housing-monitor
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Chinese房地产市场监控技能
Real Estate Market Monitoring Skill
此技能使 Claude 能够收集房地产市场数据、生成可视化图表，并分析趋势进行预测。
This skill enables Claude to collect real estate market data, generate visual charts, and analyze trends for predictions.
数据收集工作流程
Data Collection Workflow
第一步：识别数据源
Step 1: Identify Data Sources
对于中国房地产数据，使用这些高置信度的官方来源：
| 数据类型 | 主要来源 | 网址 |
|---|---|---|
| 房价指数 | 国家统计局70城房价 | https://www.stats.gov.cn/sj/zxfb/ |
| 月度房价 | gotohui.com | https://fangjia.gotohui.com/fjdata-49 |
| 成交量 | 深圳市住房和建设局 | https://zjj.sz.gov.cn/xxgk/ztzl/pubdata/ |
| 房地产信息平台 | 深圳市房地产信息平台 | https://fdc.zjj.sz.gov.cn/ |
| 研究报告 | 乐有家研究中心、中指研究院 | 各不相同 |
For Chinese real estate data, use these high-confidence official sources:
| Data Type | Primary Source | URL |
|---|---|---|
| Housing Price Index | 70-city housing prices from National Bureau of Statistics | https://www.stats.gov.cn/sj/zxfb/ |
| Monthly Housing Prices | gotohui.com | https://fangjia.gotohui.com/fjdata-49 |
| Transaction Volume | Shenzhen Municipal Bureau of Housing and Construction | https://zjj.sz.gov.cn/xxgk/ztzl/pubdata/ |
| Real Estate Information Platform | Shenzhen Real Estate Information Platform | https://fdc.zjj.sz.gov.cn/ |
| Research Reports | Leyoujia Research Center, China Index Academy | Varies |
第二步：收集的数据类型
Step 2: Types of Data Collected
- 二手房均价：元/㎡
- 新房均价：元/㎡
- 二手房成交量：套
- 新房成交量：套
- 租房价格：元/月
- 租售比：年租金/房价（标准：30-60合理，>60泡沫）
- Second-hand Housing Average Price: yuan/㎡
- New Housing Average Price: yuan/㎡
- Second-hand Housing Transaction Volume: units
- New Housing Transaction Volume: units
- Rental Price: yuan/month
- Price-to-Rent Ratio: Annual Rent / Housing Price (Standard: 30-60 is reasonable, >60 indicates bubble)
第三步：网络搜索策略
Step 3: Web Search Strategy
使用以下查询搜索每种数据类型：
深圳 2024年 二手房均价 每月 数据深圳 2024年 新房成交量 月度 套数site:gotohui.com 深圳 二手房 价格指数国家统计局 2024年 深圳 房价 指数
Use the following queries for each data type:
Shenzhen 2024 second-hand housing average price monthly dataShenzhen 2024 new housing transaction volume monthly unitssite:gotohui.com Shenzhen second-hand housing price indexNational Bureau of Statistics 2024 Shenzhen housing price index
第四步：提取和验证数据
Step 4: Data Extraction and Verification
- 尽可能交叉核对多个来源
- 标注置信度：国家统计局数据 > 地方政府数据 > 第三方平台数据
- 标注估算值与官方公布数据的区别
- 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
图表生成
Chart Generation
必需的 Python 环境
Required Python Environment
bash
python3 -m venv venv
source venv/bin/activate
pip install matplotlib numpy pandasbash
python3 -m venv venv
source venv/bin/activate
pip install matplotlib numpy pandas图表模板结构
Chart Template Structure
生成包含这6个关键指标的图表：
- 二手房成交均价趋势
- 新房成交均价趋势
- 月度成交量对比（二手房 vs 新房）
- 租房套均租金趋势
- 租售比趋势（房价/年租金，标准80㎡）
- 二手房/新房价格比
Generate charts containing these 6 key indicators:
- Second-hand housing transaction average price trend
- New housing transaction average price trend
- Monthly transaction volume comparison (second-hand vs new housing)
- Average rental price trend per unit
- Price-to-rent ratio trend (housing price / annual rent, based on 80㎡ unit)
- Second-hand housing / new housing price ratio
关键可视化要素
Key Visualization Elements
undefinedundefined租售比计算
Price-to-Rent Ratio Calculation
租售比 = 房价(元/㎡) × 80㎡ / (月租金 × 12)
Price-to-Rent Ratio = (Housing Price (yuan/㎡) × 80㎡) / (Monthly Rent × 12)
国际标准
International Standards
- 合理区间：30-60（租金收益率4%-6%）
- 警戒线：>60（可能存在泡沫）
undefined- Reasonable range: 30-60 (rental yield 4%-6%)
- Warning line: >60 (potential bubble)
undefined分析框架
Analysis Framework
1. 价格走势分析
1. Price Trend Analysis
| 阶段 | 特征 | 判断 |
|---|---|---|
| 上涨期 | 成交量放大，价格持续上涨 | 卖方市场 |
| 下跌期 | 成交量萎缩，价格持续下降 | 买方市场 |
| 筑底期 | 成交量企稳，价格跌幅收窄 | 市场底 |
| 回暖期 | 成交量回升，价格环比上涨 | 复苏信号 |
| Phase | Characteristics | Judgment |
|---|---|---|
| Rising Period | Increased transaction volume, continuous price growth | Seller's market |
