Buying a private property in District 28

Updated: May 17


(Photo credit: Wikipedia)


Date of Analysis: 17 March 2020

Period of data: Mar 2017 to Mar 2020

Number of transactions analyzed: 1223

(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 28 is one of the districts within the OCR (Outside of Central Region) of Singapore. It comprises of few neighbourhoods such as Seletar and Yio Chu Kang. Some of the private properties in this region are Mimosa Park, H2O Residences and Seletar Springs Condo etc. There are not many new launches in this district, with the most recent one being Parc Botannia. Before Parc Botannia, there wasn't any new condominium project in D28 for the past 3 years with the Riverbank@Fernvale being the newest condominium project before Parc Botannia. Being a relatively large development with more than 700 units, Parc Botannia contribute to quite a number of transactions for the past 3 years.


How do the private properties in D28 generally fare? Using box plots, here are the details for each of the properties in D28.


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 Rivertrees Residences as an example here. From the diagram, you can see that

Average price- $1225psf

Median price- $1230 psf

Price at 25th percentile- $1159 psf

Price at 75th percentile- $1273 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 D28 is Parc Botannia with an average price of $1310 psf while the most affordable condominium in D28 is Seletar Springs Condominium with an average price of $717 psf. As described earlier, Parc Botannia is the latest 99 year leasehold condominium in D28 with an expectation to TOP in 2021. Location wise, it's 2 minutes walk away from Thanggam LRT station, which is 4 LRT stops away from Sengkang main station. Hence, I won't say it's too accessible to most places.


Seletar Springs Condominium is a 99 year leasehold condominium which TOP in 2001. Its location is kinda less than ideal if you don't drive, with the nearest LRT station (Layar) being a good 10 minutes away. After which, you will still need to continue your journey for 3 more LRT stations before your reach Sengkang MRT/LRT station.

Let's take a look at the various scatter plots to have a better insight of how the property prices perform across 1223 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 0.23. At the first glance, this means that the $psf in D28 sees a positive appreciation over time. However, it's also important to note the "dense amount of data on the upper region of the line". These transaction points are mostly for Parc Botannia. Hence, transactions for Parc Botannia might have heavily skewed the results, especially since it's a new built project and $psf for new built projects are usually higher.


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 D28.


2 of the top performing projects from the graph above are Mimosa Park and Sunrise Gardens. Mimosa Park is a freehold condominium which was built in 1979 (really a long time ago). There aren't many freehold condominiums in D28. The other one which is freehold is Serenity Park. In my opinion, Mimosa Park has a really bad location with no nearby MRT stations in the vicinity of 4 to 5km. Even getting to Yio Chu Kang MRT station requires a 12 minutes car ride. With the apartment being built so long ago and a much lesser than ideal location, I'm curious to know why Mimosa Park is one of the top performing projects in terms of $psf appreciation over the past 3 years. Serenity Park, which is also freehold, has a slightly better location and is newer (built in 1995) actually has less price appreciation in terms of $psf as compared to Mimosa Park. That really got me baffled.


Sunrise Gardens is a 99 year leasehold condominium which was built in 1998. Its location is bit better than Mimosa Park, but you still need roughly 8 minutes drive to the nearest MRT station (Yio Chu Kang). Generally, I find most of the condominiums in D28 having relatively bad locations as compared to the other districts. But maybe there is some appeal in the Yio Chu Kang area which I'm not aware of? Let me know if you know!

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 0.38. However, I will not pay too much attention to this as there are only two freehold condominiums contributing to this (Mimosa Park and Serenity Park). And the amount of data points available is also hardly meaningful here.


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

Not too surprising here. Apartments with size less than 500 sqft (usually the studio or 1 bedder) performs the worst. OCR districts which are usually a little "out of the way" tend to have this issue of having little or even negative price appreciation for studio or 1 bedder. Here is a good article from CNA in 2018 highlighting that investing in small apartments in OCR districts might be less than ideal due to low resale demand.

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 1223 transactions through several machine learning models, I eventually achieve a model which provides me with suitable evaluation results (MAE of 58214, RMSE of 87300 and R2 of 0.908).


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/22260613/for-sale-high-park-residences


Project: City High Park Residences

Area: 668 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 $840,393 which is less than the asking price of $850,000 . This asking price of $850,000 translates to a $psf of $1272 which is between the median ($1239 psf) and 75th percentile pricing ($1290 psf) based on the transactions pertaining to High Park Residences in the past 3 years. Thus, the asking price listed here might be a fair value. But of course, more investigation will also be needed to look at other factors beyond these parameters.


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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