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Open Access test for equality Discussions F Sciences Natural Hazards Hazards Natural and Earth System System and Earth erences between the two groups ff 3066 3065 ected, frequency, financial loss, and psychological trauma and ff ecting flood insurance ff This discussion paper is/has beenSystem under Sciences review (NHESS). for Please the refer journal to Natural the Hazards corresponding and final Earth paper in NHESS if available. two worst flood events in(USD the 60.8 country million) vis-à-vis and the resulted 1971 to flood the that death cost of RM200 61 million persons and the successive 50 yr In the ranks ofarea natural and disaster population a floodthe is real Malaysia estate sector worst is nightmareexperienced particularly in major hit terms hard. flood Historical of event1970, records overall in show 1971, that 1988, 1886, Malaysia 1993, 1926, has 1996,It 1931, is 2000, 1947, estimated 2006, that 2007, 1954, the 2008,(USD 1957, average 2009, 912.8 annual 1965, 2010, million) flood 1967, 2011 damage (Deloitte, andRecord in 2003) 2013. also Malaysia which is show about can the RM trend be 3 has translated billion been as increasing. annual This loss is to glaring GDP. when one compares subjective risk perception wereand found flood risk more aversion. predictive of flood insurance penetration 1 Introduction two groups of residential homeownerswho in three purchased districts flood of insurance Johorpenetration and State, rate Malaysia: the those with group thatthus, Kota the did Tinggi highest not. district degree Ourof of having result group flood the revealed means, risk 34 highest % SCDFC, aversion.shown structure The penetration that Wilks’ correlation (44 %) Lambda there and canonical and areof correlation strong homeowners have significant based clearly attribute onperception, di measures and of socio-economic objective cum flood demographic risk variables. exposure, However, measures subjective of risk High impact flood has virtuallysurance become has an remained annual a experience grosslymanagement. in neglected Malaysia, Using part yet of discriminant flood comprehensive analysis, in- side integrated this flood variables risk study that best seeks predict to flood indentify insurance the penetration demand- and risk aversion between Abstract Nat. Hazards Earth Syst. Sci.www.nat-hazards-earth-syst-sci-discuss.net/2/3065/2014/ Discuss., 2, 3065–3096, 2014 doi:10.5194/nhessd-2-3065-2014 © Author(s) 2014. CC Attribution 3.0 License. Factors a penetration in residential properties in Johor Malaysia U. Godwin Aliagha, T. Ewe Jin, W. WengDepartment Choong, of and Real M. Estate, Nadzri FacultyMalaysia, Jaafar of Malaysia Geoinformation and Real Estate, Universiti Teknologi Received: 25 January 2014 – Accepted: 19Correspondence March to: 2014 U. – Godwin Published: Aliagha 30 ([email protected]) AprilPublished 2014 by Copernicus Publications on behalf of the European Geosciences Union. 5 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ect ff erently, residual risk is the por- ff 3068 3067 total land area prone to flooding, thus, exposing 2 ected. It was also estimated that the December and ff In US, UK and recently in Australia after the Brisbane flood, flood insurance has Argument for enhanced flood insurance penetration that is integral part of a compre- Already some insurance companies in Malaysia have stated reporting flood insur- Flood threat to Malaysia coastal real estate could be enormous when one realise that sive integrated flood risk management in Malaysia. Flood insurance is also profoundly been adopted as a partcomplement of nonstructural the approach. tools for Asrated residual a as flood risk result, part management flood ofcase to insurance support a in has and comprehensive Malaysia. been Flood integratednot incorpo- insurance flood a as risk common a management. practicemore non-structural This in so flood Malaysia is it risk as is not not management floodsin the a tool are Malaysia, legal still is requirement neither viewed to issurance have as there flood as an insurance an any “Act for instrument incentive flood of for2009). prone from flood God” Consequently, flood properties risk the and insurance management has government in become to the a neglected country promote aspect (Kaizrul, flood of 2004; a in- Ho, comprehen- is always the possibilityembankments of a occurring flood within event aping greater time than the period the defenses that design and mayout, capacity flooding result absolute of adjacent in flood levees properties. protection breaching or is As or impossible. Kreibich overtop- et al. (2005) pointed reasons are fourfold: (1) Publicbe Works able and to Irrigation protect Departments allconstruction have areas, not continued control or in all may flood-prone floods; not areas; (2)of (3) under climate based increasing change on urbanisation on observed private frequencyity; records, and the (4) intensity e even, of where rainfall structuralresidual has measures flood become are which an in Plate accepted place, real- (2002)the there risk and is after Merz always implementing (2006) the a describedtion probability protection of as of system. risk the Put that remaining di remains part after of flood control structures have been built. In essence there embankments, flood bypasses, riverstorage dams diversions, and poldering, flood and attenuationstriction of construction ponds. development While along of costal non-structural corridors, flood measurestion, land include use flood zoning, proofing, re- resettlement and of flood popula- forecastingmeasures and the warning incidents systems. Despite of these floods laudable and attendant losses continue to increase. The main Amongst structural measures are channel enlargement, construction of levees and some significant structural and non-structural measures to address flood problems. million). Yet, the claims representbillion only cost 7 to % the of Governmentflood (Singh, the 2007). may total The damage lie reason compared for in lowabout with the insurance 5 RM1.5 claims fact % from that (Business flood Times,portunity insurance 5 for flood penetration March insurance. rate 2007) Inpaying in contrast even massive with though, Malaysia amount the is there flood insurance relief very is industry, the damages. huge low, government business is op- hensive flood risk management inof Malaysia Public could Works be and established. Irrigation Under Departments the the auspices government has over the years taken of climate change ascover the rise insurance by firms as much operating as in 100 these % areas in the experienceance next cost claims 10 of from yr policy (Gerrit, holders 2009). January a 2007 flood cost insurance firms in Malaysia about RM100 million (USD 30.4 by increasing urbanizationbate and severity mounting of flood evidence riskAs that (IPCC, Keizrul 2007; climate Abdullah, Stern, Director-General change 2006, DrainageMalaysia 2008; will and Bubeck approaches Irrigation and exacer- 2020, Department Botzen, Malaysia 2012). warned,lenges should as owing expect serious to flood increasedthreat management severity to chal- and the entire frequencyto corridor of climates area change floods. (MNREM, may Adding 2007,that be BERNAMA, that property the 21 owners the in exposure June a largest 2007). of high-risk Pundits area Malaysia have to 189 warned expect premiums rivers double basins in the coming years RM1.5 billion (USD 456.4 million)Badrul and et led al., 2010; to Hamzah the et death al., of 2012). 