Factors Influencing Homeowners' Decision to Purchase Flood Insurance in Flood-prone Areas of Imo State, Nigeria

doi:10.21203/rs.3.rs-1842515/v1

Severe floods have become an annual experience in Imo State inflicting losses of lives and property on the population. The most affected areas are Oguta, Ohaji/Egbema and Owerri. This situation notwithstanding, most homeowners in these areas have not taken advantage of flood insurance facility. This study attempts to identify the factors affecting homeowners' flood insurance purchase decisions in Oguta, Ohaji/Egbema and Owerri, and to apply discriminant analysis technique on those factors to determine whether or not there are significant differences in perception between those willing to purchase flood insurance and those not willing. The study equally seeks to determine the variables that best account for the differences in perception between the two groups. Tests using Wilks’ lambda, eigenvalues, canonical correlation, functions at group centroids, and classification accuracy were applied on identified sets of relevant determinants. The study found that those willing to purchase flood insurance were more concerned with the mitigation of flood catastrophe, apparently because they are worst hit by the floods, while those who were not willing were more indifferent as they appear to be less certain and aware of flood insurance. However, the variables with the most predictive power in accounting for the differences in perception and have the highest impact were found to be within the measures of behavioral, attitudinal and emotional determinants. The study therefore concludes that the likelihood of purchasing flood insurance in the study areas depends greatly on information availability, consumers’ insurance literacy and level of trust on the integrity of insurance companies.

Flooding

Insurance

Houses

Perception

Imo State

Floods have become the most common and recurring disaster in Nigeria (Federal Government of Nigeria, 2013), inflicting heavy losses of lives and property on the population. A study in 2013 showed that 363 people were killed, 5,851 injured, 3,891,314 affected, and 387,153 displaced as a result of floods in Nigeria in 2012 (Federal Government of Nigeria, 2013). This study which focused mainly on the states which suffered most in the 2012 flood disaster namely, Benue, Jigawa, Edo, Adamawa, Delta, Kogi, Kebbi, Nasarawa, Taraba, Rivers, Anambra and Bayelsa put the value of the damage and losses in these states at N2.617 trillion or US$16.9 billion. Of this figure, damage and losses to housing was N204.83 billion. Imo State was equally hit by the floods of 2012; 2 persons drowned, about 25000 people were displaced, and 123 farm settlements were inundated in Oguta and Ohaji/Egbema local government areas of the state (Nkwopara, 2012).

Floods occur in Nigeria in three main forms: coastal flooding, river flooding and urban flooding (Folorunsho and Awosika, 2001) and the other cause of flooding include the rising global temperatures which result in persistent heavy downpours which were hitherto uncommon in the study area. High impact floods have become an annual event in Imo State, leading to heavy damage and losses of lives and property. Records show that the geographical area today known as Imo State has experienced major flood events in 1947, 1969, 1984, 1986, 1993, 2005, 2009, 2011, 2012, 2013, 2014, 2015, 2016, and 2017, with loss of lives and property (Nkwopara, 2012; Duru and Chibo, 2014; Emeribeole, 2015; Ogugbuaja, 2015; Alozie, 2017; Olowolagba, 2017). In August 2019, heavy rainfall in Imo State caused flooding in Oguta which displaced about 6000 people and damaged 200 homes (sunnewsonline.com, 2019). Flooding and flood disasters have become a reality that must be addressed in Imo State and Nigeria in general.

Most of the researches on flooding in Imo State have so far been limited to physical measures. For instance, studies by Emeribeole (2013), Duru and Chibo (2014), Njoku et al. (2017) limited their recommendations to physical measures. Flooding cannot be totally eradicated in Nigeria owing to the various infrastructure challenges and the unforeseen weather changes occasioned by climate change. Consequently, post-disaster strategies must go beyond physical measures for the total impact of the disasters on the victims and their families to be significantly reduced. There is need for insurance cover to enable the victims of flood disasters to quickly rebuild their lives, properties and businesses. Unfortunately, insurance penetration in Nigeria is very low, put at less than 1% by the 2018 Report of the London-based Lloyd’s Insurance (Floodlist.com, 2018). Flood insurance has not been given any serious attention in Nigeria; in the cases mentioned above, almost all the properties damaged by the floods were not insured. The Nigerian Insurers Association in January 2017 raised serious concern over the need for adequate cover for individual homes and businesses (Nnabugwu, 2018; Hougaard et al, 2018). They emphasized the need for increased awareness of flood insurance and the role the government could play in that direction to protect her citizens. An argument for enhanced flood insurance penetration as an integral part of a comprehensive flood risk management in Imo State has become necessary.

Consequently, the aim of this study is to encourage flood insurance based on a good understanding of homeowners’ perception of the factors that will affect flood insurance purchase for residential properties in the flood-prone areas of Imo State, such as Oguta and Ohaji/Egbema Local Government Areas and parts of Owerri Urban.

To achieve the fore-going, the study would:

i. identify, from literature, the factors that influence flood insurance purchase decisions and the degree of risk aversion among homeowners in Owerri, Oguta and Ohaji/Egbema Local Government Areas;

ii. determine if there are significant attribute differences in the degree of risk aversion between two groups of homeowners: those willing to purchase flood insurance and those who are not; and

iii. determine the most important variables that best differentiate and account for the degree of risk aversion between group of homeowners that are willing to purchase flood insurance and the group that are not.

The study area, Imo State, is one of the states in the southeast geopolitical zone of Nigeria. It was created in 1976. It lies within latitude 5º 10´N - 6º 00´N and longitude 6º 38´E - 7º 25´E and covers a land area of approximately 5,182.818 square kilometers. It is bordered on the east by Abia State, on the west and south by Rivers State, and on the north by Anambra State. Its population which was 3,927,563 persons in 2006 (NPC, 2006), is currently estimated at 6,299,681 persons, given the national annual population growth rate of 3.2%. Imo State consists of 27 local government areas (LGA). The study is concentrated on three major areas namely:

i. Owerri Urban comprising Owerri Municipal, and parts of Owerri North, Owerri West LGAs and a small part of Mbaitoli LGA.

ii. Oguta LGA, and

iii. Ohaji/Egbema LGA.

