Real estate is a high-involvement, long-cycle purchase. Prospective buyers usually move through several stages – from generic research to detailed comparisons and finally project-specific searches. This behaviour is well documented in real estate funnel and customer-journey literature, which emphasises multi-stage decision paths and multi-channel touchpoints.
This case study illustrates how different attribution models change the perceived value of keywords along the path to a property inquiry for a fictional new-build project, “Skyline Lofts Berlin”, and what this implies for real estate marketing strategy.
1. The Buyer Journey: Maria’s Path to “Skyline Lofts Berlin”
Maria lives in Stuttgart and considers buying an apartment in Berlin as a long-term investment and occasional city base. Her search behaviour spans several weeks.
Day 1 – Initial Exploration (Awareness)
- Query: “buy apartment berlin”
- Clicks a search ad leading to a generic listing page with multiple projects
- Browses a few offers, including “Skyline Lofts”, but does not send an enquiry
Funnel stage: Awareness (broad market scan)
Role: Entry point into the digital real estate funnel – initial exposure to the project.
Day 7 – Narrowing Down Location and Type (Consideration)
- Query: “new build apartment berlin mitte”
- Clicks a search ad from the same developer, now filtered to central Berlin projects
- Reads more about facilities, pricing ranges, and floor plans
Funnel stage: Consideration
Role: Filters market options by location and asset type; “Skyline Lofts Berlin” remains on Maria’s mental shortlist.
Day 18 – Investment & Quality Check (Late Consideration / Evaluation)
- Query: “loft apartment berlin investment”
- Clicks an ad highlighting investment-oriented benefits (rental yield, location, demand)
- Downloads a brochure and a yield calculator PDF
Funnel stage: Late consideration / evaluation
Role: Confirms that a loft-style unit in Berlin is a suitable asset from a financial and lifestyle perspective.
Day 25 – Final Project Search (Conversion)
- Query: “skyline lofts berlin 2 bedroom”
- Clicks a project-specific search ad
- Fills in a contact form for a viewing of a 2-bedroom unit, assigned an internal lead value of €3,000 to reflect expected commission and close rates
Funnel stage: Conversion
Role: Final step – Maria already knows the project and is now signalling concrete purchase intent.
This pattern – broad generic searches, then location/type refinement, followed by project- or brand-specific queries – closely matches modern descriptions of digital real estate funnels and buyer journeys.
2. Attribution Input: Touchpoints and Conversion Value
- Conversion type: Qualified contact form for property viewing
- Assigned lead value: €3,000 (based on average commission probability)
- Search touchpoints:
buy apartment berlinnew build apartment berlin mitteloft apartment berlin investmentskyline lofts berlin 2 bedroom
Attribution models define how much credit each keyword receives for the €3,000 conversion. In digital marketing, these models are used to distribute credit across multiple interactions instead of attributing everything to only the first or last click.
3. Attribution Models Applied to the Real Estate Journey
Using the same conversion value (€3,000), the six common attribution models yield the following distribution:
| Attribution model | buy apartment berlin | new build apartment berlin mitte | loft apartment berlin investment | skyline lofts berlin 2 bedroom |
|---|---|---|---|---|
| Last Click | €0 | €0 | €0 | €3,000 (100%) |
| First Click | €3,000 (100%) | €0 | €0 | €0 |
| Linear | €750 (25%) | €750 (25%) | €750 (25%) | €750 (25%) |
| Time Decay | €60 (2%) | €390 (13%) | €750 (25%) | €1,800 (60%) |
| Position-Based | €1,200 (40%) | €300 (10%) | €300 (10%) | €1,200 (40%) |
| Data-Driven | €360 (12%) | €900 (30%) | €660 (22%) | €1,080 (36%) |
These model types – single-touch (first, last) and multi-touch (linear, time decay, position-based, data-driven) – correspond to the categories widely discussed in current attribution literature and platform documentation.
Note: In Google Ads and GA4, rule-based models such as first click, linear, time decay and position-based have been deprecated for new conversions, with data-driven attribution becoming the recommended default. Conceptually, however, these models remain useful for understanding how different attribution logics shape perceived performance.
4. What the Models Suggest – and Why It Matters
4.1 Last Click – “Only Project Keywords Perform”
Under last-click attribution:
- Only
skyline lofts berlin 2 bedroomreceives credit (€3,000). - All generic and mid-funnel keywords appear to generate zero revenue.
Implications:
- Budget appears best spent solely on brand/project keywords.
- Generic terms like
buy apartment berlinlook expensive and “inefficient”.
This reflects the classic bias of last-touch models: they ignore how earlier touchpoints create demand and drive users towards branded or project-specific searches.
4.2 First Click – “Discovery is Everything”
In the first-click model, the generic keyword buy apartment berlin receives 100 % of the value:
- Emphasises the importance of market entry and discovery.
- Underestimates the role of later, more specific queries in confirming intent and closing the lead.
4.3 Linear – Equal Weight for All Stages
The linear model distributes €3,000 equally:
- Each keyword receives €750 (25 %).
Interpretation:
- Recognises that the buying decision is the result of a sequence of interactions rather than a single moment.
- Makes the length and complexity of the journey visible – typical for real estate, where decision cycles are longer and involve more research.
Limitation: assumes all touchpoints have equal impact.
