Deductible Choice in Flood Insurance: Who Chooses the Maximum?

Figures & data

Table 1. NFIP deductible menu for October 2008 – October 2019.

Standard Deductibles	10/1/2008	5/1/2009	10/1/2009	5/1/2010	10/1/2010	1/1/2011	5/1/2011	10/1/2011	5/1/2012	1/1/2013	5/1/2013	10/1/2013
Post-FIRM (all coverages)	$500	$500	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000
Pre-FIRM (all coverages)	$1,000	$1,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000
Buy-Back Deductibles (all coverages)
Pre-FIRM Only	$500	$500	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000
Emergency Program (all coverages)	$1,000	$1,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000
Higher Deductible Choices	$1,000	$1,000
	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000
	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000
	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000
	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000
Standard Deductibles	6/1/2014	10/1/2014	4/1/2015	11/1/2015	4/1/2016	10/1/2016	4/1/2017	10/1/2017	4/1/2018	10/1/2018	4/1/2019	10/1/2019
Post-FIRM
Building Coverage < = $100K	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000	$1,000
Building Coverage > $100K	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250
Pre-FIRM
Building Coverage < = $100K	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500
Building Coverage > $100K	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000
Emergency Program
Building Coverage < = $100K	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500
Building Coverage > $100K	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000
Higher Deductible Choices	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250	$1,250
	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500	$1,500
	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000	$2,000
	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000	$3,000
	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000	$4,000
	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000	$5,000
			$10,000	$10,000	$10,000	$10,000	$10,000	$10,000	$10,000	$10,000	$10,000	$10,000

Table 2. Owner-occupied single-family dwellings, NFIP policy effective 01/01/2009–06.

Variable		Mean		Std. Dev.	Median	Pct(75)	N	Pct(25)
Panel A: Descriptive Statistics for SFHA with Elevation Reported
Policy Premium		666.33		599.08	392	501	727	10,874,500
Deductible Amount		1,609.38		1,451.37	1,000	1,000	1,250	10,874,500
Total Coverage		253,326.20		86,661.04	196,100	255,900	344,900	10,874,500
Post FIRM Construction		0.74		0.44	0	1	1	10,874,500
Elevation Difference		1.67		31.31	0	1	3	10,874,500
CRS Discount		0.13		0.09	0.05	0.2	0.2	10,874,500
Panel B: Descriptive Statistics for SFHA with no Elevation Reported
Policy Premium		1,365.96		839.80	751	1,199	1,801	5,932,885
Deductible Amount		2,704.40		1,879.11	1,500	2,000	5,000	5,932,885
Total Coverage		166,089.00		91,917.52	88,700	159,100	250,000	5,932,885
Post FIRM Construction		0.04		0.19	0	0	0	5,932,885
CRS Discount		0.08		0.09	0	0.05	0.2	5,932,885
Panel C: Descriptive Statistics for non-SFHA, PRPs
Policy Premium		384.32		74.37	348	390	450	15,499,351
Deductible Amount		1,051.90		201.06	1,000	1,000	1,250	15,499,351
Total Coverage		290,287.00		91,683.57	210,000	350,000	350,000	15,499,351
Panel D: Descriptive Statistics for non-SFHA, non-PRPs
Policy Premium		868.84		507.76	475	667	1,168	1,543,867
Deductible Amount		1,588.28		1,344.72	1,000	1,250	1,250	1,543,867
Total Coverage		232,452.20		110,411.90	141,700	250,000	350,000	1,543,867

Table 3. 2015 summary statistics for matched policies (2013–2019).

Variable	Mean	Std. Dev.	Pct(25)	Median	Pct(75)	N
Total Coverage	252,613.90	92,930.62	193,200	250,000	350,000	252,280
Premium for $100,000	536.75	586.85	177.5	286.2	887.0	252,280
Coastal State	0.81	0.39	1	1	1	252,280
Contents Covered	0.64	0.48	0	1	1	252,280
Deductible Amount	2,143.91	1,702.82	1,250	1,250	2,000	252,280
Minimum Deductible	0.76	0.43	1	1	1	252,280
Maximum Deductible	0.16	0.37	0	0	0	252,280
Changed to 10000	0.02	0.14	0	0	0	252,280

Note. This table presents summary statistics for the 252,280 policies that we are able to trace over the period of 2013–2019. The sample begins with policies from 2013 for owner-occupied SFDs located in SFHAs. Subsequent policies for these properties are matched using the following variables: census tract, original policy date, construction date, city, latitude and longitude (with one decimal of precision), and the number of floors.

