How to use AI-driven geospatial analytics to gain a competitive edge in the real estate market?


How to use AI-driven geospatial analytics to gain a competitive edge in the real estate market?

Data Analytics & Artificial Intelligence for Real Estate

Data analytics can significantly improve decision-making in real estate, specifically Artificial Intelligence (AI)-driven geospatial analytics. It is a quick, lean and affordable way to provide address-specific rental predictions that are explainable and transparent.

Microanalysis on address level

The use of data is important in any sector, including real estate. The use of data analytics can improve decision-making, from valuation, sale/purchase of properties, and contracting to negotiations, risk analysis, and planning. Data is often available for bigger cities, which makes macro analyses relatively easy for these locations. However, the smaller the city, the harder it is to get this data. This becomes even more of a struggle for single addresses, micro-analysis, even though this information can be very useful.

Data challenges for real estate

Despite the value of this data in the real estate sector, there are still some challenges. The required data often is not available, not granular enough, or outdated. If the data is available, not all manual data corrections might be performed. They might not have been harmonized across geographic areas, or the data might not be cleaned yet. So, problems can arise even before starting a simple analysis. This means there is a risk of ending up with expensive but worthless or even misleading analysis results due to, for instance, personal bias by the expert 'correcting' the data issues. The alternative is buying the data, but good data comes at a price.

The benefits of AI for real estate companies

Once the challenges have been overcome, the insights that have been derived can be hugely beneficial for real estate companies. Enhancing their datasets with geographical features will justify the application of powerful analytics. This will lead to even better insight into previous market developments, sub-markets, locations, and interdependencies.

Bridging the data-knowledge gap

Using a "digital twin" approach can be of great value in real estate. A "digital twin" uses the information from data-rich environments and applies this knowledge to somewhat similar but data-poor areas. Based on the right data and a machine learning algorithm, the computer will build a model. This model will be able to render valuable answers based on simple information as the address and the construction year. For example, the model could be used to provide precise predictions of current and future rental values or recommendations for the highest yielding refurbishment options.

Explainable Artificial Intelligence (XAI)

To make this process as efficient and effective as possible, the prediction models will be integrated via API (often in the form of AI as a Service') into the workflows of real estate management software, feed planning, or risk models. This will enhance reports and provide visualization and make the decision-making process easier. However, with this much at stake, investors will doubt a machine prediction, especially if it is purchased externally as a service. That is why there is an approach called "explainable AI," which refers to AI being applied so that the results are easy to understand. Explainability will make the results more trustable and provide further insights, like why certain properties are more valuable than similar ones in the same area.

Real estate predictions 2021

This year the technology will become mature and suitable for use by the masses. Enough users will use it to make an impact on the market and unleash its full potential for the first time. For those who have invested in this technology early, data gathering and cleaning efforts will eventually become a thing of the past.

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