How to select neighborhoods for your first rental property in Sydney (2023)

This post is a cut down version of full analysis paper for Terence's IBM Data Scientist Capstone Project. For detail data file used in this post, python code and project paper please go to link below:

https://github.com/tianruisun/IBM-Data-Scientist-Certification

Introduction

Description & Discussion of the Background

Sydney, capital of New South Wales and one of Australia's largest cities covers 12,367.7 sq km land and is made up of 35 local councils. As of June 2019, Sydney has a population of 5.73 million and spread into 658 suburbs. I have been living in Sydney almost 13 years now, with all the rental and investment experience I have gain over the years. I’m now consider myself quite familiar of the most popular neighborhood in Sydney and know which suburb is might be a good choice to live at different life stages. Every now and then, I have some new friends decide to move to Sydney from China. Most of the time, they come to consult me on where should they rent when they first move to this country. Well, this might sound like a simple question, however each new comer actually always has different needs and preference on their dream property and the recommendation needs to vary depend on what feature they care the most such as: rental price, location,, quality or feature of the property, community.

For this paper, we will be mainly analysis 3 factors: rental price, neighborhood safety and available amenities in the area to see how we can use data science to help our new comer to select their dream property when they first move to Sydney.

Data Description:

For this analysis, I have collected quite a few data via the internet which most of them are publicly available for download. Detail of these data set and samples will available in paper and python project

NSW Communities & Justice provide Rent and Sales Report

NSW Neighborhoods Geo Coordinate Data

Sydney Postcode and Suburb Mapping

NSW Bureau of Crime Statistics and Research Monthly Criminal Incidents Report

Foursquare API Venues data

Methodology

For this analysis, I will be focus only on 2-bedroom and touch a bit on 3-bedroom apartment in Sydney as these are the most popular rental property types and we have most comprehensive data for most of neighborhoods in Sydney. An exploratory data analysis will come first and give us an idea of how Sydney rental market look like. Then we will be using unsupervisedclustering algorithm to identify other alternative neighborhoods which have similar venue types and have similar security rating and rental as well to give more options for us to choose.

Exploratory data analysis:

Average rental price by Suburb:

I start with get a general idea of how Sydney rental price looks like for 2/3 bedrooms apartment.As we can see, the median rent for 2 bedrooms apartment in Sydney is around 450$ per week. The region has highest average rental price will be central area which around 700$ per week and the lowest will be out Western Sydney region which is more than 30km away from central and the average medium rental price is around 330$ per week. Similar trend shows in the 3 bedrooms’ apartment rental price as well. However instead of 500$ rental per week, for 3 beds the average rental is around 650$ Sydney wide.The highest area in Sydney for 3 beds is Northern beach area which almost approaching to 1000$ per week mark.

How to select neighborhoods for your first rental property in Sydney (1)

Which area in Sydney are popular among tenants?

According to the total bonds' statistic, the most popular area in Sydney amount tenants is Central as expected. Although it has the highest average rental prices, seems to me location is definitely a very important factor when people selecting the place to rent.Also, from below chat, we can see, 2-bedroom apartments are definitely more popular than 3 bedrooms. The total bond of 2 bedrooms as of September 2019 is almost 15 times of 3 bedrooms bond in Sydney.

To explore a bit more, I have listed the top 10 most popular suburb by rental price in Sydney.This time we can see the top 2 suburbs are Parramatta and Liverpool (western region and south region) instead of central.Both of them have a relative lower rental price. (For people who never been in Sydney, these 2 areas have very good transportation systems, excellent facilities such as large shopping mall and commercial area.) To my surprise, the third popular suburb is actually Bondi, which is on eastern coastal. Given it close to the most famous Beach in Sydney (Bondi beach). The average rental price in this area for 2 beds is close to 800$ per week.

How to select neighborhoods for your first rental property in Sydney (2)

How is neighborhood rental price movement look like in 2019?

To let audience directly have a visual on how that look like in Sydney, I have plotted the rental movement onto Sydney map using Python Folium.Lime color is more than 10% increase YTD, orange color is for anything decrease and red color is more than 10% decrease YTD. It is quite clear that over all Sydney rental price has a decrease trend in 2019 in particular around out west region. The highest growth suburb is South Huntsville which have 11% increase on rent.

How to select neighborhoods for your first rental property in Sydney (3)

How is neighborhood safety look like in Sydney?

I also look into the neighborhood safety in Sydney by plot the total number of major crime incidents from Jan 2017 to end of 2018 onto Sydney map.I have split the number of crime incident for each suburb into 5 percentiles.

How to select neighborhoods for your first rental property in Sydney (4)

Light green color represents the lowest 0 – 15%; Dark green color represents 15% - 30%; Blue color represents 30% - 70%; Orange color is for 70%-85% and the rest are high risk area shows as red color on the map. As shown on below chart. The areas have most of major crime incident spread among out west, west south and Central area and the safest area in Sydney will be the northern suburbs, inner west, southern suburbs or among the east or east north coastline.

