Jabodetabek — House Pricing and Venues Analysis
By: Havez Vazirani Al Kautsar
Tags: K Means Clustering, Unsupervised Learning, House Prices, Data Science, Jabodetabek

Description & Disscusion of the Background

Jakarta is the capital and largest city of Indonesia. Situated on the northwest coast of the world’s most populous island of Java, it is the centre of economy, culture and politics of Indonesia with a population of more than ten million as of 2014. Albeit only covers 699.5 square kilometres, the smallest among any Indonesian provinces, its metropolitan area covers 6,392 square kilometres; it is the world’s second most populous urban area after Tokyo, with a population of about 30 million as of 2010. Jakarta’s business opportunities, as well as its potential to offer a higher standard of living, have attracted migrants from across the Indonesian archipelago, making it a melting pot of numerous cultures. The city proper has a very high population density of 14,464 people per square kilometer, while the metro area has a density of 4,383 people/sq km. [1]

Not only Jakarta residents, Jakarta is filled with workers from outside Jakarta every day, so it is often called Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi) or Greater Jakarta area. Lots of public transportation makes it easy for these workers to go to Jakarta even though the distance is quite far adrift. Therefore, there are a lot of investors who have started investing in the Jabodetabek area, and this study will try to explore parts of Jabodetabek related to relatively low property prices with low intensity of business competition.

When we consider all of these problems, we can create a map and information chart where the house price index is placed on Jabodetabek and each district is clustered according to the venue density.

Data Description

To consider the problem we can list the datas as belows

I used Forsquare API to get the most common venues of given cities of Jabodetabek [2].

to get the most common venues of given cities of Jabodetabek [2]. There are not too many public datas related to demographic and social parameters for the cities of Jabodetabek. Therefore you must set-up your own data tables in most cases. In this case, I collected latest House Price Averages for each Cities of Jabodetabek from housing retail web page [3].

Methodology

My master data df which has the main components Cities, and Average House Price informations of the cities.

I used python folium library to visualize geographic details of Jabodetabek and it’s cities and I created a map of Jabodetabek with cities superimposed on top. I used latitude and longitude values to get the visual as below:

With foursquare API’s, i tried to explore what and how many venues that exist in the cities of Jabodetabek. I limit the radius of 10 km for each city in Jabodetabek, Foursquare API also only limits 100 venues for each coordinate.

Then we can create a bar chart and analyze the number of venues of each cities.

Here is we can see that 8 of 9 cities reached the 100 limit of venues. On the other hand; Tangerang is below 60 venues in our given coordinates with Latitude and Longitude.

The result doesn’t mean that inquiry run all the possible results in cities. Actually, it depends on given Latitude and Longitude informations and here is we just run single Latitude and Longitude pair for each city. We can increase the possibilities with Neighborhood informations with more Latitude and Longitude informations.

There are 144 uniques categories of venue on Jabodetabek by Foursquare, then I created a table which shows list of top 10 venue category for each borough in below table.

We have some common venue categories in cities. In this reason I used unsupervised learning K-means algorithm to cluster the boroughs. K-Means algorithm is one of the most common cluster method of unsupervised learning. I will use K-Means algorithm for my study in this project.

I use the elbow method to gain the proper k for clustering.

Based on the figure above, I run K-Means to cluster the cities into 3 clusters and create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood.

We can also estimate the number of 1st Most Common Venue in each cluster.Thus, we can create a bar chart which may help us to find proper label names for each cluster.

From the result above, coffee shops are very common in Jabodetabek even though it does not always in the 1st place of the most common venue but it makes at least 2nd place on Jabodetabek (coffee shop in cluster 1 is in the 2nd place of the most common venue).

When we examine above graph we can label each cluster as follows:

Cluster 0 : “Coffee Shop and Indonesian Restaurant Venues”

Cluster 1 : “Indonesian Restaurant Venues”

Cluster 2 : “Coffee Shop Venues”

Then I analyze the housing sales prices for per square meter in specific range. Thus we can create new labels which involve pricing features, as well.

We can examine that what is the frequency of housing sales prices in different ranges.

As it seems in above histogram, we can define the ranges as below:

‘<’ 10,000,000 AHP : “Low Level HSP”

10,000,000–20,000,000 AHP : “Mid Level HSP”

‘>’ 20,000,000 AHP : “High Level HSP”

Then wecan add a label to my previous dataframe.

One of my aim was also show the number of top 3 venues information for each city on the map. Thus, I grouped each city by the number of top 3 venues and I combined those informations in Join column.

Results

Let’s merge those new variables with related cluster informations in our main Jabodetabek_merged table.

You can now see Join, Labels and Level_labels columns as the last three ones in above table.

Finally, let’s visualize the resulting clusters

Conclusion

As a result, people are turning to big cities to start a business or work. For this reason, people can achieve better outcomes through their access to the platforms where such information is provided.

Depok is considered as one of the cities with the lowest average housing prices, and Depok is in a cluster of venues that are different from other cities in Jabodetabek. So there are still good opportunities for investors to invest in Depok by bringing trends from other Jabodetabek areas. As an example of investing in the coffee shop area in Depok, considering that the venue is most common in Jabodetabek.

However, this can be studied more deeply considering the limitations of the Foursquare API which is only 100 venues per city, so investors and stakeholders will get a bigger picture related to the potential of Jabodetabek.

Not only for investors but also city managers can manage the city more regularly by using similar data analysis types or platforms.

References