# RECORD a34939ebd1d20ba654297f426501ad47056b65a4
# TITLE: Adjusting to the New Normal: River Flood Risk and the Real Estate Market
# YEAR: 2025.0  VENUE: Environmental and Resource Economics  DOI: 10.1007/s10640-025-00995-x
# AUTHORS: Henri Gruhl; Johannes Brehm; Arijit Ghosh; P. Breidenbach; Nils Aus Dem Moore
# PAGES: 26  CANDIDATE TABLE PAGES (1-based): [10, 13, 15, 21]
# ABSTRACT: Do individuals learn from salient river floods and update their risk perceptions accordingly? We exploit the Western European flood in 2021 to estimate changes in property values across German flood risk regions. Flood-prone areas in proximity to the flood or with higher climate change beliefs experience housing price declines, while we observe no statistically significant effects in other flood risk zones. Our results imply that flood risk discounts in the housing market will not adjust uniformly, which may result in suboptimal capital allocation in areas at risk of future flooding.


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Accepted: 7 May 2025 / Published online: 18 July 2025
© The Author(s) 2025
	
 Henri Gruhl
henri.gruhl@rwi-essen.de
	
 Johannes Brehm
johannes.brehm@rwi-essen.de
1	
RWI - Leibniz Institute for Economic Research, Vrije Universiteit Amsterdam, Berlin, 
Germany
2	
RWI - Leibniz Institute for Economic Research, Hertie School, Berlin, Germany
3	
RWI - Leibniz Institute for Economic Research, Essen, Germany
4	
Hertie School, Berlin, Germany
Adjusting to the New Normal: River Flood Risk and the Real 
Estate Market
Henri Gruhl1
 · Johannes Brehm2
 · Arijit Ghosh3 · Philipp Breidenbach3 · 
Nils aus dem Moore4
Environmental and Resource Economics (2025) 88:2331–2356
https://doi.org/10.1007/s10640-025-00995-x
Abstract
Do individuals learn from salient river floods and update their risk perceptions accord­
ingly? We exploit the Western European flood in 2021 to estimate changes in property 
values across German flood risk regions. Flood-prone areas in proximity to the flood or 
with higher climate change beliefs experience housing price declines, while we observe no 
statistically significant effects in other flood risk zones. Our results imply that flood risk 
discounts in the housing market will not adjust uniformly, which may result in suboptimal 
capital allocation in areas at risk of future flooding.
Keywords  Flood risk · House prices · Risk updating
JEL Codes  Q54 · Q51 · D81 · R31
1  Introduction
If the global mean temperature increases by 1.5◦C compared to pre-industrial temperatures, 
global human fatalities from river flooding are estimated to rise by 70–83% and flood dam­
ages by 160–240% without further adaptation (Dottori et al. 2018). According to the most 
optimistic IPCC pathway, it is highly likely that average global warming will cross the 
threshold of 1.5◦C, with temperatures likely exceeding this level by 2100 (IPCC Working 
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H. Gruhl et al.
cellar, and the conditions of the structure (categorical)11. We include separate indicators for 
characteristics with missing information. Zip code fixed effects capture all unobserved zip 
code-specific characteristics that are invariant over time. The average zip code in Germany 
covers 43.2 km2. Following Abbott and Klaiber (2011), in addition to the zip code fixed 
effects, we include several different spatial fixed effects in our main estimations at the larger 
county12 level or the smaller 1 km2 level. While fine-scale fixed effects effectively control 
for unobserved spatial heterogeneity, they may discard valuable between-variation, lead­
ing to incomplete estimates of broader capitalization effects (Abbott and Klaiber 2011). In 
contrast, coarse-scale fixed effects retain between-variation but risk omitted variable bias by 
failing to account for fine-scale heterogeneity. The inclusion of varying spatial fixed effects 
allows us to assess the sensitivity of the results, yielding information about capitalization of 
public goods and amenities at varying spacial scales. The year-month fixed effects control 
per-square-meter values than larger ones, especially in urban settings.
11 The categories for the condition of the house range from “simple” over “normal” and “sophisticated” to 
“deluxe”.
