Luyou666 China Housing Forecast Lite Skill China Housing Forecast Lite Skill

中国房地产趋势预测 Skill / China Real Estate Trend Forecast Skill

Use this skill to produce concise, evidence-driven forecasts for Chinese residential real estate markets. Keep the analysis centered on housing-market data: price trend, transaction volume, inventory or listings, valuation, credit conditions, policy changes, and district segmentation.

Do not turn the forecast into a broad macroeconomic report, crawler system, or machine-learning system. Economic cycle and population flow are important overlays, but they remain a small adjustment within +/-8 points.

Standard disclaimer for formal outputs: 仅供交流学习娱乐，不提供投资建议，所有解释权归作者所有。

When to Use

Use this skill when the user asks about:

• Housing price trends in a Chinese city or district
• New-home or second-hand-home market forecasts
• Whether a market is stabilizing, bottoming, or still declining
• Whether now is suitable for self-use purchase or investment
• 3/6/12/24 month probability forecasts
• Risk checks for core areas, suburbs, new districts, or satellite cities

Fresh Data Retrieval Rule

When the user question includes any of the following, prioritize latest public data retrieval before scoring:

• 最新
• 当前
• 现在
• 今年
• 最近
• 当下
• 未来几个月
• 未来 3 个月
• 未来 6 个月
• 未来 12 个月
• 某城市是否见底
• 现在是否适合买房
• 当前是否适合投资

If the runtime supports web search, perform fresh public data retrieval. Use only publicly accessible sources. Do not bypass paywalls, login restrictions, anti-bot systems, or captchas.

If the runtime does not support web search:

1. State clearly that latest data cannot be fetched automatically.
2. Ask the user to provide data, or continue with a low-confidence framework analysis.
3. Mark the missing items as Data Gap.
4. Do not fabricate current or latest data.

Traceable Source Requirement

For formal forecasts using fresh data:

• Show source name, data date, and freshness status.
• Prefer traceable official or industry sources.
• Include source links or identifiable publication names if available.
• Do not claim to have used latest data unless the source date is visible.
• If the source cannot be opened and only a snippet is available, treat it as weak evidence.
• If a key data point has unclear source or unclear date, mark it as Data Gap or Stale and lower confidence.

Fresh Data Retrieval Workflow

1. Define scope:

   • City
   • District
   • New home / second-hand home / both
   • Forecast horizon
   • User purpose: self-use / investment / research / risk check

2. Search latest data:

   • Price data
   • Transaction volume
   • Inventory / listings
   • Mortgage rate / LPR / credit policy
   • Local purchase restrictions, provident fund, subsidies, purchase support, destocking or acquisition policies
   • Population inflow / outflow
   • Income, employment, and economic-cycle data

3. Assign data quality:

   • Use data_sources.md A/B/C/D reliability levels.

4. Check freshness:

   • Use data_sources.md freshness standards.

5. Resolve conflicts:

   • If sources conflict, explain methodology differences.

6. Mark gaps:

   • If key data is missing, mark Data Gap and apply scoring_model.md confidence caps.

7. Score and forecast:

   • Use scoring_model.md for core scoring, probability mapping, economic/population adjustment, and horizon adjustment.

8. Cross-check:

   • Use Minimal Multi-Agent Mode and Bear Case Review.

9. Output:

   • Use output_template.md.

Required Data

Housing Market Data

• New-home and second-hand-home price trend
• Transaction volume and listing volume
• Inventory or months of supply
• Discount rate and price-cut ratio if available
• Rental yield or price-to-income ratio if available
• Mortgage rate, credit availability, and down-payment policy
• Local purchase restrictions, tax policy, provident fund policy, and housing support policy
• District-level split between core areas, mature urban areas, outer suburbs, new districts, and satellite cities

Economic Cycle Data

• GDP growth or regional GDP growth
• Unemployment rate
• Resident disposable income growth
• Consumer confidence if available
• PMI or local industrial activity if available

Population Flow Data

• Permanent population change
• Net population inflow or outflow
• Young population share if available
• Household registration change if available
• Employment inflow if available

If data is missing, do not block the forecast. Mark Data Gap and lower confidence according to scoring_model.md.

Economic Cycle and Population Flow Overlay

Economic cycle and population flow correct the final judgment but do not replace the core housing model.

• Economic expansion can improve income expectations, employment stability, and purchase confidence.
• Economic downturn can weaken income expectations, raise employment pressure, and reduce willingness to add leverage.
• Net population inflow supports long-term demand, especially in core districts.
• Net population outflow weakens long-term demand, especially in non-core areas and high-inventory new-home markets.
• Young population inflow matters more than total population inflow.
• Population inflow alone does not imply price growth; it must be checked against income, industry, and inventory.
• Population outflow does not mean every area declines; scarce core locations may still have support.

New Home Price Distortion Rule

New-home listed prices and official filing prices may not reflect real transaction values.

Check:

• Discount rate
• Free parking space
• Decoration package
• Channel rebate
• Down-payment installment
• Developer financing pressure
• Group-buying discounts
• Promotional payment terms

If new-home sales improve mainly because of heavy discounts, do not treat it as organic recovery.

Self-Use vs Investment Rule

A property can be acceptable for self-use but unattractive for investment.

Self-use judgment should focus on:

• Affordability
• Commute
• School, hospital, and public services
• Family stability
• Holding period

Investment judgment should focus on:

• Rental yield
• Liquidity
• Population and job inflow
• Supply pressure
• Expected resale demand
• Leverage risk

Minimal Multi-Agent Mode

For every formal forecast, simulate at least five roles:

1. Trend Agent
2. Supply and Inventory Agent
3. Credit, Policy and Valuation Agent
4. Population and Economic Overlay Agent
5. Bear Case Review Agent

Each role must output:

• Score
• Direction
• Confidence
• Key Reason
• Main Risk

The Bear Case Review Agent must challenge the main conclusion before the final probability table is produced. If it raises a major objection, lower confidence or reduce rise probability.

Workflow

1. Define scope and user purpose.
2. Trigger Fresh Data Retrieval Rule when the user asks about current or latest conditions.
3. Assign source quality and freshness using data_sources.md.
4. Score the original housing model using scoring_model.md.
5. Apply Economic and Population Adjustment within +/-8 points.
6. Apply Data Gap Confidence Cap if data is missing or stale.
7. Run Minimal Multi-Agent Mode and Bear Case Review.
8. Produce 3/6/12/24 month rise, sideways, and fall probabilities.
9. Separate self-use recommendation from investment recommendation.
10. Output using output_template.md.

Output Discipline

• Every formal forecast output must include this disclaimer: 仅供交流学习娱乐，不提供投资建议，所有解释权归作者所有。
• Always show data sources, dates, quality, and freshness for formal forecasts.
• Preserve source names, publication dates, or traceable links whenever available.
• Always output the 3/6/12/24 month probability table.
• Every probability row must sum to 100%.
• Separate short-term trend from medium- and long-term fundamentals.
• Explain conflicts between transaction signals, source types, economic cycle, and population flow.
• Use examples only as format references, never as real current data.

File Integration Map

• Use data_sources.md for source reliability, freshness, and conflict handling.
• Use scoring_model.md for score calculation, probability mapping, horizon adjustment, and confidence caps.
• Use output_template.md for final report format.
• Use risk_rules.md to avoid prohibited or overconfident conclusions.
• Use examples/ only as format references, not as real data.
• Use tests/ to validate output quality.

Source: luyou666/china-housing-forecast-lite-skill — distributed by TomeVault.