Team Name

Dwell

Team Members

Project Description

Problem Statement

For most of us the most expensive outgoing that we have is our rent or mortgage and house prices have increased by 22% last year and rents have increased by 33% in regional area and by 25% in capital cities. This is putting a huge crisis on housing affordability.

The rental crisis is reaching emergency levels in NSW and finding affordable housing is a major challenge for every NSW citizen.

Dwell brings to its users a digital solution to identify the best possible recommended and affordable housing in their locality.

Solution

Dwell uses an efficient matching engine powered by the data sourced from "Australian Housing Data Dashboard" and "NSW Rent and Sales Dashboard".
The data sets are used to provide insights to the Dwell users to identify the most suitable localities based on their work location and Demographic details.

Let's explore how Dwell can be used to search your dream home:
1. As a user you can enter the state and the city where you wanna find your dream home for buying or renting.
2. The search results will display the the heatmap of the city based on affordability index based on property price and rents in the locality.
3. You can then refine your search further to get recommendations based on your age, income and work location.
4. The result will help you zero down on a locality for your dream home for buying or renting.

Tech Stack

The backend is a simple web server that returns region rent/mortgage statistics + the affordability calculations & analysis we did in R/Python. A stretch goal would be to host the data on GCloud SQL as originally intended and provide an interface to add/modify additional SA2 regions as new housing data is released.
The frontend is a simple dashboard is built with React and the JS-native Pigeon Maps library. We use it to visualise affordability calculations by suburb/area (SA2 geographical boundaries) as a choropleth map so that users can determine visually which areas are comparatively affordable.

Data Story

We used General Community Profile data from the 2021 ABS Census Datapack. It breaks down average rent for each SA2. We’ve also used ABS’s shapefiles to construct our map.