Figure 1. Total SFHA policies by deductible (January 2010 – July 2019).

Note. Figure 1 presents the total number of NFIP flood insurance policies in effect by month for owner-occupied single-family dwellings in SFHAs. This stacked line graph makes obvious a) the raising of the minimum deductible from $500 to $1000, b) the raising of the minimum from $1,000 to $1,250, c) the availability of the $1,500 deductible, and d) the raising of the maximum deductible from $5,000 to $10,000 on April 1, 2015. The figure also reveals the considerable drop in the number of policies in effect during the two-year period following the beginning (October 2013) of the implementation of the Biggert-Waters Flood Insurance Reform Act of 2012.

Figure 2. Total non-SFHA policies by deductible (January 2010 – July 2019).

Note. Figure 2 presents the total number of National Flood Insurance Program policies by deductible ($500, $1,000, $1,250, $1,500, $2,000, $3,000, $4,000, $5,000, or $10,000) in effect by month from January 2010 through July 2019 for owner-occupied single-family dwellings that are outside of Special Flood Hazard Areas. This stacked line graph makes obvious a) the raising of the minimum deductible from $500 to $1000, and b) the raising of the minimum from $1,000 to $1,250 for building coverage greater than $100,000. The figure is dominated by Preferred Risk Policies which constitute over 90% of the non-SFHA policies in our sample.

Figure 3. Minimum deductible choice in SFHAs percentage of owner-occupied SFDs.

Note. Figure 3 presents the time series for the percentage of SFHA policies in effect that have the minimum deductible available for the given properties. The minimum deductible option depends on a variety of factors, including the amount of building coverage and whether the property is pre-FIRM, post-FIRM, or part of the Emergency Program. For more details regarding the menu of deductible options over this time frame and what minimum deductibles are applied, see Table 1.

Figure 4. Maximum deductible choice in SFHAs percentage of owner-occupied SFDs.

Note. Figure 4 presents the time series for the percentage of SFHA policies in effect that have the maximum deductible available. Prior to April 2015, the maximum deductible option for SFHA properties was $5,000, and beginning in April 2015, a new maximum of $10,000 was made available. The drop in the percentage of policies at the maximum deductible in April 2015 reflects this change, and the slow take-up of this new maximum is demonstrated by the subsequent increase.

Figure 5. $5,000 and $10,000 deductibles in SFHAs: Percentage of policies in effect before and after April 1, 2015 owner-occupied SFDs.

Note. Figure 5 presents the time series for the percentage of SFHA policies that had either a $5,000 or $10,000 deductible. This plot is identical to the one in Figure 4 up until the increase to the maximum deductible in April 2015. For the subsequent period, the percentage includes policies with either the old maximum of $5,000 or the new maximum of $10,000.

Figure 6. $5,000 and $10,000 deductibles in SFHAs: Count of policies in effect before and after April 1, 2015 owner-occupied SFDs.

Note. Figure 6 presents the total counts for SFHA policies with either $5,000 or $10,000 deductibles. This plot demonstrates the reduction in policies following the implementation of Biggert-Waters in October 2014, and then the take-up of the $10,000 deductible option after it was made available in April 2015. After the addition of the new maximum option, it can be seen that the majority of the policy growth since 2015 can be attributed to the $10,000 option.

Figure 7. Distribution of deductibles by year (for our 252,280 matched policies).

Figure 8. Fraction of individuals at the $5k deductible who chose to switch to the $10k deductible (252,280 matched policies).

Figure 9. Premiums paid by those at the maximum deductible (252,280 matched policies).

Figure 10. Selection of the maximum deductible by risk level.

Note. Figure 10 plots the estimates of β from Equation (1)(1) $$Ma{x}_{\mathit{its}}={\beta }_{}\times x+{\mu }_s+{\mu }_t+\epsilon$$(1) where the independent variable is the binned flood insurance premium for a policy with the maximum coverage and minimum deductible. The estimates for each bin indicator are plotted along with the corresponding 95% confidence interval derived from the standard errors, which are clustered at state level. The sample includes all the SFHA policies in the main analytic sample.

Figure 11. Selection of the maximum deductible by income level.

Note. Figure 11 plots the estimates of β from Equation (1)(1) $$Ma{x}_{\mathit{its}}={\beta }_{}\times x+{\mu }_s+{\mu }_t+\epsilon$$(1) where the independent variable is the binned median income in the corresponding census tract. The estimates for each bin indicator are plotted along with the corresponding 95% confidence interval derived from the standard errors, which are clustered at state level. The sample includes all the SFHA policies in the main analytic sample.