12 Counties in Germany correspond to the NUTS-3 level and are called “Landkreise” or “Kreisfreie Städte”. 
There are 400 counties in Germany.
Table 1  Sample statistics
Inside flood risk zones
Outside flood risk zones
∆in−out
mean
sd
min
max
mean
sd
min
max
ln(Price per m2)
7.404
0.624
5.487
8.865
7.483
0.603
5.486
8.865
−0.079∗∗∗
(0.017)
Price (thousand €)
331,7
238,2
20,5
2,759
348,1
244,5
21
2,780
−16,4∗∗∗
(5,518)
Living space (m2)
178.03
90.28
55.67
767
172.74
80.97
55.74
768
5.29∗∗∗
(1.44)
Construction year
1961
56
1500
2020
1972
46
1500
2020
−11∗∗∗
(1)
Number of rooms
5.458
1.722
1
10
5.422
1.641
1
10
0.036
(0.024)
Cellar
0.334
0.472
0
1
0.335
0.472
0
1
−0.000
(0.009)
Property facilities:
”simple” 0.031
0.173
0
1
0.025
0.156
0
1
0.006∗∗∗
(0.001)
”normal” 0.137
0.344
0
1
0.122
0.327
0
1
0.015∗∗∗
(0.003)
”sophisticated” 0.294
0.456
0
1
0.337
0.473
0
1
−0.044∗∗∗
(0.007)
”deluxe” 0.015
0.122
0
1
0.017
0.131
0
1
−0.002∗∗
(0.001)
Number of obs.
232,390
1,234,770
1,467,160
Notes: This table summarizes the characteristics of the houses in the final sample, i.e. houses inside flood-
risk zones (treatment group) and within 0.5–3 km of flood-risk zones (control group). The columns show 
the mean, standard deviation, minimum value, and maximum value respectively for houses within and 
outside of flood risk zones. The last column reports the difference between the means of the two groups, 
clustered at the zip code level. * p < 0.1, ** p < 0.05, *** p < 0.01
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Adjusting to the New Normal: River Flood Risk and the Real Estate Market
for all unobserved time-specific effects in a specific month and year, which are the same 
for all included regions in Germany, such as a general upward or downward shift in the 
German housing market. Furthermore, we also show specifications in which we control for 
county-specific time trends or relax the temporal fixed effects by only including year and 
month fixed effects, capturing temporal general changes between years and general seasonal 
changes between months. Additionally, we present results when we include county-specific 
quadratic trends, cubic trends, or county-by-year fixed effects. Standard errors are clustered 
at the zip code level.
The identifying assumption in the canonical difference-in-differences framework is the 
parallel trends assumption. By comparing changes in outcomes over time between a treat­
ment group (houses in flood risk zones) and a control group (houses outside flood risk 
zones but in close proximity), DiD designs assume that, in the absence of treatment, both 
groups would have developed similarly over time (parallel trends). Under this assumption, 
the effect is identified even if shocks affect the potential outcome as long as they are not 
correlated with the flood risk zones. Although it is impossible to test this assumption con­
clusively, it is common practice to perform an event study to show that there is no statisti­
cally significant effect on property prices between the groups in the period before the flood 
occurred. Methodologically, this concept can be expressed as follows:
	
log Yi,z,t =
t−1
∑
s = t
αs(flood riski × Posts) +
¯t
∑
s = t
αs(flood riski × Posts)
	
+β flood riski + γ Xi + λz + ϕt + εi,z,t,
(2)
where t is the first pre-treatment period for which we want to rule out anticipation effects, 
while ¯t represents the last post-treatment period for which we expect adjustment effects. We 
take α−1 as the reference category. Therefore, the period-specific effects in the first sum­
mation (anticipatory effects) and the second summation (reactive effects) are interpreted 
relative to the period t −1. This approach enables us to assess whether there are any statisti­
cally significant changes in property prices between the treatment and control groups in the 
periods leading up to and following the flood event, and to quantify the magnitude of these 
effects over time.
