# RECORD 13561959c7aabb108ebf10b83cf499e51f169500 # TITLE: Flood risk management and real-estate prices: between prevention and “crowding out” effect # YEAR: 2025.0 VENUE: Frontiers in Environmental Economics DOI: 10.3389/frevc.2025.1603746 # AUTHORS: Jeanne Dachary-Bernard # PAGES: 10 CANDIDATE TABLE PAGES (1-based): [6] # ABSTRACT: Adapting to climate change is a key challenge for metropolis, and it partly deals with population location and flood risk management. The objective of this article is to understand the way flood risk is actually taken into account in residential choices. To this end, we measure how flood risk influences property prices and in particular, we target two specific flood management public instruments: the flood risk prevention plan (PPRI) and the public insurance mechanism (CatNat). We applied the hedonic price method over the period 2011–2016 to the French metropolitan area of Bordeaux, extended to include its neighboring estuarine municipalities. We show that flood risk zoning produces an expected significant deflating effect on prices whereas the CatNat system's insurance mechanism would appear to have a perverse effect. These results highlight an interesting crowding-out effect on the residential market that we finally discuss. ===== PAGE 1 (score 9, numeric-paren 0, table-words 0, var-words 9) ===== TYPE Original Research PUBLISHED 23 May 2025 DOI 10.3389/frevc.2025.1603746 OPEN ACCESS EDITED BY Saul Nurick, University of Cape Town, South Africa REVIEWED BY Carsten Lausberg, University of Applied Sciences Nürtingen-Geislingen, Germany Douw Boshoff, University of Pretoria, South Africa *CORRESPONDENCE Jeanne Dachary-Bernard jeanne.dachary-bernard@inrae.fr RECEIVED 31 March 2025 ACCEPTED 30 April 2025 PUBLISHED 23 May 2025 CITATION Dachary-Bernard J (2025) Flood risk management and real-estate prices: between prevention and “crowding out” effect. Front. Environ. Econ. 4:1603746. doi: 10.3389/frevc.2025.1603746 COPYRIGHT © 2025 Dachary-Bernard. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Flood risk management and real-estate prices: between prevention and “crowding out” effect Jeanne Dachary-Bernard* UR ETTIS, INRAE Nouvelle-Aquitaine Bordeaux, Bordeaux, France Adapting to climate change is a key challenge for metropolis, and it partly deals with population location and flood risk management. The objective of this article is to understand the way flood risk is actually taken into account in residential choices. To this end, we measure how flood risk influences property prices and in particular, we target two specific flood management public instruments: the flood risk prevention plan (PPRI) and the public insurance mechanism (CatNat). We applied the hedonic price method over the period 2011–2016 to the French metropolitan area of Bordeaux, extended to include its neighboring estuarine municipalities. We show that flood risk zoning produces an expected significant deflating effect on prices whereas the CatNat system’s insurance mechanism would appear to have a perverse effect. These results highlight an interesting crowding-out effect on the residential market that we finally discuss. KEYWORDS real-estate prices, flood risk, crowding out effect, residential choices, flood management 1 Introduction Climate change is creating an increasing risk for urban areas, due in particular to more frequent and violent storms and flood phenomena (IPCC, 2022). The United Nations estimate that by 2050 almost 65% of the world’s population will be living in urban zones, thus implying a new allocation of additional resources and services (to the population) (United Nations, 2019). The population in France is expected to reach 74 million in 2050, essentially distributed around urban centers increasing their vulnerability to global change. In such a context, and in this medium and long-term perspective, our cities have to deal with new challenges in terms of governance and more particularly to respond to the imperative of implementing measures to adapt to climate change. French current flood risk management strategy involves a combination of devices, alongside a number of classic flood defense infrastructures, the high maintenance costs of which are causing local managers to consider more flexible adaptation measures. Since 1982, plans to prevent natural risks (PPRN) (known as PPRI when related to flood risk) are France’s main management and prevention tools. Through zoning and associated easements, these plans govern urbanization rights in accordance with the exposure and vulnerability of the zones at risk. In tandem with the “CatNat” compensation system, these tools constitute an original national solidarity system (Barraqué, 2014). In 1982, France created the “CatNat” system, a fund financed on the basis of national solidarity via home and car insurance premiums. When there is flooding, the mayor of a municipality can ask the State (via the Prefect) to declare the event as a natural disaster (“catastrophe naturelle” in French), hence the terminology CatNat. If, following an expertise, the State makes such Frontiers in Environmental Economics 01 frontiersin.org ===== PAGE 6 (score 63, numeric-paren 36, table-words 3, var-words 10) ===== Dachary-Bernard 10.3389/frevc.2025.1603746 and u a vector of independent and identically distributed random error terms. PPRI, CatNat and their interaction PPRI∗CatNat are the variables of main interest, as previously defined. 