Revealed Information and the Demand for Hurricane Mitigation
Features
By
Dean Gatzlaff,1 Kathleen McCullough,2 Lorilee Medders3 and Charles M. Nyce4 *
Last Updated: July 15, 2013
Abstract: This paper examines the effect of hurricane mitigation features on the transaction prices of singlefamily homes. Some of these features are obvious to the casual observer (known) and others can only be
revealed through costly verification (revealed). Merging a dataset that includes information on single-family
properties in Miami-Dade County with a database of insured properties from Citizens Property Insurance
Corporation, we look at whether hurricane mitigation features are priced by the market. We are able to
contrast the impact of observed features with unobserved features as well as the impact of verification through
an inspection certificate. Our results indicate that mitigation information is valued by market participants;
known features typically “bundled” with revealed features dominate the pricing mechanism; and that the
inspection certificate provided to the seller is priced consistent with capitalized value associated with the
reduction in premiums. The strength of these findings is bolstered by the fact that the effect of mitigation
features may be somewhat overshadowed by distressed market conditions.
* - contact author
1
The Florida State University, Center for Real Estate Education & Research, College of Business,
Tallahassee, FL 32306-1110. [email protected]
2
The Florida State University, Department of Risk Management/Insurance, College of Business,
Tallahassee, FL 32306-1110. [email protected]
3
The Florida State University, Florida Catastrophic Storm Risk Management Center, College of Business,
Tallahassee, FL 32306-1110. [email protected]
4
The Florida State University, Department of Risk Management/Insurance, College of Business,
Tallahassee, FL 32306-1110. [email protected]; phone: 850.645.9392. *Contact Author
1
Revealed Information and the Demand for Hurricane Mitigation Features
INTRODUCTION
In the housing market there is concern about the possible effect of information asymmetries
on the individual estimates of buyer and seller values, their reservations prices, and ultimately
observed transaction prices. To mitigate possible asymmetries, most states and many local
governments have enacted a set of seller (and lender) disclosure laws that cover real property
transactions. These disclosures may provide to the buyer and seller unobserved (private) or
observed (public) information. In addition, sellers may choose to convey private information
informally or more formally by obtaining available certifications (e.g., energy certificates such as
LEED).
This study provides a unique setting to test the impact of various types of information.
Some of this information would be obvious to the casual observer (known) while some of the other
information (revealed) may only be learned through costly verification (physical property
inspection). In addition, there is some ambiguity to the value of the information. With regard to
hurricane mitigation features, some are easily known, such as roof shape and the existence of
hurricane shutters, whereas others, such as roof attachment and the presence of secondary water
barriers, are difficult to know without the purchase of an inspection to reveal the characteristic. The
features potentially add value to the home due to improved resilience, but there also is the potential
for the homeowner to receive insurance credit for the features. In Florida, insurers can require a
mitigation inspection in order to provide the credit. Thus, there is an extra layer of information –
the confirmation of features through an inspection.5 The dynamics of known features versus
features that are revealed by experts combined with verification through inspections for insurance
credits creates a setting to examine whether value is created by merely the presence of the features,
the safety information gained through inspection, or the verification for insurance credits gained
through inspection. We find that known mitigation features such as varying roof styles and shutters
types are consistently found to be priced by the market, while the pricing of revealed features varies
with the inclusion of verification variables. Interestingly, the issuance of an inspection certificate to
5
The information that has been verified through the inspection process carries a statutorily defined credit on the
homeowners’ insurance policy premium. All insurers in Florida are required to accept the inspection form as proof of
mitigation features and award a premium credit based on a published schedule. Insurers may award the credits without
proof of inspection and many do “grandfather” credits to existing policyholders. However, all new policies must have
the inspection form to be awarded premium credits. Whenever a parcel of property transfers ownership, a new
homeowners’ policy must be issued.
2
the owner that confirms mitigation features and reduces the insurance premiums of homeowners is
positively valued in pricing the house even when controlling for known mitigation factors.
Additionally, the known features continue to be of significant price value separate from the
inspection.
MOTIVATION AND LITERATURE
At both the state and federal government level, policymakers are placing added emphasis on
the importance of home mitigation as a public benefit. Yet despite its value as both a private and
public good, the market for property mitigation is still underinvested. In addition to the insurance
substitute problem, cost barriers and other reasons well documented in prior research, this
underinvestment may in part be due to a lack of knowledge about the benefits of mitigating.6 The
return on mitigation investment can include a savings on annual insurance premiums, savings on out
of pocket expenses when an event occurs, or an increase in resale value of the property.
Appropriate mitigation efforts, implemented in catastrophe-prone areas, decrease expected
losses as well as decrease the burden placed on the public in the event of a catastrophic event.
Kleindorfer and Kunreuther find that mitigation decreases expected losses and decreases the burden
placed on the public in the event of a catastrophic event. Christoplos, Liljelund, and Mitchell (2001)
conclude that mitigation not only reduces direct losses (i.e., saves lives, reduces injuries, and lowers
property losses), but also measurably increases the public good through alleviation of the indirect
poverty effect of catastrophes.
As specific examples of the benefits of mitigation, consider research conducted by the
Insurance Institute for Building and Home Safety (IBHS) and Risk Management Solutions (RMS)
after the 2004-2005 storm seasons. Storms in the 2004 hurricane season damaged one in five Florida
houses, according to the Insurance Information Institute (2004). Hurricane Charlie alone destroyed
12,000 houses and left another 19,000 uninhabitable. Although substantial, the damage was reduced
Myriad economic literature tells us that risk-averse individuals generally are willing to pay a “risk premium” to purchase
insurance, and some are willing to pay a “safety premium” for risk-reducing features. But since insurance reduces the
losses sustained directly by the insured, it may be rational for a risk-averse individual to treat the purchase of insurance
effectively as a mitigation expenditure. To this point, Ehrlich and Becker, in an important 1972 study, find that insurance
and mitigation are treated as substitutes by individuals. Kunreuther and Kleffner (1992) further show that if low or no
deductible insurance is mandatory, mitigation is less likely and/or less will be spent on mitigating activities. Precisely
because homeowners insurance protects property owners from catastrophic financial loss in the event of an insured
event, it inherently results in reduced financial incentives to make mitigation expenditures, all else the same. Policy and
industry studies have repeatedly indicated that perceived lack of affordability and/or lack of return on their investment
are the primary reasons property owners do not engage in mitigation.6 Cost is ranked even higher as a constraint among
minorities and low-income homeowners, who unfortunately are also more likely to own properties in the most need of
hardening (Peacock, 2003; International Hurricane Research Center, 2004).
