OpenStreetMap (OSM) data is an increasingly valuable resource for real estate analysis. In the era of big data, real estate professionals must have access to the most accurate and up-to-date information to make informed decisions. And they want to eliminate biases from their data sources. OSM offers a wealth of data that can help real estate investors make better decisions. However, processing this data for real estate analysis also presents a number of challenges.
OSM data can assist real estate portfolio managers in making more informed decisions about the location of a property. By providing detailed information about the surrounding area, such as nearby businesses, parks, and public transportation, property decisionmakers can better understand the strengths and weaknesses of a particular location. In addition, OSM data can be used to identify and map areas of risk, such as flood zones, earthquake zones, and areas prone to wildfires. Another valuable aspect of data is its integration with other data sources. OSM data can be easily combined with data from other sources, such as demographic data, property data, and traffic data, to create a more complete picture of a particular location. All this data can be used to understand the potential risks associated with a particular location, and help make informed decisions about their real estate investments.
Data Format
One of the key difficulties of processing OSM data is the format of the data. Unlike normal geo-located data, which is often provided in a standardised format, OSM data can be stored in a variety of formats, making it difficult to process and analyse. This can lead to difficulties in combining OSM data with other data sources, such as demographic data or property data, to create a more complete picture of a particular location.
Size and complexity
Additionally, processing OSM data can be time-consuming and resource-intensive. Due to its size and complexity, it can take a significant amount of time and computing power to process and analyse the data. This can be particularly challenging for real estate analysts who need to process a large amount of data in a short amount of time.
Variability in coverage and detail
Another difficulty of processing OSM data is its variability. Unlike proprietary map data, which is often standardised and consistent, OSM data can vary in quality and detail depending on the area being mapped. This can make it difficult for real estate analysts to rely on the data to make informed decisions, as the data may not be consistent or accurate in all areas.
Technical challenges
Finally, processing OSM data can also present a number of technical challenges. For example, real estate analysts may need to have specialised knowledge of programming languages and GIS software in order to process the data effectively. This can make it difficult for those without technical skills to use the data, and can limit the potential use of the data for real estate analysis.
Extrapolation
One of the common misconceptions about using OpenStreetMap (OSM) for real estate analysis is that 100% coverage is required in order to gather meaningful insights. However, this is not the case. In fact, partial coverage of an area can still provide valuable insights for real estate investors. For example, even if there is only partial coverage of a location in OSM, it is still possible to infer important information by using machine learning algorithms. A machine learning model can be trained on data from areas with incomplete OSM coverage to identify patterns and relationships between different variables. This information can then be used to predict the values of missing data in areas with partial coverage.
In conclusion, while OSM data provides a wealth of information that can be useful for real estate analysis, processing this data can also present several challenges. These challenges include the format of the data, its accuracy, the time and resources required to process it, its variability, and the technical skills required to use it effectively. Real estate analysts must be aware of these challenges in order to use OSM data effectively and make informed decisions based on the data.
The figure illustrates the first so-called ‘swimlane’ to prepare OSM data for a real estate analysis.
