Melbourne Housing Snapshot

### 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][1].

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PI - property passed in; PN - sold prior not disclosed; SN - sold not disclosed; NB - no bid; VB - vendor bid; W - withdrawn prior to auction; SA - sold after auction; SS - sold after auction price not disclosed.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.

[1]: https://www.kaggle.com/anthonypino/melbourne-housing-market

### 背景
墨尔本房地产市场繁荣兴盛。您能从中发现洞察力，预测下一个重大趋势，成为地产大亨吗？或者，难度更大的是，能否抢购到价格合理的两居室单元？

### 内容
这是由Tony Pino先生创建的一个[数据集](#)的快照。

该数据集通过从每周公开发布的Domain.com.au网站上的结果进行抓取而来。Tony Pino先生对其进行了精心整理，现在轮到您施展数据分析的魔法。数据集包括地址、房地产类型、区域、销售方式、房间数量、价格、房地产经纪人、销售日期以及距离市中心的距离。

### 特定变量的说明

房间数量：房间数量

价格：以美元为单位的价格

销售方式：S - 已售出；SP - 之前已售出；PI - 未售出；PN - 之前未披露；SN - 未披露；NB - 无出价；VB - 出价人出价；W - 拍卖前撤回；SA - 拍卖后售出；SS - 拍卖后售出，价格未披露。N/A - 价格或最高出价不可用。

类型：br - 卧室；h - 房屋、小屋、别墅、半独立式、联排别墅；u - 单元、复式；t - 联排别墅；dev site - 开发用地；o res - 其他住宅。

SellerG：房地产经纪人

日期：销售日期

距离：距离市中心距离

Regionname：一般区域（西部、西北部、北部、东北部等）

Propertycount：区域内房地产数量。

Bedroom2：从不同来源抓取的卧室数量

Bathroom：浴室数量

Car：停车位数量

Landsize：土地面积

BuildingArea：建筑面积

CouncilArea：区域管理机构

### 致谢
本数据集旨在作为对https://www.kaggle.com/anthonypino/melbourne-housing-market的静态（不改变）快照。它于2017年9月创建。此外，已移除价格未定的房屋。

[1]: https://www.kaggle.com/anthonypino/melbourne-housing-market

搜集汇总

背景与挑战

背景概述

该数据集是2017年9月创建的墨尔本房地产市场静态快照，源自Tony Pino公开采集的数据，包含地址、房产类型、价格、销售方法、房间数等关键交易属性，旨在支持数据分析和趋势预测。数据集移除了无价格记录，聚焦于提供结构化房产信息以挖掘市场洞察。