6
3
due to stricter building codes that were in effect after Hurricane Andrew. A 2007 study by IBHS
examines Charlie’s damage and finds that newer homes suffered significantly less damage than older
homes. Furthermore, research conducted by Risk Management Solutions (RMS) following the 2004
and 2005 hurricanes in Florida demonstrates lower losses suffered by newer structures, built in
compliance with the most up-to-date (2002 Florida Building Code) building codes. In 2011, IBHS
finds further evidence of loss-cost-reduction via mitigation efforts. An experimental study wherein
wind was created and applied to houses built identically except for a difference in the roof decking –
only one was sealed. The study, conducted in a controlled environment, concludes loss estimates to
be three times greater for unsealed roof decks than for the sealed roof decks during 70 MPH
sustained winds ($16,935 versus $5,408).
Several researchers in recent years have attempted to determine whether and to what extent
home buyers may value mitigation and, although findings are mixed, overall consumers do appear to
recognize and value safety. Several studies have linked home values to various measures of, or
proxies for, mitigation investments. The majority of this research has focused on the consumer
buying response to changes in building codes for new home construction and/or homeowners
insurance premiums. A few also link selling prices to voluntary mitigation efforts.
Building codes and regulations appear to be observed and valued by land and home buyers.
Dehring (2006) examines the effect on land prices for three changes in coastal building regulations
for Florida’s barrier islands. She finds that the cost of compliance outweighs the benefits of safety.
Bin, Kruse and Landry (2008) analyze the effects of flood hazard on coastal property values. They
estimate a hedonic pricing model of the effects of flood hazard on coastal property values using real
estate transactions from 2000 to 2004 in North Carolina and GIS on flood zone. Information on
flood hazard, coastal amenities and standard structural/neighborhood attributes were available for
3,106 sales. The results show that location within a flood zone lowers property value by an average
of 7.3 percent. Dumm et al (2009a, 2009b) examine the capitalization of the 2002 Florida Building
Code in the house prices for the Miami and Jacksonville, Florida housing markets. A hedonic
pricing model is used to estimate the differential effect on house prices of the stricter 2002 Florida
Building Code. The model also tests whether the stricter building code became more valuable after
the 2004-2005 storm seasons. They find that houses in the Windborne Debris Region (area of
greatest wind risk) that were built under the new, stricter building code (1994 for Miami and 2002
for Jacksonville) sold for approximately 10.4 percent and 4.5 percent more on average than those
built under the older, less strict code, for Miami and Jacksonville, respectively. So for the area with
4
greatest risk exposure, consumers recognize the value of the stricter building code and are willing to
pay a “safety premium.” In both of these studies, consumers in the interior wind zones (the less
risky 100-110 MPH zones) there are mixed results and a change in perceived value after the 20042005 storm seasons.
Bin, Kruse and Landry (2008) calculate flood insurance premiums with sales price
differentials, and overall, conclude that flood zone designation and insurance premiums convey risk
information to potential buyers in the coastal housing market. Buyer behavior in the study differ
depending on a home’s value scale – the flood insurance premium is linked to a positive sales
differential for lower-priced homes whereas it is linked to a negative sales differential for higherpriced homes, implying that the availability of flood insurance appears to allay the buying concerns
only at the lower end of the housing market. Dumm, Nyce, Sirmans and Smersh (2011) look at the
correlation between home prices and insurance premiums differently. They examine whether and to
what extent changes in homeowners insurance premiums impact selling prices. Using Miami-Dade
County home sales and Citizens Property Insurance Corporation homeowners policy information
from 2004-2009, the researchers find that homeowners spent an average $2,145 annually during the
study period. The data reveal significant percentage changes in insurance premiums, at 18.08
percent, 41.44 percent and 72.58 percent over one-, two- and three-year periods. The findings
indicate that the one-, two- and three-year premium changes result in house price decreases of
$3,363.09, $9,963.54 and $13,484.62, respectively.
There exists limited literature that links property prices with property mitigation features
and/or efforts. Andersson (2005) found that some drivers are willing to pay a “safety premium” for
safety features (such as side airbags) when purchasing auto insurance. Miller, Morgan and Womack
(2002) find that although consumers are willing to pay for tornado safe rooms, the “safety premium”
they are willing to pay is less than the cost of producing them. Simmons and Sutter in 2007
conducted a similar tornado mitigation study and also finds that consumers are willing to pay a
premium for internal shelters. Simmons, Kruse and Smith (2002) are the only researchers who have
examined the price effects of voluntary hurricane mitigation measures by homeowners. Using singlefamily home data for a Gulf Coast city, they examined the effects of storm shutters and structural
integrity on selling price. They find that the presence of storm shutters adds approximately 5 percent
to the selling price although they do not make any comparison between the sales price differential
and the cost of the storm shutters.
5
Summarizing the findings of the prior research on the impact of mitigation on property
prices, consumers overall appear to observe and value the cost of risk when making purchase
decisions. The current research adds to the prior literature by examining whether mitigation
information – both observed and unobserved – is valued by the housing market.
METHODOLOGY
3.1
The Effect of Mitigation Information on Transaction Price
The hedonic model is widely used to estimate the marginal values of individual
characteristics of heterogeneous products that by definition are not individually traded. Credit for
the initial development of the theoretical framework that underpins the hedonic and the
interpretation of the estimated coefficients is generally attributed to Rosen (1974). He shows that
under full information conditions that the hedonic model provides reliable estimates of the marginal
value of the individual characteristics of heterogeneous assets. In the housing market research
literature, hedonic modeling is widely used to estimate the implicit prices of unique characteristics of
housing directly or indirectly. For example, the model is used in real estate economics and urban
economics to reveal the implicit marginal value of characteristics of the structure, such as size and
condition, or the characteristics associated with its location, such as air quality, school quality, noise
or the effect of crime. One key assumption of the revealed preference model is that for marginal
values to be accurately estimated, all relevant information must be available to the market
participants.
To examine the effect of the categories of mitigation information on the transaction price we
use a standard hedonic regression model, specified as
,
(1)
where SPit is the transaction price of property i at time t,; βj is a vector of j coefficients on the
property- and location-specific characteristics, Xjit; c is a vector of coefficients on the mitigationspecific characteristics, Mit ; δt are the coefficients on Dit, time dummies with values of 1 if the ith
property sold in period t and 0 otherwise; and eit is the random error with mean, 0, and variance σ.
The coefficient, c, yields an estimate of the marginal effect of the mitigation feature on the
composite price of the property, evaluated at its mean.
The property-specific characteristics include features such as the square footage (and squared
square footage) of its conditioned space (SQFT and SQFT2), the number of bedrooms (BDRS), the
6
number of bathrooms (BTHS), the number of floors (FLRS), the effective age (and squared
effective age) of the property (EFFAGE and EFFAGE2), and the square footage (and squared
square footage) of the lot (LOTS and LOTS2). We expect positive relationships between most of
these variables (negative for age) and a transaction price of the home (specified in the model as the
log of sale price, lnSP). We expect the squared values to be negatively related to price, signifying a
decreasing marginal value.
