Blog

November 08, 2018 | IN Blog

HazardHub Releases Frozen Pipe Score, Ice Dam Score and enhanced Weather Variables

An average of a quarter of a million American families have one or more rooms in their homes ruined and their lives disrupted each winter by water pipes freezing and breaking. Until today, the insurance industry has struggled to get their collective hands around this multi-billion dollar risk.

Introducing the Frozen Pipe Score and Ice Dam score from HazardHub!

Frozen Pipe Score

Numerous studies have shown the “threshold” temperature for frozen pipes is when the temperature falls below 20°F for more than 24 hours in areas where freezing is not normally expected and pipes are unprotected in crawl spaces and attics.  In areas that experience long periods of winter weather, pipes are generally better protected, but freezing pipes can still occur if bitter temperatures persist for several days.  The problem is, therefore, a function of both temperature and duration.

The HazardHub Frozen Pipe Score is based on long-term weather records from over 7,000 weather stations.  The final score is based on long-term average minimum temperatures, the number of days in which the maximum temperature is below freezing, the number of winter days below 0° and below 20°, plus several other variables.  It is a numeric scale (0 -365).  The ordinal scale has also been bucketed into 5 grades (A – F) based upon logical breaks in the data.

Ice Dam Score

Another potential risk for homes in colder areas are ice dams. An ice dam is a ridge of ice that forms at the edge of a roof and prevents melting snow (water) from draining off the roof.  The Ice dam occurs when melting snow water flows from a warmer part of a roof to the unheated portions of the eaves.  The water freezes and, over time, builds up into a dam which traps melting water, forcing it under the shingles and through the roof. The water that backs up behind the dam can leak into a home and cause damage to walls, ceilings, insulation, and other areas.

Ice dams are a function of both temperature and snowfall.  Without sufficient snow on the roof, even the coldest temperatures will not produce ice dams.  The HazardHub Ice Dam Score combines the severity and duration of below-freezing weather (all of the variables in the Frozen Pipe score) with the amount and duration of snow cover to create a numeric scale (0 – 665).  This ordinal scale has also been bucketed into 5 grades (A -F).

 

New Weather Data

In order to build our Frozen Pipe and Ice Dam scores, we needed to generate a tremendous amount of new weather data variables. Rather than keep them to ourselves, we’ve chosen to make them directly available to our users. While we’re mostly focused on our property & casualty lines of business, we also think this data will be helpful for Auto writers. Of course, there’s no extra charge for the additional data.

For a test address in Massachusetts, here’s how the enhanced Weather Data will appear in the HazardHub API –