Imo State lies within the sub-equatorial region. It has mean annual rainfall of 2250mm to 2500mm. It is characterized by two seasons namely the rainy season which lasts from April to October and the dry season which runs from November to March. The climate change has significantly affected this rainfall pattern with the result that the rains are increasing in intensity and the dry season is now experiencing more rainfall. Humidity is high in the area and varies from about 60% during the dry season to 85% in the rainy season (Duru and Chibo, 2014). The rivers Orashi, Awbana, Utu and Njaba pass through parts of the Oguta, and Ohaji/Egbema areas. There is also the Oguta lake in the area. See Figures 1 and 2. Overflow from these rivers during the rainy season contribute greatly to the flood problem of the area. Although Rivers Nworie and Otamiri pass through Owerri urban, much of the flooding problem cannot be attributed to them; rather, the problem here lies mainly with poor and insufficient drainage infrastructure, and also indiscriminate dumping of refuse which end up clogging up the drainage channels.

A GIS-aided flood vulnerability assessment of Imo State identified Oguta and Ohaji/Egbema as very high risk and Owerri as high risk areas (Okoroafor et al., 2021).

Oguta and Ohaji/Egbema are dominated by residential and agricultural land uses while Owerri Urban is covered by residential, commercial and limited industrial land uses. Reports and investigations by various authorities including some researchers show that these areas have suffered considerable damage from flooding over the years. This underscores the importance of the subject study.

3.1. Theoretical Motivation

Two popular theories used to explain decision-making under risk or uncertainty are the expected utility (EU) theory and the prospect theory (PT) (Aliagha et al, 2014). EU is the standard and rational theory of decision-making under risk that relies on a linear composite of weighted probability outcomes to compute EU. It is founded on the principle of diminishing marginal utility and uses net wealth as the only reference point (Aliagha et al., 2014). With regard to insurance decision-making, this implies that people will buy insurance cover so long as the premiums paid are less than the expected losses. Demand for flood insurance for a risk-averse person will therefore depend on the offer of a full cover premium whose benefits are not less than the insured losses. PT on the other hand, argues that because of complexities in decision-making, limited information, and analytical ability, preferences are often not consistent and an individual often does not use linear probability weights to determine values (Aliagha et al., 2014). Rather, PT contends that context and subjective values influence decisions under uncertainty. As a result, decision makers may not select the alternative with the highest payoff and may not use net wealth as the reference point as depicting EU theory. Empirical evidence suggests that people often make decisions by comparing changes in their financial status with reference to specific actions rather than the impact of the actions on final wealth utility function (Kaheman and Tversky, 1979; Tversky and Kahneman, 1992; Kunreuther and Pauly, 2005). Some people have so much phobia for loss that they may be prepared to pay a slightly higher premium than necessary to cover for any potential loss. The PT covers such decisions.

3.2. Factors Affecting Flood Insurance for Residential Properties.

3.2.1 Objective Flood Risk Exposure

Topography affects the susceptibility of houses to flooding. Houses on elevated grounds are less likely to experience flooding than those in low lying areas. The elevation of a building has been observed to be one of the factors underlying a homeowner’s perceived probability of losses and hence influences the homeowner's likelihood to purchase insurance. Dixon et al. (2006) found out that the probability of people purchasing flood insurance is considerably higher in a coastal flooding area than at high elevation or in non-coastal areas. Also, the proximity to large bodies of water exposes homes to flooding. Botzen, Aerts & van den Bergh (2009) found that houses near a river are more likely to suffer flood damage than houses far away from a river once a river overflows its banks. Distance from the river becomes another important factor.

In their study, Kriesel and Landry (2004) found that property owners near a flood zone are more likely to purchase flood insurance. Their research shows that an increase of 1% in distance from the flood zone decreases the probability of purchasing flood insurance by 0.88%.

3.2.2 Subjective hazard vulnerability

Vulnerability to flood can be described as the degree of harm that can be anticipated under certain situations of exposure, vulnerability, and resilience (UNESCO-IHE Institute for Water Education, 2011; Aliagha et al, 2013). According to Capobianco et al.(1999) and Klein and Nicholls (1999), the concept of hazard vulnerability when it comes to flood insurance of properties is a function of resistance – the ability to withstand change due to hazard; resilience – the ability to return to the original state following a hazard; and susceptibility – the current physical state, without taking into account temporal changes.

Consequently, homeowners who perceive flood risk to be very high will most likely take mitigation measures, which may include purchasing flood insurance and modifying their house structure to reduce flood effects (Preston et al., 1983). House and layout designs which include structural flood control measures such as dykes, dams, levees and by-pass channels reduce homeowner’s perception of vulnerability to the risk of flooding; as a consequence, their propensity to purchase flood insurance will reduce. The reverse will be the case where such flood control measures are lacking. Furthermore, Baumann and Sims (1978) found evidence that past experience with disasters motivates insurance adoption. They found higher insurance uptake among homeowners who had suffered previous damage from a flood. Where the probability of flood reoccurrence is low, the take-up rate of flood insurance or renewal of existing policies will be seriously reduced.

3.2.3 Adoptive Capacity and Power

Many scholars (Shaw, 2004; Kriesel and Landry, 2004) have discussed socio-economic and demographic factors as key determinants of residential flood insurance purchase. The capacity as well as decisions to buy flood insurance policy is affected by the income level of homeowners, level of education, gender and age factors and budget constraints. Some other authors have identified premium and budget constraints as important factors that influence flood insurance purchase. Before now, Shaw (2004) had contended that homeowner’s decision to purchase flood insurance policy is influenced by his or her level of income. Where income is not sufficient to meet family’s basic needs, purchasing flood insurance policy will not be a priority. As corroborated by Kriesel and Landry (2004), the wealth of a house owner will influence his decision to purchase flood insurance policy.