Construction-related variables are included as well since higher construction quality (with
construction quality represented by multiple dummy variables – LOWCOST, BELOWAVG and
ABOVEAVG, where superior construction is the omitted category) and construction after Florida’s
Residential Building Code strengthened in South Florida in 1994 to require hurricane-resistant
features (POSTFBC) are examined and expected to be positively correlated with lnSP.
To control for the location-specific variation in house prices associated with risk (including
hurricane risk), we identify and include the flood zones, both inland flood and coastal flood zones
(INFLOOD and CFLOOD, respectively), as well the distance of a property from the coast
(DTCOAST) and whether it resides in Citizens Coastal Account (HRA) area.7 The direction of the
relationships between these variables and lnSP are difficult to hypothesize because, despite the
hurricane risk of being on or near the coast for instance, the proximity to coast has an amenity value
for homeowners that may offset the diminished value of being in a higher risk hurricane zone. While
these variables are inter-related, tests did not indicate strong collinearity and all of the variables are
included in the estimation model.
Whether a property is receiving homestead exemption for property tax purposes
(HOMESTEAD) is also included in the analysis, and assumed to be positive, since the homestead
exemption provides tax information to buyers and helps to differentiate primary residences from
rental properties. To capture potential demographic and neighborhood characteristics that may be
important to the sale price we control for the many Miami-Dade census tracts. Finally, we include
variables for the quarterly period in which the property sold to control for changes in the market
through time.
There are several methods of evaluating the relationship between the mitigation feature
information and the selling price of the property. First, whether homeowners are receiving a
mitigation credit on their insurance may be related to selling price. We measure this in two ways.
7
Dummies for wind zones were initially included as well, but are omitted here due to multicollinearity with the
other location variables. Citizens Coastal Account was known as the High Risk Account until 2012.
7
First, we use a simple dummy variable for the presence of a credit. This may, however, lead to
biased results since there may be no verified information on the existence of the mitigation features
and there is no indication of the magnitude of the credit. The mitigation credit variable is a
zero/one dummy variable reported by Citizens. More than 78% of the sales in our sample had a
mitigation credit, but only 27% had a mitigation inspection.
Second, we estimate the size of the credit given for the insured’s wind premium based on
home’s features and the state’s table of insurance credits. Similar to the potential issue with our first
method, we have far more properties with mitigation features than those having that information
verified through an inspection. In both cases, we expect that the presence of a known credit will be
related to an increase in LnSP.
In terms of individual mitigation features, there are a large number of mitigation-specific
structural characteristics identified in the home inspection and deemed by the State of Florida
Department of Emergency Management and Residential Building Code to be important in
mitigating loss or damage from a hurricane. Some features are easily observed or known by buyers
and sellers and some are not. For example, known features include the roof shape (hip roofs) and
the use of shutters and their materials (e.g., hurricane shutters, basic shutters, no shutters). Features
identified by the inspection but typically unobserved by the buyer or need to be revealed through
inspection include the manner of attachment of the roof sheathing to the roof rafters (e.g., roof nail
type and spacing); the manner of attachment of the roof rafters to the wall framing (e.g., metal clips;
double wraps; or single wraps), and the inclusion of a secondary water barrier.
If mitigation features are valued by the market we expect the estimated coefficients on the
mitigation variables to be positive and consistent with the level of mitigation provided. Whether the
mitigation features that are difficult to observe without inspection (especially if there is no known
inspection) are found to be capitalized is of particular interest. For testing, we have divided the
factors into two categories, KNOWNFEAT and REVEALFEAT. As stated earlier,
KNOWNFEAT represents the presence of mitigation features observable by buyers and sellers, and
are thus reasonably expected to be known (e.g., roof shape and shutter type). REVEALFEAT
represents mitigation features that are generally difficult to observe and more likely to be revealed to
the homeowner-seller by professional inspection. This information may or may not be conveyed by
the seller to potential buyers. We expect KNOWNFEAT to be positively correlated with lnSP,
while REVEALFEAT may be positive correlated, but require verification through an inspection.
One challenge in further separating the features into individual characteristics is that their overall use
8
may not be independent. For example, in some cases, roof attachment methods and roof to wall
framing may be selected together for a complimentary effect. For this reason, alternative
specifications are evaluated using grouped features in our analysis. 8
As mentioned earlier, there is a further layer of information conveyed through the presence
of a mitigation inspection. For this reason, we also include a dummy variable, INSPECT, defined as
equal to 1 if the home was inspected and the homeowner was receiving a reduction in their
insurance premiums due to the inspection at the time of the sale, or subsequent sales; otherwise
INSPECT is equal to 0. We expect the coefficient on INSPECT to indicate that this information is
valued by buyers and sellers and is thus capitalized in the sale price of the home (lnSP).9 The
potential value of the mitigation inspection form to buyers is related to the information it provides
regarding the safety and/or insurance benefits of the home. The safety information provided is
explicit since the inspection form itemizes features the home possesses that are known to reduce
losses in the event of hurricanes and other wind events. The information regarding the insurance
benefits of owning the home may be more implicit within the mitigation form than the safety
information (given no premium discount amounts or percentages are provided on the certificate
itself), but the connection between the mitigation form and potentially large insurance discounts is
well publicized by Florida insurers, state agencies and non-governmental public relations entities.10
Our initial models run a traditional hedonic pricing regression with variations of the
mitigation related variables. We start with the base model excluding mitigation features. We then
add the presence of a mitigation credit to see if the market values the insurance credit in the price.
Next, the differences between known and revealed features are explored. Prior to adding the
verification of information through a mitigation inspection, it is important to control for the fact
that a property owner’s decision to obtain an inspection is likely related to characteristics of the
home.
3.2
Treatment Effects and the Decision to Obtain an Inspection
8
Mitigation features are not additive; several found in combination result in a different final insurance discount
than the sum of the individual discounts.
9
For a review of the My Safe Florida Home (MSFH) program, the original impetus for the mitigation certificates,
see the addendum at the end of the document.
10
For example, the Florida Department of Emergency Management posts estimated homeowners insurance
discounts for specific home features through a pamphlet, “Make Mitigation Happen,” that can be downloaded from
its website at www.floridadisaster.org. Insurers are required by Florida Statute to inform policyholders of the 1802
Form and insurance discounts available for mitigation features present on their homes.
9
The inspection variable, INSPECT, is a decision dummy, meaning that the homeowner
(seller) decides whether to obtain a certificate of inspection prior to sale of the home. It is expected
that known features are expected to be highly correlated with the decision to obtain an inspection.
To the extent that homeowner’s decision to mitigate is influenced by the appeal of insurance
premium reduction and neighborhood information, we also expect the decision to obtain an
inspection of mitigation features will be influenced by mitigation features observable by the
homeowner/seller and the potential knowledge of whether neighbors are obtaining inspections.
