The Importance of State-of-the-Art Data and Algorithms
As climate change continues to increase the severity and frequency of natural catastrophe events, it is more important than ever for insurers to make sure that the properties in their portfolio are insured to value. Insuring to value (ITV) is the process of ensuring a property has the proper amount of insurance to be able to rebuild in the event of an accident or natural catastrophe event.
When a property is not properly quoted, policyholders could have their home undervalued, and in the event of damages, the claims payout is not enough to rebuild the home. That’s why it is now more important than ever to have reconstruction cost estimators and property prefill data be as accurate as possible in underwriting.
What to Look for in a Reconstruction Cost Estimation Data Provider
1. A Total Component Approach
The total component approach is preferable because it doesn’t rely singularly on partial loss claims pricing. At CoreLogic®, our approach is to virtually build a property from the ground up from our vast construction research and validation efforts.
This starts with monitoring price points for hundreds upon thousands of materials and hundreds of labor trades across the country and ends with a process where we validate our reconstruction cost values against contractor surveys; loss analysis studies; competitive litigation studies; and relationships with design firms, builders, developers, architects, universities and construction organizations across the country.
2. Geographically Broad Construction Research
The more geographies covered, the better. We currently monitor more than 2,600 geographic areas to capture pricing data that fuels our construction research.
3. Deep Construction Research
It’s not enough to just have a broad view if it is not equivalently deep. Granularity in data is essential, as it ensures a holistic view of property, giving a carrier the ability to know everything they can from the outset.
In addition to the breadth of our research, we’ve moved from the standard five-digit ZIP code approach to the a nine-digit ZIP code level. This was introduced late 2019 and is an industry first to the market.
Five-digit ZIP codes break the U.S. into around 30,000 different regions, each having thousands of homes. When nine-digit zip codes are used, the U.S. is divided into 26 million regions, each of which is broken into subareas of a few dozen homes. This allows us to make more accurate assumptions about homes based in part on their location.
For example, assumptions about bathroom quality, kitchen quality or floor finishes are more accurate if the location is more specific. Within the same five-digit ZIP code in Detroit, there are both low- and high-income neighborhoods. But the homes within nine-digit ZIP codes contained within the same five-digit ZIP code are more similar to each other.
With this level of detail, we’re now looking at individual neighborhoods for our default construction assumptions instead of relying on the five-digit ZIP code level, which encompasses thousands of homes. As a result of our data, our reconstruction estimates are the industry standard.
What to Look for in a Prefill Data Provider
1. A Multi-Source Approach
Prefill data includes items such as the year built, home style and square footage of a property which are retrieved after a user provides an address. There is, of course, no single source of perfect property characteristic information in the market. That’s why it is important to keep in mind that relying on a single-source approach can lead to a skewed view:
- With publicly available data — such as assessor data — there’s availability problems with non-disclosure areas — and huge variability in reporting by jurisdiction.
- Appraisal data is seen as the most accurate of these but generally has lower coverage.
That’s why our idea behind property prefill is to leverage as many sources as possible, so we can derive the best data from each of them to return a more accurate prefill record. The data behind our InterChange® prefill comes from multiple sources within the housing industry — such as real estate agents, lenders, appraisers and assessors.
2. High-Quality Data Sources
It’s not just important to have a greater number of data sources; they also need to be of fair quality.
CoreLogic has access to a comprehensive database of proprietary appraisal data, which is a huge addition to our InterChange prefill data. We also have deep relationships with MLS boards across the nation, including the Partner InfoNet™ program. MLSs that participate in Partner InfoNet grant CoreLogic permissible use to utilize MLS data and imagery for risk management purposes in exchange for a revenue share.
Using as many sources as possible — and pulling out the best information from each — is what’s truly needed to create the most accurate digital property record available.
3. In-House Compilation of Mapping of Prefill Data
It’s not uncommon in the market to be in the business of buying data sets, such as buying tax roll data and reselling it as prefill. This can be an incomplete and complicated methodology.
By contrast, we compile, map and score our prefill data in-house. We refer to this as an “insurance-ready” solution.
Our prefill comes from a single master database with all the quality assurance and mappings needed to seamlessly integrate into our residential reconstruction cost estimator software. We have been continuously investing in this prefill. In fact, between 2018 and 2021, we saw a 21% increase in coverage as we continue to add sources. InterChange prefill offers higher quality data coming from more sources. It utilizes proprietary and reliable data sources, and it is mapped for use within RCT Express.
CoreLogic: Industry-Leading Reconstruction Cost Data
CoreLogic leads the pack with the best and most comprehensive reconstruction cost and prefill data.
For reconstruction cost estimation, the total component approach and broad and deep construction research work helps ensure that property can be correctly quoted — and more importantly, homeowners can be adequately insured, so in the event of a disaster, they’re covered.
And from a prefill data perspective, taking multiple sources of data, validating their quality and getting it compiled for an insurer’s specific use creates a consumable and trustworthy source of information. In this way, insurers can have greater confidence they are insuring to value and making sure their policyholders have adequate coverage.
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