Weekly Apartment Price Trends for the First Week of January 2025: How to Visualize with a Python Comparison Bar Graph

Have you ever wondered how much apartment sales and rental prices change from week to week? Let's visualize the 'Weekly Apartment Price Trends' data from the Korea Real Estate Agency in Python and explain the data in an easy-to-understand way.

Today we'll be using Python Comparison Bar Graphsto understand the changes in sale and charter prices at a glance. Follow along with the visualization code below, and stay with us as we provide line-by-line code explanations to make it easy for beginners to understand!

📊 Weekly apartment price trend data explained

Recently, the Korea Real Estate Agency announced Weekly apartment price trends as of January 6, 2025According to,

• Bid/Ask Price Index: Down -0.031 TP3T nationally
• Charter Price Index: 0.001 TP3T nationally flat

Regional datais shown below.

• Buy and Sell Price: Seoul 0.00%, metropolitan area -0.02%, provinces -0.05%
• Charter price: Seoul 0.00%, metropolitan area 0.00%, rural -0.01%

To get a clear visual understanding of this data Comparison bar graphsis an effective way to do this.

📈 Implementing a Python Comparison Bar Graph (code block)

import matplotlib.pyplot as plt

Enter the # data
regions = ["Nationwide", "Seoul", "Capital Region", "Non-Capital Region"]
sales_price_1 = [-0.03, 0.00, -0.02, -0.04]
sales_price_2 = [-0.03, 0.00, -0.02, -0.05]
jeonse_price_1 = [0.00, 0.00, 0.00, -0.01]
jeonse_price_2 = [0.00, 0.00, 0.00, 0.00, -0.01]

# graph size and settings
plt.figure(figsize=(8, 6))
bar_width = 0.35
x = range(len(regions))

Visualize the percentage change of the # Sales Price Index
plt.bar(x, sales_price_1, bar_width, label='Dec 30, 2024', color='#3498db')
plt.bar([p + bar_width for p in x], sales_price_2, bar_width, label='Jan 6, 2025', color='#2c3e50')
plt.title('Sales Price Index Change (%)')
plt.xticks([p + bar_width / 2 for p in x], regions)
plt.axhline(0, color='gray')
plt.legend()
plt.show()

Visualize the percentage change in the # Charter Price Index
plt.figure(figsize=(8, 6))
plt.bar(x, jeonse_price_1, bar_width, label='Dec 30, 2024', color='#e74c3c')
plt.bar([p + bar_width for p in x], jeonse_price_2, bar_width, label='Jan 6, 2025', color='#c0392b')
plt.title('Jeonse Price Index Change (%)')
plt.xticks([p + bar_width / 2 for p in x], regions)
plt.axhline(0, color='gray')
plt.legend()
plt.show()

📌 Code Explained

Now, let's break down the above code step by step.

1. Prepare your data

regions = ["Nationwide", "Seoul", "Capital Region", "Non-Capital Region"]
sales_price_1 = [-0.03, 0.00, -0.02, -0.04]
sales_price_2 = [-0.03, 0.00, -0.02, -0.05]
jeonse_price_1 = [0.00, 0.00, 0.00, -0.01]
jeonse_price_2 = [0.00, 0.00, 0.00, 0.00, -0.01]

• regions: Contains the name of the region.
• sales_price_1 and sales_price_2Percentage change data
• jeonse_price_1 and jeonse_price_2Charter price change data

2. Set up a bar graph and create your first graph

plt.figure(figsize=(8, 6))
bar_width = 0.35
x = range(len(regions))

• plt.figure: Set graph size
• bar_width: Sets the width of the bar.
• x: Create a list to adjust the position of the bar graph

3. visualize the percentage change in the price index

plt.bar(x, sales_price_1, bar_width, label='Dec 30, 2024', color='#3498db')
plt.bar([p + bar_width for p in x], sales_price_2, bar_width, label='Jan 6, 2025', color='#2c3e50')

• plt.bar: Commands to draw a bar graph
• xand sales_price_1 Plot the first graph based on your data.
• bar_widthto move the second bar graph to the right

4. Visualize the percentage change in the Charter Price Index

plt.bar(x, jeonse_price_1, bar_width, label='Dec 30, 2024', color='#e74c3c')
plt.bar([p + bar_width for p in x], jeonse_price_2, bar_width, label='Jan 6, 2025', color='#c0392b')

• Visualize charter price data in the same way.

✅ Conclusion: Weekly apartment price trends at a glance!

The Python Comparison Bar Graphsusing the Weekly apartment price trendseffectively visualized the data. We were able to intuitively understand changes in the data and compare trends in sales and rental prices by region.

In this way, the Real estate datathen Python can be a very powerful tool. Stay tuned for more data visualization and analysis!

As a side note, how do you visualize a stacked histogram compared to a Python comparison histogram? Changing Perceptions of Marital Childbearing: Analyzing Data with Stacked Bar Graphs Check out this post to learn how!