Therefore, we suspect endogeniety between the decision to obtain an inspection and the known
mitigation variables and certain housing characteristics (age, size, distance to coast), and so include
an analysis of potential treatment effects in our study. The study hypothesis is that the presence of
observable mitigation features (a hip roof (HIP) and the presence of hurricane shutters
(HURSHUT)) will each be positively correlated with INSPECT. We control for other variables that
can be expected to relate to the inspection decision as well, such as, distance to coast, effective age
of the home, construction quality and size of the home. Additionally, in work related to the decision
to mitigate and the extent of mitigation, Carson, McCullough and Pooser (2013) in particular
examined several factors, including reduction in insurance premiums and the reduced costs of
mitigation achieved by gathering information from neighbors.11 For this reason, we expect that the
presence of other inspections in the neighborhood will increase the chance that a homeowner has an
inspection because the homeowner heard of the positive benefits from neighbors. We include a
variable related to the number of known inspections in the property’s census track to identify the
system of equations.
DATA
The data used for this study include information obtained from the Florida Department of
Revenue’s (DOR) property tax records, the Miami-Dade County municipal property tax records and
the Citizens Property Insurance Corporation (Citizens) on windstorm-inspected single-family
residential properties. The DOR data are compiled annually from each county in the state of Florida
for auditing purposes under a statutory provision. The data include information on the three most
11
Carson, McCullough and Pooser (2013) specifically examined Florida homeowners in light of the MSFH program
setting, lending added relevance to the study here. They also provide an extensive review of prior studies on the
decision to mitigate.
10
recent transactions, if sold, on every property in the state and a limited set of property- and ownerspecific characteristics. The DOR data for Miami-Dade county was merged with data obtained from
the Miami-Dade County Property Appraiser which included additional property-specific
characteristics (e.g., information on lot size, number bedrooms and bathrooms), yielding 368,907
single-family detached housing observations.
In addition, Citizens, the largest property insurer in the state of Florida, has provided policy
level data including mitigation features and inspection dates for single-family attached and detached
residences throughout the state of Florida, including multi-peril policies for 260,740 unique parcels
in Miami-Dade. The inspection provides a source of costly information regarding the mitigation
features of a property. Some of these features (e.g. roof shape) may have already been known and
the inspection may not have been necessary to reveal that information. However, many insurers
(including Citizens) require the inspection form before providing premium discounts for any
mitigation features, including those that may be obvious for any new insurance policy issued. Other
features (roof clips, secondary water barrier, etc.) may not have been known. Therefore, the
inspection reveals information that may be relevant to a homeowner’s purchase decision. By
analyzing sales with and without this information on mitigation features, we are able to test whether
the mitigation features, both verified and revealed, are correlated with property market values.
Merging the Citizens single-family detached housing records with the property data
produced a dataset of 152,885 observations. Transactions recorded as transfers of ownership, nonqualified sales, as well as sales less than $10,000 or greater than $5,000,000, new property sales (e.g.
AGE < 1), and transactions occurring prior to the start of the MSFH inspection program in 2007
were subsequently removed to yield a final data set of 29,532 observations. Summary statistics and
variable descriptions are contained in Table 1.
RESULTS
BASE MODEL CONTROLS
The model is highly explanatory overall of selling prices (LPRICE) in the Miami-Dade housing
market during the time period studied. Column 1 of Table 2 contains the results of the base model
ignoring any mitigation feature analysis. As expected, larger homes (SQFT), greater numbers of
bathrooms (BTHS) and quality of construction are all positively related to property values (lnSP).
Additionally, homestead exemptions are related to increases in property values. In this initial model,
stories (STRS) is negatively related to selling price, and although not statistically significant result,
11
may imply that taller houses may be more exposed to storm damage. The age of the property is
negative and significant and the lot size variable is positive and significantly related to price. The
square footage, age and lot size variables are all effecting price at a decreasing rate (negative
relationship for each SQFT2, EFFAGE2 and LOTS2 with lnSP). Consistent with expectations, the
POSTFBC variable indicates higher housing prices for homes built after the state’s new, stricter
building codes took effect in South Florida in1994 than for those built prior to 1994 although this
result is not significant.
The variables included to capture flood exposure produce interesting, although mixed,
results. The category of inland properties with low risk of flood is the default, and one might expect,
all else the same, homes in this category to sell at relatively higher prices than properties within the
100-year floodplain, whether inland (INFLOOD) or coastal (CFLOOD). The inland flood variable
is not significant, although we expected it to be negative. The coastal 100-year-floodplain category in
particular reflects higher prices than the default (low flood risk) category. As discussed earlier, it is
likely the findings are distorted by the value-enhancing impact of the amenities associated with close
proximity to coastal bodies of water. Properties with coastal flood exposure are extremely close to
the coast. It appears that the distance to the coast variable and the HRA variables are not capturing
the presence of higher risk separate from the presence of amenities. Finally the controls for quality
of construction (with the leave out being the highest quality) show that higher quality of
construction is related to higher sale prices. While not included in the table for space reasons, the
controls for census track and quarter of sale are in the estimation model. The variables in Column 1
are included as controls in the subsequent models and remain relatively stable across the models.
MITIGATION FEATURE ANALYSIS
One way in which mitigation features are likely to add value to a home is through insurance credits.
Models 2 and 3 provide an initial analysis of the impact of mitigation credits on sales price. First,
model 2 contains a variable that is equal to one if the insured is receiving a mitigation credit. The
variable is positive and significant, indicating that credits are reflected in the sale value.12 This model
does not, however, contain any information related to the magnitude of the credits. While model 2
shows that the presence of the credit matters, model three attempts to quantify that amount based
12
While new homeowners are required to obtain a mitigation inspection in order to obtain the full mitigation credit,
some of the properties in the data set are receiving at least partial credits without the inspection. This may be
because they were grandfathered in or they are receiving partial credit based on known characteristics.
12
on the credit assigned in the state mandated credit table for the wind premium mitigation credit.
This is based on the characteristics of the property and provides an estimation of the credit the
property should receive if an inspection is obtained. In this case, the credit is positive and significant
as well. This model serves as a nice aggregate model. Since the loss reducing effects of mitigation
features is not additive, this grouping of the features into officially recognized subsets allows for
valuing the cumulative effect of the features.
While models 2 and 3 show that mitigation credits are positively reflected in the value of the
home, the next set of models seeks to analyze whether the impact on price varies based on the type
of mitigation feature. Model 4 includes a variable for known mitigation features. These
characteristics such as roof shape and the existence of hurricane shutters are easy to verify, even by
novice consumers. As expected, the existence of these factors as a group is positive and significantly
related to sale price. Model 5 adds features that are costly to verify and likely only revealed through
an inspection. In this case, both the known and revealed features are positive and significantly
related to price. Model 6 provides a version of model 5 in which the individual mitigation features
are broken out. The results are consistent with the prior models. As expected, hip roof shape and
hurricane shutters have a positive impact on housing value. Similarly, the attachment of the roof
also can have a positive impact on the sales price. It is somewhat surprising that the roof to wall
attachments do not appear to add value. While the results confirm the fact that mitigation factors
positively increase sales price, caution should be used in the individual characteristics as certain
combinations may be used in a group to achieve a desired effect. Thus, one should not think that
the presence of clips or wraps ultimately does not add value.
