1# RECORD 85ea3296a5d9175ea12142cf209523d5db6d0b422# TITLE: Hedonic Analysis of the Impact of Flood Events on Residential Property Values in Malaysia: A Study of Willingness to Pay3# YEAR: 2019.0 VENUE: Malaysian Journal of Economic Studies DOI: 10.22452/mjes.vol56no1.44# AUTHORS: N. Ismail; M. Z. Abd Karim; B. Hasan-Basri5# PAGES: 22 CANDIDATE TABLE PAGES (1-based): [11, 13, 15]6# ABSTRACT: The effect of flooding on residential property values (RPV) is a major concern to all property buyers and owners. Although numerous studies have already discussed the risk of devaluation of those properties situated in flood prone areas, those that focused on the impact of flooding on residential properties in Malaysia are still limited. This paper extends existing literature by also estimating consumers’ willingness to pay for flood control measures to reduce the flood risk in Malaysia. Using the hedonic pricing model (HPM), our results suggest that the market value of urban and rural residential properties significantly decreases by 18.5 percent and 13.6 percent due to flooding, respectively. The result also shows that, respondents who are risk averse are willing to pay 35.4 percent more for flood control measures to reduce impact of flood risk compared to those who are risk takers. The results of this study can help property owners to understand the factors that contribute to property devaluation due to flooding. This study also proposes flood insurance programmes to be implemented as flooding is a major concern to real property owners.789===== PAGE 1 (score 5, numeric-paren 0, table-words 0, var-words 5) =====1011 12Malaysian Journal of Economic Studies Vol. 56 No. 1, 2019 136314Malaysian Journal of Economic Studies 56(1): 63–84, 2019 15ISSN 1511-455416Hedonic Analysis of the Impact of Flood Events on Residential 17Property Values in Malaysia: A Study of Willingness to Pay18Nur Hafizah Ismaila19Mohd Zaini Abd Karimb 20Bakti Hasan-Basric21Universiti Utara Malaysia22Abstract: The effect of flooding on residential property values (RPV) is a major concern 23to all property buyers and owners. Although numerous studies have already discussed 24the risk of devaluation of those properties situated in flood prone areas, those that 25focused on the impact of flooding on residential properties in Malaysia are still limited. 26This paper extends existing literature by also estimating consumers’ willingness to pay 27for flood control measures to reduce the flood risk in Malaysia. Using the hedonic 28pricing model (HPM), our results suggest that the market value of urban and rural 29residential properties significantly decreases by 18.5 percent and 13.6 percent due to 30flooding, respectively. The result also shows that, respondents who are risk averse are 31willing to pay 35.4 percent more for flood control measures to reduce impact of flood 32risk compared to those who are risk takers. The results of this study can help property 33owners to understand the factors that contribute to property devaluation due to 34flooding. This study also proposes flood insurance programmes to be implemented as 35flooding is a major concern to real property owners. 36Keywords: Floods, hedonic price model, property, valuation37JEL classification: Q54, R21, R32381. Introduction39Flood is a natural phenomenon that can have disastrous effect on the environment and 40on those affected communities (IFRC, 2009; Jha, Bloch, & Lamond 2012). It is also one 41of the most frequent and most costly due to the damages it can cause to properties, 42livelihoods and human health. In terms of asset loss, flooding can destroy public 43infrastructures, houses and other real and personal properties. The destruction that 44floods bring disrupts people’s daily lives, with some even forced to leave their homes. 45a 46School of Economics, Finance and Banking, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia. 47Email: hafizah19@live.com, n.hafizah.mohammad@uum.edu.my (Corresponding author)48b 49Othman Yeop Abdullah Graduate School of Business, Universiti Utara Malaysia, 06010 Sintok, Kedah, 50Malaysia. Email: zaini500@uum.edu.my51c 52School of Economics, Finance and Banking, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia. 53Email: bakti@uum.edu.my54* The authors wish to thank the Ministry of Higher Education Malaysia for funding this study under the 55Long-Term Research Grant Scheme (Code LRGS/b-u/2012/UUM/Teknologi Komunikasi dan Informasi). The 56views expressed in this study are those of the authors and do not necessarily reflect the views or policies 57of the ministry or the project team. Any errors are the sole responsibility of the authors.58Article Info: Received 26 June 2018; Revised 3 February 2019; Accepted 4 March 201959https://doi.org/10.22452/MJES.vol56no1.4606162===== PAGE 11 (score 