Revealed Information and the Demand for Hurricane Mitigation Features

Transcription

1 Revealed Information and the Demand for Hurricane Mitigation Features By Dean Gatzlaff, 1 Kathleen McCullough, 2 Lorilee Medders 3 and Charles M. Nyce 4 * 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 dgatzlaff@cob.fsu.edu 2 The Florida State University, Department of Risk Management/Insurance, College of Business, Tallahassee, FL kmccullough@cob.fsu.edu 3 The Florida State University, Florida Catastrophic Storm Risk Management Center, College of Business, Tallahassee, FL lmedders@cob.fsu.edu 4 The Florida State University, Department of Risk Management/Insurance, College of Business, Tallahassee, FL cnyce@cob.fsu.edu; phone: *Contact Author 1

2 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

3 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 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 6 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). 3

4 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 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

5 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 MPH zones) there are mixed results and a change in perceived value after the 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 , 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 percent, percent and 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, 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

6 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 SP it is the transaction price of property i at time t,; β j is a vector of j coefficients on the property- and location-specific characteristics, X jit ; c is a vector of coefficients on the mitigationspecific characteristics, M it ; δ t are the coefficients on D it, time dummies with values of 1 if the ith property sold in period t and 0 otherwise; and e it 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

7 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

8 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

9 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 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

10 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

11 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

12 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

13 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

14 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

15 mitigation. It also sheds light on the importance of the mitigation features being communicated and proper measure taken to ensure insurance credits. 15

16 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), 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), 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), 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), 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,

17 Miller, D., D. Morgan and C. Womack (2002) Buying Tornado Safety: What Will it Cost? Southwestern Economic Proceedings, 29(1) Palmquist, R.B. (1979) Alternative Techniques for Developing Real Estate Price Indexes Review of Economics and Statistics, 62: Peacock, Walter Gillis 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: Simmons, Kevin, Jamie Kruse and Douglas Smith (2002) Valuing Mitigation: Real Estate Market Response to Hurricane Loss Reduction Measures Southern Economic Journal, 68(3):

18 Table 1 Table 1: Variable Definitions/Statistics Panel A Housing and Location Characteristics Variable Description Mean Std. Dev. Minimum Maximum Sale Price 344, ,070 28,000 4,825,000 Distance to in miles Coast Bedrooms Bathrooms # of Stories Square Feet Lot Size in square feet 9,481 7, ,732 Age yr sale yr built Effective Age Housing and Location Characteristics Dummies Variable Description Mean Homestead Primary residence qualifies for FL s homestead exemption.621 Wind Zone 130 Parcel located in 130 MPH wind zone.280 Wind Zone 140 Parcel located in 140 MPH wind zone.748 Wind Zone 150 Parcel located in 150 MPH wind zone.052 HRA Property located in Citizens Coastal Account Zone.2074 PostFBC Built after the 1994 South Florida Building Code.2276 Inland Flood Parcel located in Flood Zone A, AH, or AE.580 Coastal Flood Parcel located in Flood Zone VE.0005 Low Cost Assessor s opinion of construction quality lowest rating (1).0685 Below Average Assessor s opinion of construction quality 2 nd lowest rating (2).5921 Above Average Assessor s opinion of construction quality 2 nd highest rating (4).2436 Excellent Assessor s opinion of construction quality highest rating (5)

19 Table 1: Variable Definitions/Statistics Panel B Mitigation Characteristics Roof Decking Attachment Dummies Variable Description Mean Type A 6d nails every 6-12 inches.3096 Type B 8d nails every 6-12 inches.1595 Type C 8d nails every 6 inches.4385 Mitigation Characteristics Roof to Wall Attachment Dummies Variable Description Mean Clips Roof truss to top of wall connector.1181 Single Wrap Roof truss to top of wall connector that wraps around truss.3915 Double Wrap Roof truss to top of wall connector that wraps around truss & wall.0159 Toenail/Unknown Angled nail to connect truss to wall.4745 Mitigation Characteristics Opening Protection Dummies Variable Description Mean Hurricane Meet Florida Building Code for impact ratings Rated Shutters Basic Shutters Offer some protection from impact, but not Hurricane Rated No Shutters /.6921 Unknown Mitigation Characteristics Roof Variable Description Mean Hip Shape All sides of roof line are sloped.2718 Secondary Water Barrier Underlayment to prevent water penetration if roof covering fails Other Characteristics Variable Description Mean Mitigation The property is receiving a mitigation discount from Citizens.7803 Credit Wind Premium The percent credit from the OIR credit table for the property s.4632 Credit mitigation features Knownfeat Dummy variable for the presence of one of the known features.4713 Revealfeat Dummy variable for hidden mitigation features.6272 Previous Number of mitigation inspections performed in property s census Inspection tract prior to property s inspection. Mitigation Inspection Dummy variable if property had a mitigation inspection prior to sale, zero otherwise

