Damage costs incurred by properties due to flooding vary significantly depending on their age and type.
Such detailed age and type information is not commonly available and so can hamper efforts by insurers to accurately predict the costs that could be incurred due to flooding at a property level.
The availability of a new property level age and type database now offers insurers the ability to correctly identify the cost of damage for each individual property and therefore the total costs across their entire portfolio.
Having detailed property level information, and not just on location, can only help improve underwriting, claims management, and risk modelling across the industry.
Location, Location, Location
Many, if not all, insurers, intermediaries and reinsurers are aware of and price on the risk of a property being subject to flooding. This is based on its location or proximity to a flood risk zone, be that coastal, fluvial or surface water.
Thanks largely to many years of work undertaken by the Ordnance Survey, flood modellers and insurers we now know the location of properties and whether or not they sit in a flood zone.
Whilst many have previously modelled this risk at the postcode level, most are, or moving to, modelling this risk at the individual property level. So largely the problem of flood risk location is solved.
But what next? Do we treat the possible damage as being equal across all properties in the same flood risk zone?
If location is the same, and if the risk is the same, is our response the same, are the repair costs the same? No, not unless all the properties are the identical, which they are not. Not all properties are equal.
Evidence from The Flood Hazard Research Centre (FHRC), an interdisciplinary centre based at Middlesex University, clearly shows there is a variation in flood damage costs between different types of properties and different ages (see the graph below).
Graph created from tables presented in MCM-Online.co.uk. Flood Hazard Research Centre, Middlesex University, London.
FHRC have determined the 2013 repair costs, shown here for 0.1m flood depth, for a Short Duration Flood period, for different types of properties and ages, along with the residential sector average damage cost (dotted line).
Their research shows, even within one age group, there is variation (significant perhaps) in damage costs between property types, and across different property ages.
Pre 1919 detached properties exhibit the highest damage costs, and are off this chart at some £29,591. The average for each age group varies too and this means that using a single figure or a simple trend (e.g. older properties have higher damage costs) to predict this damage will produce woefully inaccurate results.
A 1945 – 1964 Terrace house, for example, has one of the lowest damage costs at £5,921, well below the age and sector average.
The residential sector average damage cost is around £10,500 (dotted line), but using such an average will again lead to incorrect individual property damage costs, as many types and ages vary about this figure.
So now we know that age and type of property is a significant factor in the resulting flood damage costs; and as a consequence one would assume the repair effort and time residents are out of their homes.
However do insurers have accurate property age and type data to correctly model and predict all this?
Solving the data void
Relying on property age and type information from the policy holder is recognised by insurers as being suspect.
Property age and type is available in the census, but it is grouped to show house types as a percentage of an area which commonly contains over 1,000 properties. So misleading when trying to work at the property level or even postcode level.
The Valuation Office doesn’t release any data it holds on property age or type. So this accurate and detailed property type and age data isn’t readily available.
Here at The GeoInformation Group we have been working on a UK wide buildings database that provides information at the property level – for both residential and non-residential buildings - including:
This work has shown the variability of property types across the UK at the individual property level revealing significant variations even within one street.
Looking at the example map below we can see even within this small area, just a few streets and postcodes, all these properties are within the flood zone and may previously have been treated as equal in terms of damage, but the property age and type differences show that isn’t realistic.
The flood zone, provided by the Environment Agency in this example, shows Late Victorian terrace houses (light blue), right next to post war regeneration terraces and semis (purple), next to Interwar terraced and detached (pink), all within a street (and postcode) of each other.
However we now know there is a damage cost variation of some £5,000 between these types of properties.
Getting this wrong for one property might be manageable but getting it wrong for hundreds or thousands of properties would be a costly mistake and one that can now be easily avoided.
The availability of property level age and type data now offers insurers the ability to correctly identify the cost of damage for each individual property and therefore the total costs across their entire portfolio.
Having detailed property level information, and not just on location, can only help improve underwriting and claims management across the industry.
As insurers are planning to implement the new Flood Re scheme they now have additional information, beyond the council tax band, to help them identify new build properties and better determine the damage costs and whether they wish to transfer these properties into the scheme.
Location does matter, but property information matters just as much and now insurers have for the first time, sufficient data at their fingertips to more accurately determine potential losses due to flooding.
I have no interest in or connection with the Flood Hazard Research Centre, Middlesex University, London. They very kindly allowed me to reproduce information from one of their tables for which I am indebted to them.
I am a director of The GeoInformation Group (an Ordnance Survey Partner) and partly responsible for the development of the UKBuildings database.