18 personsPeninsular (MNREM, Malaysia 2007; has 29 0004.82 km million people to flooding (Liu and Chan, 2003; ADRC, 2011). This is compounded and 100 yr floods that hit Johor State in December 2006 and January 2007 which cost 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | : ex- ectively ab- ff erences in degree of risk ff Expected utility (EU) theory er of an actuarially fair premium ff 3070 3069 among alternatives using linear probability weighting ff erentiate and account for the degree of risk aversion between groups of ff Owing to the fact the expected utility theory is a rational theory that operate on the Residential flood insurance penetration requires both demand and supply sides. The Though flood insurance cannot prevent actual property damages or loss of life as (Sebora, 1995; Isenberg, 1989; Marchfor and lack Shapira, 1987) of it explanatory has been power. widely Moreso, criticized Rabin (2000a) argues that if the only reason der this condition the individualThis with cover is is because not whether botheredmain whether the the there flood is same. disaster flood Perhaps or that occurstheory not. is or has why not a Kunreuther constant the and absolute utility Pauly risk (2005) from aversion said cover utility expected will function. utility assumptions re- of context invariance, availability ofof all formation all and possible complete outcomes, knowledge sistently always select predict the accurate probabilities best of payo outcomes and con- ity theory holds that peoplea will point purchase where full premiums insuranceUlrich, are only 2012) if equal In the to the premiums scenario expected arefor of losses fair expected a (Kunreuther to utility risk and (EU) averse theory Pauly, 2006a; that demand person reflect for will flood full be insurance coverage based where benefits only from on premium the equal o the expected losses. Un- 2.1 Prospect theory under flood insuranceTwo risk popular decisions theories usedexpected utility to (EU) explain theory decision andpected prospect making utility theory (EU) under (PT). is the risk standardrelies or and on rational linear uncertainty theory composite of of are decision weightedIt probability making is outcomes under founded to risk compute on that expected theonly utility. principle reference of point diminishing (Chateauneuf marginal andand utility Cohen, Pauly, and 2005; 1994; uses Desrosiers, Rabin, 2012). net 1997, Under wealth 2000a; insurance as Kunreuther decisions the making, expected util- groups on the factors that influence flood insurance purchase. 2 Review of related theories and literature (4) determine if there are areas of common ground or commonality between the two demand side is determinedtrolled by by the the insurance householdsdential firms. flood while Our insurance the and study thus supply focusesand aim on side degree to: the (1) of is demand-side determine flood mainlyJohor flood aspect risk State; con- insurance of aversion penetration (2) resi- rate among determine if residentialaversion there between homeowners are two in significant groups three attributeinsurance of districts di and residential of the homeowners: group those thatthat who who best purchased did di flood not;homeowners (3) that determine purchased the flood most insurance important and variables the group that do not purchase; and to transfer the risk tosorb an and insurance diversify company the whoprecautionary is risk. risk in reduction Hence, better measures buying taking positionet around flood to flood al., e insurance exposed 2011) buildings is (Kreibich regarded as one of the with flooding. An insuredmuch property financial damaged stress by to floodcan the can government. rebuild be A faster replaced community after quicklyas with flood. without extensive serving Kunreuther flood the and insurance Roth purposeranging (1998) for of described the reducing flood transfer theSimilarly, insurance of Bubeck economic and all impact Botzen or of (2012)measure part which conceive individual of reduces flood losses financial insurance the by consequencesThe as loss for ar- demand a to an for private individual insurance others mitigation once is who a driven share by flood individual’s occurs. the knowledge same of potential risk. risk and opt age to properties from floodresilience are and frequent phenomena. adaptations Even strategies numerous have studiesonly missed on cursory out flood attention on to flood it. insurance or have paid structural measures would do, it can significantly reduce the economic risk associated under researched not only in Malaysia but across Southeast Asia where collateral dam- 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and may not ff . 3072 3071 ceteris paribus Prospect theory was developed by Kahneman and Tversky This study is study situated primarily on the prospect theory, thus acknowledging that Moreso, prospect theory postulates that people, including modest risk individuals are Prospect theory (PT): purchase of insurancerepresents by revealed an preference individual for financial is protection. a Purchasing good flood insurance indicator is of risk aversion since it tional risk by paying moreliving than flood fair prone premium areas may in rationally orderparticularly prefer to actuarially those avoid fair premium, loss. motivated realistically, Though some byfair Malaysians loss price aversion to may avoid be expected loss willing to pay2.2 slightly more than Literature review Risk aversion house(Kriesel owner and would Landry, more 2004; likely Smith, to 1968). purchase As Borzen flood et insurance al. policy (2009) contend, actual practically, decision to purchase floodand insurance losses will as most well as likely agreementinfluence be that insurance motivated both purchase by objective decisions. gains risk The exposure studypostulation and is that risk also perceptions aligned people, with including prospect theory modest risk individuals are willing to take an addi- sky, 1979; Hershey andtendency Schoemaker, for 1980). people In to weighUnder the losses this parlance more notion, of heavily Rabin prospect thanup (2000b) gains insurance theory and is is Sydnor this determined called (2010) in “losstake pointed the aversion.” actions that loss to the domain. avoid decision Prospectthis losses theory to and notion, encourages take maximize people Eckles gain to andinsurance (Eckles Volkman decisions and (2011) in Volkman, order argue 2011). tomaximize Against that the minimize in domain the where line domain a where with gain a PT is loss experienced”. people is will