15, numeric-paren 0, table-words 2, var-words 9) =====6364 65Malaysian Journal of Economic Studies Vol. 56 No. 1, 2019 667367Hedonic Analysis of the Impact of Flood Events on Residential Property Values in Malaysia68houses are selected for this study. Meanwhile, distance variables such as distance to 69nearest bus stop, distance to nearest city centre, proximity to airport and proximity to 70school are obtained from Google Maps (Google, n.d.).714.0 Results and Discussions 72A total of 1,486 respondents were interviewed in this study. However, only about 91.52 73per cent or 1,360 respondents responded to the interview; the remaining 8.48 percent 74or 126 respondents declined to be interviewed due to time constraints. A majority of 75the respondents have busy work schedules and irregular working hours, which makes it 76difficult for them to accommodate our interview session. 77In particular, 700 respondents were property owners who had experienced 78flooding. The list of flood victims who still resides in the same areas that had been 79flooded was acquired from the Department of Social Welfare and from the local 80councils. The remaining 660 respondents who were not affected by flood in the area 81were also included in this study to avoid sample selection bias problem. 82Table 6 presents the descriptive statistics of the variables. It shows that at most, 83the study areas have been flooded three times within a five-year period (2008-2013). 84According to the Department of Irrigation and Drainage, most of the flood prone 85areas covered in this study experienced three flood events a year. For example, Larut 86Matang and Selama (one of our sample districts) were flooded five times in 2010; once 87in August 2010, three in September 2010, and another one in November 2010. The 88average cost of structural damage to the respondent’s house due to the flood event is 89RM966 including damage to the external and internal house structures. Based on the 90survey responses, the types of house structure damages recorded are paint cracks on 91Table 6. Descriptive statistics of variables used in the first stage and second stage of 92 93residential property value model94Variable 95Mean 96Minimum 97Maximum98RPV (in RM per unit property) 99266,080.73 10069,600.00 101590,000.00102Flood: 103 104 105 106FREQUENCY (number of flood 1073.13 1081.00 1095.00110 111 occurrences in 5 years)112 113DAMAGE (RM) 114966.00 115200.00 1168000.00117Location: 118 119 120 121BUS (in kilometres) 1222.36 1230.70 12410.60125 126CITY (in kilometres) 1273.78 1280.80 12911.00130Structural: 131 132 133 134LOT (per square foot) 1351,516.73 1361,200.00 1371,600.00138 139AGE (number of years) 14015.00 1418.00 14220.00143 144BED (number of rooms) 1453.13 1461.00 1477.00148 149BATH (number of rooms) 1502.04 1511.00 1524.00153Source: Authors’ survey.154155156===== PAGE 12 (score 27, numeric-paren 0, table-words 5, var-words 17) =====15715874 159Malaysian Journal of Economic Studies Vol. 56 No. 1, 2019160Nur Hafizah Ismail, Mohd Zaini Abd Karim and Bakti Hasan-Basri161the wall, spoiled flooring, jammed or broken windows and doors, and some wreckage of 162the electrical system including switches and wiring. 163This study covers both low cost and high cost properties where the sale prices 164or residential property values (RPV) are between RM69,600 to RM590,000. The 165significant variations between the low cost and high cost property prices are due to the 166differences in the study areas which cover the urban and rural areas. The residential 167properties included in this study are located about 0.7 km and 0.8 km from the nearest 168bus station and city centre, respectively. Lot sizes of the sample residential properties 169can be considered moderate to large, with lot size averaging 1,516 sq. ft. The age of 170the properties surveyed range from 8–20 years. At most, the properties have seven 171bedrooms and six bathrooms.172 1734.1 First-stage Hedonic Regression 174The first objective of this study is to investigate the effect of flood frequency on RPV in 175urban and rural areas in Malaysia. For this purpose, we use the semi-log functional form 176(dependent), which can correct heteroscedasticity and multicollinearity problems (Basu 177and Thibodeau, 1998). Table 7 presents the result of the first-stage hedonic regression.178Based on the results in Table 7, flood frequency in both urban and rural areas 179are negatively and statistically significant. The results explain that if flood frequency 180increases by one flood event per year, the residential property value significantly 181decreases by 18.5 percent and 13.6 percent in the urban and rural areas, respectively. 