Some other researchers such as Baumann and Simms (1978), and Johnson (1978) have opined that the frequency or probability of severe flooding and the extent to which the projected long run cost of damages from flooding outstrips the sum of annual insurance premiums also affect decisions to purchase flood insurance.

3.2.4 Subjective Risk Perception

Another important factor that could influence the decision to purchase residential flood insurance is what many scholars termed subjective risk perception. While many authors have debated on this as major drawback to flood insurance purchase, others have argued that the cost debate of this perception is far from settled. For example, whereas the work of Baumann and Sims (1978) found evidence that past experience with disasters motivates or discourages insurance adoption, the study of Aliagha et al (2013; 2014) argues that the perception by homeowners that flood insurance premiums are high could have clear implication on the decision to purchase insurance cover. The foregoing notwithstanding, Baumann and Sims (1978) observed higher insurance uptake among homeowners who had suffered previous damage from floods. Taking inference from past studies, this study believes that perception of unreliability of insurance firms to pay insurance claims and their reluctance to provide flood insurance coverage, perception that the flood protection system is not adequate, lack of awareness of existing insurance policies and companies that provide flood insurance and reliance on government for flood relief assistance could affect the decision to purchase flood insurance. For example, Kunreuther, 1978; Blanchard-Boehm et al., 2001; Smetters, 2004; Harrington, 2006) have argued that confidence by residents living in flood prone areas that the government would come to their rescue and aid in the event of flood remains a key factor that discourages residential property owners from wanting to purchase insurance. What this suggests is that individuals or communities 'precautionary measures and commitment against any flood in particularly prone areas like Oguta, Ohaji Egbema and some parts of Owerri are likely to be very low since there is the trust that government relief would come if the flood occurs. Experience has shown that in many cases especially in the developing nations, the expected government’s assistance never materializes (Bayer and Vari, 2003).

3.2.5 Behavioral, Attitudinal/Emotional Determinants.

Homeowners’ demand for flood insurance is influenced by the perceived behavioral, attitudinal/emotional determinants (Bradt, 2019). Various studies (Slovic et al., 1977; Lichtenstein et al., 1978; Bradt, 2019) in both the cognitive psychology and behavioral science literature have attempted to link psychological barriers that exist in individuals, organizations and institutional levels as factors that could to a large extent determine homeowners’ decision to purchase flood insurance. The tendency of individuals to make decisions regarding risks and remedies can be correlated with various factors such as their mental and emotional state, biases, consumers’ insurance literacy, trustful belief on insurance, social norms, information availability, perceived product value of insurance, the framing of the risk and outcomes and past experiences (Johnson,1978; Thaler et al., 1997; Kunreuther et al., 2001; Tom et al., 2007; Browne et al., 2015).

A specific area in which this behavior has been clearly observed is in the area of market for flood insurance (Bradt, 2019). Documentary evidences exist that some prospective flood insurance purchasers and buyers tend to neglect low-probability flood risks and do not purchase insurance, while others reveal a willingness to pay for flood insurance that exceeds the value of the expected losses (Bradt, 2019). This means that the mental perception of the risk or expected risk influences the decision to purchase insurance cover. Keller et al. (2006) and Bradt (2009) have recognized situations where mental shortcuts have led certain individuals to pay for flood insurance that exceeds the value of expected losses as a result of their inflated perceptions of the risk of the event.

4.1. Data collection

Data for this study were obtained through the use of questionnaires and key informant interviews. The study covered a population of approximately 10,000 residential households in the two riverine local government areas of Oguta and Ohaji/Egbema, and also parts of Owerri Urban Area. These areas of Imo State are prone to major flooding. A sample size of 500 was adopted based on Krejcie and Morgan (1970) determination of sample size. Physical inspection was carried out in two phases, namely in July and October 2021. A reconnaissance survey of the affected communities in July showed evidence of severe flooding of 1–8metres depth in some areas as indicated by tell-tale marks left by flooding on some buildings and trees. A second inspection of the Oguta and Ohaji-Egbema study areas in October showed the areas near the rivers already inundated from the overflows from the rivers with the flood water moving outwards as the river levels continued to rise. September is the beginning of the main flood season in these areas. The survey also involved sets of interviews which were conducted in July 2021. The key informant interviews covered both residents of the study areas and some personnel from three major insurance firms in Owerri, namely Industrial & General Insurance, Regency Insurance and Niger Insurance.

This study made use of structured questionnaires. The questionnaire was divided into three parts. Part 1 comprised respondents' demographic profiles, such as gender, race, income, and highest level of education. Part 2 contained questions that asked about residential / house types; house value; house distances from flood prone river; major source of flood in the area; the structure of the buildings in the study areas; elevation of the house's location; number of times the respondents' experienced flood; the financial value and worth of property lost to flooding within the years victims experienced flooding; respondents’ awareness of any flood insurance purchase policy; and purchase and willingness to purchase flood insurance policy. Part 3 contained statements that were designed to elicit respondent’s level of agreement using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The questions/statements were related to the factors that could influence flood insurance purchase for residential properties in flood-prone areas. This study tried to determine if there were significant differences between those who are willing to purchase flood insurance and those who are not willing, using the discriminant analysis method. The dependent variable was a dichotomous variable measured nominally as 1 = ‘yes, I am willing to purchase flood insurance’; 2 = ‘No, I am not willing to purchase flood insurance’. Few of the independent variables were in interval scale, and the independent variables were measured using a 5-point Likert scale (where 1 represents - “strongly Disagree”; 2- “Disagree”; 3- “Neutral”; 4- “Agree”; 5-“Strongly Agree).A total of 500 sets of questionnaire were distributed among randomly selected heads of owner-occupied households in each of the local government areas in the month of July2021 - the peak of rainy season. The analysis was, however, based on 401 valid responses received. Out of this figure, those willing to purchase flood insurance were 217 (54.1%) while those not willing were 184 (45.9%). Those from Owerri were 105 (26.2%); while those from Oguta and Ohaji/Egbema were 196 (48.9% and 100 (24.9%) respectively. Overall, 265 (66.1%) were male whereas 136 (33.9%) were female. Majority of the respondents were between the age of 41 and 50 years whose occupations were mainly trading and farming.