experienced “Make and ing expected losses. Prospect theoryaversion” and explanation how for people this weighoutcome rests probabilities people in often of the overvalue outcome. small concept In probabilitiesvalued of weighting while (Rabin, “loss larger probabilities 2000b; probabilities Sydnor, of 2010). are This under- more is because sensitive there to is evidence small that gains/losses people are compared to larger ones (Kahneman and Tver- modest risk people often buying insurance policy with premiums significantly exceed- (2004), Kunreuther and Pauly (2006a), Sydnor (2010), Ulrich (2012) reveal evidence of with reference to specific actionsfunction rather (Kaheman than and impact Tversky, of 1979; the TverskyPauly, actions 2005). and on Kahneman, Leaning final 1992; more wealth Kunreuther utility on and and subjective Bergh (2009) influences pointed measures out Slovicnot that (1987), only commonly Botzen people on evaluate the andperceptions basis make which risk on involve decisions intuitive objective risk risk judgments exposure or but risk also beliefs. fromwilling to the take perspective an of additionalpostulation, risk risk by studies paying by more in Pashigian order et to al. avoid loss. (1966), In Drèze support (1981), of this Cutler and Zeckhauser do not use linear probabilitytends weights that to context determine and values. subjective Rather,sult prospect values decision theory influence makers decision con- may under not uncertainty.use select As the net a alternative wealth re- with as the thegest highest reference that payo point individual often as make depicted decisions in by the comparing changes EUT. Empirical in their evidence financial sug- status abilities are objectively known orpossible at outcomes best (Sebora, as 1995. subjective However,objective Predicting maps measures insurance of may purchase the be purely objectivereuther, misleading values on 1978; of as Slovic, 1987). perception of risk is often(1979) subjective and Kun- later1992). examined Prospect and theory argues quantied thatinformation, because further of analytical complexities ability, by in preferences decision Tversky are making, often and limited not Kahneman consistent and (1981, individual often explanation for risk aversionin in modest the size EU risks. then Ordecreasing as they marginal Desrosiers utility should (2012) of be contends, wealthviduals very EU cannot purchase close theory provide moderate plausible to with or explanation its risk smallof for associated neutral scale why limitations insurance. indi- of Levin the (2006) EUT conceived that is one that it treats uncertainty as objective risk where the prob- people are risk averse is the diminishing marginal utility of wealth, which is the only 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er flood damage ff ect of this may be increase in subscription ff 3074 3073 ered previous damage from a flood. When estimating or predicting the probability ff Brinley (1972) blames the lack of knowledge and awareness of the existence couple Also, the proximity to large bodies of water exposes homes to flooding. Botzen nity for the low penetration rate of flood insurance (Browne and Hoyt, 2000). Browne trol measures, such as dykes,traditionally levees, used floodwalls, reservoirs, to and reduce bypasscreases channels susceptibility when they are and, are as notto provided. such flood The perception e insurance. of Ironically,Levees the vulnerability create opposite in- a is false sense oftenare of the security fully case among protected where coastal they residents againstinsurance who are (WMO, future 2006; believe provided. that Kunreuther disasters they and and Pauly, 2006b). therefore feel nowith the need failure to of take local flood authority in seeking eligibility for the coverage to their commu- riencing flood damage for several yearscomparison has to led other to types decline in of1991). renewal insurance What rate coverage this for (Kunreuther policies means and in inmade White, house essence 1994; owners is Palm, think that that thepolicy it is low since not probability all flood of that flood is necessary occurrence to something has renew that their flood does insurance not occur frequently. Structural flood con- a distasteful event, thenfor they next occurrence, will (Kunreuther, learn 1978;Kunreuther Kunreuther from and et White, the al.1978; 1994). event Epple Accordingpredictable and and to occurrences Lave, Burton have 1988; of and better disasters Kates preparations flood are (1964), mitigation making the and individuals rare prevention. and more Dixonas un- unlikely et a to al. reminder take (2006) of found any floodscription that rate damages flood among hence, experience property resulting serves owners. to Equally,to the higher have time an flood of influence last insurance on flood policy has the sub- been decision observed of a homeowner to purchase insurance. Not expe- Baumann and Sims (1978) findinsurance evidence adoption. that They past found experience higher with insurancesu disasters uptake motivates among homeowners who had of flood, human beings tend not to worry too much but once they have experienced 2.2.2 Subjective perception of risks the flood risk theywith face. From higher his number research, of DixonFlood et properties Hazard al. Area) under (2006) has the foundsupported higher risk out by demand Kriesel that of and for location Landry being floodare (2004) flooded insurance. finding more This that (known property likely argument as owners to isof nearer Special flood further purchase 1 % zone flood in insurance.insurance distance Their by from 0.88 research %. the shows flood that zone an decreases increase the probability of purchasing flood areas. et al. (2009)than found houses that far away houses from alevels. near river As a once Dixon a river et diketo are breaches al. coastal or more (2006) is flooding overtopped likely serves posit, by as proximity to high a to water su constant large reminder bodies to homeowners of in water the that community of is subject 2.2.1 Objective exposure and susceptibility House location elevation maysuch, well elevation determine of susceptibilitying a or homeowners’ building sensitivity perceived to has probabilityon flood. been elevated of As observed ground loses. to areet Home less be al. owners likely (2006) one whose to also ofis buildings found purchase considerably are the out insurance higher that factors for at the underly- such coastal probability properties. flooding of Dixon area people than purchasing on flood high insurance elevation or non-coastal property to flood, themographic traits house of owner’s the house perceptionvariables owner. of Numerous that literatures risk underlie have and heighted theseflood the socioeconomic insurance three specifics to cum factors protect de- that against the could risk. influence decision to purchase however contingent upon the degree of objective exposure and susceptibility of the 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ord ff 3076 3075 strongly disagree, 5 strongly agree). On the other hand = were based on a stratified random survey on residential home- s interval scale. While questionscale. 