(Photo credit: Wikipedia)
Date of Analysis: 10 January 2020
Period of data: Jan 2017 to Dec 2019
Number of transactions analyzed: 807
(transaction data extracted from URA website)
District 22 is one of the districts within the OCR (Outside of Central Region) of Singapore. It comprises of few neighbourhoods such as Boon Lay, Jurong and Tuas. Some of the private properties in this region are The Lakefront Residences, Parc Oasis and The Mayfair etc. There aren't many new launches in D22 recently, with the latest new launch probably the Lake Grande which is supposed to TOP in 2020. Also, the number of transactions in D22 is amazingly low- only 807 transactions. In the same period, D19 has almost 8 to 9 times the number of transactions!
How do the private properties in D22 generally fare? Using box plots, here are the details for each of the properties in D22.
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 Parc Oasis as an example here. From the diagram, you can see that
Average price- $870.6 psf
Median price- $872 psf
Price at 25th percentile- $828psf
Price at 75th percentile- $910 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 D22 is Lake Grande with an average price of $1317.5 psf while the most affordable condominium in D22 is Lakeside Tower with an average price of $557.4 psf. It's interesting to note that the Lake Grande with an average price of $1317.5 psf is already the most expensive condominium. This is much lesser than all the other districts which I have analyzed so far. Now, isn't this ironical so far? Everyone has been saying Jurong area is the next major development area in Singapore. However, it seems to also be the most affordable district so far.
Now, let's take a look at the various scatter plots to have a better insight of how the property prices perform across 807 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 -0.07! The first negative r coefficient for all the districts which I have analyzed so far! This means that $psf in D22 has not been growing and is relatively flat over the past 3 years! Just take a minute to absorb this!
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 $1100 psf in Oct 2018. 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 D22.
Some of the top performing projects from the graph above are Parc Vista, Lake Grande and J Gateway. Parc Vista is a 99 years leasehold project which has TOP in 2000 and yet has one of the best performances in the district. Perhaps, being near to Rulang Primary School do increase its attractiveness to potential buyers looking for a property in D22.
Next, how do freehold perform against leasehold during this 3 years period?
Nothing.
I'm not kidding you. There is no freehold condominium projects in D22. Thus, there is nothing to compare here.
Also, how about apartments of various sizes? How do they perform against each other?
There's really not too much differences between the apartments of various sizes here as they all perform relatively bad (either flat or on slight decline).
So is D22 a good district to invest in with all the hype around it?
You be the judge!
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 D22 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 D22. I will also attempt to explain or illustrate more of this in a separate post in the future.
Running through all 807 transactions through several machine learning models, I eventually achieve a model which provides me with suitable evaluation results (MAE of 28305, RMSE of 45460 and R2 of 0.965).
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: Parc Vista
Area: 1076 sqft
Floor level: Low Floor (assume to be 01 to 05)
Running through the machine learning model which I have created, the price I have obtained is $864,821 which is quite similar to the asking price of $888,888. This might then suggest that this is a fair value to pay for this property. 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!
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