We now turn the issue of verification. Model 7 includes a variable for sales in which there is
a known mitigation inspection. The variable for the inspection is positive and significantly related to
the sale price. We recognize that the insured’s decision to obtain a mitigation inspection is likely
related to the known mitigation features of the house. For this reason, we estimate a series of
treatment effect models in which the presence of a mitigation inspection is instrumented to control
for self-selection. We follow a similar progression to the prior models. First, we estimate model 7
in which the presence of a mitigation credit and the presence of a mitigation inspection are included.
Even when controlling for the presence of existing mitigation credits, the results show the presence
of an inspection is positively and significantly related to the home value. When breaking the
mitigations factors out by known and revealed features, we find that the inspection is positive and
significant. Additionally, we find that even after controlling for the presence of an inspection, the
13
presence of known features is still positive and significant while the revealed features alone are not
significant. Thus, although a homeowner may not have added mitigation features to the home, the
information contained in the report, in conjunction with the potential insurance discounts, is related
to a positive increase in housing value. This is an especially interesting finding as it indicates that
home buyers observe and value the revelation of information regarding safety/mitigation features,
apart from valuing the features themselves. Interestingly, the certificate of mitigation inspection
increases property value even in the case of mitigation factors readily observable and unlikely to
have been changed, KNOWNFEAT. The final model contains the individual mitigation features.
Once again with the inspection variable included, the hip roof shape and hurricane shutters are still
positive and significant while none of the revealed features are positive and significant.
When using the natural log of the selling price as the dependent variable in a hedonic model,
the economic interpretation of the coefficients can be roughly percentages. A parameter estimate of
.10 equates to roughly a 10% change in the selling price. Table 3A contains the dollar equivalents of
the parameter estimates of the nine models evaluated at the mean selling price of $344,482. As can
be seen in the table, properties that have been inspected are selling for approximately a $12,000
more.
Depending on the number of openings that need to be covered by hurricane shutters, it
appears that the initial investment in shutters can be offset by the increase in selling price. Table 3B
assumes a 5 percent cap rate to equate the change in selling price to the annual reduction in
insurance premiums (in perpetuity). Table 3B shows that assuming a 5 percent cap rate, insurance
premium credits would have to result in savings in the $500-$600 per year range to generate the
increases in selling price estimated in our models. These seem like reasonable savings given that
magnitude of homeowners insurance premiums in Miami-Dade.
CONCLUSION
Through the use of a series of hedonic pricing models, this paper analyzes the question of
whether there is variation in the way features are priced into home values based on the ease of
knowledge related to the existence of mitigations features. Also, we investigate whether verification
of these features impacts value. We find that known features consistently have a positive impact on
value, with or without verification. However, there is some variation in the impact of revealed
features based on the presence of outside verification. The results of the impact of mitigation on
housing price are both statistically and economically significant. This underscore the importance of
14
mitigation. It also sheds light on the importance of the mitigation features being communicated and
proper measure taken to ensure insurance credits.
15
REFERENCES
Andersson, Henrik (2005) “The Value of Safety as Revealed in the Swedish Car Market: An
Application of the Hedonic Pricing Approach” Journal of Risk and Uncertainty, 30(3), 211-239.
Bin, O., J.B. Kruse and C.E. Landry (2008) “Flood Hazards, Insurance Rates and Amenities:
Evidence from the Coastal Housing Market” Journal of Risk and Insurance, 75(1), 63-82.
Carson James, Kathleen McCullough and David Pooser (2013) “Deciding Whether to Invest in
Mitigation Measures: Evidence from Florida” Journal of Risk and Insurance, 80(2), 309-327.
Christoplos, Ian, John Mitchell and Anna Liljelund (2001) “Re-framing Risk: The Changing Context
of Disaster Mitigation and Preparedness” Disasters, 25(3)
Dehring, C.A. (2006) “Building Codes and Land Values in High Hazard Areas” Land Economics,
82(4), 513-528.
Dumm, Randy, G. Stacy Sirmans and Greg Smersh (2009a) “The Capitalization of Stricter
Building Codes in Miami, Florida House Prices” Working paper, Florida Catastrophic Storm Risk
Management Center
Dumm, Randy, G. Stacy Sirmans and Greg Smersh (2009b) “The Capitalization of Stricter
Building Codes in Jacksonville, Florida House Prices” Working paper, Florida Catastrophic Storm
Risk Management Center
Dumm, Randy, Charles M. Nyce and G. Stacy Sirmans (2011) “The Capitalization of
Homeowners Insurance Premiums in House Prices” Working paper, Florida Catastrophic Storm
Risk Management Center
Institute for Business and Home Safety (2007) The Benefits of Modern Wind Resistant Building Codes on
Hurricane Claim Frequency and Severity Tampa, Florida.
Insurance Information Institute (2004) “Insurance Companies Paying Two Million Dollar Claims
from Four Florida Hurricanes
Insurance Institute for Business and Home Safety (2011) Rating the States: An Assessment of Residential
Building Code and Enforcement Systems for Life Safety and Property Protection in Hurricane-Prone Regions,
Insurance Institute for Business and Home Safety, Tampa, Florida
International Hurricane Research Center (2004) Hurricane Loss Reduction for Housing in Florida: Final
Report to the Florida Department of Community Affairs, Florida International University
Kleindorfer, Paul R. and Howard Kunreuther (1999) “The Complementary Roles of Mitigation and
Insurance in Managing Catastrophic Risks” Risk Analysis, Vol. 19, No. 4, 1999
16
Miller, D., D. Morgan and C. Womack (2002) “Buying Tornado Safety: What Will it Cost?”
Southwestern Economic Proceedings, 29(1) 35-45
Palmquist, R.B. (1979) “Alternative Techniques for Developing Real Estate Price Indexes” Review of
Economics and Statistics, 62: 442-480
Peacock, Walter Gillis. 2003 “Hurricane Mitigation Status and Factors Influencing Mitigation Status
among Florida’s Single-Family Homeowners” Natural Hazards Review, 4(3): 1-10
RMS (2009) Special Report Analyzing the Effects of the My Safe Florida Home Program on Florida
Insurance Risk
Rosen, S. (1974) “Hedonic prices and implicit markets: Product Differentiation in Pure
Competition” Journal of Political Economy, 82: 34-55
Simmons, Kevin, Jamie Kruse and Douglas Smith (2002) “Valuing Mitigation: Real Estate Market
Response to Hurricane Loss Reduction Measures” Southern Economic Journal, 68(3): 660-671
17
Table 1
Table 1: Variable Definitions/Statistics Panel A
Variable
Sale Price
Distance to
Coast
Bedrooms
Bathrooms
# of Stories
Square Feet
Lot Size
Age
Effective Age
Variable
Homestead
Wind Zone 130
Wind Zone 140
Wind Zone 150
HRA
PostFBC
Inland Flood
Coastal Flood
Low Cost
Below Average
Above Average
Excellent
Description
in miles
Housing and Location Characteristics
Mean
Std. Dev.
