Geospatial analytics is a huge and growing market. It’s been estimated that the global geospatial analytics market grew to $12 billion by 2020, with an annual growth rate of 16%. This blog post will teach you the basics of geospatial analytics, specifically for property analysis, and its uses across business settings. You’ll learn what this field entails and how companies can use it for their own purposes.
So what is geospatial analytics?
Geospatial analytics is a form of computational analysis that leverages geographic information, spatial data, location data, and increasingly, high-resolution imagery, computer vision, and other forms of AI to extract structured data that can be used for specific applications and industries.
Geospatial Analytics: What It Is, Why It Matters
With a moniker derived from “geo,” meaning earth or land, and “spatial,” which refers to a location in space, geospatial analysis is an analytical technique that involves gathering data about the Earth’s surface (such as elevation, population density) or the built environment (roads, buildings) and using them to make predictions about human behavior or natural phenomena.
In its most primitive forms, geospatial analysis can be traced as far back as the earliest forms of cartography and surveying, if not much further. But beginning in the 1960s, the term became associated with geographic information systems (GIS) used to catalog natural resources. Since then, geospatial data has been used in a wide variety of ways, and can span many forms of technology—satellite imagery, GPS, coordinate systems, IoT sensors, and additional location-based data.
Governments use GIS-based solutions for everything from tracking extreme weather events and climate change, to measuring and managing traffic patterns, to assessing population growth and its impact on energy, transportation, and housing needs as part of urban planning and land management.
Thanks to the revolution in big data, cloud computing, and machine learning, geospatial analytics is now used to find patterns and meaning within geospatial data that can help improve business decision-making, performance, and operational efficiency.
The Role of Geospatial Analytics in Modern Business
As part of this revolution, geospatial imagery and data visualization have become important sources of truth on the grounds that they make geospatial analysis easier to verify and explain. And as the quality and availability of geospatial imagery has increased, so has its utility in a new generation of geospatial analytics-based solutions. Many of these solutions are building a better understanding of a central aspect of the global economy: property.
Property data has always been challenging to collect, and for property-related industries such as insurance and real estate, the impact of geospatial analytics on organizations is nothing short of transformational. For example, instead of the time- and labor-intensive task of conducting onsite inspections, critically important information on exterior property condition is available on-demand.
Property Condition: How Geospatial Analytics Works
Powered by AI, modern geospatial analytics use high-resolution geospatial imagery and computer vision to extract structured data from imagery. When focused on property, this includes the size of a property, type of roof construction and its condition, the presence of solar panels, whether there are overhanging trees, and whether there’s a backyard swimming pool, yard debris, or vegetation encroachment around the property that could pose a fire danger, and more.
Today’s most advanced solutions can provide this kind of data for tens of millions or even a hundred million commercial and residential properties nationally, delivered with the speed and coverage of traditional property record data.
By providing instant, accurate intel on property conditions, modern analytics enables users to assess a specific property, as well as compare it to similar properties within the same neighborhood, zip code, city, region, or by type across the nation. They can also analyze local, regional, and national trends that can impact its value or risk profile over time.
The value of this intelligence depends on the resolution and recency of the imagery it uses and the machine learning put in place to analyze it. To understand why, let’s start by examining different types of geospatial imagery used for various kinds of data visualization and analysis. As we explained in a recent post, different sources of image-capture methods come with distinct pros and cons. And manual review of these images by analysts presents its own issues.
Remote Sensing Data & Geospatial Imagery
Remote sensing is a technique that uses imaging from satellites, aircraft, and other sensors to capture information about the Earth. There are different forms of remote sensing data—but geospatial imagery is the most powerful for property-related analytics.
Geospatial imagery is available at different resolutions and refresh rates. Today, manned aircraft ranks among some of the best resources for high-resolution geospatial imagery with wide coverage of highly-populated areas every few months so the images have excellent data currency at a reasonable price. Increasingly, stratospheric balloons are becoming another source of high-resolution imagery with recency measured in days, and perhaps someday, real-time.
Machine Learning & Computer Vision
In a world awash with data, spatial analysis is often the first step to understanding and analyzing geo-referenced data. However, it can be difficult for analysts to go about this process at scale, as most tools are designed for smaller datasets or single users.
Thanks to the rise of machine learning and computer vision, we can use algorithms to create far more powerful and accurate analyses that provide new insights and correlations otherwise unachievable. And by combining machine learning and advanced data science techniques, geospatial analytics are further turned into predictive analytics, capable of spotting trends as they take shape.
Use Cases: From Imagery to Intel
Transforming imagery into structured data (i.e., useful information) is intricate and requires a substantive level of expertise.
At CAPE, for instance, we begin by knitting together a coast-to-coast view of the United States using geospatial imagery provided by our imagery partners. Then, we deploy tailored computer vision algorithms to identify key elements in the imagery relevant to risk analysis and valuation. Next, additional data, ranging from parcel information to recent weather events to hazard information, are layered in via machine learning to create a comprehensive view of a given property. Finally, the outputs are returned as structured data linked to an address.
To understand how this kind of AI-enabled geospatial analysis can be put to use—let’s look at use cases within the insurance and real estate industries.
The business intelligence gleaned from geospatial analytics help insurers select better risks, reduce expenses, and improve the customer experience in a number of ways.
