(Photo credit: Wikipedia)
Date of Analysis: 27 January 2020
Period of data: Jan 2017 to Jan 2020
Number of transactions analyzed: 295
(transaction data extracted from URA website)
District 25 is one of the districts within the OCR (Outside of Central Region) of Singapore. It comprises of few neighbourhoods such as Woodlands and Admiralty. Some of the private properties in this region are Parc Rosewood and Woodhaven etc. There aren't any new condominium projects in this district in the recent years. In fact, there are only 295 private property (condominium) transactions in this district in the past 3 years. This is really a low number as compared to the other districts. D19 has almost 23-24 times the amount of transactions in the same time period. It's also interesting to note that Woodlands has the most affordable HDB resale flats in whole of Singapore (more of that will be discussed in a separate post)
How do the private properties in D25 generally fare? Using box plots, here are the details for each of the properties in D25.
That's it. Only 7 projects.
To help you better understand the data, I will use Rosewood Suites as an example here. From the diagram, you can see that
Average price- $750 psf
Median price- $793 psf
Price at 25th percentile- $665 psf
Price at 75th percentile- $814 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 D25 is Parc Rosewood with an average price of $1090 psf while the most affordable condominium in D25 is Woodgrove Condominium with an average price of $665 psf. In most districts, a $psf of around $1000 tends to represent the more affordable range of private condominiums. In fact, the upcoming Parc Canberra EC is already around this price and bear in mind it is an executive condominium, not a private condominium. Hence the prices of private condominiums in D25 are really very affordable.
Parc Rosewood is a 99-year property which was completed in 2014. It does not enjoy good proximity to any MRT stations. However, it is within walking distance to some of the schools such as Innova JC and Singapore Sports School.
Let's take a look at the various scatter plots to have a better insight of how the property prices perform across 295 transactions in the past 3 years.
Not too many transaction points ah? Unlike most of the other districts which have graphs full of transaction points.
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 an alarming -0.12. So far, D25, D22 and D16 are the only districts which I have seen the $psf drops in this period of 3 years. Similar to these districts, the lack of new projects in the any district tend to cause the $psf to have a decreasing trend. So D25 is also no exception.
Based on the graph above, 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 $900 psf in Feb 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 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 D25.
The top performing project from the graph is Rosewood. Please do not confuse this with Parc Rosewood. Rosewood is a 99 year leasehold project which TOP in 2003. Location wise, it is also not near to any MRT station within a comfortable walking distance though it is still nearer to Woodlands MRT as compared to Parc Rosewood.
Next, how do freehold perform against leasehold during this 3 years period?
There are no freehold condominium projects in D25.
Also, how about apartments of various sizes? How do they perform against each other?
As expected, not too well generally. The $psf for most of the apartments sizes either stagnates or drops, with apartments of sizes less than 500 sqft (usually the studio or 1 bedder) being the only exception. Even then, it does not have a significant increase in $psf as shown in the graph above.
What you have seen above are largely data insights that we have derive using the various data science tools. But, what if we could actually use these insights to build machine learning model to attempt to predict the prices of the properties in D25 and understand if the prices the seller is asking for is reasonable? How could we do that?
We could try various different machine learning models to attempt to do so. Some examples of such machine learning models we could use are random forest and linear regression. They are methods which we could generally use to apply regression techniques to attempt to construct a linear relationship between price and various other variables (in this case, it will be project name, date of sales, size of flat etc). What we ultimately try to construct is a predictive model which allows us to have the highest confidence in prediction by attempting to reducing as much prediction errors as possible (think about Mean Absolute Error and Root Mean Squared Error)
If you are already feeling confused at this point of time, don't be as these information are highly technical in nature. You may read up more about them if you want to. Otherwise, I believe the information above in the box plots and scatter plots are more than enough for you to better understand the property prices in D25. I will also attempt to explain or illustrate more of this in a separate post in the future.
Running through all 295 transactions (little but that's all we have) through several machine learning models, I eventually achieve a model which provides me with suitable evaluation results (MAE of 33152, RMSE of 53220 and R2 of 0.951).
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: Rosewood Suites
Area: 710 sqft
Floor level: NA
Running through the machine learning model which I have created, the price I have obtained is $703,920 which is slightly more but quite close to the asking price of $690,000. The asking price is thus deem to be reasonable in this perspective. But of course, more investigation will also be needed to look at other factors beyond these parameters.
Of course, the above example is just a glimpse of what is achievable as you could actually use it to determine a lot more property prices in the region. In the future, I will also consider uploading this machine learning model online so you could actually use it to determine/predict property prices based on this model. But that's a story for another day.
Now, with these data in mind, go be a data science investor!
Psst.. If you like what you read, please scroll down and subscribe for regular updates!