4.4 Time Decay – Emphasis on Decision-Stage Queries
The time-decay model gives more credit to the interactions closer to conversion:
buy apartment berlin= €60 (2 %)new build apartment berlin mitte= €390 (13 %)loft apartment berlin investment= €750 (25 %)skyline lofts berlin 2 bedroom= €1,800 (60 %)
This model:
- Reflects the intuition that late reminders and detailed searches are more influential right before the enquiry.
- Still acknowledges early generic searches as part of the path, but weights them lower.
4.5 Position-Based – Entry and Exit as Co-Heroes
The position-based (U-shaped) model:
- First and last interactions receive the largest shares (40 % each).
- Middle interactions share the remaining 20 %.
In this case:
buy apartment berlin= €1,200 (40 %)skyline lofts berlin 2 bedroom= €1,200 (40 %)new build apartment berlin mitteandloft apartment berlin investment= €300 each (10 %).
Implications:
- The awareness keyword and the project keyword are recognised as co-key events:
- First contact: introduces Maria to the developer and the project.
- Last contact: triggers the actual enquiry.
- Mid-funnel queries still receive credit for refining preferences.
4.6 Data-Driven – Evidence from Many Buyer Paths
In data-driven attribution, algorithms analyse many successful and unsuccessful journeys to estimate how much each interaction changes the probability of conversion.
In the example distribution:
buy apartment berlin= €360 (12 %)new build apartment berlin mitte= €900 (30 %)loft apartment berlin investment= €660 (22 %)skyline lofts berlin 2 bedroom= €1,080 (36 %)
Interpretation:
- The location- and type-specific query (
new build apartment berlin mitte) has particularly high explanatory power for predicting enquiries. - The project keyword remains the strongest single driver but not the only one.
- Upper- and mid-funnel searches still receive significant credit.
For AIRVOLKSMARKETING, such data-driven outputs guide budget shifts towards those keywords that empirically contribute most to lead generation – across the entire funnel.
5. Business Impact for Real Estate Marketing
5.1 Risks of Relying on Last-Click Only
If performance is judged purely on last-click attribution:
- Generic and mid-funnel keywords appear unprofitable.
- Awareness and consideration campaigns for new projects or new markets are systematically underfunded.
- Project/brand terms seem to “do all the work”, even though they depend on prior exposure.
In a real estate context – where buyers often require multiple weeks or months to decide – this can result in:
- Fewer new buyers entering the funnel,
- Overdependence on existing branded demand,
- Lower long-term lead volume and slower project sell-out.
These dynamics mirror broader findings on the limitations of single-touch attribution in complex buyer journeys.
5.2 Funnel-Aware Strategy with Multi-Touch and Data-Driven Attribution
A funnel-aware, attribution-informed approach allows AIRVOLKSMARKETING to:
- Maintain Upper-Funnel Visibility
- Preserve or selectively scale generic queries like
buy apartment berlinto feed the top of the funnel with new potential buyers.
- Preserve or selectively scale generic queries like
- Invest Strategically in Mid-Funnel Queries
- Allocate sufficient budget to high-intent, non-branded queries (e.g.
new build apartment berlin mitte,loft apartment berlin investment) that are statistically linked to qualified enquiries.
- Allocate sufficient budget to high-intent, non-branded queries (e.g.
- Use Brand / Project Keywords as Closers, Not the Only KPI
- Continue to bid strongly on project and developer names, while recognising that their performance depends on prior funnel stages.
- Adopt Multi-Touch and Data-Driven Models Where Possible
- Use platform-level data-driven attribution in Google Ads and GA4 as default if data volume allows, complementing it with path analyses and offline CRM data (e.g. call tracking, on-site visits).
6. Conclusion
The “Skyline Lofts Berlin” case demonstrates that:
- Real estate enquiries emerge from multi-stage, multi-keyword journeys, not from isolated last clicks.
- Generic and mid-funnel search terms play crucial roles in discovery, qualification and confidence-building.
- Attribution choice strongly shapes which campaigns appear “profitable” and which are mistakenly cut.
By integrating search funnel analysis, conversion path reporting and multi-touch / data-driven attribution, AIRVOLKSMARKETING can:
- allocate budgets more accurately along the entire buyer journey,
- protect essential top- and mid-funnel activities from short-sighted cuts,
- and support developers and agents in achieving faster absorption and more predictable lead flows across their real estate portfolios.
References
- Tracify. (2025). Real Estate Marketing in 2026: Key Strategies.
- Oricmeta. (2025). The Real Estate Digital Marketing Funnel Explained.
- REX Software. (2023). The 3-Step Real Estate Marketing Funnel.
- Codedesign. (2025). Customer Journey Maps for Real Estate: How to Create Effective Ones.
- Google Ads Help. (2024). About attribution models.
- Nielsen. (2019). Methods & Models: A Guide to Multi-Touch Attribution.
- WhatConverts. (2025). Multi-Touch Attribution: Models, Benefits & Best Practices.
- Salesforce. (n.d.). Multi-touch attribution: What it is & best practices.
- Marketlytics. (2023). Google Removes Four Attribution Models: How It Affects Advertisers in 2024.
- UXAX. (2023). Ads Attribution Models Google is Sunsetting in 2024.
- Webstar Research. (2024). GA4 Data-Driven Attribution: Complete Guide.
- Funnel Leasing. (2025). Why multitouch attribution is essential for multifamily.
