The Impact of Uncovered Flood Events on the Demand for Homeowners Insurance

Transcription

1 The Impact of Uncovered Flood Events on the Demand for Homeowners Insurance Vijay Aseervatham LMU Munich Patricia Born Florida State University J. Tyler Leverty University of Wisconsin - Madison July 7, 2014 Abstract Flood coverage is excluded from the standard homeowners insurance policy, but many American homeowners are not aware of this exclusion. As a result, flood events may provide new information to policyholders and allow them to update the probability of homeowners insurance losses. We model this possibility and find that floods decrease the demand for homeowners insurance coverage as policyholders switch to higher deductible policies. Using a nationwide panel dataset of flood events and total homeowners insurance premiums by state, we find flood events decrease homeowners insurance premiums. We postulate that homeowners use the savings on the homeowners insurance to purchase flood coverage in the aftermath of flood events, supporting budget considerations. All-hazards insurance might provide a solution for the problems arising with these reactions. Keywords: Catastrophic Risks, Insurance Demand, Property/Casualty Insurance Vijay Aseervatham Munich School of Management, Ludwig-Maximilians-Universitaet Munich, Schackstr. 4, Munich, Germany, Aseervatham@bwl.lmu.de Patricia Born Dept. of Risk Management/Insurance, Real Estate & Legal Studies, College of Business, Florida State University, 821 Academic Way, Tallahassee, Florida , USA J. Tyler Leverty Wisconsin School of Business, University of Wisconsin Madison, 975 University Avenue, Madison, WI 53706, USA, tleverty@bus.wisc.edu 1

2 1 Introduction Floods pose significant risks to the property of homeowners all over the world. In 2012 hydrological events such as river floods, flash floods, storm surges, and mass movements (landslide) accounted for 36 % of the loss events, 48 % of the fatalities, and 13 % of overall losses (Munich Re, 2013). Nevertheless people, even in high risk areas, do not adequately insure against floods (e.g., Michel-Kerjan and Kousky, 2010; Dixon et al., 2006; Kriesel and Landry, 2004). Instead, flood insurance coverage spikes after flood events and then steadily declines in the years following the event (Gallagher, 2013). Flood damage is excluded in the standard homeowners insurance policy, so flood events would seemingly have no impact on coverage decisions for homeowners insurance. 1 We argue, however, that these events may provide valuable (and potentially new) information to homeowners about the adequacy of their homeowners insurance coverage. A 2007 survey by the National Association of Insurance Commissioners (NAIC) finds that 33 percent of homeowners believe flood damages are covered by their homeowners insurance policy (NAIC, 2007). 2 This suggests many homeowners do not have flood insurance coverage, but it also suggests that flood events may provide additional information to homeowners about their coverage and, in particular, that they bought too much homeowners insurance given that it does not cover flood. We propose a simple theoretical model which analyzes the impact of a change in (perceived) loss probability on the deductible choice. The model reveals that a decrease in loss probability is attended by an increase in deductible. We then analyze the impact empirically using a state-level panel dataset for the years 1996 to 2010 and find evidence for our hypotheses. Analyzing how homeowners react to flood events provides several contributions to the literature. First, recent studies find that the purchase of flood insurance spikes the year after a flood and then it steadily declines in subsequent years (e.g., Gallagher, 2013). Michel-Kerjan et al. (2012) conjecture that homeowners budget considerations may be the reason for this observed pattern as people may be forced in some cases to choose between alternative protective behaviors, such as buying healthier food, or replacing bald tires, or purchasing health insurance or fire insurance. We find evidence of this supposition. In particular, we find that some homeowners pay for their flood insurance by reducing their homeowners insurance coverage. Second, the observed drop in the homeowners insurance coverage after flood events might have an important implication with respect to optimal levels of insurance. It is possible that the 1 Flood insurance is provided by the National Flood Insurance Program (NFIP). 2 Kunreuther and Michel-Kerjan (2009) support this finding by emphasizing that [d]espite the fact that the NFIP was created over forty years ago, some homeowners contend that they were not aware of this exclusion and that [i]n fact, many lawsuits were filed in Gulf Coast states following Katrina and other hurricanes. Most of these requested that the courts overturn flood exclusions in their homeowners policies. 2

