Use machine learning to improve your data quality and business decision making

Location Based Machine Learning for Housing

The best ML methods, which we improved by adding location data.

Clustering houses

Calculating neighborhoods

Predictions

Housing value/ price estimations

Other studies, scenarios and statistics

Turn data into insights and uncover hidden aspects of your world

By using machine learning, your business can build models that can analyze larger, more complex data and deliver faster, more accurate results. You have the chance to identify a profitable opportunity and avoid risk.

Harvest the power of Machine Learning, we guide you through the process

Let our experts handle the technicalities and take you through the process of how we maximise its potential for you, step by step.

We include user-friendly explanations, demo sessions, unique geodata inputs and continuous communication throughout the geospatial project so that you get to reap the rewards while understanding how we add value to your projects and clarify expectations.

Clustering Houses

By clustering houses, we can identify others based on their similar attributes or values. 

We have used it to successfully estimate taxation information based on an array of geographical factors.

Thanks to the clusters, we knew which properties were comparable. The user can compare his property with a reference buildings and assess whether his property is correctly valued.

Calculating neighbourhoods

Everyone has their own view of what constitutes their neighbourhood. It can be a family-friendly residential area, a student neighbourhood, or a business zone. And it can vary depending on the size and type of city. At Stratopo, we can calculate it based on geo-data to make accurate real estate market analyses.

We use demographic, real estate and unique StraTopo data. This information is important in spatial analysis and neighbourhood comparisons.

Housing value estimations

At Stratopo, our Machine Learning models combine open-source data (National Registration Dutch buildings and land use, geo-data) with characteristics of the property and its environment and results from our unique internally-developed routing engine to compute the most accurate value for your house.

Our data model estimates realistic transaction prices of houses, at this moment in Limburg.

LoBaML uses not only the technical data and information about properties but also geo factors as inputs, which are generated by means of complex geodata models within StraTopo. We use a postgres database with the postgis and pgrouting extension. With these extensions we can run the geo data models which provide unique input features

Want to know more? Request a complete presentation through our contact page/ contact s.reulen@stratopo.nl or m.jastrzebowska@stratopo.nl.