weather_params: {

  • annual_average_days_less_than_0: “1.3 Days”,
  • annual_average_days_less_than_10: “10.6 Days”,
  • annual_average_days_less_than_20: “39.4 Days”,
  • annual_average_days_more_than_40: “309.4 Days”,
  • annual_average_days_more_than_50: “245.4 Days”,
  • annual_fall_days_less_than_0: “0.0 Days”,
  • annual_fall_days_less_than_10: “0.0 Days”,
  • annual_fall_days_less_than_20: “1.0 Days”,
  • annual_fall_days_less_than_32: “15.9 Days”,
  • annual_spring_days_less_than_0: “0.0 Days”,
  • annual_spring_days_less_than_10: “0.4 Days”,
  • annual_spring_days_less_than_20: “4.4 Days”,
  • annual_spring_days_less_than_32: “28.4 Days”,
  • annual_winter_days_less_than_0: “1.3 Days”,
  • annual_winter_days_less_than_10: “10.1 Days”,
  • annual_winter_days_less_than_20: “34.1 Days”,
  • annual_winter_days_less_than_40: “86.9 Days”,
  • average_annual_precipitation: “50.3 Inches”,
  • average_annual_temperature_max: “58.9 Degrees F”,
  • average_annual_temperature_min: “41.1 Degrees F”,
  • avg_num_days_below_32_degrees: null,
  • avg_num_winter_days_below_32_degrees: “74.2 Days”,
  • cooling_degree_days: “525.0 Degrees F”,
  • fall_diurnal_range: “18.4 Degrees F”,
  • fall_days_with_max_temp_less_than_32: “0.1 Days”,
  • heating_degree_days: “5999.0 Degrees F”,
  • spring_days_with_max_temp_less_than_32: “1.3 Days”,
  • spring_diurnal_range: “17.9 Degrees F”,
  • winter_days_with_max_temp_less_than_32: “16.7 Days”,
  • winter_diurnal_range: “17.5 Degrees F”,
  • average_annual_snowfall: “36.1 Inches”,
  • average_days_snowfall_greater_than_10_inches: “29.2 Days”,
  • average_days_snowfall_greater_than_1_inch: “29.2 Days”,
  • average_fall_snowfall: “1.0 Inches”,
  • average_spring_snowfall: “7.5 Inches”,
  • average_winter_snowfall: “27.6 Inches”,
  • avg_days_snow_depth_above_10_in: “29.2 Days”,
  • avg_days_snowfall_above_1_in: “29.2 Days”,
  • fall_days_snow_depth_greater_than_10_inches: “0.1 Days”,
  • fall_days_snow_depth_greater_than_1_inch: “0.4 Days”,
  • fall_days_snow_depth_greater_than_3_inches: “0.2 Days”,
  • fall_days_snow_depth_greater_than_5_inches: “0.2 Days”,
  • spring_days_snow_depth_greater_than_10_inches: “0.2 Days”,
  • spring_days_snow_depth_greater_than_1_inch: “4.5 Days”,
  • spring_days_snow_depth_greater_than_3_inches: “2.8 Days”,
  • spring_days_snow_depth_greater_than_5_inches: “1.2 Days”,
  • winter_days_snow_depth_greater_than_10_inches: “2.5 Days”,
  • winter_days_snow_depth_greater_than_1_inch: “24.3 Days”,
  • winter_days_snow_depth_greater_than_3_inches: “14.3 Days”,
  • winter_days_snow_depth_greater_than_5_inches: “8.8 Days”

(This home scores a “D” for both Frozen Pipes and Ice Dams.)

We’re super-excited to add these new data elements to our API. If you’re interested in learning more, let us know!

October 17, 2018 | IN Blog

HazardHub Releases Enhanced Property Characteristics

At HazardHub, data is our passion. We aim to provide as much data as possible to make the insurance process both way faster and way MORE accurate.

The one key element we thought we could improve upon? Property characteristic data. We currently provide county assessor data and – while it does a great job at proving things important for the assessor (like beds, baths, and sq footage) – it does not do such a great job at proving the level of detail that makes a property come alive. Of course, at HazardHub we love a good data challenge. We did some digging and determined that we could be better. A LOT better.

Introducing Enhanced Property Data from HazardHub – our data source that combines county tax assessor data with in-depth real estate transaction data to give you unprecedented insight into the characteristics of an individual property.

Here’s a sample of data that’s available for 637 Hibiscus Glen in Escondido, CA.

Here’s how the complete data looks in the HazardHub API – the BOLD data represents new data elements previously unavailable with HazardHub.

  • enhanced_property_data: {
    • property: {
      • address: “637 HIBISCUS GLN, ESCONDIDO, CA 92025”,
      • apn: “272-672-06-30”,
      • use_code: “Condominium”