3 increase in deductibles may leave some homeowners underinsured with regard to risks that are covered in the classic homeowners insurance policy. Common deductibles are 100, 250, 500, and 1000 (Sydnor 2010) so the new deductible might not be optimal. Our study provides insight on an almost unnoticed potential benefit of an all-hazard policy. All-hazards policies would first remove the flood exclusion (as argued in Kunreuther and Michel-Kerjan, 2009) and second, homeowners would then not be able to shift money from one coverage to the other, a social benefit that has been neglected to date. 3 Without all-hazard policies, the occurrence of floods might also help people to make better choices with regard to their homeowners insurance policy: Sydnor (2010) shows that homeowners over-insure small risks. If people appropriately adjust their homeowners insurance deductibles in the aftermath of floods this could be even seen as a positive side effect. The paper is structured as follows: we first develop a model to analyze the impact of a decrease in loss probability on the deductible choice. In part 3, we present our data and the empirical method. Part 4 provides our results. Part 5 includes a discussion and the policy implications. 2 Model The following model, which represents a very simplistic setting on the basis of Mossin (1968), is used to analyze theoretically how a perceived decrease in loss probability in homeowners insurance affects the deductible decision. We assume that homeowners who purchase the standard homeowners insurance policy under the false assumption that flood losses are covered realize they are excluded after major flood events that affect them or those around them. This realization changes the perceived loss probability in the standard homeowners insurance policy. While homeowners only have limited options to vary their homeowners coverage because of policy standardization and mortgage lender requirements, homeowners can adjust the level of their homeowners insurance deductible. As Sydnor (2010) argues, [w]hile lenders require coverage, the homeowner is generally free to choose the insurance company and the details of the policy, including the deductible level. We assume a risk averse decision maker (u > 0, u < 0). The individual has initial wealth w and decides to purchase insurance with deductible. The perceived loss probability for the insured is p and the loss is L. The insured pays premium. is the estimated probability of loss for the insurance company and includes a loading factor. If the insured thinks that flood risk is covered whereas the insurer knows this not to be the case, the perceived loss probability of the 3 For a general discussion of all-hazard insurance policies, see Kunreuther and Michel-Kerjan (2009). 3

4 insured will diverge from the estimated loss probability of the insurer, which should be smaller in this case. We assume no moral hazard. Thus, expected utility is given as: with and. (1) We want to analyze the effect of a change in the perceived loss probability on the deductible choice, i.e.,. (2) Thus, the sign of is given by the sign of. (3) This term is unambiguously smaller than zero which would mean that a decrease in perceived loss probability for the insured leads to the choice of a higher deductible if (4) (5) Intuitively, this means that if the loading factor of the insurance company is too high, insureds would not opt for a lower deductible even if there is an increase in perceived loss probability. The term on the RHS is larger or equal 1. So, even if is equal to 1, the whole expression (4) would be smaller than zero. In terms of fair insurance, means that the insurer calculates the premiums as if the loss case would happen with certainty. Obviously, this is an unrealistic case. This is why we assume expression (5) holds indicating that after realizing flood losses are not covered in the standard homeowners insurance individuals will choose a higher deductible. Thus, our first hypothesis is: H I: Homeowners who purchase the standard homeowners insurance policy under the misguided assumption that flood risks are covered will raise their deductible for the standard homeowners insurance policy after realizing that flood risks are excluded, i.e. after major flood events affecting them. This effect might be even stronger if budget constraints are considered. After major flood events an increase in flood insurance purchases has been observed (Gallagher, 2013). If one thinks about a single budget for securing the house, then some money from the homeowners insurance coverage may be shifted to the flood insurance coverage. Michel-Kerjan et al. (2012) use a related argument to explain why people lapse their flood insurance policies after a few years. This reasoning might even 4