Problem to solve

As most of my friend are Asian background. When they first come to Australia, I normally recommend them to rent at a suburb surround by Asian community such as, Burwood, Huntsville, Chatswood, Ashfield etc. However, these suburbs normally have a higher rental price.For students come to Sydney to study, some time they prefer place cheaper and have the similar community as these listed 4.Hence, I plan to use Kmeans algorithm as part of this clustering study to see if we can identify any other neighborhood in Sydney that have similar venues and facilities like these 4 Asian suburbs.

Collect additional venue data using Foursquare API

To start our model, we need additional venue info to be collected which I have utilized the Foursquare API to explore the neighborhood and segment them. To get as much info I can, I did not put a limit on the number of venues return from API and marked the radius 600 meter for each neighborhood from their given latitude and longitude which normally the town center (where the post office located). After I call the API, I store the rerun JSON object in a data frame and keep information as below:

How to select neighborhoods for your first rental property in Sydney (5)

Data Prepare for our K-means Algorithm

First 1 hot encoding has been performed on the data set by neighborhood by venue type. Then we calculate the appearance of each venue type in each neighborhood so we can sort and create a new table to show what are the most common venues in each neighborhood.

I have used the K-Means with elbow method to identified that 14 clusters is optimum k of the K-Means as shown below. Euclidean distance has been chosen by me to check on the result for each level of clustering.

How to select neighborhoods for your first rental property in Sydney (6)

A table with first 10 most common venues in each suburb has also been created later on to for us to view what are the common venues in these neighborhood that has been clustered together. After the clustering get done, a new field called cluster label has been created in the dataset shows which neighborhood has been grouped together.

Result

Lets have a look how our clustering perform.I have specifically pull out these 4 Chinese suburbs I have mentioned previously. As show on below screen shot, Chatswood, Ashfield has been grouped into cluster 2 and Burwood, Huntsville has been clustered into Group 8.All four of them have the most common venue as Chinese Restaurants, Shanghai Restaurant etc.

So do we have any other similar neighborhood like these 2 cluster in Sydney? Lets plot them on the map. It seems to me that we have a few alternative neighborhood that similar to these 4 I normally recommend to my friend.Also, a few of these neighborhood has a bit lower medium rental price for 2-bedroom apartment while the safety still remains relatively okay.

Blue shows on the map is cluster 2 and Orange for cluster 8.

How to select neighborhoods for your first rental property in Sydney (7)

If the budget is enough, they can consider neighborhood in cluster 2 which are similar like Chatswood and Ashfield. e.g.:North Sydney, Camperdown Crows Next, West Ryde, Epping etc.

The cheaper option for neighborhoods like Burwood and Hurstville will be in cluster 8.e.g. : Kingsford, Carlingford, Parramatta, Auburn and Wolli Creek, Macquarie Park.

Discussion

Sydney is one of the largest cities in the world and also one of the most multicultural cities as well.The type of venues exists in Sydney shows a very complex mix and the size of each neighborhood vary significantly. With all the 9000+ venues I have extracted from Foursquare API, just restaurant itself I can see 50+ different types. A few different approaches have been tried on to clustering these neighborhood together and finally I have selected K means.Unfortunately, these has no perfect method to find out all the details on Sydney neighborhood with the limited data we have, and not all customer will yield the best quality results however, based on my 10+ year experience living in Sydney, I’m quite happy with what I have found using above method.A few “wow” interesting finding also give me new knowledge about this city I have never knew before.

The important part is, I have used most of the knowledge I have gain during this data scientist cause. Such as using K means algorithm as part of this clustering study.Understand how to use the Elbow method to identify the optimum k value.If more data is available, we can certainly drill down to more details and expanded our analysis in to more granular blocks.

I hope this analysis and all the visualization created during this project will help new comer to Sydney to find their first idea rental property and for future studies as well.

Conclusion

There are a lot of community hidden in Sydney and most of them have very similar culture and facilities. However, dig in deeper you would be surprised to see the rental price for these areas could vary significantly.If you would like to save money while also enjoy a high quality life style that fits your need. Be patient and do some research yourself and you might be surprised to what you can find.

To data science!

Terence Sun

References:

[1] “Greater Sydney”

https://www.cityofsydney.nsw.gov.au/learn/research-and-statistics/the-city-at-a-glance/greater-sydney

[2] “Sydney rental vacancy rate drop”.Su-Lin Tan Reporter

https://www.afr.com/property/residential/sydney-rental-vacancy-rate-drops-20191011-p52ztk”

[3] “NSW Rent and Sales Report”.NSW Communities & Justice

https://www.facs.nsw.gov.au/resources/statistics/rent-and-sales/dashboard

[4] NSW Postal Geo Data

http://www.corra.com.au/australian-postcode-location-data/

[5] “Sydney metropolitan Postcode”. Equifax

https://www.prospectshop.com.au/resources.aspx

[6] “NSW all criminal incidents recorded”. NSW Bureau of Crime Statistics and Research

https://www.bocsar.nsw.gov.au/Pages/bocsar_datasets/Datasets-.aspx

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