alaaelgendy22/egypt-real-estate-analytics

End-to-end Egypt real estate analytics with Python, SQL, data quality checks, and executive visual reporting. # Egypt Real Estate Analytics An end-to-end analytics project that turns six linked real-estate tables into a validated analytical layer, business KPIs, PostgreSQL analysis, and an executive-ready visual suite. The one-command runner builds a compact, non-identifying demo fixture locally, so the complete workflow can be reviewed without requesting additional files. ## Business questions - Where are transaction volume and market value concentrated? - Which property types and transaction channels drive performance? - How do payment methods, discounts, and commissions affect the financial mix? - Which client segments and acquisition sources deserve attention? - How do price, demand, supply, rental yield, and time on market change over time? - How much does the cleaning pipeline improve data quality? ## Headline findings The default demo run produces the following portfolio results: - **EGP 5.43B** in completed transaction value across **571 completed deals**. - **EGP 9.5M** average completed deal value. - **Cairo** leads completed deal volume, followed by Alexandria, Giza, New Capital, and Matrouh. - **Apartments account for 190 completed deals**, the largest property segment in the demo. - The 2024 average price-per-square-metre indicator is **6.21% higher year over year**. - Cash and bank mortgages are the two largest payment channels, representing roughly **60%** of transactions combined. ## Business recommendations 1. Prioritize inventory and campaign spend in Cairo and Alexandria, then test focused growth plays in Giza and New Capital. 2. Keep separate conversion funnels for cash and mortgage buyers; together they represent the majority of payment behavior. 3. Use apartments as the core acquisition segment while monitoring higher-value office, studio, and villa opportunities separately. 4. Track price growth beside demand, supply, rental yield, and days on market to avoid making pricing decisions from a single indicator. 5. Review data-quality excepti …