182The value of flood coefficient in the urban area is higher compared to the estimate 183in the rural area. This indicates that flood frequency causes the residential property 184value in the urban area to decline more than the property value in the rural area. 185Most of the urban residents are wealthier than the rural residents where the average 186income of urban residents is RM2,000 more than the average income of rural residents 187(DOSM, 2013). Therefore, urban residents can afford to pay a higher price for residential 188property in the urban area whereby most of the property has more valuable extras 189(NAPIC, 2014). Thus, floods that hit the urban area will have a greater effect on the 190urban property value compared to the rural area. 191Location is another important attribute that affects potential buyers’ housing 192choices. The urban-area CITY coefficient is positive and statistically significant but the 193coefficient of CITY2 is negatively significant implying that residential properties located 194near the city centre of urban areas have lower market values than those that are 195farther from the city centre. However, up to a certain distance, the farther the distance 196from the city centre, the lower the value of the residential properties. Buyers prefer 197to acquire residential properties farther away from the city but to a certain extent – 198especially away from industrial zones – because such city and industrial areas are more 199exposed to air pollution, which is hazardous to health (Schwarz & Marcus, 1990). Thus, 200this causes low demand for residential properties located in the city and its surrounding 201areas, thereby affecting their property market value. 202The structure of a house can be analysed in terms of lot size, house age and 203number of bathrooms and bedrooms. Coefficients for the lot size (LOT), number of 204bedroom (BED) and number of bathroom (BATH) for both urban and rural areas are 205206207===== PAGE 13 (score 78, numeric-paren 41, table-words 10, var-words 12) =====208209 210Malaysian Journal of Economic Studies Vol. 56 No. 1, 2019 21175212Hedonic Analysis of the Impact of Flood Events on Residential Property Values in Malaysia213positive and significant. An increase in the lot size usually adds to the value, because 214it gives the property owners room to expand such as adding more bedrooms and 215bathrooms. In fact, building additional rooms (bathroom or bedroom) will increase 216construction costs, which will consequently increase the value of a residential property.217The coefficients of house age (AGE) for both urban and rural areas are negative 218and statistically significant. Generally, as a property gets older, it becomes less valuable 219because a lot of work will be needed for repairs and upgrading the condition as well as 220to maintain it. In fact, based on the assumption that older homes may have outdated 221technology as well as lacking a number of features available in newer homes, this would 222decrease the benefits provided by the property. Therefore, this will lower the value of 223the property. 224In the neighbourhood characteristics, all of the urban and rural areas DAIRPORT 225coefficients show insignificant results. Conversely, coefficient of proximity to school 226(DSCHOOL) shows a positive and significant result only in the rural area. This probable 227Table 7. Estimation results of the first-stage hedonic regression228 229Coefficient230Variable 231Urban area 232Rural area233 234Coefficient 235Standard error 236Coefficient 237Standard error238Constant 23911.553*** 240(0.173) 24111.336*** 242(0.216)243Flood: 244 245 246 247 248FRE 249-0.185*** 250(0.070) 251-0.136* 252(0.081)253Location: 254 255 256 257 258BUS 259-0.016 260(0.011) 2610.003 262(0.006)263 264BUS2 2650.0001 266(0.0001) 267-0.0001 268(0.0003)269 270CITY 2710.041*** 272(0.012) 273-0.010 274(0.008)275 276CITY2 277-0.001*** 278(0.0003) 2790.0003 280(0.0003)281Structure: 282 283 284 285 286LOT 2870.0001*** 288(0.00005) 2890.0001*** 290(0.00004)291 292AGE 293-0.012*** 294(0.003) 295-0.010*** 296(0.002)297 298BED 2990.077*** 300(0.034) 3010.059** 302(0.045)303 304BATH 3050.233*** 306(0.035) 3070.180*** 308(0.042)309Neighbourhood: 310 311 312 313 314DAIRPORT 3150.059 316(0.074) 317-0.112 318(0.090)319 320DSCHOOL 321-0.139 322(0.094) 3230.256* 324(0.137)325No. of observations 326680 327680328R2 32953.90 33047.00331Adjusted R2 33252.40 33345.30334F-Statistic 33536.57 33627.811337 338 339(0.000) 340(0.000)341Note: *, **, and *** refer to figures that are significant at 10%, 5%, and 1% levels of significance, respectively.342343344===== PAGE 14 (score 8, numeric-paren 0, table-words 2, var-words 4) =====34534676 347Malaysian Journal of Economic Studies Vol. 56 No. 1, 2019348Nur Hafizah Ismail, Mohd Zaini Abd Karim and Bakti Hasan-Basri349reason may be due to the limited number of schools in the rural area while a number 350of them are located far from certain residential areas. For example, in Selangor, a rural 351area such as Kapar only has ten primary schools and two secondary schools, compared 352to the number of schools in urban areas, such as the city of Klang, which has a total of 35375 primary schools and 36 secondary schools (Ministry of Education Malaysia, 2014a; 3542014b). For these reasons, buyers in the rural area prefer to choose a residential 355property located near to a school. 