Melbourne Housing Snapshot

by user1
17 February, 2022

Snapshot of Tony Pino’s Melbourne Housing Dataset

Tagssocial science, real estate, demographics, housing, australia

Context

Melbourne real estate is BOOMING. Can you find the insight or predict the next big trend to become a real estate mogul… or even harder, to snap up a reasonably priced 2-bedroom unit?

Content

This is a snapshot of a dataset created by Tony Pino.

It was scraped from publicly available results posted every week from Domain.com.au. He cleaned it well, and now it’s up to you to make data analysis magic. The dataset includes Address, Type of Real estate, Suburb, Method of Selling, Rooms, Price, Real Estate Agent, Date of Sale and distance from C.B.D.

Notes on Specific Variables

Rooms: Number of rooms

Price: Price in dollars

Method: S – property sold; SP – property sold prior; PI – property passed in; PN – sold prior not disclosed; SN – sold not disclosed; NB – no bid; VB – vendor bid; W – withdrawn prior to auction; SA – sold after auction; SS – sold after auction price not disclosed. N/A – price or highest bid not available.

Type: br – bedroom(s); h – house,cottage,villa, semi,terrace; u – unit, duplex; t – townhouse; dev site – development site; o res – other residential.

SellerG: Real Estate Agent

Date: Date sold

Distance: Distance from CBD

Regionname: General Region (West, North West, North, North east …etc)

Propertycount: Number of properties that exist in the suburb.

Bedroom2 : Scraped # of Bedrooms (from different source)

Bathroom: Number of Bathrooms

Car: Number of carspots

Landsize: Land Size

BuildingArea: Building Size

CouncilArea: Governing council for the area

Acknowledgements

This is intended as a static (unchanging) snapshot of https://www.kaggle.com/anthonypino/melbourne-housing-market. It was created in September 2017. Additionally, homes with no Price have been removed.

Size: 451 KB Price: Free Author: Unknown Data source: kaggle.com