Abstract

Public housing has been said to be a Singapore icon, with HDB housing more than 80% of Singapore’s resident population. (http://www.hdb.gov.sg/cs/infoweb/about-us/our-role/public-housing--a-singapore-icon). Indeed, home ownership has been the aspiration for many. For first timers looking to purchase a unit in public housing, there is always the dilemma of buying a flat direct from HDB or a resale flat from the open market. Purchasing a flat direct from HDB, though cheaper, has a longer wait time depending on how long the developer takes to build the new units. With Cash over Valuations (COVs) dropping in recent years, a resale flat seems within reach for first timers again. This analysis targets the HDB resale transactions which occurred in 2015 and first half of 2016 only.

Problem and Motivation

Buying a flat is a major commitment, and one does not want to fall into the trap of behaving irrationally, and thus making a wrong decision.

One will look at time fluctuation of the resale flat prices in 2015 and first half of 2016, for price is always a major consideration in being able to service the housing loan without getting an indebtedness situation. In addition to that, it has been commonly quoted that when it comes to a property purchase, it is always about “Location, location and location”. In this analysis, besides analyzing price and location, one will also look at other attributes of the resale flat such as location, age, size, design and height of flat.

Through this analysis, it is hoped that one who is looking for a home currently is able to make an informed and rational decision for oneself.

Approaches

One aims to look at the time-series price fluctuations, and to examine the influencing attributes. In this visualization work, the following document the approaches.

Data Source

The data sets available at Data.gov.sg are explored. In narrowing down, specifically, the “resale-flat-prices-based-on-registration-date-from-march-2012-onwards” data set is used. There are 10 variables in this data set of 79,650.

Data Preparation

The data set is reviewed for missing or incomplete data. It is noted that Storey Range in the data set has some overlaps. However, there is no other source of data available to re-bin them without overlapping ranges. This could be due to how HDB had organized them with passing time. General analysis could still proceed without major impediments.

There are 2 variables which were transformed:

month - This variable holds the string of numeric year-month. A new variable, Year, is calculated for the correct year and month format, using DateParse function.
lease_commence_date - This variable holds the string of numeric year. A new variable is calculated for the correct year format, using DateAdd function.

General Price & Transaction Observations

The following 2 diagrams shows the number of resale transactions across the months and the price distributions in 2015 and the first half of 2016,

One observes that the number of resale transactions tends to taper of during beginning and end of the year.
One also observes the right skewness of the distribution of prices across the transactions, and the price of resale flat range from 200K to slightly over $1Mil

General Price Trending

The following diagram shows the minimum, average, and maximum price trending across 2015 and the first half of 2016.

One observes that the average price movement is generally flatter than the minimum and maximum price movement. There do not appear to be any correlation between minimum price and maximum price

One uses line charts across time, and stack them vertically to allow comparison of the movement of prices.

One also encodes the quarterly values (in 2015), and 2-monthly values (in 2016) for better visualization.

Note: The average price is used here for comparison, as it was checked during visual exploration that the median and average prices are close in range.

The following 2 diagrams show the price trending across 2015 and the first half of 2016 in various towns and flat types.

One observes that towns such as Bukit Timah, Central Area, and Marine Parade have greater price fluctuations, and their average resale prices do hit beyond 700K.
One uses line charts across time, and uses the color marks to distinguish between each town to allow comparison.
One also encodes the peak values for better visualization.

Location, Transaction & Price

The following diagram shows average resale prices across each town in 2015 and first half of 2016.

One observes that Bukit Timah, Central Area, and Bishan are the top 3 towns which sees the highest average prices. Marine Parade which was 4th in 2015, was replaced by Queenstown in the 4th position in 2016.
One also observes that the average resale prices generally drops comparing across the 2 years.
In addition, though Bukit Timah sees the highest average resale price, it is the Central Area which sees the maximum resale price transaction.
In this instance, bar charts of average resale prices are used to make categorical comparisons across the towns. For maximum resale price, the colour palette of blue-teal is used to highlight the town with the highest transaction prices.