2 was in rank scale, question 3 and 4 were in nominal premium to be highnon but reliability willing of to insurance firmsprovide pay to flood slightly pay insurance higher insurance cover; than claims andadequate. fair as (6) Except price; well perception question as that (5) 1 their flood perceptioncontained all reluctance protection statements of to the system that are questions were not designed tappinging to into a elicit subjective 5-point respondent’s risk likert levelsocio-economic of perception scale cum agreement (1 us- demographic variables(2) education were level; assessed (3) gender; using: (4) (1) race; and income (5) age. level, Questions 1 and 5 were measure measures of objective flood risk(2) exposure house are: location (1) distance elevationscale. from of On flood property. subjective prone risk The river; perception, two the and experienced; measures variables (2) are were (1) expectation measured numbers of of in high futureping interval impact increase flood flood flood insurance frequency; if (3) likelihood flood of is drop- not experienced for 2 yr; (4) perceive flood insurance A total of 315districts. sets However, the of analysis questionnaire was were based distributed on among 207 usable the3.2 responses homeowners received. in each Instrument and measures The questionnaire isobjective designed risk to exposure,graphic tap subjective variables risk into that perception homeowners’ couldand determine and response hence, their influences socio-economic to likelihood their cum flood of measures risk demo- purchasing of aversion flood orientation. insurance, Two variables used to elicit 3.1 Data source The data used in this owners in three districts of Johor State, namely Kota Tinggi, Segamat and Johor Bahru. 3 Data and method that have low probabilitycoverage. and Hence, when house the ownersment losses feel (Baumann are and that Sims, likely expenditure 1978; to on Johnson,Lamond 1978; be et insurance Kunreuther al. less et is (2007) al., than a observed 1978;coverage subscription that Palm, poor because 1981). house invest- they owners expect might that,be choose lower not in than to long the purchase term, sum flood found of the that annual cost premiums. those In of who contrast, have damagesto Blanchard-Boehm purchased from et cover flood the al. flood insurance (2001) cost felt will that of flood damages. insurance will able may also influence thewith decision higher of income purchasing is flood morelead likely insurance to to policy, higher purchase a penetration floodhouse house rate. insurance owners owner According and who to higher have a incomethe not may research premium purchased of conducted flood flood by insurance insurance.significant FEMA felt Also part (1997), it that is of they hard their could to income not convince to a property purchase owners to flood allocate insurance in order to reduce losses straints. The decision to purchasedent upon a the flood income insurance of policy themeet property to his owner. a immediate Where certain needs, ones income purchasing levelbudget. is flood is Smith not (1968) insuring depen- even noted policy enough in to will hismium not model price that be people for including will flood in forgo insurance the floodIn is insurance their higher if own the than view, pre- the Browne probabilityfor and of flood Hoyt the (2000) insurance total noted lost policyflood that from a would insurance. flood. decrease eventually Kriesel in increase and the price the Landry charged probability (2004) of added purchasing that the of wealth of a house owner danger posed by the flood may increase the penetration2.2.3 of flood insurance. Socioeconomic and demographic determinants Homeowners feel reluctant to insure their property due to premium and budget con- and Hoyt added that increasing information seeded into the public’s awareness of the 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - er ffi 30 ff ≥ ceteris erences ff erences. Inferential erentiation between ff ff cients used to assess for Wilks’ lambda pro- erences between those ffi F ) for Wilks’ lambda test if ff F 108) and Indians constitute = “yes, I have flood insurance”; N = erence. ANOVA ( ff 3078 3077 er much on that variable. The canonical correla- ff erences. In other words, ff 76), Chinese 52 % ( erence analysis, discriminant analysis has an advan- = ff cient on the hand shows the correlation between each N ffi test for Wilks’ Lambda, model Wilks’ Lambda, standardized canon- 91) where 48 % (44) of them were male and 55 % (47) were female. F test because it compares the groups in terms of group centroids, thus, = t N 22). In terms of age profile the pattern of response from the highest order = . The flood penetration rate could be considered somewhat low and below aver- N “no, I do not have flood insurance”. The variable was also used to classify the The study attempted to discover if there were significant di = insurance could be describedparibus as more risk averse than the 66 % that did not, tage over the takes into account the interactions between the individual variables (Ndubisi, 2008). 4 Result and discussion On flood insurance penetration rate, thesampled result in revealed that the out study, of only(66 the 34 %) % 207 respondents or (72) insured 135 their did property not. against This flood also while the implies rest that 34 % homeowners who purchased flood tion depicts the multipletion. correlation The between structure the matrix predictorspredictor coe and variable the and discriminant func- theare discriminant considered function. significant Correlations and thatNdubisi, therefore 2011). have have In loadings practical group mean significance di (Hair et al., 1998; ical discriminant function (SDFC), eigenvalues,group canonical centroids. correlation, The lambda and is functionsand varies at closer from to 0 1 to imply 1,there less closer are group to significant means 0 di group imply meanvides group useful di means statistics di to identifyor variables among that groups. make The significanteach di standardized variable’s discriminant unique function contribution coe tocient discriminant implies function. that A the low groups do standardized not coe di statistical technique for testingmodel for of equality group membership ofet based group al., on 1987). means Discriminant a and analysis set provides buildingmean) of descriptive and statistics a observed (total inferential predictive discriminating mean statistics and variablestatistics group identifying (Hair include and analyzing group di surance based on a set of predictive variables. Discriminant analysis is an appropriate nale for its use liesbetween with two the fact groups that of the respondents: study involved those testing who group “do” mean di and “do not” purchase flood in- 11 % ( was 29.5 % (61) from within(47) the within age the of age 31–50; ofthe 25 31–40; % age 15 (52) of % within 60. (31) the within age the of age 