Table 5. Logistic regression models for selecting the maximum deductible option.

	Dependent Variable: Max Deductible
	(1)	(2)	(3)	(4)	(5)
log(Policy premium) (s.e.)	0.314*** (0.007)				0.519*** (0.009)
log(Median income) (s.e.)		0.412*** (0.007)			0.506*** (0.009)
Fraction with a college degree (s.e.)			−0.900*** (0.049)		−0.234*** (0.057)
Fraction of claims (s.e.)				0.097*** (0.034)	0.038 (0.035)
Fixed Effects	State, Year	State, Year	State, Year	State, Year	State, Year
Observations	1,350,973	1,328,826	1,326,537	1,096,904	1,096,904

Note. This table presents the regression estimates for the logistic regression model described in Equation (1)(1) $$Ma{x}_{\mathit{its}}={\beta }_{}\times x+{\mu }_s+{\mu }_t+\epsilon$$(1) . In each regression, the dependent variable is an indicator equal to one if a policy has the maximum available deductible, and zero otherwise. The independent variables refer to proxies for the risk level of a property, policyholder income, education, and prior claims history respectively.

*p < 0.1.

**p < 0.05.

***p < 0.01.

Table 4. Linear probability models for selecting the maximum deductible option.

	Dependent Variable: Max Deductible
	(1)	(2)	(3)	(4)	(5)
log(Policy premium) (s.e.)	0.040*** (0.010)				0.068**** (0.010).
log(Median income) (s.e.)		0.049*** (0.014)			0.064**** (0.013)
Fraction with a college degree (s.e.)			−0.109 (0.068)		−0.027 (0.073)
Fraction of claims (s.e.)				0.008 (0.028)	−0.002* (0.024)
Fixed Effects	State, Year	State, Year	State, Year	State, Year	State, Year
Observations	1,350,973	1,328,826	1,326,537	1,096,904	1,096,904
Adjusted R^2	0.063	0.063	0.061	0.054	0.059

Note. This table presents the regression estimates for the linear probability model described in Equation (1)(1) $$Ma{x}_{\mathit{its}}={\beta }_{}\times x+{\mu }_s+{\mu }_t+\epsilon$$(1) . In each regression, the dependent variable is an indicator equal to one if a policy has the maximum available deductible, and zero otherwise. The independent variables refer to proxies for the risk level of a property, policyholder income, education, and prior claims history respectively. Standard errors are clustered at state level.

*p < 0.1.

**p < 0.05.

***p < 0.01.

Table 6. Linear probability models for switching from $5,000 to $10,000 deductible.

	Dependent Variable: New Max Deductible
	(1)	(2)	(3)	(4)	(5)
log(Policy premium) (s.e.)	−0.009** (0.004)				−0.0004 (0.005)
log(Median income) (s.e.)		0.033*** (0.009)			0.036*** (0.008)
Fraction with a college degree (s.e.)			0.043 (0.074)		0.093 (0.061)
Fraction of claims (s.e.)				−0.025 (0.026)	−0.030** (0.013)
Fixed Effects	State	State	State	State	State
Observations	37,834	36,772	36,679	30,105	30,105
Adjusted R^2	0.004	0.005	0.004	0.005	0.006

Note. This table presents the regression estimates for the linear probability model described in Equation (2)(2) $$d1{0000}_{is}=\mathrm{ }{\beta }_{}\times x+\mathrm{ }{\mu }_s+\mathrm{ }\epsilon$$(2) . In each regression, only policies that had a deductible of $5,000 (the old maximum) prior to April 2015 are included. The dependent variable is an indicator equal to one if a subsequent policy for the same property switches to the new maximum ($10,000), and zero otherwise. The independent variables respectively refer to proxies for the risk level of a property, policyholder income, education, and prior claims history. Standard errors are clustered at state level.

*p < 0.1.

**p < 0.05.

***p < 0.01.

Table 7. Logistic regression models for switching from $5,000 to $10,000 deductible.

