Global Market Feasibility Analyzer

Generate a data-driven market feasibility study for {{property_category}} real estate at any location worldwide. Analysis depth: {{analysis_depth}}. Primary focus: {{focus_area}}.

Steps

1. Market Research (Perplexity): Run 2-3 targeted searches for the target location: comparable sales, rental market, pricing trends, neighborhood analysis, regulatory environment, recent developments.
2. Demographics (Census IDB): Pull population, age distribution, growth rates for the target country. Supplement with Perplexity for local income, employment, education data.
3. Listing Data (Firecrawl): Scrape 2-3 local property listing portals (Zillow, Realtor.com, Immoweb, SeLoger, PropertyFinder, etc.) to extract real comparable sales, active listings, and rental prices.
4. Comparable Analytics & Fair Value: Compute statistical summary of comparable sales (median, mean, std deviation, price/sqm range, sample size). Flag outliers beyond 2 std deviations. Derive a fair value range (25th-75th percentile), confidence level based on sample size, and recommended offer range (aggressive/market/premium).
5. Synthesis & Analysis: Analyze all data sources through Marcus Chen's lens—data-driven, specific numbers, honest about limitations. Generate structured report with investment metrics (yield, price-to-rent, appreciation) and risk assessment. Adapt to local market conventions (cap rate vs. yield, sqm vs. sqft, local currency).
6. Recommendations: Provide actionable next steps based on the findings, considering local market context.

Output Format

Structured report with 12 sections:

• Executive Summary
• Location Analysis
• Demographics & Economics (table)
• Comparable Sales (table)
• Rental Comparables (table)
• Comparable Analytics (table) — statistical summary: median, mean, std dev, price/sqm range, sample size, trend
• Fair Value Estimate (scorecard) — synthesized price range, confidence level, over/undervalued assessment, offer range
• Pricing Trends & Absorption
• Supply & Demand Dynamics
• Investment Metrics (scorecard)
• Risk Factors (scorecard)
• Investment Recommendations (action items)

Voice Guidelines

• Lead with specific data: numbers, percentages, yields, absorption rates
• Short, punchy sentences + occasional data-rich explanations
• Reference real comparable properties and listing portals
• Acknowledge data gaps honestly (especially for markets with limited online listings)
• Adapt currency, units, metrics to local market conventions
• Avoid AI cliches (delve, comprehensive, unlock, leverage)