# Buying a private property in District 20

Updated: a day ago

(Photo credit: Wikipedia)

**Date of Analysis: 3 January 2020**

**Period of data: Dec 2016 to Dec 2019**

**Number of transactions analyzed: 1603**

(transaction data extracted from URA website)

District 20 is one of the districts within the RCR (Rest of Central Region) of Singapore. It comprises of few neighbourhoods such as Ang Mo Kio, Bishan and Thomson. Some of the private properties in this region are **Centro Residences**, **Rafflesia Condominium **and **The Panorama **etc. Recent new properties in the area are **Jadescape** and **Lattice One.**

How do the private properties in D20 generally fare? Using __ box plots__, here are the details for each of the properties in D20.

To help you better understand the data, I will use **Thomson Plaza** as an example here. From the diagram, you can see that

Average price- $548 psf

Median price- $548 psf

Price at 25th percentile- $498psf

Price at 75th percentile- $598 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 D20 is **Lattice One** with an average price of $1770.6 psf while the most affordable condominium in D20 is **Thomson Plaza **with an average price of $548 psf. **Lattice One** is a freehold property which was launched in 2019 and expected to TOP in 2022. Considering it's a freehold and in a RCR region, Lattice One seems to be priced rather competitively.

Now, let's take a look at the various __ scatter plots__ to have a better insight of how the property prices perform across 1603 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 0.37. This means that the $psf in D20 is enjoying a healthy increment over the past 3 years.

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 $1450 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 D20.

One of the top performing projects from the graph above is **The Panorama**. **The Panorama **is a 99 years leasehold project which has recently TOP in 2019. It's in rather strategic location, being near to Ang Mo Kio and Yio Chu Kang MRT stations, and within short walking distance to Mayflower station when it opens in 2020.

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 0.35 is quite comparable to the r coefficient of 0.38 for the scatter plot with all transactions. This means that the freehold transactions actually perform similarly to the leasehold properties in D20 and might not actually worth the premium.

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

The results are rather shocking. Apartments with size less than 500 sqft actually suffer a decrease in $psf over the past 3 years (this is similar to __D20__)! That's really a shock as you will generally expect $psf to increase over the years. Granted you might not expect a big increase, but you certainly do not expect a decrease! This might suggest that 1 bedder/studio apartment in D20 might not be ideal investments. Apartments of other sizes have rather similar positive trend lines, with apartments of size between 1000 and 1500 sqft (usually 3 bedders) performing the best.

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 D20 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

__. 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__

**linear regression****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 D20. I will also attempt to explain or illustrate more of this in a separate post in the future.

Running through all 1603 transactions through several machine learning models, I eventually achieve a model which provides me with suitable evaluation results (MAE of 954129, RMSE of 147268 and R2 of 0.926).

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/22045431/for-sale-rafflesia-condo__

Project: Raffesia Condominium

Area: 915 sqft

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

Running through the machine learning model which I have created, the price I have obtained is __$1,041,390__ which is quite similar to the asking price of $1,080,000. 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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