5 hold for people who have purchased the homeowners insurance policy without misguided assumptions. After a flood event people might realize that they need to secure their house against flood risks assuming one account for saving your house, people might then shift money from one coverage to the other. The effect is likely to be stronger in low flood risk areas compared to high flood risk areas, as homeowners in high risk areas are more likely to know that flood losses are excluded in the standard homeowners insurance policy. Kousky (2010), for instance, provides evidence that after major floods, risk perception in 500-year floodplains changes (measured as a drop in property prices), whereas there is no change in 100-year floodplains. Thus, our second hypothesis is: H II: The effect will be stronger in low risk areas than in high risk areas. Although we are not able to analyze neither policy level data nor qualitative information, we expect to find evidence of these reactions on an aggregate state level because a significant fraction of the population seems to be unaware of the flood exclusion (NAIC survey 2007). 3 Data and Empirical Model Data For our analysis we use information for the period Our key variables of interest are obtained from insurer financial statement data aggregated to the state level. The financial statement data is obtained from the NAIC. Hazard data on state-year level is obtained from Sheldus TM, which is a natural hazard database that provides information on property damages on the county level for each hazard event. Flood damages are identified if the Hazard_Type_Combo is Flooding. The Hazard_Type_Combo might have more than one entry per observation such as Severe Storm/Thunder Storm Wind. To reduce supply effect distortions in our analysis, we do not consider events in which flooding appears as a multiple entry event it. We analyze the impact of flood events on insurers premiums earned. We standardize premiums by state population. Since flood losses are not covered by the standard homeonwers insurance policy, there should be no supply-side effects (e.g., a capacity constraint effect, Winter 1988; Doherty and Garven, 1995; and Klimaszweski-Blettner, 2010) after flood events. Thus, these events will only elicit an impact on the demand-side. We exclude earthquake events from our analysis because these events are also covered partially by non-private institutions. Furthermore, we use information on migration (number of new residential 5

6 housing units), mean income on state level, FEMA expenditures on emergency management, planning and assistance, FEMA expenditures on disaster relief and seasonally adjusted median home prices per state. The FEMA data are collected from the annual Federal Aid to States reports published by the U.S. Census Bureau. If people move or leave high risk regions this might have an effect on our dependent variable. The mean income on state level proxies for the value of the properties in a state this also has an impact on premiums earned. The FEMA expenditures might have an impact on the flood insurance purchase decision, which again according to our model has an impact on the deductible decision in the homeowners insurance. Home prices also affect the amount of premiums earned in a state. If home prices change after floods, this might also influence premiums earned. The median home prices are compiled from two sources. Median house prices are collected from the Census Bureau for the years 1990, 2000, and For the intermediate years, we calculate the median house prices using the House Price Index (HPI) published by the Federal Housing Finance Agency (FHFA) using data provided by Fannie Mae and Freddie Mac. The HPI is a broad measure of the movement of single-family house prices. The HPI is based on transactions involving conforming, conventional mortgages on single-family properties purchased or securitized by Fannie Mae or Freddie Mac. To assign risk zones (above average flood risk, average flood risk, below average flood risk) to states we use information on cumulative flood payments by the NFIP. We standardize this variable by the cumulative income per state. Empirical Model We analyze the impact of flood damages on premiums earned on the state-year level. We control for other natural hazard damages in the respective state and year. We include a lagged damage variable in the estimation. We also control for other state and year specific variables. We include year dummies and control for a time trend. The time trend variable takes on the value 1 for the year 1996, 2 for the year 1997, and so on. We use a fixed-effects estimation (supported by a Hausman Test) and thereby control for time invariant unobserved heterogeneity between states. We use heteroskedasticity robust and clustered standard errors on state-level. Our first estimation does not include risk zone information: (1) The unit of observation is the state calendar year. The dependent variable in Equation (1),, is the natural logarithm of homeowners insurance premiums earned for state s and year t. The independent variables of interest are the natural logarithm of flood damage ( in t and t-1. The 6