– Residential Property
Carriers like Kin are turning to geospatial analytics for the most recent and accurate property information, giving them the confidence to accurately price risk when offering online quotes to customers shopping for homeowners insurance online. Geospatial analytics also helps insurers make faster and more accurate underwriting decisions while reserving physical inspections for situations that require additional scrutiny.
For example, homeowners policies on properties with a bad roof have had loss ratios three times higher than those on properties with roofs in good or excellent condition. In fact, 40% of insurance claims are related to the roof. Roof Condition Rating (RCR), created by the team here at CAPE, analyzes every roof on more than 110 million structures nationwide, rating them on a five-point scale ranging from severe to excellent. All available through API and easily integrated with underwriters’ existing workflows. It’s worth pointing out that this is also without the need for expensive, time-consuming physical inspections.
Then there are renewals. After a property is underwritten, the profile on that property is often never updated again. By incorporating timely geospatial property data analytics into renewal workflows, insurers like CSAA reduce inspections of roofs known to be high-quality, provide more accurate pricing at renewal, and helping members proactively mitigate future risk by cutting vegetation back before damage occurs. With automated change detection, insurers can also know when the risk profile of a property has meaningfully changed and right-size insurance coverage to offer the best protection.
– Commercial Property
Consistent, structured property condition data enables commercial property insurers to power more scalable workflows. Solutions offering at-a-glance condition scoring and visual highlights of structures that need attention are especially valuable. Our own customers report that at least 20% of past underwriting or inspection decisions would have been different if more accurate property condition data had been available.
External property condition data unearthed through geospatial analytics is also correlated to overall property risk and internal claims. Building roof condition, for instance, is linked to higher wind and hail losses and business interruption claims. Likewise, lot debris is linked to liability losses. In addition to helping insurers avoid unnecessary inspection costs, post-bind pricing changes, and unexpected, downstream losses, geospatial analysis enables innovative new underwriting strategies.
– Natural Catastrophes: Wildfire, Wind, and Hail
Homes and businesses with a large amount of foliage overhanging a roof experience 90% higher wind-related losses. And in the western US, a tremendous number of homes are being built in areas with high wildfire risks. In fact, wildfire is one of the most granular perils in terms of how risk can deviate from one property to the next.
Modern geospatial analytics solutions can provide insurers with on-demand access to structured data on important wildfire risk down to the specific property level, including proximity to surrounding fuel load. According to our own data, for instance, homes in very high hazard zones with heavy vegetation coverage have 5X higher losses than those with more defensible surroundings. By identifying property attributes such as roof geometry, insurers are better able to capture premium, triage inspections, and avoid unexpected losses.
Just as with insurers, AI-driven geospatial analytics is rapidly gaining the attention of the real estate industry , precisely because viable alternatives for fully understanding property risk are often not available, not granular enough, or are outdated. According to Deloitte, these technologies represent a quick, lean, and affordable way to gain an understanding of assets all the way down to zip code, neighborhood, and individual-address level. And this can dramatically enhance decision-making for interests throughout the real estate ecosystem, including the following.
– Loan Trading
Geospatial analysis is increasingly used to augment traditional data with additional condition variables that can significantly improve valuation and pricing models for better-informed decisions. For many, broker price opinions (BPOs) and appraisals are too costly and time-consuming for a marketplace that demands split-second decisions. And even sophisticated automated valuation models (AVMs) can miss critical details that impact financial decisions.
Using geospatial analytics, loan traders can identify outlier properties and quickly determine whether target properties are in good condition or estimate rehab costs. They can also optimize or expedite the property inspection process while monitoring existing positions to make sure collateral remains in good condition.
– Multi-Property Investment and Management
These and other factors are of particular interest to investors competing in what has become a red-hot market for single-family rentals (SFRs). Because traditional AVMs and even drive-bys can miss critical and current property features that impact rehab cost and value, their usefulness in the modern world of SFR investing is limited—at least on their own. Whether they’re exploring a single property, or shopping nationwide, geospatial analysis enables investors to instantly access neighborhood, census tract, zip code, or CBSA-level data so they can zero in on the most promising regions and properties. They can also review aggregate changes over time to unearth trends (both good and bad) in order to identify the best markets to enter.
– Loan Origination and Servicing
Originators and servicers can use geospatial analysis to better value and assess property. They can use geospatial intelligence to identify properties that differ from existing data sources, with new fields such as roof condition, pool, solar panel, and number of structures, and more. With this augmented information in hand, they can optimize or expedite the property inspection process and automatically understand if there are important changes to property condition that may affect the collateral value.
Endless Additional Possibilities
These are just a few examples of how we at CAPE apply geospatial analytics today. But there are many other potential use cases for these data-drive technological capabilities. Roofing, solar panel installation companies and others can use it to conduct remote inspections for prospecting and quoting. So can property and landscape management companies.
Local governments can conduct property tax assessments far more efficiently. Telecoms and other utility companies can identify towers at risk from vegetation overgrowth to manage clearance operations. And geospatial analytics’ value to FEMA and other emergency response organizations may be immeasurable in the aftermath of catastrophic events. For geospatial analytics, the sky’s the limit.
To learn more about geospatial analytics and its use cases in industries such as insurance and real estate, visit capeanalytics.com