3 Estimation results The spatial autoregressive error model (SEM) is estimated via maximum likelihood (ML), and estimations were performed under R (package spdep). Results are set out in Table 2. The OLS base hedonic model is an a-spatial model that serves as a benchmark against which the SEM effects can be compared. We will present and interpret the results in two stages. Before focusing on the impacts of the main variable of interest (flood risk variables), we comment first on the other determinants of housing prices and underline some stylized facts. 3.1 Stylized facts In line with the literature (Baumont and Legros, 2013), the results relating to the intrinsic characteristics of property (variables group Z) suggest that the greater its built area, the more land it has, the more rooms, whether it has a terrace, a swimming pool, TABLE 2 Results of the MCO and SEM for hedonic valuation of real estate prices (The test’s risk levels are noted: 1% “∗∗∗”, 5% “∗∗” and 10% “∗”.). Variables groups Variables MCO SEM Constant (α) 9.3516 (<0.0000)∗∗∗ 9.4305 (0.1485) Z surf _build 0.0048 (<0.0000)∗∗∗ 0.0047 (0.0001)∗∗∗ surf_area 0.0450 (<0.0000)∗∗∗ 0.0455 (0.0016)∗∗∗ Rooms 0.0115 (<0.0000)∗∗∗ 0.0112 (0.0021)∗∗∗ Terrace 0.0770 (<0.0000)∗∗∗ 0.0688 (0.0046)∗∗∗ Swim 0.1860 (<0.0000)∗∗∗ 0.1777 (0.0068)∗∗∗ Bathroom 0.0763 (<0.0000)∗∗∗ 0.0709 (0.005)∗∗∗ Garage 0.0287 (<0.0000)∗∗∗ 0.0331 (0.0041)∗∗∗ Condition 0.0611 (<0.0000)∗∗∗ 0.0579 (0.005)∗∗∗ E dist_road 0.1278 (<0.0000)∗∗∗ 0.1231 (0.0187)∗∗ dist_BX ≤0.000 (0.000)∗∗∗ ≤0.000 (0.000)∗∗∗ dist_river 0.0472 (<0.0000)∗∗∗ 0.0487 (0.0036)∗∗∗ Isolation 0.1041 (<0.0000)∗∗∗ 0.0986 (0.0166)∗∗ dist_road∗isolation −0.0129 (<0.0000)∗∗∗ −0.0123 (0.0021)∗∗∗ surf_nature 0.0125 (<0.0000)∗∗∗ 0.0121 (0.0022)∗∗∗ surf_built_neigh −0.0760 (<0.0000)∗∗∗ −0.0786 (0.0049)∗∗∗ height_built_neigh 0.1493 (<0.0000)∗∗∗ 0.1485 (0.0053)∗∗∗ social_compo_G1 Ref Ref social_compo_G2 0.0279 (0.0006)∗∗∗ 0.0318 (0.0094)∗∗∗ social_compo_G3 0.1560 (<0.0000)∗∗∗ 0.1615 (0.0071)∗∗∗ BM 0.1946 (<0.0000)∗∗∗ 0.1962 (0.0096)∗∗∗ V CatNat 0.0748 (<0.0000)∗∗∗ 0.075 (0.0066)∗∗∗ PPRI −0.1143 (<0.0000)∗∗∗ −0.1153 (0.0158)∗∗ CatNat∗PPRI 0.0583 (0.002)∗∗∗ 0.0532 (0.0178)∗∗ T T2011 Ref Ref T2012 0.0092 (0.1435) 0.0148 (0.0061)∗∗∗ T2013 0.0160 (0.0103)∗∗ 0.0223 (0.006)∗∗∗ T2014 0.0136 (0.0353)∗∗ 0.0152 (0.0062)∗∗∗ T2015 0.0177 (0.0046)∗∗∗ 0.0209 (0.006)∗∗∗ T2016 0.0550 (<0.0000)∗∗∗ 0.0577 (0.006)∗∗∗ Lambda (p) 0.1609 (<0.0000)∗∗∗ N 24,871 I moran on residuals (p-value) −0.0069 (0.8047) Frontiers in Environmental Economics 06 frontiersin.org ===== PAGE 7 (score 14, numeric-paren 0, table-words 0, var-words 14) ===== Dachary-Bernard 10.3389/frevc.2025.1603746 more than one bathroom, a garage, and the better its condition, the higher its price is. In addition to these structural variables, temporal variables also prove to have a positive effect on prices (taking 2011 as the year of reference), which relates to the general price increase seen in the real estate market over that period in this area (A’Urba, 2018). Regarding the property’s environment (variables group E), the distance to the Bordeaux urban center has a significantly negative (but very low) effect on prices meaning that the prices decrease with the distance to city center. Conversely, we notice positive coefficients for the variables of distance from the river and distance to the main road. The dichotomous BM variable is highly significant in the model, which means that the metropolitan effect is captured into prices. Several other variables that refer to the environment surrounding the property affect prices. Thus, a large green area, a small built area, and a significant average building height within 200 m of the house, will increase its value. The market therefore appears to valorize a green and dense neighborhood. The social environment also affects house prices through positive and significant effects relating to the social composition of the neighborhood. Socio_compo_G2 and Socio_compo_G3 have highly significant and positive coefficients. It illustrates the search for neighborhoods that are socioeconomically homogenous and above all without diversity (this mixed composition is represented by the reference level for social composition). Finally, the isolation index has a positive effect on prices. All other things being equal, the market valorizes isolated properties but it should be noted that the interaction term between this isolation index and the distance from the road variable has an overall negative effect. It means that people valorize isolated houses, but that a property far from the main transport infrastructures will be less expensive, all other things being equal. People look for isolated housing but easily accessible. 