7 estimated coefficients are interpreted as the percent change in the premiums earned in state s given a 1 percent increase in flood damage. X is a vector of the control variables that affect the demand for homeowners insurance (natural logarithm of other natural hazard damages in t and t-1, natural logarithm of the number of new residential housing units, natural logarithm of per capita income, natural logarithm of median home prices, FEMA expenditures on disaster relief, FEMA expenditures on mitigation, and time trend). Equation (1) also includes state fixed effects ( ), year fixed effects ( ), and a stochastic error term ( ). The fixed effects non-parametrically control for unobserved (and unchanging) state characteristics and year factors. The errors are clustered on state-level. To make sure that we are not estimating any supply effects we check whether flood damages have an impact on insurers losses incurred. This should not be the case. We finally investigate whether the effects differ between risk zones. Therefore we run the regression separately for the average, below average and above average risk zones. 4 Results Table 1 shows our descriptive statistics. Since we are estimating a log-specification, if there is no damage we assign a value of 1 for Flood_Damage and Other_Damage. Only 6 state-year observations do not have any other natural hazard property damage and 44 state-year observations do not have any flood property damage. Table 1 Descriptive Statistics Variable Obs Mean Std. Dev. Min Max Homeowners Premiums Earned e e e e+12 Flood_Damage e e e+09 Other_Damage e e e+10 housing pcapinc Homeowners Losses Incurred e e e+10 Median Home Price FEMA Disaster Relief FEMA Mitigation The results for our first estimation are shown in Table 2. As hypothesized, we find a significantly negative effect of flood events on homeowners premiums earned. A 10 percent increase in flood damage leads to a 0.02 percent decrease in homeowners premiums earned in the respective year. In the following year, this effect is reduced by half. We do not find any statistically significant effects of our control variables on the dependent variable. In general, there seems to be a negative trend over 7

8 the years in premiums earned. These results support our first hypothesis: people adjust their homeowners insurance coverage after flood events. As a support for our assumption that flood events should not have an impact on insurers capacity we estimate whether recent flood damages have an impact on insurers losses incurred. The results are presented in Table 3. Our results show that, as suggested, there is no impact of flood damages on insurers losses incurred. Not surprisingly, other natural hazards increase losses incurred. A 10 percent increase in other damage causes an increase in losses incurred of about 0.9 percent. Again, the controls except for per capita income do not have a significant impact on losses incurred. Finally, we classify the states in our data to three risk zones: average flood risk, below average flood risk, and above average flood risk. The classification of the states is summarized in Table 4. 8

9 Table 2 Fixed-Effects Estimation / Premiums Earned VARIABLES (1) log_home_pe_pc ln(flood Damage) t *** (0.001) ln(flood Damage) t * (0.001) ln(other Damage) t (0.002) ln(other Damage) t (0.001) ln(housing) (0.039) ln(per capita income) (0.234) ln(homeowners losses incurred) (0.009) ln(median home price) (0.063) disaster_exp (0.000) mitigation_exp (0.000) Year dummies Trend *** (0.009) Constant 9.822*** (2.140) Observations 689 Number of staten 50 Adj. R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 9

10 Table 3 Fixed-Effects Estimation / Losses Incurred VARIABLES (1) log_home_li_pc ln(flood Damage) t (0.003) ln(flood Damage) t (0.003) ln(other Damage) t 0.087*** (0.023) ln(other Damage) t (0.007) ln(housing) (0.089) ln(per capita income) * (0.706) ln(median home price) (0.205) disaster_exp (0.000) mitigation_exp (0.000) Year dummies Trend *** (0.025) Constant ** (5.610) Observations 689 Number of staten 50 Adj. R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 We then conduct our estimation separately for the three risk zones. These regressions help to answer our second hypothesis that the spill-over effects are stronger in low flood risk areas than in high flood risk areas. The results for our risk zone analyses are shown in Table 5. 10

