem for the population. And the term supply will refer only to the residential properties available and habitable on the market. But a quick look to the planned and under construction projects is interesting too. Prices of the residential properties will be also considered given that everyone has not the same budget and revenues. All this will be considered for the 2015 year, last whole year with available data. Because population is growing, is the supply also following it? How is the share of the supply by type of housing and also geographically? What type and region were the most expensive during 2015? And have the prices fluctuated a lot? It is important to know the answers to this question to make good decisions about housing, so for personal decisions as well as for policy development.

Data choices and cleaning

Once the area of study and specification of the problem to be cover is well done and understood, the collection of the data can be made. The data collected have been found on REALIS. There are three big categories for real estate: residential, commercial, industrial. The one interesting for the problem here is the Residential one.

Once our category is selected, different areas are proposed such as time series, project, transaction, rental or stock for example. Transaction dimension has been useful to get the data for availability by prices, but also by different region, selected for January 2015 till December 2015. We cannot download all the data, so I’ve been using Data analysis option to be able to do it and also take all the essential information. That means that Number of Unit were the analysis variable, Vertical axis was Unit price range in steps of 100 dollars psm and Horizontal axis was Property range, I also took the data for Planning region. This has be done for Landed – All and also Non-Landed - All only because getting all the property type at the same time we’re not allowed.

What about data necessary for the supply? In section Times Series, the sheets of data relative to availability and vacancy in total and also for each region have been collected. The stock part has been also useful to know the number of units per type but also per region. Data analysis option function has been used to collect it because of the huge amount of data.

Every information necessary collected, for everey single files, Excel has been used to split the only cell where all information was to convert it in several columns. And for every file, the rows not about 2015 of one of her quarter and extra information have been deleted. More modifications hae been made for the files related to transactions. Indeed, for example, the range of "1200(incl) till 1300 (excl)" had one housing in total in his row. But with the price in text, it was impossible to calculate something. Plus it was not continous, every single range of price is not always representend because housing not neccessary has this price. I have than changed it and replace "1200(incl) till 1300 (excl)" by 1200, for example, and fullfill automatically by zero the emply cells from the new rows for which a housing value was not covered. Once all that done, they can finally be used on Tableau Software that has been used for graphical constructions and images.

Inforgraphics

Click on the image to enlarge and zoom.