The Need for Data Analytics to Flood-Proof Property Investment

Did you know that the total risk of floods isn't accounted for in urban planning in the US due to a denial of climate change and a lack of awareness of potential dangers?

Consequently, homebuyers end up paying inflated prices that don't take into account the financial implications of insurance, flood repair, and property devaluation.

The truth is that inland flooding has resulted in an astronomical $177.9 billion in damages in the US over four decades. And a study highlights that flooding, exacerbated by climate change, is getting more lethal and financially devastating. 

To mitigate risk, investors, local authorities, insurance agents, and lenders currently leverage flood maps to spot at-risk zones and floodplain boundaries. With this information, they aim to make smarter investment choices and design strategies, including elevating building foundations, restricting some development in vulnerable areas, and mandating insurance for properties.

Here's the catch: These maps use outdated data and fail to account for the effect of climate change on extreme weather events and the ongoing changes in land use.

Therefore, spatial data analytics and artificial intelligence (AI) tools are becoming the go-to option for real estate investors looking to make more informed decisions. Let's dive into the details.

The Need For Data Analytics To Flood-Proof Property Investment

As spatial data analytics and AI analyze various factors, such as elevation and historical flooding activity, they allow decision-makers to gain critical insights into a property's flood risk potential. Armed with this knowledge, real estate investors can avoid putting their money in high-risk flood zones.

Bear in mind that there is no federal requirement in the US for home sellers to disclose information regarding flood risk. In fact, doing it can decrease a property's value by approximately 4%. Therefore, data analytics solutions can protect homebuyers and property investors from financial losses by providing them with a comprehensive overview of a property's price. 

Besides, these tools can develop rapid estimations of property prices without the need for manually collecting and updating the input data, which is particularly beneficial in the fast-paced real estate market.

Estimating Flood Risk With Spatial Data Analytics And AI

Through radar and satellite imagery, spatial data analytics and AI tools collect high-resolution images of floodplains, improving the accuracy and timeliness of flood mapping models. Then, machine learning algorithms, such as Decision Tree and Random Forest, analyze rainfall, soil moisture, and environmental factors to predict flood locations, size, and severity.

The National Oceanic and Atmospheric Administration (NOAA), for instance, uses this technology to gain a deeper understanding of flooding risks and make cities more resilient in the US.

Added to that, machine learning algorithms utilize social media data to identify flooding-related tweets, posts, and images, revealing the flooding's scope and intensity. This enables the development of more focused and efficient response strategies.

With climate change worsening extreme weather conditions, flooding has become a pressing concern for the real estate world. Traditional maps can provide some information about flood risk in a region, but they aren't as effective as data analytics and AI tools ’real-time insights. By leveraging these innovative solutions, investors can avoid sinking their money into properties at risk of flooding.

The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "a flood destroying a city".