| Declining Period | Shrinking transaction volume, continuous price drop | Buyer's market |
| Bottoming Period | Stabilized transaction volume, narrowed price decline | Market bottom |
| Recovery Period | Rising transaction volume, month-on-month price growth | Recovery signal |
2. 政策影响识别
2. Policy Impact Identification
需要注意的中国房地产关键政策：
- 208政策（2021.02）：二手房参考价制度 → 成交量暴跌
- 金融16条（2022.11）：房企融资支持
- 认房不认贷（2023.09）：限购松动
- 930政策（2024.09）：史诗级救市
Key Chinese real estate policies to note:
- 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. 市场指标
3. Market Indicators
| 指标 | 健康值 | 风险值 |
|---|---|---|
| 成交量荣枯线（深圳） | >5000套/月 | <3000套/月 |
| 租售比 | 30-60 | >60 |
| 二手房/新房比 | 0.9-1.1 | >1.3 |
| Indicator | Healthy Value | Risk Value |
|---|---|---|
| Transaction Volume Boom-Bust Line (Shenzhen) | >5000 units/month | <3000 units/month |
| Price-to-Rent Ratio | 30-60 | >60 |
| Second-hand Housing / New Housing Price Ratio | 0.9-1.1 | >1.3 |
4. 预测框架
4. Prediction Framework
进行预测时：
- 短期（1-3月）：基于政策暖风和成交量趋势
- 中期（6-12月）：基于经济环境和供需变化
- 长期（1-3年）：基于人口结构和政策导向
When making predictions:
- Short-term (1-3 months): Based on policy stimulus and transaction volume trends
- Mid-term (6-12 months): Based on economic environment and supply-demand changes
- Long-term (1-3 years): Based on population structure and policy orientation
输出格式
Output Format
数据表格式
Data Table Format
始终以此格式呈现数据：
| 年份 | 二手房均价(最高) | 二手房均价(最低) | 新房均价(最高) | 新房均价(最低) | 二手房成交(万套) | 新房成交(万套) | 租售比 |
|------|-----------------|-----------------|----------------|---------------|-----------------|----------------|--------|Always present data in this 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 |
|------|-----------------|-----------------|----------------|---------------|-----------------|----------------|--------|图表文件输出
Chart File Output
将图表保存到用户可访问的位置：
/Users/lumin/skills/shenzhen_real_estate_charts.pngSave charts to a user-accessible location:
/Users/lumin/skills/shenzhen_real_estate_charts.png分析报告结构
Analysis Report Structure
undefinedundefined[城市]房地产市场分析（[时间区间]）
[City] Real Estate Market Analysis ([Time Period])
1. 价格走势
1. Price Trends
- 描述价格变化趋势
- 对比同比/环比数据
- Describe price change trends
- Compare year-on-year/month-on-month data
2. 成交量分析
2. Transaction Volume Analysis
- 二手房成交量趋势
- 新房成交量趋势
- 荣枯线分析
- Second-hand housing transaction volume trend
- New housing transaction volume trend
- Boom-bust line analysis
3. 租售比分析
3. Price-to-Rent Ratio Analysis
- 当前租售比水平
- 与历史数据对比
- 国际标准对照
- Current price-to-rent ratio level
- Comparison with historical data
- Alignment with international standards
4. 政策影响因素
4. Policy Impact Factors
- 重要政策节点
- 政策效果评估
- Important policy nodes
- Policy effect evaluation
5. 趋势预测
5. Trend Predictions
- 短期预测（1-3月）
- 中期预测（6-12月）
- 风险提示
- Short-term prediction (1-3 months)
- Mid-term prediction (6-12 months)
- Risk warnings
6. 数据置信度说明
6. Data Confidence Statement
- 数据来源
- 估算方法
- 置信度评估
undefined- Data sources
- Estimation methods
- Confidence assessment
undefined质量检查清单
Quality Check List
交付结果前，请验证：
- 所有数据来源均已引用
- 图表有正确的标签和图例
- 单位清晰标注（元/㎡、套、元/月）
- 已标注置信度
- 政策事件已在图表上标注
- 分析基于证据而非推测
Before delivering results, verify:
- 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
示例工作流程
Example Workflow
用户请求："分析深圳2024年房价走势"
Claude 响应：
- 搜索"深圳 2024年 二手房均价 每月"
- 从 gotohui.com 和官方来源收集数据
- 生成显示月度价格趋势的图表
- 识别政策影响（930政策）
- 计算关键指标（租售比、成交量）
- 提供带有预测的结构化分析
（文件结束 - 共195行）
User Request: "Analyze Shenzhen's 2024 housing price trends"
Claude Response:
- Search for "Shenzhen 2024 second-hand housing average price monthly"
- Collect data from gotohui.com and official sources
- Generate a chart showing monthly price trends
- Identify policy impacts (930 Policy)
- Calculate key indicators (price-to-rent ratio, transaction volume)
- Provide structured analysis with predictions
(End of document - Total 195 lines)