Location insights for your use-cases
Present dream places effortlessly
Impress your users AND sellers.
Locations explained: clear and comprehensible
All place evaluations are available via API for easy and quick integration into any system.
Do you need more indicators than shown below? Let us know!
New: AI location descriptions for property profiles
Details about all our location indicators
“How loud is it here?”
A third of home seekers want quietness. Convince those with your offering.
Our unique noise check API returns an up-to-date, precise and accurate analysis about environmental noise. It has been created in collaboration with one of the leading noise pollution experts in Germany, Prof. Birger Gigla. Easy to integrate into your online property profiles.
Your noise-sensitive users will become fans!
The problem: is it quiet here?
A third of renters and three quarters of buyers in Germany are looking for a quiet location. Noise pollution is one of the top “No-Go’s” when people decide against a property.
To evaluate noise pollution correctly is hard and time-consuming.
This is how the noise check works
The noise check AI finds noise sources in the environment, quantifies their loudness and returns an easy-to-understand result to quickly see if a place is quiet. The result is calculated for front and back of the property and split into day, evening and night.
We include street noise, train noise, flight noise, noise from industry (e.g. power plant) and commerce (e.g. shopping center). In addition we analyse over 100 further noise sources such as churches, playgrounds or bars and quantify noise pollution from passers-by and party goers (e.g. close to a party mile).
Results are accurate down to a 3 meters, validated through hundreds of user feedbacks and based on up-to-date geo data.
“Is this place well connected to transport?”
Younger people increasingly want good public transport.
Our connectivity check provides all relevant insights about getting to and from the property. We don’t just look at stops and available lines but analyse timetables to get a most accurate result.
“Ended up in the middle of nowhere” was yesterday!
The problem: how fast can I get to the places that are important for me?
More than half of home seekers in Germany want a good connection to public transport. This increases to three quarters among under 30’s.
To check this for every interesting property is time-consuming and leads to errors.
How the Transport Check works
First, the algorithm checks the surroundings, finds all close stops of every means of transportation and returns their respective distances. Subsequently, it calculates footpaths and how long it would take to reach stations by walking.
Now what if the connections are bad?
In the next step, the algorithm calculates a connectivity analysis. Based on real travel times to important locations, different times and weekdays.
Finally, we determine a total score which we return together with details about stops and walking times there.
The result: A full understanding of a place’s connectedness from all important angles.
“Can I buy everything I need here?”
Day-today needs are still largely bought offline. Show your users where.
Despite online shopping, the largest part of food is still bought in local shops. A good selection of stores, especially supermarkets, is an important selection criterion for home seekers. Our Shop Check finds all close shops and evaluates offering and reachability.
We show your users if they can find what they need for their daily needs.
The problem: can I buy here what I need?
Only 2% of revenue for food is turned over online (in Germany). Especially small things people like to buy around the corner.
Researching shops is annoying and often not even done.
How the Shopping Check works
At first, the algorithm surveys the vicinity and finds all types of shops and their distances.
Supermarkets are important for most home seekers, so for them we calculate how to get there by foot and how long this would take.
Afterwards the algorithm calculates the variety of stores: the more the better.
The API returns an overall score which we return together with close shops, their details and positions. From this you can easily show a local shopping assessment and visualise stores on a map.
“Is my child well provided for?”
A third of movings in Germany are families. Respond to their questions.
Parents have high expectations but little time. Infants need nurseries and playgrounds, later sport clubs and schools become important. No parent wants to live next to a dangerous crossing or the red light district.
“I already know all about what my child needs!”
The problem: can our children grow up well here?
More than 8 million people in Germany have underage children and change houses more often than average. Moving with a family is particularly stressful.
Checking the child-friendliness of a location costs time which parents don’t have.
How the Kids Check works
The GeoSci AI evaluates three dimensions which are important for families:
– Education and childcare: How is the availability of day-care and schooling?
– Quality of free-time activities: How about sports fields, swimming pools or a music school? Are there parks to have fun in?
– Health and safety: Are there dangerous locations or polluters nearby?
Every dimension is assessed individually and at the end we create a total score. We also communicate the reasoning behind it.
To answer questions about where exactly schools and nurseries are, we also return those, together with available details like websites for easy visualisation on a map.
We are currently working on this API (First version completed).
“Who lives here?”
The right neighbours are important for most.
And “right” generally means similar. But who wants to survey the entire neighbourhood? We provide the answers to questions about the social structure of an area, e.g. age, income or household sizes.
So your users feel at home in their new neighbourhood.
The problem: how do I find out who will live next to me?
A good neighbourhood is for half of home seekers within the top 3 of wishes (in Germany). What contributes to a good neighbourhood? People who match. Neighbours have children, too? A similar age?These are questions we often get.
Most people don’t have access to this data or don’t understand these statistics.
