(Photo credit: Wikipedia)
Date of Analysis: 1 March 2020
Period of data: Feb 2017 to Feb 2020
Number of transactions analyzed: 790
(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 4 is one of the prime districts within the RCR (Rest of Core Central Region) of Singapore. It comprises of few neighbourhoods such as Harbourfront and Telok Blangah. Some of the private properties in this region are Reflections at Keppel Bay and The Interlace etc (there are quite a few iconic condominiums in this district). There are no new launches in this district for the past few years, hence the little number of transactions you see here. In fact, the number of transactions in this district is even lesser than that of D22 (Boon Lay, Jurong and Tuas).
How do the private properties in D4 generally fare? Using box plots, here are the details for each of the properties in D4.
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 Skyline Residences as an example here. From the diagram, you can see that
Average price- $1835 psf
Median price- $1866 psf
Price at 25th percentile- $1646 psf
Price at 75th percentile- $1990 psf
I personally think that box plot is 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 tell at one glance how wide the spread of prices are for any of the condominium projects. Pretty neat, I will think.
The metric used here is $psf as it is a common indicator to reflect property prices.
The most expensive condominium in terms of average price in D4 is Corals at Keppel Bay (avg price of $2080 psf) and the most expensive condominium in terms of median price in D4 is Seascape (median price of $2028psf). Both provide stunning views with the Corals at Keppel Bay overlooking the sea and Seascape being at the edge of Sentosa cove.
The most affordable condominium in D4 is Teresa Ville with an average price of $1094 psf. This is considered a bit higher than usual as the most affordable condominium in most other districts are usually less than $1000 psf. Teresa Ville is a freehold condominium which TOP in 1986. Location wise, it's not too fantastic with the nearest MRT station (Tiong Bahru MRT station) being a 2 to 3km away (which usually not a walkable distance for most)
Now, let's take a look at the various scatter plots to have a better insight of how the property prices perform across 790 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 0.13. This indicates that the $psf is increasing with time in the past 3 years, albeit not a huge one.
From this line of best fit, you could also better understand if you are "over-paying" for your property purchase (eg. if you property is above the line of best fit). Taking a quick glance at the scatter plot, your transaction will be on the high side if you are paying more than $1600 psf in May 2019. Of course, there could be many factors such as location, tenure etc that could influence your buying price. This is still a general assumption.
So, which projects perform remarkably well comparatively in the past 3 years?
The plot above shows a myriad of lines of best fit from various different projects in D4.
2 of the top performing projects from the graph above are Corals at Keppel Bay and The Interlace. So not only is Corals at Keppel Bay the most expensive condominium in D4, it's also one of the top performing ones.
The Interlace probably needs not much introduction as most of us will remember the project as the "uniquely shaped" project. Well, in this case, being "uniquely shaped" can have a positive effect as The Interlace is one of the top performing projects. The Interlace is a 99 year leasehold project which TOP in 2013. Location wise, The Interlace is fantastic if you are working in Hewlett Packard Enterprise. Otherwise, it's not really a convenient location if you are relying on public transport as it's right in the middle between the stations on the East West Line (Redhill, Queenstown) and stations on the Circle Line (Pasir Panjang, Labrador Park)
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 0.12 is almost the same as the r coefficient of 0.13 for the scatter plot with all transactions. This means that the freehold properties in D4 perform quite similarly to the leasehold properties in D4 in terms of $psf over the past 3 years. Key to note though is that there aren't too many freehold condominiums in D4, resulting in not too many freehold transactions as illustrated above.
Also, how about apartments of various sizes? How do they perform against each other?
There are too little transactions for apartments with sizes of less than 500 sqft, hence I think it's not statically meaningful to derive anything out of it with the exception that there are really little bedrooms with less than 500 sqft in D4. Among the apartments of other sizes, apartments with sizes between 500 and 1000 sqft performs the best. This usually represents the 2 bedders.
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 790 transactions through several machine learning models, I eventually achieve a model which provides me with suitable evaluation results (MAE of 339112, RMSE of 638178 and R2 of 0.817).
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.
Project: Reflections at Keppel Bay
Area: 1210 sqft
Floor level: Middle Floor (assume to be 06 to 10)
Running through the machine learning model which I have created, the price I have obtained is $1,809,253 which is almost the same as the asking price of $1,830,000. Considering the $psf requested over here is $1512.40psf which is less than 25th percentile of the $psf for the transactions pertaining to Reflections at Keppel Bay in the past 3 years coupled with the fact that it's almost the same value as what I have obtained from my model, this asking price might be worthwhile to consider or look at. Please bear in mind though other facts such as renovation status, flat history etc are not taken into consideration here and they could also have an effect on the "fair price".
Now, with these data in mind, go be a data science investor! #datascienceinvestor
Refer here for analysis on the other districts!
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