Singapore apartment resale prices analysis
By: Luis Conti
Tags: Data Analysis, Python, Udacity, Singapore

Photo by Swapnil Bapat on Unsplash

This blog post is part of the Udacity Data Scientist Nanodegree Program. Full analysis, as well as the Python code used for this purpose, can be found in my GitHub repository.

Introduction

Singapore is known to be one of the most expensive cities in the world, especially regarding apartment prices. The city-state has a heavily regulated property market, and as a consequence there is a lot of information about apartment resale prices in the official website of the Singaporean Government. For this project, a database from this website was selected.

The aim of this blog, and of the related analysis, is to provide answers to the following questions:

Which is the district of Singapore with the highest average apartment resale price per square meter?

Apart from the district, which other features significantly affect the resale price?

Which is a reasonable resale price for an apartment in Singapore with specific characteristics?

Question 1: Which area of Singapore has the highest average apartment resale price per square meter?

Singapore is divided into different districts. Apartment resale prices vary significantly from one district to another. Therefore, it would be interesting to have a look at the average prices per square meter, and compare between different districts:

It is not surprising at all that the highest price per square meter (over $7000 per square meter) is found in Singapore Central Area, followed by Queenstown with a price per square meter almost $1000 below. On the other hand, districts of Choa Chu Kang and Woodlands have the lower average prices, with values barely above $3500 per square meter.

The comparison between the average resale prices, not divided by the floor area of the apartment, could provide misleading results and could lead us think that the most expensive district is Bukit Tiham:

But it is clear that the price of the apartment does not depend only on the district. The size of the apartment is another important feature (if not the most important) to take into account when evaluating the price of an apartment. The differences found out between apartment prices and prices per square meter can be explained by the differences in the average size of the apartment between different districts:

As many cities and towns around the world, apartments in the Central Area in Singapore are in general terms smaller, in comparison with the size of the apartment in other districts.

Question 2: Apart from the district, which other features significantly affect the resale price?

As observed beforehand, floor area seems to play an important role in the resale price of an apartment, something that is confirmed if we represent the price of all apartments of the dataset against their size:

We can also observe that the lineal relationship between floor area and price is significantly strong.

However, we are interested in finding out which other features have a strong influence on the apartment prices:

Apartment type: In the first box plot below it can be observed how the average price of the apartment increases with the number of rooms, with the highest average prices observed for “executive” and “multi-generation” apartments. However, this is observation is tightly related to the size of the apartment for each apartment type, as shown in the second box plot.

Apartment model: There is a wide variety of apartment models available for those living or willing to move to Singapore. Average prices are highly dependent on the apartment model, according to the box plot below. Therefore, the apartment model is an important factor to take into account at estimating the resale price of an apartment.

Month of sale: The dataset analyzed includes sales price from January 2017 to October 2020. Throughout this period, no significant change is observed in terms of average price or dispersion of prices. Therefore, it can be concluded that the date of sale has no strong influence on the apartment resale price. This parameter can be discarded for further analysis.

Lease commence date: The lease commencement date refers to the day when the tenant moves into the flat and starts paying the rent. As observed in the box plot below, it seems it has a considerable influence on the resale price of the apartments in Singapore. The general trend shows that, the latest the lease commencement date, the higher the average resale price. Particular events might have taken place in 2011, 2018 and 2019, resulting in higher prices (and higher price dispersion in 2011 as well) and whose reasons cannot be explained only with this plot.

Storey range: It comes as no surprise that apartments at higher floors were sold at higher average prices. This trend is common in those cities with breathtaking skylines and Singapore is not an exception, as can be observed in the box plot below.

Summarizing, the type of apartment and the storey range are the features with higher influence to the resale price of the apartments in Singapore, apart from the floor area (directly related to the apartment type). The lease commencement date also seems to play an important role on the price, whereas the date at which the apartment was sold, at least in the period from January 2017 to October 2020, does not reflect any significant impact on the prices.

In this context, it is important to point out that prices remained stable, and even slightly higher, since the beginning of the coronavirus pandemic in the first quarter of 2020 and the subsequent economical crisis.

Question 3: Which is a reasonable resale price for an apartment in Singapore with specific characteristics?

In order to predict the price of an apartment, a multiple linear regression model was built in Python. This model was applied to three aparments extracted from the dataset:

Apartments extracted from dataset for model assessment

The rows corresponding to these three apartments were removed from the dataset, in order to ensure that the model remains completely independent from them during its “fit” phase.

The multiple lineal regression model provided a r-square value of approximately 0.865 for both train set and test set, proving therefore that it can be a good tool for the prediction of the resale price of apartments in Singapore.

Prices predicted for the sample of three apartments showed an error lower than 5% with respect to the real resale price in all cases, corroborating the robustness and accuracy of the model.

Comparison between predicted resale price and actual resale price

Conclusion

From the dataset analysed it was possible to identify which features have a higher impact on the resale price of apartments and which ones barely affect it. Based on those with a higher influence on the price, it was possible to build a multiple lineal regression model that provides good r-square values and also a very accurate estimation of the resale price of a sample of 3 apartments, with an error lower than 5% in all cases.