/melbourne-housing

Analyzed the Melbourne Housing dataset as a part of our HackUTD challenge. Gained some interesting insights from the dataset which can help provide buyers with some useful information before they invest their money in property.

Primary LanguageJupyter Notebook

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How old is that house?

  • The red (darkest) regions or spots represent older property
  • Lighter the colors get, lesser the age of your house
  • In central areas of Melbourne, most of the old houses are located in concentration.
  • Suburb areas are less concentrated and have newer houses

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Restaurants and Parks near me

  • Correlation between Houses and People
  • The bigger the circle, the more restaurants you have
  • The greener the circle, the number of parks increases

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Is Age related to Price?

  • For a wide range in Age, the price remains the same
  • Decision: Do not correlate age with price when you are investing in your property in Melbourne

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More Insights on house prices

  • The warmer the red, more is the price.
  • The cooler the color, the lesser the price.
  • The most expensive houses are in the dense central zone.
  • If you are looking for cheaper options, you can look in thin zones which are in the away from the center.

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Choosing the right suburb that suits you

  • A straight comparison of the price of the house with respect to the suburb where it is located
  • Are you looking for a posh area or just a regular one? This decides for you
  • The average price in the richest suburb is almost double of the lowest prices suburb

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Relation between the Type of the house and its price

  • If it’s a bungalow in Melbourne, its probably relatively old and the priciest.
  • Townhouse is new and the price is moderate
  • Apartment are the cheapest

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Family matters after all

  • A direct correlation between the number of people and the number of children
  • With increase in the number of children and people (adults), the number of houses in Melbourne also increases

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