      },

    • assessment: {
      • Assessed_Improvement_Value: 213490,
      • Assessed_Land_Value: 103929,
      • Assessment_Year: “2017”,
      • Building_Area: 2163,
      • Building_Area_1: 0,
      • Garage_Cars: 2,
      • Garage_Type: “Yes”,
      • LSale_Price: “0”,
      • LSale_Price_Code: “Transfer Tax on document indicated as EXEMPT.”,
      • LSale_Recording_Date: “20180504”,
      • LotSize_Acres: 5.23,
      • LotSize_Frontage_Feet: “0000000000”,
      • LotSize_Square_Feet: 227798,
      • Main_Building_Area_Indicator: “Living Area”,
      • N_of_Plumbing_Fixtures: “0”,
      • Number_of_Baths: 2,
      • Number_of_Bedrooms: 3,
      • Number_of_Partial_Baths: 0,
      • Number_of_Units: 1,
      • Owner1FirstName: “JOHN C”,
      • Owner1LastName: “SIEGMAN”,
      • Owner2Firstname: “ROXANNE A”,
      • Owner2LastName: “SIEGMAN”,
      • PSale_Price: “605000”,
      • School_Tax_District_1: “ESCONDIDO CITY”,
      • School_Tax_District_1_Indicator: “Tax”,
      • School_Tax_District_2: “ESCONDIDO UNION”,
      • School_Tax_District_2_Indicator: “School”,
      • Standardized_Land_Use_Code: “1004”,
      • Tax_Amount: 3662.2400000000002,
      • Tax_Rate_Code_Area: “4-167”,
      • Total_Assessed_Value: 317419,
      • Total_Market_Value: 0,
      • Total_Number_of_Rooms: “0”,
      • Year_Built: “1983”,
      • No_of_Stories: null,
      • Owner_Occupied: null,
      • School_Tax_District_3: null,
      • School_Tax_District_3_Indicator: null

      },

    • listing_record: {
      • list_date: “3/11/2018”,
      • list_price: 649000

      },

    • mls_listing_record_details: {
      • ex_construction_features: “Stucco”,
      • ex_fence_features: “FN”,
      • ex_garage_features: “Attached”,
      • ex_garage_spaces: 2,
      • ex_parking_features: “Driveway”,
      • ex_roof_features: “Tile”,
      • ex_sewer_features: “Sewer Connected”,
      • ex_style_features: “Spanish/Mediterranean”,
      • if_cooling_features: “Central”,
      • if_cooling_yn: “YES”,
      • if_fireplace_yn: “YES”,
      • if_heating_features: “Forced Air, Gas Natural”,
      • if_levels_features: “02 Story”,
      • if_water_features: “Meter On Property, Gas Water Heater”,
      • in_living_square_feet: 2163,
      • in_property_type: “Residential”,
      • rm_baths_full: 2,
      • rm_baths_total: 2,
      • rm_bedrooms_total: 4,
      • rm_room11_type: “BD”,
      • rm_room13_type: “BD”,
      • Ad_geo_altitude: null,
      • ad_zone_features: null,
      • ex_exterior_wall_features: null,
      • ex_foundation_features: null,
      • ex_general_features: null,
      • ex_location_features: null,
      • ex_lot_size_acres: null,
      • rm_family_yn: null,
      • rm_room11_features: null,
      • rm_room13_features: null,
      • rm_rooms_total: null,
      • ex_patio_features: null,
      • ex_patio_yn: null,
      • ex_road_features: null,
      • ex_wateraccess_features: null,
      • ex_waterfront_features: null,
      • if_appliance_features: null,
      • if_basement_features: null,
      • if_basement_yn: null,
      • if_floor_features: null,
      • if_general_features: null,
      • if_security_features: null,
      • if_security_system_yn: null,
      • if_utilities_features: null,
      • if_window_features: null,
      • in_association_dues1: null,
      • in_sold_date: null,
      • in_sold_price: null,
      • in_subtype: null,
      • rm_baths_half: null,
      • sc_school_district: null

      },

Gathering data about a property is a painful, manual process that introduces numerous errors into the insurance process. With the introduction of Enhanced Property Data, we’ve taken a 30-40 minute data gathering exercise and reduced it to less than 30 seconds, while increasing the accuracy of your data.