Figure 3 displays event studies based on Eq. (2). We do not detect any statistically sig­
nificant pre-trends in the quarters leading up to the flood. We also do not find longer-term 
systematic pre-trends in the years before the flooding. The event studies support the parallel 
trends assumption, indicating that the potential outcomes for the treated and control groups 
follow parallel trends when conditioned on the specified model. This further strengthens our 
analysis, as it suggests that any changes in the outcomes after the flood event can be attrib­
uted to the flood and not to other external factors.
For identification, we rely on intertemporal variation in prices. However, as noted by 
Kuminoff and Pope (2014), capitalization may not precisely reflect households’ willingness 
to pay if the gradient of the equilibrium price function shifts following the flood. Specifi­
cally, if the flood changes residential sorting in flood-risk areas, altering the hedonic gradi­
ent, panel variation may yield biased estimates of risk valuation updating. Therefore, our 
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Adjusting to the New Normal: River Flood Risk and the Real Estate Market
approach assumes a stable hedonic gradient over time. Since this assumption is difficult to 
test, the estimated effects should be interpreted with caution in terms of welfare.
4  Results
4.1  Main Results
Our analysis shows that after the flood, risk perceptions are updated in some regions of 
Germany while remaining unchanged in others. Specifically, we observe localized effects 
in regions near the flooding that are statistically significant at the one percent level, which 
diminish with distance, and significant effects at the five percent level in regions with higher 
baseline beliefs in climate change.
Table 2 presents the average effect of the flood on house prices in flood risk regions that 
were not directly affected. We do not detect any statistically significant effects at the one, 
five or ten percent level across model specifications with varying restrictiveness. The result 
indicates that, on average, the flood did not lead to a statistically significant change in flood 
risk valuation across German flood risk zones. We run our model using five specifications. 
Column 1 estimates the effect according to Eq. (1). In Column 2, we loosen the restrictive­
ness of the model by removing the year-month fixed effect and only include year fixed 
effects for unobserved differences between years and month fixed effects to control for sea­
sonality across years. Similarly, Column 3 allows for more spatial variation by expanding 
the spatial fixed effect to the county level. In Column 4, we restrict the model by including 
a 1 km2 fixed effect, capturing more unobserved spatial differences, but in turn reducing 
variation in the estimation of the effect. Finally, Column 5 includes linear time trends at the 
county level to account for different trends between regions.
Table 2  Average effect
Dependent variable: log (price per m2)
(1)
(2)
(3)
(4)
(5)
Treatment effect
-0.0042
-0.0042
0.0035
-0.0066
-0.0071
(0.0098)
(0.0098)
(0.0104)
(0.0083)
(0.0100)
Treatment group dummy
✓
✓
✓
✓
✓
Post-treatment dummy
✓
✓
✓
✓
✓
Property characteristics
✓
✓
✓
✓
✓
County×year linear trends
✓
Year fixed effects
✓
✓
✓
✓
✓
Month fixed effects
✓
✓
✓
✓
✓
Year-Month fixed effects
✓
✓
✓
✓
Zipcode fixed effects
✓
✓
✓
County fixed effects
✓
1km2 fixed effects
✓
Number of observations
1,466,509
1,466,509
1,468,567
1,457,476
1,466,509
Notes: This table presents the average results following Eq. (1). Property characteristics include the 
construction year of the house (categorical), the size of the living space, the number of floors, the number 
of rooms, the presence of a cellar, and the conditions of the structure. Standard errors are clustered at the 
zip code level and shown in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
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H. Gruhl et al.
While the average effect is a null result, statistically and economically significant effects 
may be averaged out across heterogeneous groups. For example, economic theory and 
empirical evidence from the behavioral economics literature suggest that the distance to 
an event may play a role in how economic agents react (Gallagher 2014; Alok et al. 2020). 
Moreover, empirical evidence – primarily from the United States – suggests that climate 
change belief heterogeneities could play a role in how flood risk information, e.g., induced 
by a natural disaster, is processed (Barrage and Furst 2019; Bernstein et al. 2019; Baldauf et 
al. 2020; Bakkensen and Barrage 2022).
