Buying a private property in District 2

Updated: May 17


(Photo credit: Wikipedia)


Date of Analysis: 22 April 2020

Period of data: Apr 2017 to Apr 2020

Number of transactions analyzed: 798

(transaction data extracted from URA website)


This is part of an ongoing series "Singapore Private Condominium Guide". Please refer to the link for analysis on the other districts.


District 2 is one of the districts within the CCR (Core Central Region) of Singapore. It comprises of few areas such as Anson Road, Shenton Way and Tanjong Pagar. Some of the private properties in this region are Icon, Skysuites@Anson and Spottiswoode Residences etc. Some of the new launches in this district in the recent years are Sky Everton, Spottiswoode Suites and Wallich Residence. Similar to D1, D2 is also a business district and hence there aren't many residential apartments in this district. The number of transactions in this district for the past 3 years is just a mere 798.


How do the private properties in D2 generally fare? Using box plots, here are the details for each of the properties in D2.


More box plots of other condominiums in this district (together with all the other districts) could be unlocked when you become a patron (https://www.patreon.com/datascienceinvestor)



To help you better understand the data, I will use Sky Everton as an example here.

From the diagram, you can see that


Average price- $2557psf

Median price- $2548 psf

Price at 25th percentile- $2498 psf

Price at 75th percentile- $2637 psf


Box plot is generally a good way to present the data. In this case, you can easily see the average price, median price, price at 25th percentile and price at 75th percentile from the plots. You could also easily tell at one glance how wide the spread of prices are for any of the condominium projects.


The metric used here is $psf as it is a common indicator to reflect property prices.


The most expensive condominium in D2 is Wallich Residence with an average price of $3427 psf while the most affordable condominium/apartment in D2 is Spottiswoode Park with an average price of $802 psf. Wallich Residence is a 99 year leasehold condominium which was completed in 2016. The location for Wallich Residence is unparalleled. It is housed in the tallest building (Tanjong Pagar Center) in Singapore, and is seated right above Tanjong Pagar MRT Station. If you are living right above Tanjong Pagar MRT station, you are probably residing in the heart of Singapore. I guess there is no further need to introduce how well located Wallich Residence is. And this probably explains its hefty price tag.


Spottiswoode Park is a 99 year leasehold apartment which was completed in 1977. Despite it being old, Spottiswoode Park could potentially still be attractive to some buyers as it is located near to Tanjong Pagar Plaza and Tanjong Pagar Market and Food Centre. Nearby MRT stations like Outram Park MRT station is only a 7 minutes walk away.


Let's take a look at the various scatter plots to have a better insight of how the property prices perform across 798 transactions in the past 3 years.

First, a scatter plot of the $psf against date.


In scatter plot, we could derive r coefficient, which is used to explain the strength of the linear relationship between 2 variables. Since we are using $psf and date as the variables, r coefficient allows us to better understand how the $psf changes with time. To some extent, if the r coefficient is high, we could roughly assume that the $psf increases positively with time.

The r coefficient (or much simply/loosely put, the gradient for the line of best fit) in the scatter plot above is 0.24, which goes to show a good increment of $psf over the past 3 years. Such a value is probably above the average as compared to many other districts I have analysed thus far.


So, which projects perform remarkably well comparatively amid the general decline in the district in the past 3 years?

The plot above shows a myriad of lines of best fit from various different projects in D2.


Unlike most of the other districts, the performances across different projects in D2 are not too different from each other. This is pretty unique as you will usually see distinct groups of "winners" and "losers" in each of the districts. In D2, you don't see too much of a difference.


One of the top performing projects from the graph above is Spottiswoode Park and I have already covered this project in the earlier part of the article. Despite its excellent location, Wallich Residence did not enjoy too much of a price increment in terms of $psf over the years. So this goes to show not all well located projects are guaranteed to see good price movement. I have to admit though more years of data will probably be needed to validate this as we are only utilising 3 years of data here.


Next, how do freehold perform against leasehold during this 3 years period?

I have only included freehold transactions in this plot and you could see that the r coefficient of 0.53 is much higher to the r coefficient of 0.24 for the scatter plot with all transactions. At first glance, you might think that this means that the freehold properties in D2 are distinctly better buys as compared to leasehold properties in the same district with regards to price appreciation. However, we have to take note that there is a huge number of transactions for Sky Everton last year and that has unavoidably skewed the results due to its higher $psf.


In the longer term though, this will probably also benefit the price movement of the other freehold properties in the district.


Also, how about apartments of various sizes? How do they perform against each other?


Apartments of all sizes all show a positive r coefficient. Hence, they are all on a positive trend. Apartment with sizes greater than 1500 sqft has the greatest r coefficient, but there are also very few transactions for apartments of such sizes. Thus, this might not be too statically meaningful. If we were to exclude that, apartments of sizes between 1000 and 1500 sqft (usually the 3 bedders) will be the top performer in terms of growth for $psf.

For my regular readers, you will know that this is where I will briefly talk about the various different machine learning models and attempt to apply my machine learning model to determine a fair value for a certain property listing on PropertyGuru. If you have not read about this before, you may just refer to any of the district analysis I have done in my previous articles and you should be able to find it.


For the benefit of the regular readers, I'm going to remove the chunk of text and go straight to the analysis. Like mentioned in the earlier articles, I will talk more about these machine learning models and will probably do so when I have finished analyzing all 28 districts in Singapore.


Running through all 798 transactions through several machine learning models, I eventually achieve a model which provides me with suitable evaluation results. (MAE of 180789, RMSE of 410230 and R2 of 0.999).


I then now try to put this machine learning model to practice and use it to determine what should be a reasonable price for the following property.


https://www.propertyguru.com.sg/listing/21931353/for-sale-wallich-residence


Project: Wallich Residence

Area: 915 sqft

Floor level: High Floor (assume to be 11 to 15)


Running through the machine learning model which I have created, the price I have obtained is $3,597,115 which is not too different from the asking price of $3,418,000. This asking price of $3,418,000 translates to a $psf of $3735 which is above the 75th percentile pricing ($3692 psf) based on the transactions pertaining to Wallich Residence in the past 3 years. While the price might be reasonable from the model's perspective, it is also worthwhile to explore a bit more as such pricing is on the higher end of the spectrum for $psf transacted. More investigation will also be needed to look at other factors (such as flat orientation and renovation status etc) beyond the parameters such as floor level, area size and project name.


Now, with these data in mind, go be a data science investor! #datascienceinvestor

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Refer here for analysis on the other districts!


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