Buy to let Audience in Germany
A data profile of the people who buy, follow and care about Buy to let in Germany — modelled from more than twelve digital signal sources. Buy to let has an estimated audience of 299,955 people in Germany.
The average Buy to let fan in Germany is 45.0 years old, balanced, and lives primarily in Nordrhein-Westfalen.
The audience is concentrated in Nordrhein-Westfalen, Bayern, Baden-Württemberg.
Top brand affinities include Franz Kafka, Emily Dickinson, Oscar Wilde, with strongest over-indexing on Franz Kafka (123.69× the country average).
Demographically, the Buy to let audience skews balanced with an average age of 45.0, and over-indexes on personality traits such as Travelling, Career Orientation.
Compared to the country baseline, this audience shows distinctive patterns across 20 brand affinities and 16 regions tracked by Rascasse.
The typical Buy to let fan in Germany is balanced, around 45.0 years old, with strong Travelling tendencies and a notable affinity for Franz Kafka.
At a glance
The key figures that characterise the Buy to let profile in Germany.
Search interest · 12-month trend
Demographics of Buy to let fans
49.4% are female, 50.6% are male, average age 45.0.
Gender split
| Metric | Value |
|---|---|
| Female | 49.4% |
| Male | 50.6% |
| Average age | 45.0 |
| Estimated audience size | 299,955 |
Age distribution
| Age bracket | Share | % |
|---|---|---|
| 16-19 | 13% | |
| 20-29 | 8% | |
| 30-39 | 17% | |
| 40-49 | 29% | |
| 50+ | 33% |
Worldwide distribution
of the worldwide Buy to let audience comes from Germany.
| Country | Share |
|---|---|
| United Kingdom | 38.0% |
| United States | 19.7% |
| Ireland | 3.5% |
Geography
Where the Buy to let audience in Germany is strongest.
Top regions by affinity
| # | Region | Reach | Affinity | × |
|---|---|---|---|---|
| 01 | Bremen | ~4K | 1.50× | |
| 02 | Thüringen | ~9K | 1.11× | |
| 03 | Sachsen-Anhalt | ~8K | 1.08× | |
| 04 | Saarland | ~4K | 1.08× | |
| 05 | Brandenburg | ~10K | 1.06× | |
| 06 | Hamburg | ~7K | 1.00× | |
| 07 | Mecklenburg-Vorpommern | ~6K | 1.00× | |
| 08 | Schleswig-Holstein | ~10K | 0.97× | |
| 09 | Rheinland-Pfalz | ~10K | 0.93× | |
| 10 | Berlin | ~10K | 0.92× | |
| 11 | Sachsen | ~10K | 0.91× | |
| 12 | Hessen | ~20K | 0.85× | |
| 13 | Niedersachsen | ~20K | 0.84× | |
| 14 | Bayern | ~40K | 0.82× | |
| 15 | Baden-Württemberg | ~30K | 0.79× | |
| 16 | Nordrhein-Westfalen | ~50K | 0.71× |
Affinity by region
Audience interests & affinities
The strongest cross-interests of the Buy to let audience — brands, topics and people combined.
Top cross-interests, ranked by affinity
| # | · | Interest | Category | Affinity | × |
|---|---|---|---|---|---|
| 01 | Rabindranath Tagore | Literature | 200.22× | ||
| 02 | São Miguel dos Milagres | Travel & Leisure | 191.86× | ||
| 03 | Emily Dickinson | Literature | 171.45× | ||
| 04 | Pier Paolo Pasolini | Movies & TV | 139.19× | ||
| 05 | Franz Kafka | Literature | 123.69× | ||
| 06 | Sylvia Plath | Literature | 116.89× | ||
| 07 | Dante Alighieri | Literature | 115.74× | ||
| 08 | F. Scott Fitzgerald | Literature | 112.96× | ||
| 09 | Alexander Pushkin | Literature | 105.81× | ||
| 10 | Homebase | Shopping | 97.52× | ||
| 11 | Oscar Wilde | Literature | 95.83× | ||
| 12 | Edgar Allan Poe | Literature | 88.33× | ||
| 13 | Nobel Prize in Literature | Literature | 88.29× | ||
| 14 | Rawalpindi | Travel & Leisure | 87.35× | ||
| 15 | Google Books | Literature | 81.79× | ||
| 16 | Nobel Prize | Politics & Society | 52.66× | ||
| 17 | Johann Wolfgang von Goethe | Literature | 47.04× | ||
| 18 | Rabobank | Business & Career | 44.55× | ||
| 19 | Google Earth | Internet & Social Media | 20.00× | ||
| 20 | Ukrainian language | Politics & Society | 20.00× |
Personality of the Buy to let audience
Values above 1.00× are above the country average, values below 1.00× below it.
Deviation from the national baseline
| Trait | Cluster | Deviation | Score |
|---|---|---|---|
| Travelling | THRILL | 1.95× | |
| Career Orientation | POWER | 1.65× | |
| Luxury Orientation | PREMIUM | 1.44× |
| Trait | Cluster | Deviation | Score |
|---|---|---|---|
| Extroversion | THRILL | 0.79× | |
| Price Sensitivity | PREMIUM | 0.98× | |
| Spirituality | BALANCE | 0.99× |
Frequently asked questions
How many fans does Buy to let have in Germany?
Buy to let has an estimated audience of 299,955 people in Germany, concentrated in Nordrhein-Westfalen and Bayern.
What is the gender split and age of Buy to let fans?
49.4% of Buy to let fans are female, 50.6% are male, with an average age of 45.0 years.
Which brands do Buy to let fans like most?
Buy to let fans show strongest brand affinity for Franz Kafka (123.69×), Emily Dickinson (171.45×), and Oscar Wilde (95.83×) over the country average.
Where do Buy to let fans live in Germany?
Buy to let fans in Germany are most concentrated in Nordrhein-Westfalen (reach ~50K), Bayern (reach ~40K), and Baden-Württemberg (reach ~30K). These three regions account for the largest share of the active audience.
What other brands do Buy to let fans also like?
Beyond Buy to let itself, the audience over-indexes on Emily Dickinson (171.45×), Oscar Wilde (95.83×), Edgar Allan Poe (88.33×), and Google Books (81.79×) compared to the Germany average.
Explore more audiences
Related profiles, rankings and the same audience in other markets.
More Home & Garden audiences in Germany
- Food and drink (39,623,635)
- Entertainment (39,502,829)
- Business and industry (39,157,865)
- Shopping and fashion (38,134,645)
- Arts and music (37,163,288)
How to read this data
Audience size is the estimated number of people in Germany who actively search for Buy to let. Affinity is an over-index ratio: 2.0× means the audience is twice as likely to engage with that brand or trait as the country average. Reach is the estimated number of audience members in a region. Regional and brand-affinity tables are sorted from strongest signal to weakest.
About this audience profile
This audience profile is generated by Rascasse from anonymized search-behavior signals across Germany. For methodology see methodology. Affinity values are over-index ratios vs. the country average (1.0 = baseline). Audience sizes are estimated, not measured.
From global mass-market brands to niche local segments — explore the full library and see which intelligence layers are included.