3564.2 Results of the Second-stage Regression 357The second objective of this paper is to determine the consumer’s willingness to pay 358for flood control measures to reduce the impact of flood risk. Implicit price measures 359the amount of money that individuals are willing to pay to keep their utility at original 360levels. The economic welfare that we used in this study involves a household’s comfort 361and satisfaction of their current living standards of being able to avoid the damages 362and inconveniences brought by flooding. Thus, in our study, implicit price refers to the 363estimates of economic welfare of households, as measured by their willingness to pay 364for flood mitigation measures to reduce the flood risk. 365The calculation of implicit price represents an empirically implementable measure 366of consumer’s willingness to pay for flood mitigation project to reduce the impact of 367flood risk. The implicit price can be calculated by using the formula as in Equation (4)3:368 369(4)370The total implicit price provided by respondents is calculated as RM4,461,878 for 371approaches to avoid losses in residential property value due to flood risk problems. 372The estimation of the individual amount can be calculated by dividing the total implicit 373price with the number of respondents. On average, the individual respondent’s implicit 374price for a flood management programme to reduce the flood risk problems in their 375residential area is about RM2,280. The total value of implicit price of RM4,461,878 376or RM4.4 million calculated corresponds with the cost incurred by the government 377in the implementation of several flood mitigation projects such as river widening and 378deepening work, upgrading and modifying infrastructure and irrigation system and river 379maintenance work. For example, based on the report from the Perak Department of 380Drainage and Irrigation 2015/2016, the total cost of the flood mitigation project and the 381maintenance of several rivers in Perak such as Sungai Bidor and Sungai Lamin is about 382RM4.1 million. 383The close value between the flood control project’s actual cost and the estimated 384amount of households’ implicit price shows that this study can provide a good estimate 385Implicit Price386RPV387FREQUENCY388=389∂390∂391= coefficient of FREQUENCY392FREQUENCY393RPV394×3953 396For example, for observation 1 (or sample 1), suppose the residential property value (RPV) is 397RM100,000, coefficient of flood frequency (FRE) is 0.185 and variable flood frequency (FRE) is 3 times. 398The implicit price using the formula would be (0.185/3) x 100000 = 6166.67. Therefore, the implicit 399price is RM6,166.67. To compute the total implicit price, we need to add all the implicit prices for each 400observation.401402403===== PAGE 15 (score 29, numeric-paren 5, table-words 8, var-words 7) =====404405 406Malaysian Journal of Economic Studies Vol. 56 No. 1, 2019 40777408Hedonic Analysis of the Impact of Flood Events on Residential Property Values in Malaysia409for flood management policy implementation, where the government can collect 410money from households for flood mitigation purposes rather than fully financing 411the flood project, since the respondents are willing to spend more money on flood 412mitigation projects in order to reduce the flood risk problems.413The purpose of estimating the second stage hedonic regression model where the 414measure of willingness to pay is included as an dependent variable is to see the effect 415of socio-economic factors and risk on the willingness to pay to reduce flood risk. Table 8 416presents the results of the second-stage hedonic regression. 