# Buying a private property in District 19

Updated: 3 days ago

(Photo credit: Wikipedia)

**Date of Analysis: 6 January 2020**

**Period of data: Jan 2017 to Dec 2019**

**Number of transactions analyzed: 6989**

(transaction data extracted from URA website)

District 19 is one of the districts within the OCR (Outside of Central Region) of Singapore. It comprises of few neighbourhoods such as Hougang, Punggol and SengKang. Some of the private properties in this region are **Jewel@Buangkok**, **Kovan Melody **and **Parc Vera **etc. Recent new properties in the area are plenty (eg **Sengkang Residences**, **The Florence Residences**,** Riverfront Residences**,** Affinity at Serangoon**,** The Lilium** and **The Gazania)**

That also explain the huge number of transactions which happened in the past 3 years. With a volume of nearly 7000 transactions in the past 3 years, it's more than twice the number of transactions in __D9__, more than thrice the number of transactions in __D13____,__ and more than four times the number of transactions in __D20__!

How do the private properties in D19 generally fare? Using __ box plots__, here are the details for each of the properties in D19.

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

Average price- $850.2 psf

Median price- $841 psf

Price at 25th percentile- $705psf

Price at 75th percentile- $992 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 D19 is **The Lilium** with an average price of $2134 psf while the most affordable condominium in D19 is **Florence Regency **with an average price of $648.8 psf. **The Lilium** is a freehold property which was launched in 2019 and expected to TOP in 2025. Considering it's in a OCR region, I will say that **The Lilium** has a really high price point.

Now, let's take a look at the various __ scatter plots__ to have a better insight of how the property prices perform across 6989 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 0.38. This means that the $psf in D19 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 $1300 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 D19.

One of the top performing project from the graph above is **The Tembusu**. **The Tembusu **is a freehold project which TOP in 2017. This result is not surprising as **The Tembusu **is within 5 minutes walk to Kovan MRT and has also won awards for its environmentally conscious features.

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 0.09 is much lower to the r coefficient of 0.38 for the scatter plot with all transactions. This means that the freehold transactions in D19 might not be good buys generally.

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

Apartments of all different sizes generally shows an uptrend in $psf over the past 3 years. Apartment with size less than 500 sqft (usually studio or 1 bedder) performs the best among all the apartments of various sizes as it has the highest r coefficient of 0.46.

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 D19 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 D19. I will also attempt to explain or illustrate more of this in a separate post in the future.

Running through all 6989 transactions through several machine learning models, I eventually achieve a model which provides me with suitable evaluation results (MAE of 139692, RMSE of 3512733 and R2 of 0.917).

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/21863772/for-sale-jewel-buangkok__

Project: Jewel @ Buangkok

Area: 732 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 __$900,000__ which is lesser than the asking price of $1,100,000. This might suggest there is some room for negotiation. 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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