To assess the role of proximity, we interact the treatment effect with a continuous mea­
sure of the distance of a property to the closest area that was flooded in July 2021. Our find­
ings, presented in Panel A of Table 3, show an economically meaningful negative effect of 
the flood on nearby houses across various specifications that is statistically significant at the 
one percent level (Column 5). The statistically significant (one percent level) positive inter­
action term indicates that the discount on house prices decreases as houses are located far­
ther away from the flood. Specifically, the square meter price of at-risk houses immediately 
next to the flood decreases by about 5.9% (Column 5) compared to houses outside flood 
risk zones. The effect then decreases by approximately 0.03% points with each kilometer 
distance to the flood. While the direct comparison of effect sizes from previous studies is 
challenging due to differences in research settings, designs, and flooding types, our findings 
align most closely with Ortega and Taṣpınar (2018). The authors document an 8% price 
penalty for flood zone properties not directly damaged by Hurricane Sandy. Hallstrom and 
Smith (2005) find that Hurricane Andrew conveyed new risk information, causing property 
prices in high-risk storm zones to decline by at least 19%. Gibson and Mullins (2020) report 
an 11% price reduction for properties newly designated as being in a flood zone after nar­
rowly escaping damage from Hurricane Sandy, attributing this reduction to belief updating. 
These findings are consistent with our proposed learning mechanism increasing perceived 
flood risk.
Since distance can be categorized in various ways with varying assumptions about the 
functional form, we employ various alternative measures to test the robustness of our main 
finding. Figure B.1 illustrates the distribution of distances between properties listed for sale 
and the flooded area. We observe a large concentration of properties near the flooded areas, 
which gradually declines with distance, a pattern driven by the high population and property 
density in the state of North-Rhine Westphalia. To avoid relying on a linear functional form 
assumption, we employ alternative distance bins rather than a continuous measure. This 
approach effectively captures potential non-linearities and thresholds in the relationship. 
Table B.1 displays the results relying on the median split, which creates two distance bins, 
allowing us to estimate effects independently for properties above and below the median 
distance to the flooded areas, without imposing any functional form. The result holds: we 
find a statistically significant negative effect for properties geographically close to the flood 
at the one percent level, but no significant effect for properties located above the median 
distance (with α = 0.05). Further, Fig. 4 displays the event studies for the median split-
based distance heterogeneity, which show no systematic pre-trends. Figure B.2 displays 
the event studies for 50 kilometer distance bins. Due to decreasing precision from smaller 
sample sizes in each distance bin, we refrain from interpreting the precise location where the 
distance-based effect reaches zero. Nonetheless, Fig. B.2 displays a pattern where the effect 
size is largest for those properties listed for sale closest to the flood and then tapering off. 
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Adjusting to the New Normal: River Flood Risk and the Real Estate Market
Smaller bins generally introduce more noise due to fewer observations, this is particularly 
the case for the distance bins far from the flooded area (see Figs. B.1 and B.2). Conse­
quently, we exclude regions that are more than 400 kilometers away from the flooded areas 
and re-estimate the main distance result. Table B.2 shows that the results remain statistically 
significant at the one percent level and are slightly smaller than the main estimates. Lastly, 
we take inspiration from Callaway et al. (2024) and treat the distance-based effect as a 
continuous treatment. Comparing houses in flood risk areas relatively close to the flood to 
flood risk areas far from the flood does not display significant pre-trends (with α = 0.05) 
and identifies an imprecise effect of around ten percent (Fig. B.3).