417The positive and statistically significant coefficient of risk taking (DMOVE) shows 418that respondents who are risk averse, indicated by those who are planning to move to 419other flood free location, are willing to pay more money compared to those who are 420risk takers. A study done by Cameron and Shah (2015) found that natural disasters can 421impact real life behaviour through decreased risk taking behaviour. Commonly, after the 422post flood events, developers usually do not take on the full risk of responsibilities of 423the property in areas that are prone to flooding. Therefore, for the flood risk adverse 424group, they are willing to pay higher for flood control measures to reduce the impact of 425flood risk, so as not having to move from the area. 426Table 8. Estimates of the second-stage hedonic regression427Variable 428Coefficient 429Std. deviation430Constant 4318.414*** 4320.229433Flood 434 435 436DAMAGE 4370.00046 4380.0039439 440DMOVE 4410.357*** 4420.115443 444(1= planning to move;445 4460 = not moving) 447Socio-economic profile 448 449 450DINCOME 4510.132* 4520.096453 454(1= above RM3,050;455 4560 = below RM3,051) 457 458DEDUCATION 4590.347** 4600.137461 462(1= university;463 4640 = otherwise) 465 466AGE 467-0.010** 4680.004469 470DFINCON 471-0.065 4720.043473No. of observations 474700475R2 47647.00477Adjusted R2 47839.00479F-Statistic 48015.67481Note: *, **, and *** refer to values that are significant at 10%, 5%, and 1% significance482 483levels, respectively. 484485486===== PAGE 16 (score 22, numeric-paren 0, table-words 5, var-words 12) =====48748878 489Malaysian Journal of Economic Studies Vol. 56 No. 1, 2019490Nur Hafizah Ismail, Mohd Zaini Abd Karim and Bakti Hasan-Basri491However, the value for the coefficient of DAMAGE is statistically insignificant. This 492shows that the cost of structural damage to the house due to a flood event does not 493affect the respondent’s willingness to pay for flood control measures. This may be due 494to the fact that most of the respondents in this study had experienced flooding more 495than once. As such, they have made preparations such as installing temporary barriers 496and upgrading permanent defences and resilient equipment on the house structure 497to reduce damage and loss from floods. In fact, based on the survey (as presented in 498the descriptive statistics), the average house structural damage cost incurred by the 499631 respondents who experienced flood events more than once is small (even under 500RM1000). Therefore, the cost of structural damage to the house due to a flood event 501does not significantly affect the decision of respondent’s willingness to pay for flood 502control measures to reduce flood risk. 503Other socio-economic factors such as income, age and education level have in-504fluenced the level of willingness to pay (Devkota et al., 2014; Maraseni et al., 2008). 505The coefficient of household income (DINCOME) is positive and statistically significant. 506Generally, when household income increases, households’ spending ability will improve, 507thereby improving their economic welfare. This means that they would be willing to pay 508to maintain the same level of economic welfare. Based on the Malaysian Household 509Income and Basic Amenities Survey Spending Report 2013, there is an increase of 6.13 510percent in household income over two years between 2012 and 2013. Therefore, with 511the increase in income and spending ability, residents willingness to pay for flood 512management programmes also increases. 513A positive and statistically significant coefficient of education level (DEDUCATION) 514indicates that households that possess a university qualification tend to have a higher 515willingness to pay for flood reduction programmes compared to those households with-516out a university education. This may be because university graduates are more exposed 517to environmental issues (i.e., flood disasters), which would make them more likely to 518be more concerned and knowledgeable about flood and its management programmes. 519Thus, they would be more capable of assessing flood risks and the potential damage 520to properties as compared to those with a lower educational attainment. This is also 521consistent with the findings of an income report where according to the Public Services 522Commission of Malaysia, the starting salary of a university graduate is on average 523RM2,500 higher than those with a lower educational attainment. Therefore, this would 524make the university graduate respondents willing to spend more money for flood 525control measures due to their higher financial capacity. 526Based on the result, the coefficient of age (AGE) is negative and statistically 527significant. As age increases, some of the older households who are phasing into 528retirement may deal with lower pay and also some are likely to retire early due to 529poor health. As a result of the uncertainty and lower income received by the older 530household, this may affect their willingness to spend for flood control measures to 531reduce flood risk. Hence, increase in age will reduce the household willingness to pay 532for flood management programmes. On the other hand, the coefficient of financial 533constraint (DFINCON) is statistically insignificant. This shows that the effect of respondent’s 534income on willingness to pay for flood control measures is not influenced by the 535respondent’s decision to move or not from the flood prone area (DMOVE). 536