11 below average flood risk Table 4 Risk Classification by State average flood risk above average flood risk AK AR AL AZ DE CT CA GA FL CO HI IA ID IL LA MI IN MO MN KS MS MT KY NC NE MA ND NM MD NJ NV ME NY OH NH PA OR OK RI UT SD SC WI TN TX WY VA VT WA WV 11

12 Table 5 Fixed-Effects Estimations by Risk Zones / Premiums Earned (above average) (average) (below average) VARIABLES log_home_pe_pc log_home_pe_pc log_home_pe_pc ln(flood Damage) t * (0.002) (0.001) (0.001) ln(flood Damage) t ** (0.002) (0.000) (0.002) ln(other Damage) t (0.005) (0.001) (0.004) ln(other Damage) t (0.002) (0.002) (0.003) ln(housing) (0.077) (0.044) (0.043) ln(per capita income) 0.623* (0.314) (0.352) (0.410) ln(homeowners losses incurred) (0.024) (0.019) (0.021) ln(median home price) * ** (0.111) (0.091) (0.108) disaster_exp *** (0.000) (0.000) (0.000) mitigation_exp (0.000) (0.000) (0.000) Trend *** *** *** (0.014) (0.012) (0.021) Year dummies Constant 9.662** 9.003** *** (3.443) (3.354) (3.442) Observations Adj. R-squared Number of staten Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 In the above average flood risk zone we do not find any significant effect of recent flood damages on homeowners premiums earned. We find a significantly negative effect of recent flood damages on homeowners premiums earned in the average flood risk zone. These results would support our second hypothesis. However, we do not find any significant effect in the below flood risk area. At first sight this seems to undermine our suggestion. 12

13 Robustness One might argue that the number of clusters is too small (particularly in the split sample estimations) to get reliable estimates. We re-estimate our equations without clustered standard errors and find no difference in the statistical significance of our main results. Although we already address the problem of joint occurrences of floods and other natural hazards by our flood event definition, we conduct several robustness checks. We additionally control for the interaction of flood_damage and other_damage. The results are presented in Table A.1 and Table A.2. We do not find any significant effect of flood damages on premiums earned in the whole sample. However, again splitting the sample into the different risk zones gives us some significant effects for the average risk sample. These results, although weaker than before, support our initial evidence for a spill-over effect, which does not exist for very high risk and very low risk areas. We also check robustness using another specification. Instead of ln(other_damage) and the lag of this variable, we include a dummy (OtherEvent) that is 1 if any other natural hazard event has occurred in the state in a given year. This way we also can control for the interaction effect of flood property damage and the joint occurrence of other hazard events. The results are presented in Table A.3 in the Appendix. The results support our analysis and make the results even stronger. In the same year of a flood a 10 percent increase in flood damage is associated with a decrease in statewide homeowners premiums earned of about 0.09 percent. This effect is reduced by one half one year after the event. However, this result has to be treated with caution because only a few observations exist without any other natural hazard property damages. 5 Discussion and Policy Implications Our results suggest that people increase their homeowners insurance deductibles after flood events. Homeowners who were not aware of the flood risk exclusion in the standard homeowners insurance policy receive this information after the occurrence of a flood. The initial loss probability for the standard homeowners policy is now reduced this is why we expected the drop in demand for the homeowners insurance coverage in the aftermath of flood events. This finding is robust to different model specifications. Thus, we can support our first hypothesis. The estimated effect a 10 percent increase in flood damage leads to a 0.02 percent decrease in homeowners premiums earned is likely underestimated: if all homeowners are aware that flood damage is excluded from their homeowners insurance coverage, then the main variable of interest, Flood Damage, will not have been statistically significantly different from zero. However, in contrast, if all homeowners are not aware that flood damage is excluded from their homeowners insurance coverage, then the main 13