4.2 Instrument and measures

The distributed questionnaires tapped into the opinions of those willing to purchase flood insurance' and those not willing on the following measures as shown in Table 1. The items were presented in 5-point likert scale (1 = strongly disagree, 5 strongly agree).

Table 1

Representative Factors and their measures for Analysis.
Factor	Variables & their Descriptions
Factor 1: Objective flood risk exposure	• The distance from a flood prone river • A house location’s elevation • The topography/geography of the location and environment • The reality and effect of Climate change
Factor 2: Subjective hazard vulnerability	• Number of high-impact floods experienced • Expectation of an increase in future flood frequency • Perception on the adequacy of early warning system • Self-assessment of level of vulnerability under climate change
Factor 3: Adoptive capacity and power	• Income level • Budget constraints • Level of education • Perception of flood insurance as a good investment • Gender factor • Age Factor
Factor 4: Subjective risk perception	• Perception that flood insurance premiums are high • Perception of unreliability of insurance firms to pay insurance claims and their reluctance to provide flood insurance coverage • Perception that the flood protection system is not adequate • Reliance on government for flood relief assistance • Unaware of existing insurance policies and companies that provide flood insurance
Factor 5: Behavioral, attitudinal/emotional determinants	• Biases • Perceived Product Value of Insurance • Consumers’ Insurance Literacy • Trustful Belief on Insurance • Social norms • Information availability

4.3. Method of Analysis.

The method of analysis used in this study is the Discriminant analysis technique. The basis for the use of this technique is that it allows for testing of mean difference between two groups based on a set of predictive variables. The respondents in this case are those who are willing to purchase flood insurance for residential properties and those who are not willing. Discriminant analysis mostly tests for equality of group means and builds a predictive model of group membership based on a set of observed discriminating variables (Aliagha et al, 2013; Hair et al., 1987). It involves creating a linear mixture of two or more discriminating variables that differentiates best between groups. This could be done by applying a statistical decision rule of maximizing between-group variance relative to the within-group variance. The relationship is expressed as a ratio of between-group to within-group variances and the linear grouping is a derivative of the following equation:

Z = ₁X₁ + W₂X₂ + W₃X₃.....+W_nX_n;

Where

Z = the discriminant score,

W = the discriminant weights (discriminant coefficients), and

X = the independent discriminating variables.

The beauty of discriminant analysis is that the stepwise method for deriving discriminant functions is most effective. In discriminant analysis, a discriminant function, also known as a canonical root, is a latent variable that is a linear combination of discriminating (independent) variables. The stepwise method applied in this study selects only variables that significantly contribute to a discriminant function and predict group membership by selecting variables that minimize the overall Wilks’ lambda (Aliagha et al, 2013). An essential criterion for using discriminant analysis is achieving the supposition that the variance-covariance matrices are comparable for the groups. This is usually confirmed by Box’s M test of the null hypothesis that the covariance matrices do not vary among groups (Hair et al, 1987; Aliagha et al, 2013). However, if the result shows that the likelihood of similar log-determinants and variances that are not significantly different is greater than 0.05, then the hypothesis that the groups do not differ is accepted, suggesting that it is appropriate to apply discriminant analysis (Aliagha et al, 2013). In situations where the result contradicts the condition of equal variance–covariance matrices (multivariate normal distribution), the researcher can still continue with the analysis because of the robustness of discriminant analysis (Aliagha, et al, 2013). Klecka (1980) states that dichotomous variables, which very often contradict the hypothesis of multivariate normal distribution, do not influence the deductions from a discriminant analysis.

5.1. Presentation and Discussion of Results.

Table 2 provides the columns for tests of equality of group means. The table gives the detailed analysis of variance statistics used to identify variables that make significant differentiation between those willing to purchase flood insurance and those who are not willing on the subject matter.

Table 2: Group Mean Differences and Test of Equality of Group Mean on Residential Flood Insurance Purchase in the Study Area.