344,482
335,070
9.77
1.22
Minimum
28,000
0
3.31
.7601
1
2.11
.7403
1
1.17
.3845
1
2076
766.30
1000
in square feet
9,481
7,729
1000
yr sale – yr built
35.10
20.76
1
30.60
18.58
1
Housing and Location Characteristics Dummies
Description
Primary residence – qualifies for FL’s homestead exemption
Parcel located in 130 MPH wind zone
Parcel located in 140 MPH wind zone
Parcel located in 150 MPH wind zone
Property located in Citizens’ Coastal Account Zone
Built after the 1994 South Florida Building Code
Parcel located in Flood Zone A, AH, or AE
Parcel located in Flood Zone VE
Assessor’s opinion of construction quality – lowest rating (1)
Assessor’s opinion of construction quality – 2nd lowest rating (2)
Assessor’s opinion of construction quality – 2nd highest rating (4)
Assessor’s opinion of construction quality – highest rating (5)
18
Maximum
4,825,000
11.20
8
7
5
5000
204,732
99
98
Mean
.621
.280
.748
.052
.2074
.2276
.580
.0005
.0685
.5921
.2436
.0957
Table 1: Variable Definitions/Statistics Panel B
Variable
Type A
Type B
Type C
Variable
Clips
Single Wrap
Double Wrap
Toenail/Unknown
Variable
Hurricane
Rated Shutters
Basic Shutters
No Shutters /
Unknown
Variable
Hip Shape
Secondary
Water Barrier
Variable
Mitigation
Credit
Wind Premium
Credit
Knownfeat
Revealfeat
Previous
Inspection
Mitigation
Inspection
Mitigation Characteristics – Roof Decking Attachment Dummies
Description
6d nails every 6-12 inches
8d nails every 6-12 inches
8d nails every 6 inches
Mitigation Characteristics – Roof to Wall Attachment Dummies
Description
Roof truss to top of wall connector
Roof truss to top of wall connector that wraps around truss
Roof truss to top of wall connector that wraps around truss & wall
Angled nail to connect truss to wall
Mitigation Characteristics – Opening Protection Dummies
Description
Meet Florida Building Code for impact ratings.
Offer some protection from impact, but not Hurricane Rated.
Mean
.3096
.1595
.4385
Mean
.1181
.3915
.0159
.4745
Mean
.2902
.0177
.6921
Mitigation Characteristics – Roof
Description
All sides of roof line are sloped
Underlayment to prevent water penetration if roof covering fails.
Mean
.2718
.0233
Other Characteristics
Description
The property is receiving a mitigation discount from Citizens
Mean
.7803
The percent credit from the OIR credit table for the property’s
mitigation features
Dummy variable for the presence of one of the known features
Dummy variable for hidden mitigation features
Number of mitigation inspections performed in property’s census
tract prior to property’s inspection.
Dummy variable if property had a mitigation inspection prior to sale,
zero otherwise.
19
.4632
.4713
.6272
94.47
.2715
Table 2 Panel A
OLS (Dependent Variable= LN (SALE PRICE))
Model 1
Variable
Coef
Model 2
SE
Coef
INSPECT
MITCREDIT (D)
WPCREDIT
KNOWNFEAT
.0373***
Model 3
SE
Coef
SE
.0037
.0618***
.0053
REVEALFEAT
HIP
BASICSHUT
HURRSHUT
BROOF
CROOF
SWB
SWRAP
DWRAP
CLIPS
BDRS
BTHS
.0041
.0184***
.0027
.0031
.0043
.0185***
.0026
.0031
.0043
.0187***
.0026
.0031
FLRS
SQFT
SQFT2
LOTS
LOTS2
EFFAGE
-.0030
.0005***
-3.93e-08***
.00002***
-6.74e-11***
-.0042***
.0045
.00001
1.93e-09
4.48e-07
4.09e-12
.0005
-.0024
.0005***
-3.86e-08***
.00002***
-6.67e-11***
-.0042***
.0045
.00001
1.92e-09
4.48e-07
4.08e-12
.0005
-.0016
.0005***
-3.88e-08***
.00002***
-6.68e-11***
-.0044***
.0045
.00001
1.92e-09
4.47e-07
4.08e-12
.0005
EFFAGE2
HOMESTEAD
LN DTC
HRA
INFLOOD
CFLOOD
-1.01e-06
.0156***
-.0831***
.0165***
.0008
.3839***
5.89e-06
.0029
.0048
.0059
.0043
.0621
-1.41e-06
.0105***
-.0823***
.0234***
.0009
.3850***
5.88e-06
.0029
.0048
.0059
.0043
.0619
3.34e-06
.0097***
-.0818***
.0217***
.0009
.3886***
5.89e-06
.0029
.0048
.0059
.0043
.0619
.0083
-.3066***
-.1792***
-.1255***
yes
yes
.0068
.0184
.0102
.0083
.0037
-.3046***
-.1768***
-.1245***
yes
yes
.0067
.0184
.0102
.0083
.0041
-.3040***
-.1774***
-.1237***
yes
yes
.0067
.0184
.0102
.0083
POSTFBC
LOWCOST
BELAVG
ABVAVG
CENSUS D INCL.
TIME D INCL.
Cons.
N
ADJ. R2
12.53***
29532
.8973
.2368
12.48***
29532
.8977
20
.2364
12.49***
29532
.8978
.2362
***= Significant at the 1% level, **= significant at the 5% level SE= Robust Standard Errors. Cluster based on parcels that were
resold during the period.
Table 2 Panel B
OLS (Dependent Variable= LN (SALE PRICE))
Model 4
Variable
INSPECT
MITCREDIT (D)
WPCREDIT
KNOWNFEAT
Coef
.0228***
Model 5
SE
Coef
.0030
REVEALFEAT
HIP
BASICSHUT
HURRSHUT
BROOF
CROOF
.0031
.0244***
.0033
Coef
.0134***
.0122
.0116***
.0162***
.0265***
.0042
.0185***
.0027
.0031
.0044
.0187***
FLRS
SQFT
SQFT2
LOTS
LOTS2
EFFAGE
-.0024
.0005***
-3.92e-08***
.00002***
-6.69e-11***
-.0042***
.0045
.00001
1.92e-09
4.48e-07
4.08e-12
.0005
EFFAGE2
HOMESTEAD
LN DTC
HRA
INFLOOD
CFLOOD
-9.91e-07
.0138***
-.0822***
.0184***
.0012
.3855***
.0018
-.3053***
-.1777***
-.1245***
yes
yes
Cons.