14 variable of interest, Flood Damage, will be negative and statistically different from zero. The NAIC survey evidence suggests that a majority of homeowners are aware of the exclusion (67 percent), which means that the results will be biased away from finding a significant effect. The fact that we are trying to identify the effect for a minority of policyholders suggests that if we assume that the NAIC survey results hold for our full sample (for all the years and all the states), i.e., that 33 percent of policyholders are unaware of the flood exclusion, then for 67 percent of the observations this is zero, but the responses by the remaining 33 percent are strong enough to make it negative and statistically significant. Another reason for the relatively small effect is given in Gallagher (2013): the author finds that flood risk beliefs change after floods. Gallagher (2013) also finds that residents in non-flooded communities in the same television media market also increase take-up of flood insurance. This could explain why the effect on the state level is rather small, as we are comparing the effect of flood state to states that did not experience a flood but that may be aware of the event via the media. The findings with regard to our second hypothesis are less clear: we do not find any evidence for a statistically significant reaction in high flood risk areas, whereas we find a significantly negative effect for the average flood risk category. This would be in line with our hypothesis that people in high risk areas probably already know about the flood risk and the exclusion. Interestingly, we did not find any significant effect for the low risk area. This could have a simple answer: the risk is just too small to be recognized. People in this case would not react to flood events at all. Interestingly, neither migration nor income seems to be a relevant factor for purchasing homeowners coverage. Mortgage lender requirements might be a reason why there is no income effect. This study provides several contributions and policy implications: 1) combined with the general finding that homeowners purchase flood insurance in the aftermath of flood events, our results let us speculate that homeowners use the money that they save when they increase their deductible in the standard homeowners policy to purchase flood risk coverage. This is the first study to present a valid basis for this suggestion. 2) Our results also have implications for the discussion about all-hazards policies: people who save money in the homeowners insurance policy to buy flood insurance coverage might be underinsured with regard to all other risks then. Although Sydnor (2010) states that people in general over-insure small risks, which would mean that increasing deductibles in the aftermath of floods are arguably a good decision, at least some people might face underinsurance problems. All-hazards policies could provide a solution: all-hazards policies would abandon the coexistence of NFIP, earthquake, and standard homeowners insurance policies (Kunreuther and Michel-Kerjan, 2009). Instead, there could be one so called all-hazards policy capturing all the risks. Homeowners would then not be able to shift money from one coverage to the other. In this paper 14

15 we have not answered the question whether the increase in deductible is a good or bad insurance decision for the homeowner. Further research should aim at answering this question. 3) Our results with regard to the second hypothesis also contribute to the findings of Kousky (2010). Instead of a one way track the more experience with flood risk, the less probability updates our results let us speculate that there might be a lower level of experience with flood risk where there are also no probability updates. 15