Variables		Group Mean		Test of equality of group mean
Variables	Total Mean/ Std	Those Willing Mean/ Std	Not Willing Mean/Std	Mean Diff	Wilks’ Lambda	F^a	Sig
The distance from a flood prone river ^A1	4.22(0.845)	4.26(1.040)	4.18(0.532)	0.16	0.998	0.748	0.001
A house location’s elevation^A2	4.20(0.761)	4.21 (0.965)	4.19(0.406)	0.02	1.000	0.053	0.818
The topography/geography of the location and environment^A3	4.14(0.461)	4.55(0.492)	4.10 (0.421)	0.45	0.999	0.383	0.000
The reality and effect of Climate change^A4	4.11(0.706)	4.16(0.733)	4.07 (0.671)	0.09	0.996	1.568	0.411
Number of high-impact floods experienced^B1	4.22(0.756)	4.32(0.635)	4.10(0.865)	0.22	0.980	8.173	0.000
Expectation of an increase in future flood frequency^B2	4.12(0.673)	4.19(0.659)	4.04(0.680)	0.15	0.987	5.379	0.001
Perception on the adequacy of early warning system^B3	3.67(0.936)	3.85 (0.903)	3.52(0.944)	0.33	0.969	12.951	0.000
Self-assessment of level of vulnerability under climate change^B4	3.42(0.860)	3.49(0.817)	3.34(0.903)	0.15	0.992	9.110	0.002
Income level^C1	4.20(0.901)	4.14(1.009)	4.28(0.750)	0.14	0. .994	2.376	0.584
Budget constraints^C2	4.29(0.573)	4.00(0.547)	4.09 (0.686)	0.9	1.000	0.001	0.980
Level of education^C3	4.02(0.804)	3.92(0.897)	4.15(0.665)	-0.23	0. .980	8.267	0.004
Perception of flood insurance as a good investment^C4	3.63(0.806)	3.58 (0.813)	3.68 (0.795)	0.1	0. .996	1.666	0.697
Gender factor^C5	3.21(1.040)	3.19(1.000)	3.23 (1.087)	0.04	1.000	0.116	0.733
Age Factor^C6	3.69(0.902)	3.60(0.882)	3.77 (0.918)	0.17	0. 990	3.836	0.002
Perception that flood insurance premiums are high^D1	4.26(0.652)	4.25(0.677)	4.28(0.622)	0.04	1.000	0.132	0.717
Perception of unreliability of insurance firms to pay insurance claims and their reluctance to provide flood insurance coverage^D2	4.16(0.680)	4.06(0.718)	4.29(0.610)	0.23	0. .969	12.557	0.000
Perception that the flood protection system is not adequate^D3	4.18(0.672)	3.98(0.720)	4.41(0.552)	0.43	0. .900	44.416	0.000
Reliance on government for flood relief assistance^D4	3.74(1.110)	3.55(1.182)	3.97(0.972)	0.42	0.964	15.080	0.000
Unaware of existing insurance policies and companies that provide flood insurance^D5	4.25(0.616)	4.18(0.564)	4.31 (0.666)	0.13	0. .988	4.764	0.550
Biases E1	4.15(0.809)	4.20(0.795)	4.10(0.824)	0.1	0. 996	1.534	0.516
Perceived Product Value of Insurance E2	4.14(0.603)	4.02(0.623)	4.29(0.544)	0.27	0. 948	21.766	0.000
Consumers’ Insurance Literacy E3	4.45(0.517)	4.21(0.529)	4.88(0.449)	0.67	0. .909	39.897	0.000
Trustful Belief on Insurance E4	4.21(0.870)	4.20 (0.877)	4.86(1.861)	0.66	0. .996	1.607	0.000
Social norms E5	3.80(0.764)	3.70(0.724)	3.98(0.806)	0.28	0. 995	1.994	0.000
Information availability E6	4.38(0.588)	4.10(0.617)	4.99(0.505)	0.89	0. .931	29.389	0.000

A¹ to A⁴ = Objective flood risk exposure; B¹toB⁴= Subjective hazard vulnerability; C¹to C⁶ =Adoptive capacity and power; D¹to D⁶ =Subjective risk perception; E¹to E⁶ =Behavioral, attitudinal/emotional determinants

Table 2 shows that total mean scores for the four variables measuring objective flood risk exposure were all > 4 which overall is considered significantly high. This implies the variables have strong influence on purchasers' decision to purchase flood insurance. It further suggests that both those willing to purchase flood insurance and those not willing agree that objective flood risk exposure measures have strong power on choice of flood insurance. However, a close look at the group mean reveals that two out of the four variables measuring objective flood risk exposure exhibited strong discriminant power, which suggests that there were significant group mean differences in perception between those willing to purchase flood insurance and those not willing on the variables (A1 λ 0.998, F = 0.748, p < 0.05; A3 λ 0.999, F = 0.383, p < 0.05).Largely, those willing to purchase flood insurance recorded higher group mean values (4.26, 4.55) than developers (4.18; 4.10), the group mean differences (0.16;0.45) were large enough to make the statistical difference. The inference is that objective flood risk exposure measures such as the distance from a flood prone river and the topography/geography of the location and environment are to a great extent perceived by those willing to purchase residential flood insurance to constitute greater challenge to flood insurance purchase decisions than those not willing to purchase.

The reason for this may not be far-fetched, the topography/geography of the location and environment of the study areas are associated with moisture laden maritime southwest trade winds from the Atlantic Ocean while the temperature in the study area ranges from 23° to 26°C. Besides, the terrain of the study area is characterized by two types of land forms; gently undulating ridges and nearly flat topography. The ridges trend in the north-south direction and have an average elevation of about 40m - 55m. This could have clear implication on flood disasters in the area. Moreover, 29.2% and 22.4% of the respondents willing to purchase flood insurance live less than 1km -3km distance from the flood prone river Orashi River as a major source of flood and 68.6% of them live in bungalows. However, house location’s elevation (A2) and the reality and effect of Climate change(A4)” the variables exhibited weak discriminant power, which suggest there were no significant group mean differences in perception between the two groups on the variables (A2 λ,1.000, F = 0.053.,p > 0 : 05; A4 λ,0.996, F = 1.568.,p > 0 : 05). Rather, it could be said there was more commonality of opinions than differences between the two groups on the two variables. The group mean difference was too small to imply significant differences.