3.2 Flood risk As far as the results specifically relating to vulnerability to flooding are concerned, the two variables, and their interaction, reveal significant effects on house prices, albeit in different ways. The three flood components of the hedonic equation are highly significant. In the presence of the interaction term, the coefficients γ and δ are marginal effects and must be interpreted as holding the other variable equal to zero. For example, γ is the impact of being located in the PPRI zone of a municipality that has not experienced a high number of CatNat. Similarly, δ captures the impact of being located in a municipality with a high number of CatNat outside a PPRI zone. The interaction term θ informs about the marginal effect of being located in the PPRI zone of a municipality that has experienced a high number of CatNat flood events. PPRI variable has a negative and significant effect ( ˆγ = −0.1143) on prices. This means that all other things being equal, a property located in a flood area in a municipality that has not experienced many CatNat events is less expensive than a property located outside that zone. The discount due to a flood zone location is ∼11% in municipalities that have not experienced many CatNat events. The CatNat variable, on the other hand, has a positive effect on price (ˆδ = 0.0748), which means that all other things being equal, the fact of being located in a municipality that has experienced numerous floods recognized by decree as being natural disasters leads to higher prices for houses that are not located in flood zones. At first glance this is a counter-intuitive result that requires an explanation. It needs to be interpreted in relation to the compensative dimension that the variable CatNat conveys. Indeed, recognition of a flood event as a CatNat leads to financial compensation. The market would therefore appear to value significant activity by a municipality in terms of post- flood crisis management. According to the estimations, this CatNat variable leads to an appreciation in property value of ∼7.8%. Finally, cross-referencing the two flooding variables allows us to capture their interaction effect. This interaction effect is significantly positive ( ˆθ = 0.0583). It means that people positively value being in the PPRI area of a municipality that has had numerous CatNat events. This marginal effect is positive and may be qualified as an “additional premium”. The impact of CatNat is therefore almost twice that in a non-flooded zone, i.e., almost 12% of the value of the property. In the same way, and symmetrically, comparing with the PPRI variable, the discount is therefore reduced to 5.6% of the value of the property: the drop in value due to being located in a flood zone is lower when the municipality has shown itself to be in a position to trigger aid. 4 Discussion/Conclusion The results relating to stylized facts highlight the presence of the traditional process of peri-urbanization driven by the search for accessibility to the city center and to green amenities in the immediate vicinity of the home. On the other hand, as the literature has shown (Travers et al., 2009), proximity to the main transport infrastructures appears to be mainly perceived as nuisances which outweigh the advantages in terms of accessibility. This point requires qualification regarding isolated housing for which access to the road network is a sign of accessibility. The phenomenon of socio-spatial segregation often highlighted in contemporary urban forms would seem to be confirmed here, with the search for homogeneous and non-mixed socio-economic environments (Baumont and Legros, 2013). This result had already been demonstrated on the smaller scale of the Bordeaux metropolis alone (Décamps and Gaschet, 2013). Moreover, given the broader metropolitan scale that we have chosen, our results also ask questions about the emergence, parallel to this process of peri- urbanization, of a process of urbanization. This is characterized by a growing attractiveness for spaces far from the metropolis, spaces that will (re-)become attractive in their own right (Bailly and Bourdeau-Lepage, 2011). However, alongside these more general processes, our main results regarding the flood risk variables focus on two important phenomena that must be taken into account when considering the strategy for adapting cities to climate change. The first phenomenon concerning the PPRI zoning, which is standard in the literature, relates to people risk aversion. Indeed, locating in a flood prone area is negatively valued, meaning that people tend to move away from the risk and choose not to live in a Frontiers in Environmental Economics 07 frontiersin.org