21–30; 23 of3.4 % 51–60; and 7.7 % Method (16) above Discriminant analysis was adopted as the analytical statistics for this study. The ratio- Kota Tinggi ( On the other hand 26.6 %and (55) were 56 % from Segamat (31) where(33) 44 were % of (24) female. them of 29 them male %46 were % and (61) male female (95) were respectively. of In fromthe them other respondents Johor are were words, Bahru males Malays out ( while 46 the % and 207 (28) 54 respondents, % and (112) 54 % female. On race profile, 37 % of et al. (2009) contendof that risk real aversion because purchase it ofeven represents insurance more revealed applicable preference by for in a financial Malaysia person protection. where This is flood insurance a is good3.3 voluntary. indicator Participants The questionnaires were self-administeredaged 21 randomly yr and to above. owner-occupied Out of households the 207 usable questionnaires received, 44 % were from who purchase flood insurancemethod. and To this those end, who theis do dependent not a grouping using variable dichotomous the (do2 variable you discriminant measured have analysis flood nominally insurance?) ashomeowners into 1 whether they are risk averse or not. As pointed out earlier, Borzen 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | eren- ff erence of 1.08 0.001) di ff p < erences we performed ff 30.767, erentiation between group of = ff F ect on flood insurance purchase. ff erences in group mean we carried ff 0.870, = λ 3080 3079 erentiating between group of respondents who ff 0.001) in di p < 26.564, = F 0.885, erence of 1.0 between respondents who have purchased flood insurance (GROUP1) = To investigate further the underlying sources of these di The distance of respondents’ property in the study area influenced their willingness in To further understand the explanation for the di Table 1 provides group mean scores and tests of equality of group means statis- ff λ (1978), Dutta et al. (2003), and Dixon et al. (2006) which show that probability of pur- 6 km from river while onlythan 6 6 % km of from the river. respondents What whois could bought located be flood to deduced insurance flood from live more prone thisflood result river, insurance. is the In that higher other the therisk nearer words tendency a averseness proximity the such house to house that homeowners flood owner locatedaverse will prone less than subscribe river than homeowners contributes 3 km located to fromwith beyond. degree flood the This are of findings result more of risk conforms Krieselthat and with proximity Landry to of (2000, a 2004), a great LandryOur property extent and finding to is Jahan-Parvar (2011), consistent a with river previous works has by positive Baumann and e Sims (1978), Kunreuther purchase. The result revealed thatance 76 % live of in respondents houses located whowithin less subscribed than to 3–6 km. 3 flood km In insur- from contrast,insurance flood live prone only houses river 46 and located % another less ofthe 18 than % the people 3 live km respondents who from who did flood not did prone purchase river. not Moreover, flood subscribe 41 % insurance flood of are from houses located more than purchasing flood insurance. As shownriver in Table (DISTFD) 1 distance registered of strong( property from discriminatory flood power prone purchased and flood therefore insurance was (GROUP1) significant flood and insurance group (GROUP2. respondents The whobetween the variable did groups. The shows not mean purchase a value for very GROUP1 is high 1.38 as mean against 2.46a di for cross GROUP2. tabulation between house distance from flood prone river and flood insurance individuals also voluntarily buy insurance.by Our McPherson finding and is consistent Saarinen with (1977),ple previous Kunreuther and study (1978), Lave Kunreuther (1988), et Kunreutherpurchasing al. and flood (1978), White insurance increases Ep- (1994) with that frequency which of show flood that experience. probability of flood insurance supports prospect theory assertion that moderate and small size risk that experienced flood once and 6 % that never experienced flood actually purchased flood two or more46 % times of compare the to respondentsflood who 42 % did while for not only purchase those 6 flood %These who insurance that suggest did had has there never not experienced is flood purchase.to inter-relationship insurance Moreover purchase between never flood experienced flood insurance. flood experienceexperiences and This and translates to the may 7 % higher tendency be subjective once. attributed riskcomitantly perception could to and lead the vulnerability to which fact demand con- with that higher for number increase flood of in insurance. flood experience In flood those are essence, with most the likely lower to property number purchase of owners flood experience. insurance than However, the fact that 7 % of the homeowners and those who have notas purchased against 2.22 (GROUP2). for The GROUP2. mean value for GROUP1 isout 3.23 cross tabulation betweensult flood showed insurance that purchase 88 % and of flood respondents who experience. purchased The flood re- insurance had experienced tics used torespondents identify that variables purchased that floodcolumn insurance make for and significant test the di of group equality that(NUMEXP) of do the group not respondents’ means purchase. have shows The emerged had that in as the the number the past of mosttiating flood has significant experience strong between variable discriminant group power ( ofrespondents and who respondents did not who purchase flood purchaseddi insurance. flood The result insurance shows a and very group high mean of that Kota Tinggi hadBahru the 8.2 %. highest In rate otheraverse (48.3 than %) words Johor followed homeowners Bahru homeowners by in inas Segamat Kota terms the 40 Tinggi flood incidence % insurance. and and This and severity Segamat is of Johor not are flood unexpected more is higher risk in Kota Tinggi and Segamat. age. The breakdown of flood insurance penetration amongst the three districts showed 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erences 0.001) in ff p < ects their deci- ff 0.05) Rather, there 25.370, erence in level of risk erence between group ff = p > erence. The group that ect on flood insurance ff ff ff F 0.002, 0.890, = = F λ ect risk aversion and therefore deter- ff 0.999, erences between groups who have pur- ff = λ 3082 3081 erence. ff erentiating between group respondents who pur- ff erence may be seen in group mean score. On the cient evidence to reject the hypothesis of equal group ff ffi 0.05) in di P < erentiation of the two groups on their propensity to purchase flood ff 4.178, = F erence in their mean value with respect to perception of non reliability of ff 0.974, erence is too marginal to make a di = The respondents view about the insurance companies and how it a The price or flood insurance premium (FLPREM) was found to be a major factor in The result tests of equality of group means for the three variables above (NUMEXP, Expectation of increased flood