Having instantaneous, automated access to property characteristic data is critical for agents, carriers, AND insureds. Agents can focus on being a trusted advisor to clients and get out of the data gathering business. Carriers can speed up both the accuracy and timing of a policy quote with instant access to our risk data, our fire station and hydrant data, and in-depth property characteristic data. Applicants and agents will no longer need to take the time to answers questions that can easily be pulled from a trusted and reliable third-party source.

Here’s the best part! The improvements are absolutely FREE for all existing property data customers. Like with all of our data, when we develop a massive improvement you get it at no additional cost.

If you want the Enhanced Property Data, just let us know and we’ll add it to your API account

September 28, 2018 | IN Blog

HazardHub Releases Advanced Wildfire Model

SAN DIEGO – HazardHub has launched its advanced Wildfire Model for the United States. The new model represents an incredible improvement over current wildfire models, combining fuel load, urban/wildland interface data, rainfall data, wind data, satellite imagery, and known wildfire perimeters to create the highest resolution, most detailed wildfire assessment ever available.

Brady Foust, Chief Science Officer at HazardHub, said “I have been developing wildfire models for many years. I have never felt more confident in a wildfire model. The HazardHub Advanced Wildfire Model captures and concentrates risk in the areas that are most susceptible to wildfire while resisting the temptation to simply label an entire area as dangerous.”

Foust adds “most static models concentrate on fuel load and distance to the Wildland/Urban Interface (WUI).  These are important variables, but we add an additional five: 1) fire season rainfall; 2) vegetation burn points; 3) distance to high or very high areas [based on fuel load and WUI]; 4) distance to historic wildfire perimeters; and 5) katabatic wind zones.  Properties located in these zones (e.g., the Santa Ana wind zone in southern California) are at a much higher risk than those outside these zones.

Most fire departments are fantastic at knocking down wildfires in the absence of wind. However, if a Santa Ana-type wind is blowing, all bets are off. Normal winds can blow dangerous firebrands a 1,000 feet or more, but a Santa Ana or El Diablo wind can propel them at least 2.5 kilometers (1.55 miles). The integration of wind parameters – along with satellite imagery and rainfall data – into our model is helping to highlight those areas that are at the highest risk of wildfire and concentrate the risk where it’s most likely to happen.”

In an analysis of 60,000 California properties, here’s the distribution showing that 97.9% of properties inside of known wildfire perimeters were in 20.5% of housing units, with “F” properties showing nearly 11 times the fire risk of the average California property.

Bob Frady, CEO of HazardHub states, “as a California company, we are keenly aware of Wildfires and the destruction they can bring. These additional data elements add incredibly important factors that can make the difference between firefighters successfully saving properties or a fire over-running an area. We are excited to bring the best Wildfire Risk Model ever available to any insurer who works in wildfire-prone states, as well as to consumers via our www.freehomerisk.com site. We are constantly striving to build incredible accurate, actionable and comprehensive data to the market. With our Advanced Wildfire Model, we’re pushing far beyond what is currently available. Best of all, carriers are welcome to test the model absolutely free.”

 

 

September 25, 2018 | IN Blog

Insuretech Hartford Pitch Night

Last week we traveled to beautiful Hartford, CT (where the Connecticut River was REALLY high) for a series of meetings, including participating in the Insuretech Hartford Pitch event. We were one of nine startups, presenting to a large and engaged crowd from insurance companies all around the Hartford area.

Frankly, it was a LONG day in Hartford. It seemed like the entire day was one long “here’s who HazardHub is” presentation. It’s great but it’s tiring. So when pitch time rolled around, Bob poured that entire day into 5 sizzling minutes of pitch magic (at least, that’s how he described it.) You can judge for yourself, as the pitch was captured in a video!

 

Once the pitch was done, we thought “that was fun! Let’s mingle, watch a few pitches and grab a bite to eat.” So imagine our surprise when they announced the first winner for “Highest Potential.”

A couple of minutes later, here came the second award for “Best Problem/Solution Fit.”

 

A few minutes later, the grand prize. HazardHub was voted “All Around Favorite.” Which is a pretty cool award!