20 Table 2 Panel A OLS (Dependent Variable= LN (SALE PRICE)) Model 1 Model 2 Model 3 Variable Coef SE Coef SE Coef SE INSPECT MITCREDIT (D).0373***.0037 WPCREDIT.0618***.0053 KNOWNFEAT REVEALFEAT HIP BASICSHUT HURRSHUT BROOF CROOF SWB SWRAP DWRAP CLIPS BDRS BTHS.0184*** *** ***.0031 FLRS SQFT.0005*** *** *** SQFT2-3.93e-08*** 1.93e e-08*** 1.92e e-08*** 1.92e-09 LOTS.00002*** 4.48e *** 4.48e *** 4.47e-07 LOTS2-6.74e-11*** 4.09e e-11*** 4.08e e-11*** 4.08e-12 EFFAGE *** *** ***.0005 EFFAGE2-1.01e e e e e e-06 HOMESTEAD.0156*** *** ***.0029 LN DTC *** *** ***.0048 HRA.0165*** *** ***.0059 INFLOOD CFLOOD.3839*** *** ***.0619 POSTFBC LOWCOST *** *** ***.0184 BELAVG *** *** ***.0102 ABVAVG *** *** ***.0083 CENSUS D INCL. yes yes yes TIME D INCL. yes yes yes Cons *** *** ***.2362 N ADJ. R

21 ***= 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 Model 5 Model 6 Variable Coef SE Coef SE Coef SE INSPECT MITCREDIT (D) WPCREDIT KNOWNFEAT.0228*** ***.0031 REVEALFEAT.0244***.0033 HIP.0134***.0035 BASICSHUT HURRSHUT.0116***.0034 BROOF.0162***.0055 CROOF.0265***.0045 SWB SWRAP DWRAP CLIPS BDRS BTHS.0185*** *** ***.0031 FLRS SQFT.0005*** *** *** SQFT2-3.92e-08*** 1.92e e-08*** 1.92e e-08*** 1.92e-09 LOTS.00002*** 4.48e *** 4.48e *** 4.48e-07 LOTS2-6.69e-11*** 4.08e e-11*** 4.08e e-11*** 4.08e-12 EFFAGE *** *** ***.0005 EFFAGE2-9.91e e e e e e-06 HOMESTEAD.0138*** *** ***.0029 LN DTC *** *** ***.0048 HRA.0184*** *** ***.0059 INFLOOD CFLOOD.3855*** *** ***.0619 POSTFBC LOWCOST *** *** ***.0184 BELAVG *** *** ***.0102 ABVAVG *** *** ***.0083 CENSUS D INCL. yes yes yes TIME D INCL. yes yes yes Cons *** *** ***.2362 N ADJ. R

22 ***= 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 Model 8 Model 9 Variable Coef SE Coef SE Coef SE INSPECT.1170*** *** ***.0081 MITCREDIT (D).0143***.0038 WPCREDIT KNOWNFEAT.0180***.0031 REVEALFEAT HIP.0146***.0035 BASICSHUT HURRSHUT.0100***.0033 BROOF CROOF SWB SWRAP **.0047 DWRAP CLIPS **.0057 BDRS BTHS.0190*** *** ***.0031 FLRS SQFT.0005*** *** *** SQFT2-3.85e-08*** 1.90e e-08*** 1.90e e-08*** 1.90e-09 LOTS.00002*** 4.42e *** 4.41e *** 4.42e-07 LOTS2-6.65e-11*** 4.03e e-11*** 4.02e e-11*** 4.03e-12 EFFAGE *** *** ***.0005 EFFAGE2 5.35e e e e e e-06 HOMESTEAD.0052* ** **.0029 LN DTC *** *** ***.0047 HRA.0394*** *** ***.0060 INFLOOD CFLOOD.3975*** *** ***.0611 POSTFBC.0139** **.0068 LOWCOST *** *** ***.0182 BELAVG *** *** ***.0101 ABVAVG *** *** ***.0082 CENSUS D INCL. yes yes yes TIME D INCL. yes yes yes Cons *** *** ***.0566 N Wald chi