How the Socio-demographics Check works
Here it’s less about sophisticated algorithms or AI but about explaining statistical data – Correctly and simply.
We access different data sources which contain this information, merge and clean them.
Subsequently we return the result: suitable filtered and configured for the requested neighbourhood for easy display in your frontend.
This API we’re currently working on.
“Is it green here?”
Nature and parks are important for almost all home seekers.
Going for a run? Walk the dog? Barbecue with friends? Many of our freetime activities are more fun in the nature. And this doesn’t even account for the better air and nicer views. Our nature checks finds green spaces and evaluates attractiveness and recreational value.
Highlight places to unwind.
The problem: can I easily reach green spaces?
Two thirds of home seekers want easy access to nature. But research this for every property? Not what people want.
Not every green spot on a map is a park and one cannot just ask local residents online.
How the Nature Check works
Algorithms search the vicinity to find green spaces, parks or biotopes.
We then filter the spots with real recreational value (the shrubbery next to the train tracks is out).
On this basis, we calculate a summary score which also includes size and distance of the parks.
The API response includes the parks as polygons, details about each park and the overall score.
“How’s the nightlife?”
Half of people under 40 like to have easy access to places to go out
Partying in a club or a relaxed Sushi dinner – entertainment close by is a plus for a location. The Nightlife Check has the answers.
“This place is dead” doesn’t happen to your users.
The problem: can you go out here?
More than half of home seekers under 40 prefers locations with access to nightlife. This can be very different from one neighbourhood to the next.
Non one wants to scan Google Maps for every interesting property.
How the Nightlife Check works
At first, we find and categorise places to go out in the neighbourhood. This mean bars and nightclubs but also more relaxed locations such as restaurants or cinemas.
Then, we calculate the accessibility of each place, check customer reviews and, on that basis, calculate a summary score.
The API response contains the geometry of close places related to nightlife and their details together with that score.
“How’s the weather like here?”
Everyone talks about the weather. Join the conversation!
A third of Germans move out of their district and may not know the local weather. How warm is it? Is it sunny? Not really a decision criterion but definitely an eye-catcher.
Surprise home seekers with location-specific climate infos!
The problem: is the climate pleasant?
Alright, we admit: “How is the average weather?” is not a top question of home seekers. Still: weather.com alone has over 20M unique visitors per month. Weather is a topic that interests almost everyone.
Your users won’t find local temperature curves and season-dependant hours of sunshine anywhere else.
How the Weather Check works
We aggregate local weather data from the last 5 years for the location of the property.
On that basis, we calculate average temperature and hours of sunshine per month.
You will get the summarised result lightning-fast via API. Everything you need to display a good-looking diagram within your property profiles.
This feature is currently in development (Proof of Concept completed).
“Am I at the right place here?”
Dream locations are personal.
Beyond the standard questions, people’s desires are often very individual. Can I go model flying? Is there a bio market? Dog training ground? Many of those questions we are able to answer.
Little things make the different between a good and the right place.
The problem: does this place have what I need?
Many home seekers have individual wishes beyond the standard location requirements. These need to be researched anew for every interesting property.
In case of several needs, this quickly becomes time-consuming and frustrating.
How the Dream Place Check works
For every property, we store a comprehensive set of points of interest, optimised for quick searching and fast loading.
Within your system you can then ask users for their location preferences and store those (an additional advantage: thanks to this deeper understanding of your users, you can display better-targeted content or improve your services).
Dependent on user preferences, we supply the right information and places for every property. So you can show on a map what interests a particular user most.
We are currently working on this feature (Proof of Concept concluded).
“What would the locals say?”
Outstanding location descriptions spark emotions
“Hamburg inner city with balcony” or “sunbathing at the Alster”: What appeals to you? Great location texts create pictures and emotions but still need to contain all important information. Right (or wrong) texts directly influence user satisfaction and conversion rates. They also affect how well search engines can find your properties.
Support your sellers with outstanding location texts!
The Problem: no time or lack of marketing skills
An excellent location description creates work to research and write and demands sales skills.
Deficient real estate profiles lead to fewer conversions and can damage the image of the portal.
How the AI text-generation works
Depending on the location of the property, our algorithm finds the right frame of reference: is it the city district or, for example in the countryside, the administrative area.
Then, the GeoSci location check assesses all location highlights and the important points of interest in the area.
Based on this input and the right prompt, we request a text from a GPT-3-based language model.
We have just concluded a proof of concept and are currently developing the feature which will go live in Q2 23.
Who wants to live at a hot spot of crime? Probably not your users. Through an AI-informed methodology, we have identified areas with a higher incidence of crime (in Germany). So you can help your users find a place where they will feel safe.
This feature has been developed and tested as a proof of concept. If the capability interests you, please let us know.
Spatial resolution / Precision
Accuracy: Test of crime classification