Nicola Garbarino, Benjamin Guin and Jonathan Lee
5.2 million properties in England are at risk of flooding. To ensure the availability and affordability of flood insurance to households in flood-prone areas, the UK Government introduced an innovative reinsurance scheme, Flood Re, in April 2016. It provides insurers with an option to pass the flood-risk element of their policies on to the reinsurer at a lower fixed price according to property council tax band. In a recently published Staff Working Paper, we employ a granular data set of all property transactions and flood events in England. We estimate the effect of Flood Re on property values. We also explore if Flood Re effects are heterogeneous across different subpopulations in England.
Flood Re reduces insurance premiums for households in flood-risk areas by over half for four out of five properties with previous flood claims. This should increase the values of flood-prone properties. However, in theory, the magnitude of this property price effect depends on the expected reduction in future insurance premiums caused by Flood Re as well as the rate in discounting future insurance premiums. Moreover, the Flood Re effect might vary by area and demographic groups due to differences in flood risk and home ownership.
Empirical approach to identify the house-price effect of Flood Re
In our research, we examine properties in England. We employ a ‘repeat sales approach’ examining only those 1.5 million properties built before 2002 and sold multiple times. Specifically, we regress the change in property price on their exposure to flooding. In our regression, we interact this flood exposure with a binary variable capturing the time period when Flood Re was operating. The nature of this research design allows us to account for unobservable and time-invariant property characteristics, by examining the effect of floods on property prices both before and after Flood Re came in. To gauge heterogeneous effects of the policy, we then estimate our regression in different subsamples. To that end, we slice our data by property values and different regional characteristics, such as their income level, obtained from the Office of National Statistics.
Average impact of Flood Re on property prices
Chart 1 illustrates the effect of Flood Re on prices of flooded properties. We first run our regression on the full sample of properties in England. We find that flood events reduce property values by more than 1.5% relative to their first transition before the introduction of Flood Re. This negative effect is completely mitigated after the introduction of Flood Re.
Chart 1: Effect of Flood Re on property prices
For an average property, the introduction of Flood Re increases the relative price of properties as a result of flooding by £4,083. We then conduct a back-of-the-envelope calculation among the 5.2 million properties that are at risk of flooding in England. It suggests that the subsidisation of Flood Re increases the total value of flooded properties by £212.3 million per year, assuming only 1% of the at-risk properties are flooded annually. The total effect of Flood Re on property values would double to £424.6 million if flood-risk probability further increases to 2%.
Our results suggest that Flood Re also increases the transaction volumes of properties in at-risk areas. Chart 2 outlines the effects of Flood Re on the transaction volumes of flooded properties. Our results suggest that a flooded property has a 3.6% reduction in the annual probability of transacting before Flood Re came into place. Flood Re more than offsets this negative effect on the transaction probability and increases the transaction of flooded properties.
Chart 2: Effect of Flood Re on market liquidity
Regional heterogeneity in the response of property prices
We also explore heterogeneous effects of Flood Re in different areas across England. To that end, we create subsamples by employing median values of key variables to split the original sample in half. The effect of Flood Re is stronger in urban areas with a higher income and an older population as well as areas with more rental properties. While it would be interesting to identify the channels leading to the heterogeneous effects, our study leaves this question to future research due to the lack of more granular data.
Conclusion and discussion
Our results illustrate two key implications. First, we document heterogeneous Flood Re effects. Interestingly, our results suggest that Flood Re has a weak impact in lower-income areas but a stronger impact in higher-income areas. These results can be seen as evidence hinting at distributional consequences of this reinsurance scheme in terms of wealth among homeowners in England.
Second, the results are relevant for the transition risk of public policy interventions. Flood Re is expected to be phased out in 2039. The flood-risk component of property insurance may therefore be fully priced into premiums by that time. Consequently, the value of properties at flood risk may experience a sudden adjustment, reflecting the increase in current and future premiums. However, the magnitude of the effect that we estimate does not appear large enough to disrupt property and financial markets, but this may change if flood-risk perceptions change in the next decades.
Nicola Garbarino works at the Ludwig Maximilian University of Munich and the Ifo Institute, Benjamin Guin works in the Bank’s Strategy and Policy Approach Division and Jonathan Lee works at the University of Glasgow.
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There are a number of aspects in this article and the supporting working paper 995 which raise concerns. The work does not seem to have considered that >96% of leasehold flats are excluded from flood re: – the only exception is blocks of 3 or less flats where the owners live at the site.
The exclusion creates a number of problems which have not been considered in the study including the fact that sites previously considered insurable for flood peril without difficulty, per Grenfell, now find themselves facing difficulties obtaining cover or in some cases are unable to obtain cover.
The section headed “Robustness tests” at 5.5 points out that 15% of the sample were flats. Then says:
“Flood events might have different impact on flats, because some of the flats are possibly above the ground floor. To alleviate such concern, we replicate the estimation with the sample without any flats. The results are shown in column1 of Table 7. The inference of our estimated coefficients of interest stays the same, suggesting that the estimation of our key coefficients of interested is not biased by the inclusion of flats.”
Ignoring the oversimplified assumption that somehow a flooded lift or electrical system in the basement has less impact on someone on the 30th floor some of the other inferences drawn in this section seem open to question.