Property-Market-Analysis


Project maintained by Ajeethaa Hosted on GitHub Pages — Theme by mattgraham

Analysing Property Market in Perth, Western-Australia

Introduction

This project explores the property market in Perth, Western Australia to help a fictional investor in Switzerland who wishes to purchase a property in Perth. Perth is the capital city, also known as the most isolated city in the world according to Bloomberg. It is Australia’s fourth most populous city with an estimated population standing at about 2 085 973 and will continue to grow. The property prices in Perth have been volatile in the past 5 years. However, the demand for units and houses has always stayed steady.

With this idea in mind, an investor approached for a review of property prices and housing developments in Perth. The investor plans to migrate to Perth and is on the lookout for a property in which she could live, although she does not have a fixed envision of a property type or budget. She has never invested in Western Australia and would like a report that would highlight the critical details on housing market there as well on the current and future developments within Perth, before she meets a decision.

To assist the investor, this analysis will examine the historical data on housing prices in Perth city and analyse the surrounding infrastructures which includes restaurants, cafes, and bakeries. The analysis will propose the price range based on property type and location within the city. A recommendation is made to this investor on the choice of properties.

In this project, there will be two jupyter notebooks; Notebook and Report and two csv files. Notebook has all the code work done necessary for the Report. The Report entails visualisation and write up on the analysis. For more on the project, please click here.

Tools used in this projects:

  • Python;
  • Numpy;
  • Pandas;
  • Jupyter Notebooks;
  • BeautifulSoup for web scraping;
  • Nominatim for geographical data extraction:
  • Folium to display a map of Perth city with restaurants, cafes and bakeries alongside the properties. When clicked on each property, one will find the location and its price.
  • Matplotlib, Seaborn and Plotly for data visualisation.