Housing price in Beijing

In this repository, we have analyzed and reviewed the housing sales data of Beijing (see the dataset on Kaggle).

About Dataset

This dataset contains 318,851 rows and 26 columns. The name of each column along with its description is provided in the table below.

column	Description
url	the url which fetches the data
id	the id of transaction
Lng	Lng coordinates, using the BD09 protocol.
Lat	Lat coordinates, using the BD09 protocol.
Cid	community id
tradeTime	the time of transaction
DOM	active days on market.Know more in here.
followers	the number of people follow the transaction.
totalPrice	the total price
price	the average price by square
square	the square of house
livingRoom	the number of living room
drawingRoom	the number of drawing room
kitchen	the number of kitchen
bathroom	the number of bathroom
floor	the height of the house. I will turn the Chinese characters to English in the next version.
buildingType	including tower (1), bungalow (2)，combination of plate and tower (3), plate (4).
constructionTime	the time of construction
renovationCondition	including other (1), rough (2), Simplicity (3), hardcover (4)
buildingStructure	including unknown (1), mixed (2), brick and wood (3), brick and concrete (4), steel (5) and steel-concrete composite (6).
ladderRatio	the proportion between number of residents on the same floor and number of elevator of ladder. It describes how many ladders a resident have on average.
elevator	have (1) or not have elevator (0)
fiveYearsProperty	if the owner have the property for less than 5 years.
subway	have access to subway (1) or not (0)
district	district number
communityAverage

Preprocessing

First, we preprocessed the existing dataset. We removed unnecessary data. Then we imputed or deleted missing data. After that, we formatted the data into appropriate types. Finally, we removed outliers from the dataset. After cleaning the data, we added new information to the dataset that would be useful in the analysis phase using other data. The final processed dataset is saved in the file housing-data-proccessed.csv.

Analysis

First, we obtained general information from the dataset using bar charts. After that, we examined the impact of various factors on prices using different charts. In the next step, we displayed the distribution of houses based on price on the city map. Additionally, we categorized the houses based on their districts and obtained general information for each district. Finally, we plotted and examined various time-series charts, such as price changes over time.

Data Visualization

Effect of distanceToCapital on price

Effect of elevator on price

Effect of subway on price

This scatter map shows the distribution of houses with respect to their prices.

This scatter map shows the distribution of houses with respect to their districts.

This chart illustrates the variations in house prices over time.

This chart displays the number of houses sold in the city center.