 

 

While winning these votes was certainly very cool, it’s only because they point out one really important fact – the industry is starving for this sort of data and believes that HazardHub has the potential to deliver far better answers than are currently available in the market. If YOU want great data, just let us know!

 

September 11, 2018 | IN Blog

Now THIS is Some Awesome Flood data!

At HazardHub, we’re always working on ways to provide better, cooler, more meaningful data to our clients. It’s just how we roll.

Flood is a hot topic for insurers right now. Between new entrants trying to write private flood policies to current carriers trying to get a feel for their commercial flood risk, we’ve seen an enormous uptick in interest for accurate, actionable and affordable flood data.

One of the problems with FEMA flood data is that it’s not organized as well as it could be. It’s messy, it’s cryptic, and the data is located in a number of different data repositories – flood map data elements are in a number of different places, participating communities are in another and FIRM map metadata is in yet another location. What happens is a community doesn’t have a map or if you simply want a second look at flood that does not consider FEMA’s flood zones? What about ocean water?

At HazardHub, we give you three looks at water that may come racing into your property from the outside. You know, the kind of water damage NOT traditionally covered by a personal lines insurance policy.

FEMA Flood – FEMA data, all in one place. See the details of the flood map, along with the FIRM dates. NEW – We’ve just added FEMA CRS (Community Rating Service) data that shows whether your community participates, what “level” they’re at and any discounts available for NFIP policies.

HazardHub Flood – What would happen in FEMA didn’t exist? What do nature and science have to say about water, regardless of pre-defined FEMA boundaries? Our HazardHub flood model considers distance, elevation, and water type to give you an unbiased look at flood risk.

HazardHub SurgeMax – Sometimes the water danger does not come from the land but instead from the ocean. Our SurgeMax score provides the highest detail of storm surge risk ever available!

Here’s how the data looks when returned through the HazardHub API – this location highlights the CRM data.

If you want to put the BEST flood tools available to work for you, contact us today!

 

July 22, 2018 | IN Blog

A deeper look at the Klamathon Fire

Natural disasters are by definition a bad thing.  One of the problems of being in the hazard determination business is that for us to prove ourselves correct in our assessment, something really bad has happened.  We take no joy in being right, as it comes due to the tragedy of someone else.

One of our competitors recently claimed they had successfully identified 61% of the properties in the Klamathon Fire as being at high or very high risk. That number is WAY too low.  If you’re relying on that tool, you would be undercounting your potential risk by more than a third.

 

So what’s your data showing? 

In looking at the Klamathon Fire we rated every property inside the burn perimeter as either high or very high.  Here’s the actual data –

HazardHub Wildfire Score % of Locations
D: High 5.2%
D: High  < 1,000 feet from High or Very High Risk Area 11.3%
F: Very High 83.5%
Grand Total 100.0%

 

Had an insurance company or municipality used our scoring, they would have been able to work with their property owners to take the proper mitigation steps like clearing grass lots or moving wood piles away from the side of the house.  Proper mitigation cannot save every property and prevent every tragedy but it can and does save numerous properties in every type of natural disaster.

 

But doesn’t declaring an area high or very high just mean that HazardHub is simply overexaggerating the risk?

We’re glad you asked.

However random they seem, natural hazards tend to have some consistency as the conditions need to be just right for a wildfire to get out of control.  It’s also why you don’t see hurricanes in Chicago and Tsunamis in Atlanta – the conditions need to be right.

We believe that areas that look like wildfire areas should be classified that way.  We look at a combination of factors to decide the overall risk of a property. Sometimes people think we’re just being too cautious…but we leave opinion out of it and strictly look at as much data as we can.

One way we can see the potential risk is by looking at areas that have burned before.