N
ADJ. R2
SE
.0165***
SWB
SWRAP
DWRAP
CLIPS
BDRS
BTHS
POSTFBC
LOWCOST
BELAVG
ABVAVG
CENSUS D INCL.
TIME D INCL.
Model 6
12.50***
29532
.8975
SE
.0035
.0104
.0034
.0055
.0045
.0026
.0031
-.0009
-.0045
-.0190
.0029
.0043
.0187***
.0091
.0048
.0116
.0057
.0027
.0031
-.0019
.0005***
-3.91e-08***
.00002***
-6.68e-11***
-.0046***
.0045
.00001
1.92e-09
4.48e-07
4.08e-12
.0005
-.0023
.0005***
-3.93e-08***
.00002***
-6.67e-11***
-.0046***
.0045
.00001
1.92e-09
4.48e-07
4.08e-12
.0005
5.89e-06
.0029
.0048
.0059
.0043
.0620
4.09e-06
.0104***
-.0821***
.0230***
.0010
.3856***
5.92e-06
.0029
.0048
.0059
.0043
.0619
3.29e-06
.0109***
-.0817***
.0258***
.0012
.3852***
5.95e-06
.0029
.0048
.0059
.0043
.0619
.0068
.0184
.0102
.0083
.0069
-.3051***
-.1770***
-.1237***
yes
yes
.0068
.0184
.0102
.0083
.0028
-.3054***
-.1771***
-.1241***
yes
yes
.0069
.0184
.0102
.0083
.2368
12.50***
29532
.8977
21
.2364
12.50***
29532
.8977
.2362
***= Significant at the 1% level, **= significant at the 5% level SE= Robust Standard Errors. Cluster based on parcels that were
resold during the period.
Table 2 Panel C
Treatment-Effects Model (Dependent Variable= LN (SALE PRICE))
Model 7
Variable
INSPECT
MITCREDIT (D)
WPCREDIT
KNOWNFEAT
REVEALFEAT
HIP
Coef
.1170***
.0143***
Model 8
SE
Coef
.0079
.0038
Model 9
SE
.1220***
.0081
.0180***
-.0036
.0031
.0036
BASICSHUT
HURRSHUT
BROOF
CROOF
SWB
SWRAP
Coef
SE
.1241***
.0081
.0146***
.0035
.0133
.0100***
-.0010
.0066
.0076
-.0115**
.0103
.0033
.0054
.0046
.0090
.0047
DWRAP
CLIPS
BDRS
BTHS
FLRS
SQFT
.0040
.0190***
-.0025
.0005***
SQFT2
LOTS
LOTS2
EFFAGE
EFFAGE2
HOMESTEAD
LN DTC
-3.85e-08***
.00002***
-6.65e-11***
-.0048***
5.35e-06
.0052*
-.0830***
1.90e-09
4.42e-07
4.03e-12
.0005
5.81e-06
.0029
.0047
-3.87e-08***
.00002***
-6.64e-11***
-.0047***
4.64e-06
.0059**
-.0827***
1.90e-09
4.41e-07
4.02e-12
.0005
5.84e-06
.0029
.0047
-3.88e-08***
.00002***
-6.63e-11***
-.0046***
3.41e-06
.0062**
-.0824***
1.90e-09
4.42e-07
4.03e-12
.0005
5.87e-06
.0029
.0047
HRA
INFLOOD
CFLOOD
POSTFBC
LOWCOST
BELAVG
.0394***
.0008
.3975***
.0139**
-.3000***
-.1775***
.0060
.0043
.0611
.0067
.0182
.0101
.0383***
.0011
.3988***
.0104
-.2995***
-.1775***
.0060
.0043
.0611
.0069
.0182
.0101
.0386***
.0012
.4011***
.0070**
-.2999***
-.1770***
.0060
.0043
.0611
.0068
.0182
.0101
ABVAVG
CENSUS D INCL.
TIME D INCL.
Cons.
N
Wald chi2
-.1221***
yes
yes
11.98***
29532
142840.35
.0082
-.1217***
yes
yes
11.98***
29532
142910.15
.0082
-.1218***
yes
yes
11.98***
29532
143052.56
.0082
.0027
.0031
.0045
.00001
.0566
.0040
.0190***
-.0023
.0005***
22
.0026
.0031
.0045
.00001
.0566
-.0167
-.0116**
.0040
.0190***
-.0028
.0005***
.0114
.0057
.0027
.0031
.0045
.00001
.0566
***= Significant at the 1% level, **= significant at the 5% level SE= Robust Standard Errors. Cluster based on parcels that were
resold during the period.
Table 2 Panel D
Treatment-Effects Model First Stage (Dependent Variable=INSPECT)
Model 7
Variable
HIP
HURRSHUT
PREV INSP
SQFT
SQFT2
EFFAGE
LN DTC
HRA
LOWCOST
BELAVG
ABVAVG
YEAR07
YEAR08
YEAR09
YEAR10
Cons.
Lambda
Rho
Sigma
Coef
Model 8
Model 9
SE
Coef
SE
Coef
SE
-.0139
.2967***
.0044***
.0002***
-2.59e-08**
-.0086***
.0230
.0210
.00007
.00006
1.18e-08
.0006
-.0139
.2967***
.0044***
.0002***
-2.59e-08**
.0086***
.0230
.0210
.00007
.00006
1.18e-08
.0007
-.0139
.2967***
.0044***
.0002***
-2.59e-08**
-.0086***
.0230
.0210
.00007
.00006
1.18e-08
.0006
.0074
-.3754***
.0057
-.1375***
-.0726*
-2.066***
.0108
.0327
.0563
.0396
.0401
.0565
.0074
-.3754***
.0057
-.1375***
-.0726*
-2.066***
.0108
.0327
.0563
.0396
.0401
.0565
.0074
-.3754***
.0057
-.1375***
-.0726*
-2.066***
.0108
.0327
.0563
.0396
.0401
.0565
-1.159***
-.0597**
.0426*
-1.51***
-.0336***
-0.1472
.0466
.0266
.0233
.1419
.00496
-1.159***
-.0597**
.0426*
-1.51***
-.0346***
-0.1514
.0466
.0266
.0233
.1419
.0050
-1.159***
-.0597**
.0426*
-1.51***
-.0360***
-0.1576
.0466
.0266
.0233
.1419
.0051
.2285
.2285
.2285
***= Significant at the 1% level, **= significant at the 5% level SE= Robust Standard Errors. Cluster based on parcels that were
resold during the period.