frequency (EXPFRQ) exhibited poor discriminant Elevation of their property (ELEVTN) has weak discriminat power but still significant ff λ nificant di also argue here that ifthan the actuarially homeowner fair are premium willing that to couldflood take exceed insurance additional expected risk may loss be by then paying located theirexpected more within decision loss loss to than buy aversion expected because gains. they(1968), weigh This MacDonald more result on et is their al. consistentreuther with (1987), et the Browne al. findings and (1978), of(2001). Palm Hoyt Smith (1981), (2000), Lamond Dixon et et al. al. (2009), (2006), and Blanchard-Boehm Kun- etsion al. to purchase flood insurance or not was tested. Results show that there is a sig- to pay slightly higher thanrisk fair averse. price The to result protectcluding against is modest loss consistent which risk with also individualsprice prospect imply are they in theory more willing order postulation to to thatand increase pay Zeckhauser, people, 2004; for premiums in- Kunreuther expected and somewhat losses Pauly, than 2006a; (Pashigian Sydnor, fair 2010; et Ulrich, al.,1966; 2012). Drèze, We 1981; Cutler contributing to di insurance. The basisvariable, of group this of respondents di whomean did score not (3.08) purchase than floodin group insurance group that recorded mean purchased higher (0.63) flood wasdid insurance large not (2.45). enough purchased The to flood di insurance makeis has significant expensive and the di more notion unwilling that towho the pay have premium slightly purchased for higher flood flood than insurance. fair insurance Thus, price GROUP compared 1 to demonstrated those more willingness aversion and likelihood ofdi purchasing flood insurance is not clear as thethe group decision mean to purchasenant flood power insurance. and The emerged as variable third demonstrated most strong significant discrimi- ( that will increase in the future, but the extent this could make di frequency. Both groups shared the same opinion on anticipation that of flood frequency ELEVTN, DISTFD) provide su means and we hence, conclude thatof there is respondents significant who mean di purchasepurchase flood flood in insurance terms and of grouption, house and distance of numbers from respondents of flood who prone flood river, experience. do house not location eleva- power and therefore wasare not significant more ( commonalitychased of and opinion have than not di purchased flood insurance on expectation of increased flood mine whether the propertyfinding owner is is also in likely agreement towhich with subscribe that holds of to Dixon that flood (2006),purchase. insurance higher Kriesel or and elevation Landry not. of (2000, The 2004) property has positive e mean elevation value ofwith 1.49. flood More insurance purchase so,insurance shows cross reside that tabulation at 68 of % lowOn of house elevation the respondents location area other who elevation hand while purchasedproperty 54 the % flood live of 32 on % the low resideis respondents elevation at while expected who as the did high low 46 not elevation % elevationflood subscribe area. reside location which to at increases culminates flood high to physical insurance elevation exposure riskelevation area. of and aversion. The What a vulnerability this property result to result in suggests the therefore study is area that does the a lower ground location. ( chased flood insurance and groupWhile respondents GROUP1 who did had not lower purchase flood mean insurance. elevation value of 1.32, GROUP2 recorded higher chasing flood insurance increases with frequency of flood experience, flood depth and 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0.05). 0.743; 1.659, 0.964, = = p > λ F Λ = 2.682, 0.922, ordability of flood ff = = λ F erence on the propensity. erence to flood insurance ff ff erences. However, examin- ff 0.987, = λ 0.01). Respondents who did not ered loss of income from severe ff p < ered greater loss of wealth (accumulated 7.856, ff = 3084 3083 erentiate between group of homeowners who ff ective (Huberty, 1994). A discriminant function, F ff erentiate and separate the two groups of home- erentiation was found to be significant ( ff ff ), the function is considered significant ( Λ 0.963, ) of variance in the group di = 2 λ 0.01). Thus, we substantively infer that there is a significant p < erentiating between the groups ( ff 0.01.) but level of education (EDUCTN) was not ( 60.254 = p < erence was too marginal to make a significant di 5) ff cult get the insurance companies keen or interested to cover flood insurance. erence that existing technical flood protection systems are not adequate (FL- = ff ffi surance 0.01). On income level, examination of the group mean shows that the group that 9.645, df Table 2 shows that at 30 terations and at.05 significant level, 5 out of the 11 vari- On the perception of state of flood defense measures, there was common opinion The roles of income level and education were also examined. The contribution on = 2 ( basis of flood risk aversion. erty from flood proneprovides statistics river for (V-DISTFD); verifying andtify the income the significance level variables of (V-INCOML). that thefunction. Table 2 The have discriminant table the also function reveal and greatest thefunction canonical iden- impact explained correlation 42 and (CCr) % of.507 correlation (CCr whiching with implies the the that function’s the discriminant Wilks’χ lambda ( discriminant function that clearlyowners di on the basis of likelihood purchasing flood insurance or concomitantly on the ables entered the modelWilks’ in Lambda: the number followingpremium descending of to order flood be of experience high magnitude but(V-FLPREM); (V-NUMEXP); perception of willing perceive of to stepwise non-reliability flood pay of slightly insurance insurance well firms higher as to than their pay fair reluctance insurance price claims to as to provide flood insure insurance my cover house (V-INSREL); distance of prop- group’s degree of riskriving aversion. discriminant To this functions is end mostalso stepwise e called method of a enter/remove canonicalcriminating for root, (independent) de- is variables. a Stepwisenificantly latent method contribute variable selects to discriminant which only function is variables andvariable predict a that that group sig- linear membership minimizes by the combination selecting overallvariables of were Wilks’ dis- subjected Lambda to at stepwise each method. step. As a result, all the 11 One of the objectivesportant of predictive this variables study that is bestpurchased di flood to insurance build and a the model group that that include who only did the not most as im- well as account for the 4.1 Predicting discriminant function for group propensity to purchase flood in- than di PROT), Hence the variablesignificantly displayed poor in discriminant power di Though and examining did the not group contribute mean means di shows thatpurchase group or 1 degree of recorded risk higher aversion. mean, the savings