Table 2 also indicates that the total mean scores for the four variables determining subjective hazard vulnerability were all > 3 and 4, which overall provides sufficient evidence to suggest that both respondents (those willing to purchase flood insurance and those not willing) recognize subjective hazard vulnerability measures as strong influencers of flood insurance purchase decisions. However, this perception is not balanced as those willing to purchase flood insurance have stronger opinion on the variables than those who are not willing to purchase. Looking at the Table, there is clear evidence that the four variables displayed strong discriminant power which revealed there were significant group mean differences in perception between those willing to purchase flood insurance and those not willing on the four variables (B1, λ 0.980, F = 8.173, p < 0.05; B2 λ 0.987, F = 5.379, p < 0.05; B3, λ 0.969, F = 12.951, p < 0.05; B4, λ 0.992, F = 9.110, p < 0.05). Moreover, further evidence from the result shows that those willing to purchase flood insurance registered higher group mean values (4.32, 4.19; 3.85; 3.49) than those not willing (4.10, 4.04; 3.52; 3.34), the mean differences were large enough to make significant group difference. The result infers that subjective hazard vulnerability measures such as number of high-impact floods experienced, expectation of an increase in future flood frequency, perception on the adequacy of early warning system. self-assessment of level of vulnerability under climate change constitute greater motivation to those willing to purchase flood insurance than those not willing to commit to flood insurance purchase in the study areas. The reason for this may not be far-fetched. A cross tabulation conducted shows that 54.6% of the respondents who have thought of purchasing flood insurance had experienced high impact flooding four or more times in a season with more than 82.5% of them losing properties estimated between N 5,000,000 - N 10, 000,000 million compared to 45.4% of those who are not willing. Again, apparently for fear of an increase in future flood frequency and possibly the absence of early warning system majority of those willing to purchase flood insurance agreed that subjective hazard vulnerability measures remain an interrelationship between flood experience and the tendency to purchase flood insurance.

This finding is consistent with previous studies of Aliagha et al. (2014) which observed that increase in flood experiences could translate to a higher subjective risk perception and vulnerability which concomitantly could lead to demand for flood insurance. Thus, it means that property owners with more flood experience are more likely to purchase flood insurance than those with less experience. However, the fact that 45.4% of the respondents have never thought of or are not willing to purchase flood insurance even though they had experienced high impact floods call for concern and creates a huge gap in the awareness campaign on flood insurance within the study areas. Therefore, regardless of the lop-sidedness and differences in perception on these variables which appear to favour those willing to purchase flood insurance, the finding nevertheless shows that sizeable number of the respondents are not aware or are less certain of residential flood insurance. The inference is that property owners are likely to commit to flood insurance if they are knowledgeable about it. Thus, being aware of flood insurance increases the decision to buy flood insurance and a first step towards mitigating adverse effect of flood.

On variables eliciting measurement on adoptive capacity and power, the two respondents displayed high total mean scores on the four variables (all >3 & 4), which overall could be regarded as high. This implies that the variables have strong motivational effects on both those willing to purchase and those not willing on residential flood insurance. However, a close look at the group means reveals that two out of the six variables exhibited strong discriminant power, which suggests there were significant group mean difference between those willing to purchase flood insurance and those not willing on the two variables (C3, λ 0. .980, F = 8.267, p < 0.05; C6 λ 0. 990, F = 3.836, p < 0.05). A key and significant finding onthese two measures is that those who are not willing to purchase flood insurance recorded higher group mean values (4.15, 3.77) than those who are willing (3.92, 3.60), the group mean differences were large enough to make significant difference. It therefore suggests thatadoptive capacity and power measures such as level of education and age factor constitute greater challenge to those not willing to commit to flood insurance than those who are willing.

The explanation for this may be because the greater number of respondents especially those from Oguta are secondary school and first school leaving certificate holders who may not be familiar with flood insurance policies. For example, in a cross tabulation 59.9% of this group agreed that their level of education and age affects their decisions on flood insurance. It is part of the spillover effects of poor awareness on flood insurance policies. However, four variables: income level, budget constraints, perception of flood insurance as a good investment and gender factor showed weak discriminant power, which suggest there were no significant group mean differences in perception between those willing to purchase flood insurance and those not willing on the variables (C1, λ 0. .994, F = 2.376., p >0:05; C2, = λ 1.000, F = 0.001., p >0:05; C4, = λ 0. .996, F = 1.666., p >0:05; C5, = λ 1.000, F = 0.116., p >0:05). Rather, there was more commonality of opinion than differences between the two groups on the four variables.

Though, those not willing to purchase flood insurance recorded higher group mean values (4.28; 4.09; 3.68 and 3.23) than those willing to purchase (4.14, 4.00; 3.58 and 3.19) both groups however, shared the same anticipation that income level, budget constraints, perception of flood insurance as a good investment and gender factor will affect their choice, but the extent to which this could make a difference in the level of risk aversion and likelihood of purchasing flood insurance is not clear because the group mean differences were too marginal to make a difference. Accordingly, both respondents were of the opinion that the propensity to purchase flood insurance increases significantly with income level while education does make a difference in making the right choice. While higher income could increase the affordability of flood insurance, education could tutor flood insurance prospective purchasers on risk mitigations. This finding is consistent with As Luigi and Paiella (2008); Cameron and Shah (2011) and Aliagha et al (2014) assertion that households that face income uncertainty or suffered loss of income from severe natural disaster exhibit a greater degree of risk aversion.

The respondents’ opinions about the subjective risk perception and how it affects the decision to purchase flood insurance or not were examined. Results show that there is a significant difference in their mean value with respect to the perception of unreliability of insurance firms to pay insurance claims and their reluctance to provide flood insurance coverage, perception that the flood protection system is not adequate, and reliance on government for flood relief assistance(D2, λ 0. 0.969, F = 12.557, p < 0.05; D3, λ 0.900, F = 44.416, p < 0.05; D4, λ 0.964, F = 15.080, p < 0.05). Those who are not willing to purchase flood insurance had a higher mean value (4.29; 4.41; 3.97) compared to those who are willing(4.06; 3.98; 3.55) It could therefore be interpreted that those who never thought or are willing to purchase flood insurance felt that insurance companies are somewhat unreliable when it comes to insurance claims. As such, that it was difficult to trust insurance companies in flood insurance coverage; rather the victims of flood disasters rely greatly on government relief and interventions which rarely come and are not sustainable. On the perception that "flood insurance premiums are high and "unaware of existing insurance policies and companies that provide flood insurance", there was a common opinion that the existing flood insurance premiums are high while many are unaware of existing insurance policies and companies that provide flood insurance in the study. Hence the variables displayed poor discriminant power and did not contribute significantly in differentiating between the groups (D1, λ 1.000., F = 0.132., p >0:05; D5, = λ 0. .988, F = 4.764., p >0:05). Though a closer look at the group means column shows that those who are not willing to purchase flood insurance recorded a higher mean on the two variables than those willing. The mean difference was too marginal to make a significant difference to flood insurance purchase or to the degree of risk aversion.