	Dependent Variable: New Max Deductible
	(1)	(2)	(3)	(4)	(5)
log(Policy premium) (s.e.)	−0.082*** (0.009)				−0.006 (0.041)
log(Median income) (s.e.)		0.323*** (0.049)			0.368*** (0.054)
Fraction with a college degree (s.e.)			0.300 (0.315)		1.009*** (0.341)
Fraction of claims (s.e.)				−0.226*** (0.087)	−0.344*** (0.088)
Fixed Effects	State	State	State	State	State
Observations	37,834	36,772	36,679	30,105	30,105

Note. This table presents the logistic regression estimates for the model described in Equation (2)(2) $$d1{0000}_{is}=\mathrm{ }{\beta }_{}\times x+\mathrm{ }{\mu }_s+\mathrm{ }\epsilon$$(2) . In each regression, only policies that had a deductible of $5,000 (the old maximum) prior to April 2015 are included. The dependent variable is an indicator equal to one if a subsequent policy for the same property switches to the new maximum ($10,000), and zero otherwise. The independent variables refer to proxies for the risk level of a property, policyholder income, education, and prior claims history respectively. Standard errors are clustered at state level.

*p < 0.1.

**p < 0.05.

***p < 0.01.

Figure 13. Selection of the maximum deductible by claim history.

Note. Figure 13 plots the estimates of β from Equation (1)(1) $$Ma{x}_{\mathit{its}}={\beta }_{}\times x+{\mu }_s+{\mu }_t+\epsilon$$(1) where the independent variable is the binned mean percentage policies with claims in the previous three years. The estimates for each bin indicator are plotted along with the corresponding 95% confidence interval derived from the standard errors, which are clustered at state level. The sample includes all the SFHA policies in the main analytic sample.

Figure 14. Likelihood of increasing deductible to new maximum by risk level.

Note. Figure 14 plots the estimates of β from Equation (2)(2) $$d1{0000}_{is}=\mathrm{ }{\beta }_{}\times x+\mathrm{ }{\mu }_s+\mathrm{ }\epsilon$$(2) where the independent variable is the binned flood insurance premium for a policy with the maximum coverage and minimum deductible. The estimates for each bin indicator are plotted along with the corresponding 95% confidence interval derived from the standard errors, which are clustered at state level. The sample includes all the SFHA policies in the main analytic sample that are active from 2013 to 2019 and are observed to have a $5,000 deductible prior to 2015.

Figure 15. Likelihood of increasing deductible to new maximum by income level.

Note. Figure 15 plots the estimates of β from Equation (2)(2) $$d1{0000}_{is}=\mathrm{ }{\beta }_{}\times x+\mathrm{ }{\mu }_s+\mathrm{ }\epsilon$$(2) where the independent variable is the binned median income in the corresponding census tract. The estimates for each bin indicator are plotted along with the corresponding 95% confidence interval derived from the standard errors, which are clustered at state level. The sample includes all the SFHA policies in the main analytic sample that are active from 2013 to 2019 and are observed to have a $5,000 deductible prior to 2015.

Figure 16. Likelihood of increasing deductible to new maximum by education level.

Note. Figure 16 plots the estimates of β from Equation (2)(2) $$d1{0000}_{is}=\mathrm{ }{\beta }_{}\times x+\mathrm{ }{\mu }_s+\mathrm{ }\epsilon$$(2) where the independent variable is the binned percentage of the population with a college degree at the census tract level. The estimates for each bin indicator are plotted along with the corresponding 95% confidence interval derived from the standard errors, which are clustered at state level. The sample includes all the SFHA policies in the main analytic sample that are active from 2013 to 2019 and are observed to have a $5,000 deductible prior to 2015.

Figure 17. Likelihood of increasing deductible to new maximum by claim history.

Note. Figure 17 plots the estimates of β from Equation (2)(2) $$d1{0000}_{is}=\mathrm{ }{\beta }_{}\times x+\mathrm{ }{\mu }_s+\mathrm{ }\epsilon$$(2) where the independent variable is the binned mean percentage policies with claims in the previous three years. The estimates for each bin indicator are plotted along with the corresponding 95% confidence interval derived from the standard errors, which are clustered at state level. The sample includes all the SFHA policies in the main analytic sample that are active from 2013 to 2019 and are observed to have a $5,000 deductible prior to 2015.

Figure 12. Selection of the maximum deductible by education level.

Note. Figure 12 plots the estimates of β from Equation (1)(1) $$Ma{x}_{\mathit{its}}={\beta }_{}\times x+{\mu }_s+{\mu }_t+\epsilon$$(1) where the independent variable is the binned percentage of the population with a college degree at the census tract level. The estimates for each bin indicator are plotted along with the corresponding 95% confidence interval derived from the standard errors, which are clustered at state level. The sample includes all the SFHA policies in the main analytic sample.