from income) fromter previous their multiple risk aversion. high Asout, impact Luigi households flood and who Paiella experiences face (2008); thatnatural income Cameron disaster fos- uncertainty and exhibit Shah greater or (2011) degree su pointed of risk aversion. income level (INCOML) on groupF di P > has purchased flood insurancethat registered did higher mean not (1.83) (1.48).cantly compared In with to higher other the income words, while group While the education group does propensity not 1 to make registerd purchase di insurance, higher flood it income increase is that signifi- highly could likely increase the their group a su surance cover (V-INSREL) ( purchase insurance had a higher mean(2.92). value It (3.36) compared could to therefore thosewas who be di purchased said that those who never purchased flood insurance felt insurance firms to pay insurance claims as well as their reluctance to provide flood in- 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0.428 ) test, F = β cult to ex- 0.452 and 0.369 and ffi = = erent) to risk ff β β cients and struc- ffi test for equality of group means, F cients (SCDFC) and structure matrix corre- ffi 0.333). 3086 3085 ciency and predictive accuracy of the discrim- = ffi 0.658; distance from flood prone river ( ce to say they are either risk-neutral (indi = ffi 0.611; and perception of non reliability of insurance firms 0.598); number of high impact flood experienced ( erences between the two groups of homeowners based on = ff = erentiate between group of homeowners who purchase flood insur- ff 0.325 and within group correlation = The study probed into the reasoning for not purchasing flood fnsurance. Figure 1 The classification result provides e The table also displays the standardized discriminant function coe β SCDFC, structure correlation andare canonical correlation strong have significant clearly di shown that there The cardinal objective ofamong this residential study homeowners in isables three to that districts best determine di of floodance Johor insurance and and those penetration predicts that rate do thein not as best on well vari- their as likelihood34 of determine % purchasing the penetration flood rate group’s insurance with degree asthe Kota of well Tinggi highest flood as having degree risk the of highest aversion. floodetration penetration Our risk rate (44 result %) aversion. is Overall and revealed below we thus, average. can The say Wilks’ the Lambda flood insurance pen- count for low degree of riskand aversion perception include: that low their probability building of is experiencing flood too (2 old %) (1 %). 5 Conclusion for flood insurance purchaseto decision. 7 cover % property stated the aspay refusal premium their of for main their insurance property reason. companies vide but the This are cover. High category unable premium to are charged6 get % by risk insurance insurance of averse companies company the was agree and respondents. awealth to This willing major pro- are reason category to unwilling for is to riskthan buy averse expected an but value unfair given of their insurancereasons the level policy given of claims in by income should respondents which or for the the not averse premium purchasing event is flood (flood) insurance higher occur. which Some could other also ac- group may be livingsubstitute in to area flood of insurance. They highercause may elevation they be perceive and said probability to therefore of have see lossknowledge low about from higher degree flood flood of elevation insurance very risk as which low. aversion again 16aversion. be- % is This did a underscores not potential market have source failure adequate of in low providing degree adequate of information risk necessary not purchase flood insurance because they were not living in flood prone area. This ance if they underestimate probability of its occurrence. 21.32 % noted that they did shows that 31 % ofinsurance respondents while 14 did % not feltplain is state their not any position necessary. reason For but for these weof not two flooding su categories subscribing and it therefore to unwilling is flood likelihood to di of take up a flood future insurance floodthink or risk. they Flood that underestimate insurance the flood will not isCamerer be not and attractive to coming Kunreuther individuals soon who Rees (1989), have and Kunreuther the Wambach (1996), (2008) perception who Kunreuther that argue loss and that may Paul households not will (2006a), be likely not much. buy flood insur- residential homeowners were correctlyno classified flood into “have insurance” flood andtion. insurance” The consequently or achieved according “have impressive toin hit their predicting ratio demand-side flood suggests factors risk thepurchase distinguishing flood model aversion between insurance has orienta- and groups practical those of that significance respondents does that not. high and willing to paywithin slightly group higher correlation than fair priceand to within insure my group house correlation within ( group correlation to pay insurance claims( as well as their reluctance toinant provide function. flood The insurance model cover achieved a ht ratio of 80.2 % indicating that 80.2 % of the ture matrix correlation usedimpact to and assess correlation each with variable’s thethe unique discriminant standardized discriminant contribution function. function Consistent in coe with termslation ANOVA (within of ( group correlation) showcorrelation that with the variables the that discriminant have strongest function impact are and perceive flood insurance premium to be 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 21 June 2007. 3088 3087 erence in flood insurance purchase. Specifically 7 % ff , 2009. 10.1029/2009WR007743 http://www.piam.org.my/index.php/news/test-insurance-news/115-insurance-news/2007/ It was noted that 16 % of the group of homeowners who did not purchase insurance It was clear from our findings that non-reliability of insurance firms in paying insur- The research has some notable implications. The result showed the two groups have The most important variables distinguishing between those who did purchase flood factors that influence flood mitigation behaviour, Risk Anal., 32,Resour. 1481–1495, J., 2012. 3, 412–441, 1964. Uncertainty, 20, 291–306, 2000. risk perceptions on socioeconomicdoi: and objective risk factors, Water Resour., 45, W10440, tory? Insights in theGeogr., wake 21, of 199–221, the 2001. 1997 New Year’s Day flood in Reno-Sparks, Nevada, Appl. 431-malaysia-faces-serious-challenge-in-handling-floods-and-drought ogr., 54, 189–196, 1978. at: Community Health, 15, 1–14, 2010. BERNAMA: Malaysia Faces Serious Challenge in Handling Floods and Drought, availavle Baumann, D. D. and Sims, J. H.: Flood insurance: some determinants of adoption, Econ. Ge- Badrul, H. A. S., Marzukhi, M. I., and Daud, A. R.: The worst flood in 100 yr: Johore experience, policies takers and also increase flood insurance subscription. 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31% No reason 23% area