On Behavioral, Attitudinal/Emotional determinants, five out of the six variables registered strong discriminant power, which suggests significant group mean differences in perception between those willing to purchase flood insurance and those not willing on the variables (E2, λ 0. 948, F = 21.766, p < 0.05; E3, λ 0 .909, F = 39.897, p < 0.05; E4, λ 0.996, F = 1.607, p < 0.05; E5, λ 0. 995, F = 1.994, p < 0.05; E6, λ 0.931, F = 29.389, p < 0.05;). The variable shows very high mean differences (0.27; 0.87; 0.66; 0.28 and 0.89). To further explore the underlying sources of these differences, a cross tabulation was performed, 61.6% of respondents who are not willing to purchase flood insurance agree that they place priority on the perceived product value of insurance before investing.

On perception of consumers’ insurance literacy, it was found that 53.4% strongly agreed that inadequate knowledge of insurance policies and operation affects them while 51.9% believe they don't have confidence in the available insurance companies within the study areas. Furthermore, 53.9% considered social norms as influencers of flood insurance purchase decisions whereas 51.1% were of the opinion that information availability on property insurance remain a critical factor in their decisions to commit to flood insurance purchase. What could be deduced from this result is that information contributes greatly to the degree of flood insurance purchase. Thus, information would have positive effect on flood insurance purchase in the study area. On the other hand, one variable Biases registered poor discriminant power and did not contribute significantly in differentiating between the groups (E1, λ 0. 996., F = 1.534., p >0:05). Thus, there is commonality of opinion on this variable. A closer look at the group means column shows that those who are willing to purchase flood insurance recorded a higher mean on the variable than those not willing. The mean difference was too minor to make a significant difference to flood insurance purchase or to the degree of risk aversion.

5.2. Predicting Discriminant Function for Propensity to Purchase Flood Insurance for Residential Properties

One of the objectives of this study is to identify the most significant predictive variables that best differentiate or discriminate between those willing to purchase flood insurance and not willing. To achieve this, the “stepwise method of enter/remove” for deriving discriminant functions was used (Huberty and Barton, 1989). At 0.05 significant level, Table 3 shows that 15 out of 25 variables entered the model with significant discriminatory power, which could be said to be effective in discriminating between the groups on the status of willingness and reluctance to purchase flood insurance and predicting their group relationship in that reason. In a descending order of degree, the following variables entered the model in this order: information availability>consumers’ insurance literacy>trustful belief on insurance >the topography/geography of the location and environment>perception that the flood protection system is not adequate>Reliance on government for flood relief assistance>perception on the adequacy of early warning system, >social norms,>perceived product value of insurance>perception of unreliability of insurance firms to pay insurance claims and their reluctance to provide flood insurance coverage>number of high-impact floods experienced, >age factor>the distance from a flood prone river>expectation of an increase in future flood frequency,>self-assessment of level of vulnerability under climate change

Table 3: Predictive Model of Flood Insurance Purchase for Residential Properties, Variables entered/removed ^a,b,c,d.

		Wilks’ Lambda
						Exact F
Step	Entered	Statistic	df1	df2	df3	Statistic	df1	df2	Sig
1	Information availability	0.866	1	1	399.000	61.902	1	399.000	0.000
2	Consumers’ Insurance Literacy	0.783	2	1	399.000	55.298	2	398.000	0.000
3	Trustful Belief on Insurance	0.727	3	1	399.000	49.687	3	397.000	0.000
4	The topography/geography of the location and environment	0.658	4	1	399.000	51.434	4	396.000	0.000
5	Perception that the flood protection system is not adequate	0.628	5	1	399.000	46.750	5	395.000	0.000
6	Reliance on government for flood relief assistance	0.609	6	1	399.000	42.167	6	394.000	0.000
7	Perception on the adequacy of early warning system	0.585	7	1	399.000	39.842	7	393.000	0.000
8	Social norms	0.564	8	1	399.000	37.925	8	392.000	0.000
9	Perceived Product Value of Insurance	0.554	9	1	399.000	34.979	9	391.000	0.000
10	Perception of unreliability of insurance firms to pay insurance claims and their reluctance to provide flood insurance coverage	0.541	10	1	399.000	33.129	10	390.000	0.000
11	Number of high-impact floods experienced	0.534	11	1	399.000	30.893	11	389.000	0.000
12	Age Factor	0.527	12	1	399.000	29.020	12	388.000	0.000
13	The distance from a flood prone river	0.520	13	1	399.000	27.522	13	387.000	0.000
14	Expectation of an increase in future flood frequency	0.513	14	1	399.000	26.134	14	386.000	0.000
15	Self-assessment of level of vulnerability under climate change	0.505	15	1	399.000	25.138	15	385.000	0.000

This study aimed at identifying the variables that have the greatest impact and correlation with the discriminant function. Thus, Table 4 provides further statistics for confirming the significance of the discriminant function and identifying variables that have the greatest impact and correlation. The Table shows a canonical correlation (CCr) of 0.6756, which implies that the function explained 67% (CCr²) of variance in the group differences. This provides an overall index for assessing the fit of the model. Besides, the model’s Wilks’ lambda (ᴧ), the function is considered significant (ᴧ = 0 .505, ᵡ² (df = 5) = 267.318, p < 0.05). On this basis, the study conclude that there is significant discriminant function that evidently splits the two respondents on the basis of their perception of the factors affecting flood insurance purchase. Table 4 also displays the standardized discriminant function coefficients and structure matrix correlation used to assess each variable’s exceptional influence in terms of impact and correlation with the discriminant function.