These distance-based results may be attributed to greater amount of information about 
the flood in regions closer to the inundated areas, and cognitive biases. Geographically 
closer regions likely experienced more media coverage of the event through local news 
Table 3  Heterogeneous effects: Distance to flooded areas and belief in climate change
Dependent variable: log (price per m2)
(1)
(2)
(3)
(4)
(5)
A: Distance heterogeneity
Treatment effect
−0.0592***
−0.0589***
−0.0361**
−0.0421***
−0.0585***
(0.0165)
(0.0165)
(0.0179)
(0.0127)
(0.0166)
Treatment effect × distance to 
flooded area (km)
0.0003***
(0.0001)
0.0003***
(0.0001)
0.0002***
(0.0001)
0.0002***
(0.0001)
0.0003***
(0.0001)
Distance to flooded area (km)
−0.0006**
−0.0006**
−0.0029***
−0.0012
−0.0030***
(0.0002)
(0.0002)
(0.0005)
(0.0045)
(0.0007)
B: Climate change belief 
heterogeneity
Treatment effect
0.0134
0.0130
0.0165
0.0029
0.0088
(0.0122)
(0.0122)
(0.0131)
(0.0109)
(0.0129)
Treatment effect × high climate 
change beliefs
−0.0422**
(0.0178)
−0.0410**
(0.0179)
−0.0313*
(0.0187)
−0.0218
(0.0149)
−0.0377**
(0.0180)
High climate change beliefs
0.0252*
0.0254*
0.0915***
−0.0125
0.0227
(0.0143)
(0.0144)
(0.0204)
(0.0148)
(0.0150)
Treatment group dummy
✓
✓
✓
✓
✓
Post-treatment dummy
✓
✓
✓
✓
✓
Property characteristics
✓
✓
✓
✓
✓
County×year linear trends
✓
Year fixed effects
✓
✓
✓
✓
✓
Month fixed effects
✓
✓
✓
✓
✓
Year-Month fixed effects
✓
✓
✓
✓
Zipcode fixed effects
✓
✓
✓
County fixed effects
✓
1km2 fixed effects
✓
Number of observations
1,466,509
1,466,509
1,468,567
1,457,476
1,466,509
Notes: This table presents the heterogeneous effects of the flood. Panel A interacts the treatment effect 
with the continuous distance of a house to the nearest potentially flooded area. Panel B interacts the 
treatment effect with an indicator for counties that have an above-median share of the population that 
believes in human-made climate change. Property characteristics include the construction year of the 
house (categorical), the size of the living space, the number of floors, the number of rooms, the presence of 
a cellar, and the conditions of the structure. Standard errors are clustered at the zip code level and shown 
in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
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H. Gruhl et al.
Fig. 4  Event studies of the heterogeneous effects Notes: the figure presents event study coefficients and 
95% confidence intervals using Eq. (2) and specification (5) from Table 3 that show how flood risk af­
fects the house prices above and below the median of the distance to the flooded area (a) and the regional 
believe in climate change (b) before and after the Western European flood in July 2021. The coefficient 
in the quarter prior to the flood is normalized to zero. Standard errors are clustered at the zip code level
 
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Adjusting to the New Normal: River Flood Risk and the Real Estate Market
Table 4  Robustness checks
Dependent variable: log (price per m2)
Spillovers
Control Groups
Exclusions of flooded areas
Spatial trends
1 km buffer
1.5 km buffer Distance to 
nearest treated 
house ≤3km
Distance to 
flood risk 
area ≤2km
Only certainly 
flooded area
Poten­
tially flooded 
counties
Placebo 
flood July 
2019
Placebo 
flood July 
2020
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Average effect
Treatment effect
−0.0063
−0.0156
0.0026
−0.0021
−0.0053
−0.0080
0.0047
0.0049
(0.0103)
(0.0106)
(0.0103)
(0.0102)
(0.0107)
(0.0097)
(0.0071)
(0.0074)
Distance heterogeneity
Treatment effect
−0.0629***
−0.0729***
−0.0428***
−0.0503***
−0.0690***
−0.0734***
−0.0070
−0.0092
(0.0168)
(0.0156)
(0.0166)
(0.0165)
(0.0197)
(0.0163)
(0.0108)
(0.0112)
Treatment effect × distance to flooded area (km)
0.0003***
0.0004***
0.0003***
0.0003***
0.0004***
0.0003***
0.0001
0.0001
(0.0001)
(0.0001)
(0.0001)
(0.0001)
(0.0001)
(0.0001)
(0.0001)
(0.0001)
Climate change belief heterogeneity
Treatment effect
0.0108
0.0064
0.0152
0.0125
0.0141
0.0141
0.0029
0.0068
(0.0129)
(0.0134)
(0.0131)
(0.0132)
(0.0137)
(0.0122)
(0.0096)
(0.0101)
Treatment effect × high climate change beliefs
−0.0401**
−0.0490***
−0.0297*
−0.0341*
−0.0460**
−0.0503***
0.0042
−0.0044
(0.0179)
(0.0172)
(0.0179)
(0.0181)
(0.0192)
(0.0172)
(0.0123)
(0.0127)
Full set of controls and FEs
✓
✓
✓
✓
✓
✓
✓
✓
N
1,050,981
831,395
1,024,595
1,125,285
1,263,985
1,559,132
1,466,509
1,466,509
Notes: This table presents the robustness checks for the results in Tables 2 and 3. All estimations include a treatment group dummy, a post-treatment dummy, property 
characteristics, annual spatial trends, year-month fixed effects, and zip-code fixed effects. Regressions for distance to the flood heterogeneity include the uninteracted distance 
to the flooded area. Regressions for the belief in climate change heterogeneity include the uninteracted indicator for high climate change belief. Property characteristics 
include the construction year of the house (categorical), the size of the living space, number of floors, number of rooms, presence of a cellar, and conditions of the structure. 