Proximity to Previous Known Wildfires  % of Locations
risk: <= 1 mile from historic wildfire perimeter 52.6%
risk: > 1 mile from historic wildfire perimeter 30.5%
risk: Inside historic wildfire perimeter 16.9%
Grand Total 100.0%

 

What’s telling about this chart is that almost 17% of these properties are in an area that has burned before. What’s more notable is that more than 83% are some distance away. People often get a false sense of security that “it’ll never happen to me.” However, the conditions in this area were (and are) ripe for fire.

 

Have there been other wildfires in that area in the past?

As a matter of fact, there have been several. While many of the properties in this current wildfire area had not been hit by a wildfire, the area is prone to the conditions where wildfire can thrive.

 

Previous wildfires that have occurred in this area include:

2000 Horn Fire

2001 Hornbrook Fire

2001 Hutton Fire

2004 Irongate Fire

2010 Bailey Fire

2013 Cottonwood Fire

 

What about the impact of fire stations?

Another issue with this area is that there are not a lot of fire stations.  We look at the number of fire stations within five (5) drive miles of each property, as it’s a critical factor in suppressing the spread of a fire.  Properties with fewer than five (5) fire stations in a five (5) drive mile radius are twice as likely to be destroyed in a fire – any type of fire.  In the Klamathon Fire, 26% of the properties have no fire stations with five (5) drive miles.

Number of Fire Stations Within 5 Miles  % of Locations
0 26.0%
1 7.6%
2 46.6%
3 19.8%
Grand Total 100.0%

 

When you add up all the data, this is a dangerous area for wildfires.  The fire comes where it can get fuel.  This area had all of the elements needed to feed a growing wildfire. What’s even more important is that the surrounding area still maintains a high degree of wildfire risk, especially given that the area also has an elevated drought score.

The HazardHub Wildfire model – and our supporting data for vegetation burn points, previous wildfires, fire stations, fire hydrants, precipitation levels, and fire determining winds – is your best source for properly assessing wildfire risk. To learn more, contact us at support@hazardhub.com today.

July 10, 2018 | IN Blog

HazardHub Releases Massive Data Update

We’ve been pretty quiet over the last month or so with our communications. It turns out that we’ve been heads-down and building out both improved and new datasets for your data enjoyment!

We’ve got a massive amount of new data under review by our data sciences team. In order to get you the best and freshest data possible, we’re breaking things up to not one but TWO data releases in July. We just went live with the first update today – here’s what it contains.

     * Hydrant locations – We are now just over 8 million hydrant locations across the US, with a focus on the most populated states. Check and see if your address is covered at http://hazardhub.com/fire_hydrant/


     * Fire Station Locations – Our most recent update contains over 100 additions, closes and moves of fire station locations. We track this data every single day and make updates every other month. Check for your nearest Fire Station at http://hazardhub.com/firestation/ 

     * Drought Layer – We’ve added the most recent Drought data to our API. 

     * NEW – Mudslide Risk  – We’ve added a brand new data element called Mudslide Risk. You’ll see these areas downhill of areas that have recently suffered from Wildfires.

We’re working on adding even more granularity to our wildfire model…but more about that in Part 2 of our data release later this month!

All of these data elements are live and immediately available via the HazardHub API. If you’re not on the API, click http://hazardhub.com/api/ to find out how.

As always, thanks for your support. If there’s anything we can do to help make your program better, just let us know. 

June 04, 2018 | IN Blog

HazardHub releases first of its kind Sinkhole Susceptibility database

HazardHub, the nation’s fastest-growing supplier of geospatial risk data, has announced the release of Sinkhole Susceptibility, the nation’s first database that scores every address in the United States by the risk that the ground beneath them contains formations that lead to the ground collapsing upon itself  – aka Sinkholes.

Currently, sinkhole tools are limited to “Distance to Known Sinkholes” calculations, like the one currently available from HazardHub. While effective, they only tell part of the story as new sinkholes will often appear far from where an existing sinkhole is located. For example, a sinkhole at the Villages, FL was more than 1.5 miles away from the nearest known sinkhole. Sinkhole Susceptibility shows that property as a “D” and highly susceptible to sinkholes.  Another example is the National Corvette Museum in Bowling Green, Kentucky, infamously known for a sinkhole that swallowed seven Corvettes on display, which shows a “D” for Sinkhole Susceptibility.