Table 4: Standardized Canonical Discriminant Function Coefficient and Structure Matrix of Flood Insurance Purchase for Residential Properties

Standardized Canonical Discriminant Function Coefficient					Structure Matrix
	Function 1			Impact Ranking	(Within group Correlation)
Information availability		0.765		1	0.083
Consumers’ Insurance Literacy	-0.491			3	0.071
Trustful Belief on Insurance	-0.668			2	0.072
The topography/geography of the location and environment	0.269			9	0-.049
Perception that the flood protection system is not adequate	-0.324			7	0.068
Reliance on government for flood relief assistance	-0.610			4	0.070
Perception on the adequacy of early warning system	0.245			11	-0.337
Social norms	0. 234			13	0.274
Perceived Product Value of Insurance	0. 240			12	0.320
Perception of unreliability of insurance firms to pay insurance claims and their reluctance to provide flood insurance coverage	0.360			5	-0.090
Number of high-impact floods experienced	0.308			8	0-.059
Age Factor	0.229			14	-0.236
The distance from a flood prone river	0.333			6	-0.082
Expectation of an increase in future flood frequency	0.267			10	0.398
Self-assessment of level of vulnerability under climate change	-0.221			15	-0.231
Function at Group Centroids
Those Willing to Purchase Flood Insurance			0.909
Those not Willing to Purchase Flood Insurance			-1.072
Model Validation Statistics
Canonical Correlation (CCr)			0.703
(CCr²)			0.6756
Eigenvalue			0.979^a
Wilks’ Lambda			0 .505
Chi – Square (df = 5)			267.318
Classification accuracy (hit ratio)			83.3%
Sig			0.000

It is evident in Table 4 that the variables in the model with the most discriminatory power between the two groups were mainly measures of behavioral, attitudinal/emotional determinants“ information availability (β = 0.765 and within-group correlation = 0.083); trustful belief on insurance (β = -0.668and within-group correlation = 0.072); consumers’ insurance literacy (β = -0.491 and within-group correlation = 0.071). Others include: reliance on government for flood relief assistance (β = -0.610 and within-group correlation = 0.070); perception of unreliability of insurance firms to pay insurance claims and their reluctance to provide flood insurance coverage(β = 0.360 and within-group correlation = -0.090); the distance from a flood prone river(β = 0.333 and within-group correlation = -0.082); perception that the flood protection system is not adequate(β = -0.324 and within-group correlation = 0.068); number of high-impact floods experienced(β = 0.308 and within-group correlation = 0-.059 ); the topography/geography of the location and environment(β = 0.269 and within-group correlation = 0-.049);expectation of an increase in future flood frequency(β = 0.267 and within-group correlation = 0.398); perception on the adequacy of early warning system(β = 0.245 and within-group correlation = -0.337); perceived product value of insurance (β = 0.154and within-group correlation = 0. 240); social norms(β = 0. 234 and within-group correlation = 0.274); age factor(β = 0.229 and within-group correlation = -0.236); self-assessment of level of vulnerability under climate change(β = -0.221 and within-group correlation = -0.231). Moreover, the function at group centroid defines optimal Z-value on which the homeowners could be categorized as having knowledge of flood insurance or not. A careful look at Table 4 indicates that at the group centroids, those willing to purchase flood insurance had Z-value of 0.909 while those not willing had -1.072. The deduction is that a respondent with a Z-value equal to 0.909 is considered as having more knowledge of flood insurance whereas a respondent with a Z-value that is equal to -1.072 is categorized as having less and limited knowledge of flood insurance purchase. Nevertheless, the distribution result as shown in Table 4 provides efficiency and predictive accuracy of the discriminant function. Thus, the model achieved a hit ratio of 83.3%, indicating that 83.3% of the respondents from the two groups were rightly grouped and classified as having opinion on the variables according to their perception on the subject matter. The achieved significant hit ratio of 83.3% shows that the model has practical significance in also predicting the perception of those willing and not willing to purchase flood insurance.

One of the key objectives of the study was to identify the factors affecting homeowners’ decision to purchase flood insurance. Through the literature the study found five factors with twenty five variables. The study further examined the perception of those willing to purchase flood insurance and those not willing on the identified factors. It was found that those willing to purchase flood insurance were more knowledgeable and familiar with residential flood insurance than those who are not willing apparently because they are worst hit by the flood and would want to mitigate it. Again, it was found that those who are not willing were more indifferent in purchasing flood insurance as they don't have absolute confidence in insurance companies and moreover, they lack the necessary information on flood insurance policies. The variables with the most predictive power in accounting for the differences in perception and have the highest impact were found to be within the measures of behavioral, attitudinal/emotional determinants. The study also found that both those willing to purchase flood insurance and those not willing have common grounds on some variables. For example, those willing to purchase flood insurance and those not willing have common grounds and opinion on the income level and budget constraints which tend to suggest that affordability and the financial plans of a prospective flood insurance purchaser could have practical implication on his or her decision to commit to residential flood insurance purchase within the study areas. Finally, this study contributes to the body of literature on residential flood insurance and risks mitigation. However, a limitation of this study was on its inability to explore how the supply side of flood insurance could address the unwillingness to purchase flood insurance in the study area; a good subject for further study.

COMPLIANCE WITH ETHICAL STANDARDS:

The authors declare that they have no conflict of interest.

All respondents to questions/questionnaires provided information without compulsion.

The field data are original and obtained by our research team.

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Factors Influencing Homeowners' Decision to Purchase Flood Insurance in Flood-prone Areas of Imo State, Nigeria

Status:

Version 1

Abstract

Figures

1.0. Introduction

2. The Study Area

3. Review Of Related Theories And Literature