Spatial×year linear trends are at the county level, except for Column 5, where we instead include state-level trends for computational reasons. Standard errors are clustered 
at the zip code level and are shown in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
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H. Gruhl et al.
The average effect of the flood remains consistent with our main findings, and the heteroge­
neous effects by distance are also robust. Specifically, we estimate a smaller but statistically 
significant (at the five percent level) negative effect of approximately 2.6% for houses closer 
to the flood, which decreases, statistically significant at the ten percent level, for houses 
farther away. However, the climate change belief-based heterogeneous effects are not robust 
in the repeat-listings approach, as we find no statistically significant effects or interactions 
(α = 0.05). These results indicate that differences related to climate change beliefs warrant 
further investigation across various contexts and models to validate the indicative evidence.
Finally, we aim to ensure that our results are not sensitive to different time trends across 
regions in Germany. Although we control for county-specific time trends in the regressions, 
we confirm that we do not pick up any remaining regional differences by conducting pla­
cebo tests in time. In Columns (7) and (8), we estimate the effects of hypothetical floods that 
occurred one and two years before the actual flood, respectively. If our estimation strategy is 
biased by differences in time trends across space, we would observe (heterogeneous) treat­
ment effects in these tests. However, we find no effects in either placebo exercise, indicating 
that our main results are not picking up on general regional developments.
5  Conclusion
This study investigates the impact of a significant fluvial flooding event on flood risk valua­
tion in the German real estate market. Specifically, we focus on the Western European flood 
in July 2021. Using a hedonic pricing model and a difference-in-differences approach, we 
explore the changes in flood risk valuation across different flood risk areas in Germany.
By excluding physical flood damages using satellite imagery, we disentangle changes in 
flood risk valuation from direct flood damages and find that, on average, flood risk valuation 
across flood-prone areas in Germany did not change. However, the average effect across 
the entire country masks substantial regional heterogeneity. First, house prices declined by 
approximately 5.9% in flood risk areas near inundated areas, with the effect diminishing as 
distance from the flooding increases. Second, house prices decrease by approximately 3% 
in flood-prone regions, with a relatively higher share of people believing in climate change. 
Regions with below-median belief in climate change show no statistically significant change 
in flood risk valuation. Our findings highlight the importance of considering regional differ­
ences in the flood risk valuation updating following fluvial floods. These findings suggest 
that flood risk undervaluation may persist despite increasing fluvial flood frequency, par­
ticularly in regions distant from past floods or with lower belief in human-induced climate 
change. This heterogeneity in flood risk perceptions may lead to suboptimal capital alloca­
tion in regions at risk of future flooding (Harrison and Kreps 1978; Scheinkman and Xiong 
2003; Bakkensen and Barrage 2022).
The lack of change in flood risk valuation in distant regions or areas with below-median 
climate change beliefs may necessitate government intervention. The policy implications 
hinge on diverging perspectives of who is responsible to adapt to future climate change: the 
government or the market. The government-centric perspective argues that governments 
should act by implementing protective measures against fluvial flash floods, enforcing land-
use and zoning restrictions to discourage development in flood-prone areas, or mandat­
ing elementary insurance for at-risk properties. In contrast, the market-centric perspective 
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