23
Table 3A: Change in Property Selling Price Implied by Model Parameter Estimates
Variable
Model 2
Model 3
Model 4
Model 5
Model 6
INSPECT
MITCREDIT
WPCREDIT
KNOWNFEAT
REVEALFEAT
$13,092
Model 7
Model 8
Model 9
$11,889
$12,406
$12,623
$4,961
$9,933
$7,944
$5,731
$6,257
$8,509
HIP
$4,647
$5,066
HURRSHUT
$4,019
$3,462
BROOF
$5,626
CROOF
$9,251
BASICSHUT
SWB
SWRAP
-$3,939
DWRAP
CLIPS
-$3,973
24
Table 3B: Implied Annual Insurance Savings (5% Cap Rate)
Variable
Model 2
Model 3
Model 4
Model 5
Model 6
INSPECT
MITCREDIT
WPCREDIT
KNOWNFEAT
REVEALFEAT
$655
Model 7
Model 8
Model 9
$594
$248
$620
$631
$497
$397
$287
$313
$425
HIP
$232
$253
$201
$281
$173
BROOF
CROOF
$463
BASICSHUT
HURRSHUT
SWB
SWRAP
-$197
DWRAP
CLIPS
-$199
25
ADDENDUM
A1. An Overview of the My Safe Florida Home (MSFH) Program
A unique set of circumstances occurred in Florida during the last decade that allows a test of
whether home buyers observe and value hurricane mitigation features and/or voluntary mitigation
efforts. In the wake of devastating hurricanes during the 2004 and 2005 seasons, the State
recognized that the continued availability of property insurance is critical to all aspects of the state’s
economic stability and created the Task Force on Long-Term Solutions for Florida’s Hurricane
Insurance Market (the Task Force) to identify solutions. The Task Force made sweeping
recommendations, including the establishment of a Mitigation Consumer Assistance Program to
include (1) free consumer mitigation retrofit inspections by trained and qualified inspectors, (2)
provision of retrofit grants for low-income families, and (3) provision of low- or no-interest loan
programs for established, effective mitigation techniques.
The Florida Legislature in 2006 created the Florida Comprehensive Hurricane Damage
Mitigation Program (see Appendix H-9) and appropriated $250 million for the program. The
program was directed to perform 400,000 windstorm inspections and provide at least 35,000 grants
to assist homeowners in hardening their homes through the Department of Financial Services
(DFS). DFS made a determination to conduct a pilot program to create, test and implement the
above-described program that the Department named, “My Safe Florida Home (MSFH).”
Florida Statutes directed the My Safe Florida Home program to provide the free hurricane
mitigation inspections to homeowners residing in single-family, site-built homes statewide. The
program was required to contract with a “Wind Certification Entity” that, at a minimum, must use
inspectors who:





Were certified as a building inspector under Section 468.607, Florida Statutes;
Were a licensed general or residential contractor under Section 489.111, Florida Statutes;
Were a licensed professional engineer under Section 467.015, Florida Statues, and had
passed the appropriate equivalency test of the Building Code Training Program as required
by Section 553.841, Florida Statutes;
Were a licensed professional architect under Section 481.213, Florida Statutes; or
Had at least two years of experience in residential construction or residential building
inspections and have received specialized training in hurricane mitigation procedures.
The MSFH program also was required to provide homeowners with a report that:
26




Recommended up to seven improvements that could be made to better protect the home
against hurricanes;
Provided cost estimates to make recommended improvements;
Outlined potential insurance discounts available based on the current structure of the home
if improvements are made;
Provided a hurricane-resistance rating of the home’s current and prospective abilities with
improvements.
Inspection reports contained recommendations that focused on protecting openings and
strengthening roofs in the following categories:







Roof deck attachment
Secondary water barrier
Code-plus roof covering
Bracing gable end walls
Strengthening roof-to-wall connections
Protecting or replacing window openings
Protecting or replacing doors
On average, homeowners who participated in the program received an inspection report
within 30 days (see Appendix H-16 for a sample report). The report provided the homeowner with a
hurricane resistance rating (based on the Home Structure Rating Scale) designed to provide a general
indication of how well the home was expected to perform in the event of a hurricane. The report
indicated the beneficial features of the home that contributed to the rating and improvement plans
to increase its rating. Improvement plans provided the homeowner with the new hurricane wind
resistance rating the home would receive if specific improvements were completed, the estimated
cost of the plan and the estimated annual wind insurance savings. The report also included
contractor bid sheets to assist the homeowner in collecting estimates for the recommended
improvements.
The advent of the My Safe Florida Home (MSFH) program caused the number of
inspections to dramatically increase as the inspections were offered free to homeowners of single
family dwellings. In addition, the MSFH program raised awareness of the inspections and caused a
surge in private market inspections. By the time the program ended on June 30, 2009 (as a result of
the end of legislative funding), it had performed 400,000 state-sponsored windstorm inspections, a
number representing approximately 10% of the site built single family residences in the state, and
funded grants to assist homeowners in hardening their homes through the Department of Financial
Services (DFS), local governments and nonprofit organizations. By the conclusion of the program,
33,547 homes had been retrofitted at a cost of $111.2 million. The average grant award was $3,317.
27
DFS reimbursed 24,486 in grants to homeowners while 525 were direct pay grants issued to low
income homeowners. Some 6,203 grants were issued through non-profit partnerships and local
government partnerships issued 2,333 grants.
In 2005, the Florida Legislature (s. 22, ch.2005-111 Laws of Florida, see Appendix H-6)
directed insurance companies to notify homeowners that windstorm mitigation discounts were
available on their homeowners insurance policies. “Using a form prescribed by the Office of
Insurance Regulation, the insurer shall clearly notify the applicant or policyholder of any personal
lines residential insurance policy, at the time of the issuance of the policy and at each renewal, of the
availability and the range of each premium discount, credit, other rate differential, or reduction in
deductibles, and combinations of discounts, credits, rate differentials, or reduction in deductibles,
for properties on which fixtures or construction techniques demonstrated to reduce the amount of
loss in a windstorm can be or have been installed or implemented.”
Rule 69O-170.017 F.A.C. was amended effective December 16, 2006, requiring insurers to make
new rate filings by March 1, 2007 to double the credits to 100% or provide actuarial justification for
an alternative. Informational Memorandum OIR-07-03M (see Appendix H-7) issued February 27,
2007, stated that the “windstorm mitigation discount filing shall not include any modification of the
rating factors or base rates for any purpose, including the offset of revenue impact on current
business.”
On July 1, 2007, at the direction of the Florida Legislature, the Office of Insurance
Regulation developed and implemented a standardized form for compiling the data and reporting
features of the home to an insurance company. The development and approval of the Uniform
Mitigation Verification Inspection Form (OIR-1802) greatly aided in the standardization of
inspection data. At the same time, the MSFH program received a 90-day authorization
(EMERGENCY OIR –B1- 1804) to complete the Uniform Mitigation Verification Inspection Form
for the homes that had a MSFH inspection completed between the beginning of the program and
the end of October 2007. A program was developed to do this electronically and issue the report to
the homeowner. The State maintained this data, resulting in an immense dataset, rich with
information regarding the mitigation features of individual homes across the state.
28