Der immocation Podcast | Lerne Immobilien · immocation

638 🎙️ | Akquisestrategien für Top-Deals im aktuellen Markt

·1 hr 5 min·5 clips

Ferdi from Immometrica reveals how his 24-page data guide uncovers hidden real estate deals in today's market.

1. Der immocation Podcast presents an acquisition strategy episode with Martin Groschewski hosting Ferdi from Immometrica, Immocation's data partner for property search and market analysis. 2. Ferdi is described as an active real estate investor building his own portfolio alongside his role at Immometrica, a meta-search platform aggregating listings from all major German portals plus regional newspapers. 3. The episode's core thesis is that the post-2022 interest-rate-pivot market has structurally elevated both price reduction rates and listing durations, and that investors who understand these trends can use specific filter combinations to find deals with double seller pressure. 4. March 2026 ETW (condominium) offer price comparison shows Munich at 8,700€/sqm, Frankfurt at 6,300€/sqm, and Hamburg third; A-city offer prices have risen in the three preceding months across Germany. 5. Ferdi notes that Immometrica enables individualized offer statistics by specific property criteria (size, rooms, features) and individual districts within cities, which is useful in bank financing presentations to show precise market evidence for a target asset. 6. Leipzig shows the longest average online duration for apartment listings in the city comparison; Heidelberg, Munich, and Cologne show the shortest—indicating faster market clearance in those cities. 7. Stuttgart shows the highest price-reduction rate in the current city comparison, while the national trend has improved slightly relative to the peak of October 2022. 8. Germany's national price-reduction rate timeline: 4–6% in 2020–2021; sharp rise to nearly 16% in October 2022 following the ECB rate pivot; gradual decline through 2023–2025 but stabilized above the pre-pivot baseline. 9. Ferdi defines the reduction rate as the share of listings in a given month where the seller lowered the asking price; individual price history on each Immometrica listing shows how many times, by how much, and when descriptions were also changed. 10. Online duration data mirrors the reduction rate story: listing times extended sharply after the 2022 rate change, showed a slight counter-movement in early 2025, but remain above pre-pivot levels in aggregate. 11. Ferdi's first filter recommendation is 'only offers with price reduction' combined with a Sprengneter undervaluation threshold of at least 20%, surfacing properties that are both already discounted and marked as undervalued by the automated pricing model. 12. He cautions that Sprengneter's automated valuation depends entirely on seller-entered data and should be treated as a first-pass screening tool, not a definitive valuation. 13. A new filter added to Immometrica two weeks before recording—'minimum age of the offer in days'—allows searching for properties that have been listed for 150–180 days, a period that often aligns with expiring broker mandates. 14. Combining the price-reduction filter with the minimum-age filter produces what Ferdi calls 'the filter combination of the year': a hit list of properties with both confirmed seller discounting and likely elevated motivation due to a stalled, aging listing. 15. Martin cautions against setting any filter combination too strictly, citing coaching experience where participants filtered themselves into too few results and stopped visiting properties entirely. 16. Ferdi's rule of thumb: set search parameters broad enough to manually review each result; if too many objects arrive, tighten filters; if too few arrive, loosen them. 17. For investors focused on a primary location, Ferdi recommends maintaining immediate-notification search for hot markets and daily-digest notifications for secondary exploratory locations, using both modes simultaneously. 18. Immometrica's new micro-location rating feature provides colorful family-friendliness and student-friendliness scores per address, derived from proximity to schools, kindergartens, and public transit—useful for target-group analysis before visiting a property. 19. Investors focused on German major or secondary cities who want to use data-driven acquisition filters, particularly in the current post-pivot market with elevated price reductions, will find this episode directly applicable. 20. Listeners outside Germany, those new to real estate investing without basic knowledge of German property search platforms, or those looking for deal analysis rather than search methodology will get limited value.

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