Sig. .000 .000 .000 .000 .000

No flood in the

df2 205 204 203 202 201

.598 .333 .611

-.658 -.328 Exact F

correlation) 1 2 3 4 5 df1 se (Within (Within Group

Structure Matrix

1% 30.767 24.993 18.978 15.955 13.932 Statistic

Old building 2 1 4 3 5 Impact Ranking

Wilks' Wilks' Lambda df3 205 205 205 205 205

a,b,c,d

1 1 1 1 1 1

df2 .452 .325 .369 -428 -.300

Function

1 2 3 4 5 cients computation. 2% df1

of flood

a

3096 3095 ll ll Wilks' Lambda entered.is ovide .507 .743 .000 .870 .803 .781 .760 .743 .347 .4251 80.2%

Low probability 60.254 Statistic

Variables Entered/Removed of

: 6%

of insurance firms to pay

Entered the premium Table 2: Predictive ModelTablePurcha Predictive of 2: Insurance Flood Could not afford Maximum partial F to remove 2.71is Maximum number of steps is 30 Minimum partial F to enter is 3.84 F level, tolerance, or VIN insufficient for further

) Numbers of high impact flood experienced (V-NUMEXP) Perceive flood insurance premium to be but high to paywilling slightly higher than fair price to insure house (V-my FLPREM) Perception of non-reliability insurance firms to pay insurance claims as as theirwell reluctance to provide flood insurance cover (V-INSREL) Distance from flood prone river (V- DISTFD) Income level (V-INCOML 2

b. c. d. a.

4 5 1 2 3 Step 7% Sig Standardized Canonical Discriminant Function Coeffi Number of impacthigh flood Experienced Perception floodthat insurance premium is but high willing to pay slightly higher than fair price Perception of non-reliability insurance claims as aswell their reluctance to pr flood insurance cover Distance from flood prone river Income level Canonical Correlation (CCr) (CCr Eigenvalue Wilks' Lambda Chi-square (df=5) Classification Accuracy (hit ratio) At eachAt step, the variable that minimizes the overa Functions at Group Centroids Has flood insurance .423 Has no flood insurance -.811 16% Lack of Insurance Figure 1: Reasoning for Not Purchasing Flood Insurance want to cover flood insurance company do not knowledge about 14% Predictive model of flood insurance purchase. Not necessary

Reasoning for not purchasing flood insurance. Fig. 1. Table 2.