16 References Browne, M. J., & Hoyt, R. E. (2000). The demand for flood insurance: empirical evidence. Journal of Risk and Uncertainty, 20(3), Dixon, L., N. Clancy, S. A. Seabury, & Overton, A. (2006). The National Flood Insurance Program s Market Penetration Rate: Estimates and Policy Implications. Santa Monica, CA: RAND Corporation. Doherty, N. A., & Garven, J. R. (1995). Insurance cycles: interest rates and the capacity constraint model. Journal of Business, 68(3), Gallagher, J. (2013). Learning about an Infrequent Event: Evidence from Flood Insurance Take-up in the US. American Economic Journal: Applied Economics, Forthcoming. Gron, A. (1994). Capacity constraints and cycles in property-casualty insurance markets. The RAND Journal of Economics, 25(1), Klimaszewski-Blettner, B. (2010). Management von Katastrophenrisiken-Herausforderungen, Ansatzpunkte und Strategien im Rahmen einer Public-Private-Partnership (Vol. 63): Verlag Versicherungswirtsch. Kousky, C. (2010). Learning from extreme events: risk perceptions after the flood. Land Economics, 86(3), Kriesel, W., & Landry, C. (2004). Participation in the National Flood Insurance Program: An empirical analysis for coastal properties. Journal of Risk and Insurance, 71(3), Kunreuther, H. C., & Michel-Kerjan, E. O. (2009). At war with the weather: Managing large-scale risks in a new era of catastrophes: MIT Press. National Association of Insurance Commissioners (2007). Homeowners Insurance Survey. Michel Kerjan, E. O., & Kousky, C. (2010). Come rain or shine: Evidence on flood insurance purchases in Florida. Journal of Risk and Insurance, 77(2), Michel-Kerjan, E. Lemoyne de Forges, S., & Kunreuther, H.(2012). Policy tenure under the National Flood Insurance Program. Risk Analysis, 32(4), Mossin, J. (1968). Aspects of rational insurance purchasing. The Journal of Political Economy, Munich Re (2013). Topics Geo Natural Catastrophes 2012 Analyses, Assessments, Positions Issue. 16

17 Sydnor, J. (2010). (Over)insuring Modest Risks. American Economic Journal: Applied Economics, 2, Winter, R. A. (1988). Liability Crisis and the Dynamics of Competitive Insurance Markets. The Yale J. on Reg., 5,

18 Appendix Table A.1 Control for interaction effect VARIABLES (1) log_home_pe_pc ln(flood Damage) t (0.003) ln(flood Damage) t (0.003) ln(other Damage) t (0.002) ln(other Damage) t (0.002) ln(flood Damage_OtherDamage ) t (0.000) ln(flood Damage_OtherDamage) t (0.000) ln(housing) (0.039) ln(per capita income) (0.235) ln(homeowners losses incurred) (0.010) ln(median home price) (0.063) disaster_exp (0.000) mitigation_exp (0.000) Year dummies trend *** (0.010) Constant 9.825*** (2.155) Observations 689 Number of staten 50 Adj. R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 18

19 Table A.2 Control for interaction effect / risk zones (above average) (average) (below average) VARIABLES log_home_pe_pc log_home_pe_pc log_home_pe_pc ln(flood Damage) t ** (0.005) (0.002) (0.010) ln(flood Damage) t (0.003) (0.002) (0.010) ln(other Damage) t (0.003) (0.001) (0.009) ln(other Damage) t * (0.003) (0.002) (0.010) ln(flood Damage_OtherDamage ) t ** (0.000) (0.000) (0.001) ln(flood Damage_OtherDamage) t * (0.000) (0.000) (0.001) ln(housing) (0.072) (0.045) (0.043) ln(per capita income) 0.572* (0.307) (0.352) (0.418) ln(homeowners losses incurred) (0.027) (0.019) (0.021) ln(median home price) ** ** (0.103) (0.093) (0.111) disaster_exp *** (0.000) (0.000) (0.000) mitigation_exp (0.000) (0.000) (0.000) Year dummies Trend *** *** *** (0.017) (0.013) (0.021) Constant 9.820*** 9.431** *** (3.343) (3.348) (3.493) Observations Adj. R-squared Number of staten Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 19

20 Table A.3 Control for joint occurrence VARIABLES (1) log_home_pe_pc ln(flood Damage) t *** (0.002) ln(flood Damage) t *** (0.002) ln(other Event) t *** (0.009) ln(other Event) t ** (0.019) ln(flood Damage_Other Event ) t 0.007*** (0.002) ln(flood Damage_Other Event) t ** (0.002) ln(housing) (0.038) ln(per capita income) (0.232) ln(homeowners losses incurred) * (0.007) ln(median home price) (0.064) disaster_exp (0.000) mitigation_exp (0.000) Year dummies trend *** (0.010) Constant *** (2.106) Observations 689 Number of staten 50 Adj. R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 20