 

The geologic formations that cause sinkholes occur in 40 of the 50 States, with a higher concentration of sinkhole risk in Florida, Tennessee, Kentucky and Missouri. Hazardhub’s Sinkhole Susceptibility is a national database that scores every property in the United States.

 

According to Joe Litchfield, Chief Data Officer at HazardHub, “Sinkhole Susceptibility fills a need in the risk community by providing an unbiased look at the substrates that cause sinkholes to form.  When we tested Sinkhole Susceptibility against a database of known sinkholes in the State of Florida, we found that more than 99% of know sinkholes were in our highest risk zones. At HazardHub, we’ve digested and modeled an incredible amount of data to provide information that can help consumers, businesses, and insurers to make better and more informed decisions.”

 

For one client, Sinkhole Susceptibility identified that more than 10% of their Florida book of business was concentrated in very high zones yet more than 10 miles from known sinkhole locations – a surprising revelation to their underwriters and actuaries.

 

Bob Frady, CEO of HazardHub, says “Our goal at HazardHub is to continually push the boundaries of hazard risk data that is available and easily accessible to consumers, businesses, and insurers. With Sinkhole Susceptibility, we’re providing a new ingredient that will help drive more knowledge about the risks of a specific property. For existing HazardHub clients, this data is absolutely free to access as part of our overall solution. While nobody is excited to find out that their property is in a high-risk zone, we believe that knowledge is the power that can lead to more protection for a property. ”

May 24, 2018 | IN Blog

HazardHub releases major HydrantHubTM update

HazardHub, the nation’s fastest-growing supplier of geospatial risk data, has announced a major update HydrantHubTM, the nation’s first addressable database of fire hydrant locations. This growing database contains more than 5.2 Million hydrant locations across thousands of cities, states, counties and water districts across the United States. The new release contains 62% more hydrants than the prior version of HydrantHub, all of which are available via HazardHub’s Distance to Nearest Fire Hydrant web tool.

 

Distance to a fire hydrant is one of the most critical components to properly price homeowners and property insurance. Yet – too often – hydrant data has been unobtainable or relied on a homeowner’s best guess.  Worse, companies that claim to have hydrant data often charge people to look at it.

 

The updated release of HydrantHub covers more than 70% of the hydrant-served population in the US. States with a high level of focus include Texas, California, New York, Pennsylvania, Illinois, Ohio, North Carolina and South Carolina.

 

According to Joe Litchfield, Chief Data Officer at HazardHub, “we have been working nonstop in collecting as much data as possible to add to HydrantHub. We’ve not only added major cities like Columbus, Ohio, we’ve also added smaller places like Wink, Texas. This latest data update represents a great leap forward in our coverage. The best news is that we’re hard at work collecting the next round of hydrant data. We expect to add many, many more locations by the end of the summer.”

 

For insurers who rate with AAIS’ Fire Protection Class methodology, the combination of HydrantHub and HazardHub’s Fire Station Database allows for automatic determination of the proper FPC code for any location in the US.  HazardHub automatically provides the two components needed for AAIS’ FPC – Distance to Nearest Fire Station and Distance to Nearest Hydrant.

 

Bob Frady, CEO of HazardHub, says “We are incredibly excited to add so many more hydrant locations in the latest update to HydrantHub. It’s been an obsession of ours to build not only the most comprehensive hydrant data available but one that can easily be accessed by anyone wanting to know the location of the nearest hydrant.  Plus, we’re now covering over 70% of the population that is served via a hydrant, making our AAIS partnership and consumer strength even more powerful. ”

 

To see the location of your nearest hydrant – or to learn more about HydrantHub, visit www.hazardhub.com or reach us directly at support@hazardhub.com.