Real Estate Market Monitoring Skill

Data Collection Workflow

Step 1: Identify Data Sources

Step 2: Types of Data Collected

1. Second-hand Housing Average Price: yuan/㎡
2. New Housing Average Price: yuan/㎡
3. Second-hand Housing Transaction Volume: units
4. New Housing Transaction Volume: units
5. Rental Price: yuan/month
6. Price-to-Rent Ratio: Annual Rent / Housing Price (Standard: 30-60 is reasonable, >60 indicates bubble)

Step 3: Web Search Strategy

• Shenzhen 2024 second-hand housing average price monthly data

• Shenzhen 2024 new housing transaction volume monthly units

• site:gotohui.com Shenzhen second-hand housing price index

• National Bureau of Statistics 2024 Shenzhen housing price index

Step 4: Data Extraction and Verification

• Cross-check from multiple sources whenever possible
• Label confidence levels: National Bureau of Statistics data > Local government data > Third-party platform data
• Distinguish between estimated values and officially released data

Chart Generation

Required Python Environment

python3 -m venv venv
source venv/bin/activate
pip install matplotlib numpy pandas

Chart Template Structure

1. Second-hand housing transaction average price trend
2. New housing transaction average price trend
3. Monthly transaction volume comparison (second-hand vs new housing)
4. Average rental price trend per unit
5. Price-to-rent ratio trend (housing price / annual rent, based on 80㎡ unit)
6. Second-hand housing / new housing price ratio

Key Visualization Elements

# Price-to-Rent Ratio Calculation
Price-to-Rent Ratio = (Housing Price (yuan/㎡) × 80㎡) / (Monthly Rent × 12)

# International Standards
- Reasonable range: 30-60 (rental yield 4%-6%)
- Warning line: >60 (potential bubble)

Analysis Framework

1. Price Trend Analysis

2. Policy Impact Identification

• Policy 208 (Feb 2021): Second-hand housing reference price system → Plunge in transaction volume
• 16 Financial Measures (Nov 2022): Financing support for real estate enterprises
• Recognize Property, Not Loans (Sep 2023): Relaxation of purchase restrictions
• 930 Policy (Sep 2024): Epic market rescue policy

3. Market Indicators

4. Prediction Framework

1. Short-term (1-3 months): Based on policy stimulus and transaction volume trends
2. Mid-term (6-12 months): Based on economic environment and supply-demand changes
3. Long-term (1-3 years): Based on population structure and policy orientation

Output Format

Data Table Format

| Year | Second-hand Housing Avg Price (Max) | Second-hand Housing Avg Price (Min) | New Housing Avg Price (Max) | New Housing Avg Price (Min) | Second-hand Housing Transactions (10k units) | New Housing Transactions (10k units) | Price-to-Rent Ratio |
|------|-----------------|-----------------|----------------|---------------|-----------------|----------------|--------|

Chart File Output

/Users/lumin/skills/shenzhen_real_estate_charts.png

Analysis Report Structure

## [City] Real Estate Market Analysis ([Time Period])

### 1. Price Trends
- Describe price change trends
- Compare year-on-year/month-on-month data

### 2. Transaction Volume Analysis
- Second-hand housing transaction volume trend
- New housing transaction volume trend
- Boom-bust line analysis

### 3. Price-to-Rent Ratio Analysis
- Current price-to-rent ratio level
- Comparison with historical data
- Alignment with international standards

### 4. Policy Impact Factors
- Important policy nodes
- Policy effect evaluation

### 5. Trend Predictions
- Short-term prediction (1-3 months)
- Mid-term prediction (6-12 months)
- Risk warnings

### 6. Data Confidence Statement
- Data sources
- Estimation methods
- Confidence assessment

Quality Check List

• All data sources are cited
• Charts have correct labels and legends
• Units are clearly marked (yuan/㎡, units, yuan/month)
• Confidence levels are labeled
• Policy events are marked on charts
• Analysis is evidence-based rather than speculative

Example Workflow

1. Search for "Shenzhen 2024 second-hand housing average price monthly"
2. Collect data from gotohui.com and official sources
3. Generate a chart showing monthly price trends
4. Identify policy impacts (930 Policy)
5. Calculate key indicators (price-to-rent ratio, transaction volume)
6. Provide structured analysis with predictions