# RECORD 819ec939f0241cca8513d3a72e45a56254da737d # TITLE: House Prices and Flood Risk Exposure: An Integration of Hedonic Property Model and Spatial Econometric Analysis # YEAR: 2022.0 VENUE: Journal of real estate finance and economics DOI: 10.1007/s11146-022-09930-z # AUTHORS: Nam Bui; Le Wen; B. Sharp # PAGES: 32 CANDIDATE TABLE PAGES (1-based): [14, 15, 16, 17, 18, 19, 22, 24, 25, 26] # ABSTRACT: The impact of climate change is devastating in developing countries where flood protection and insurance schemes are limited. Certain parts of Ho Chi Minh City, Vietnam are under the constant threat of inundation due to sea-level rise. We integrate the hedonic property model in a difference-in-differences framework and spatial econometric analysis into a single analytical framework to estimate the economic effect of pluvial flooding. We find prices for affected houses were discounted by 9% after a large flood event on 30 September 2017. This research contributes to the existing literature as follows. First, we study the economic impact of pluvial floods, which has received less attention in existing studies where large and irregular floods are their focus. Second, the inclusion of legal status as a control variable accounts for the unique character of the Vietnamese housing market. Third, we also identify the recovery of house prices after the flood event. ===== PAGE 1 (score 9, numeric-paren 0, table-words 0, var-words 9) ===== Vol:.(1234567890) The Journal of Real Estate Finance and Economics (2024) 69:100–131 https://doi.org/10.1007/s11146-022-09930-z 1 3 House Prices and Flood Risk Exposure: An Integration of Hedonic Property Model and Spatial Econometric Analysis Nam Bui1 · Le Wen2   · Basil Sharp2 Accepted: 31 October 2022 / Published online: 15 November 2022 © The Author(s) 2022 Abstract The impact of climate change is devastating in developing countries where flood protection and insurance schemes are limited. Certain parts of Ho Chi Minh City, Vietnam are under the constant threat of inundation due to sea-level rise. We inte- grate the hedonic property model in a difference-in-differences framework and spatial econometric analysis into a single analytical framework to estimate the eco- nomic effect of pluvial flooding. We find prices for affected houses were discounted by 9% after a large flood event on 30 September 2017. This research contributes to the existing literature as follows. First, we study the economic impact of pluvial floods, which has received less attention in existing studies where large and irregu- lar floods are their focus. Second, the inclusion of legal status as a control variable accounts for the unique character of the Vietnamese housing market. Third, we also identify the recovery of house prices after the flood event. Keywords  House price · Flood risk · Hedonic property model · Spatial analysis · Vietnam Introduction Sea level rise is a constant threat to many shorelines around the world, especially in dense urban areas. Vojinović (2015) differentiates pluvial floods from fluvial and coastal ones. Fluvial floods break or overflow levees and enter the city while coastal floods follow storm surges and enter the city by sea or delta. Pluvial floods happen in large towns where the amount of rainfall is greater than the ability of * Le Wen l.wen@auckland.ac.nz 1 Department of Economics, University of Auckland, 1142 Auckland, New Zealand 2 Energy Centre, Department of Economics, University of Auckland, 1142 Auckland, New Zealand ===== PAGE 14 (score 18, numeric-paren 0, table-words 2, var-words 2) ===== 113 1 3 House Prices and Flood Risk Exposure: An Integration of Hedonic… ^ standard deviation Table 4   (continued) Pre-flood listings Post-flood listings Control group Treatment group Post-treatment group Mean sd^ Mean sd^ Mean sd^ Mean sd^ Mean sd^   3 (pink book) 0.387 0.487 0.369 0.483 0.367 0.482 0.419 0.494 0.363 0.482 Direction   0 (unknown) 0.628 0.483 0.725 0.447 0.719 0.449 0.510 0.500 0.558 0.498   1 (north) 0.064 0.244 0.030 0.171 0.034 0.182 0.096 0.296 0.068 0.253   2 (northeast) 0.038 0.190 0.022 0.146 0.028 0.166 0.037 0.189 0.011 0.102   3 (east) 0.039 0.194 0.040 0.195 0.039 0.193 0.041 0.198 0.026 0.160   4 (southeast) 0.077 0.266 0.051 0.220 0.062 0.242 0.074 0.262 0.079 0.270   5 (south) 0.086 0.280 0.067 0.250 0.067 0.251 0.111 0.315 0.116 0.321   6 (southwest) 0.020 0.140 0.011 0.106 0.013 0.111 0.028 0.165 0.021 0.144   7 (west) 0.030 0.170 0.024 0.152 0.021 0.143 0.048 0.214 0.037 0.189   8 (northwest) 0.019 0.137 0.031 0.174 0.016 0.125 0.054 0.226 0.084 0.278   LONG_X 106.716 0.015 106.717 0.015 106.717 0.017 106.715 0.008 106.717 0.008 LAT_Y 10.735 0.013 10.736 0.013 10.735 0.015 10.735 0.003 10.734 0.003   Observations 1,305 1,062 1,828 539 190 ===== PAGE 15 (score 31, numeric-paren 6, table-words 9, var-words 6) ===== 114 N. Bui et al. 1 3 prices among the treatment group from before to after the event. Because these houses lie within the radius of the affected roads, their prices were reduced 1.6% (= 0.075—0.091). Last, based on the coefficient of the DD term, house prices among the treatment group experienced a discount by 9.1% compared to the control group from before to after the event. A difference-in-differences plot is used to show the parallel trends (Di Maggio et al., 2019). Thus, Fig. 3 is provided to illustrate the results of the dif- ference-in-differences estimation for the flood event on 30 September 2017. Results in Table 6 reveal that house prices are determined by house characteris- tics. For example, house prices are expected to increase with proximity to the city centre. It is desirable to stay far away from traffic congestion spots, nearer to large parks and schools, and inside secured areas. The directions views to west and south- west boost house prices. Listings in the red book, which indicates houses with land use rights and ownership, are expected to command a higher price. The current interpretation of direct impacts can be improved further. To identify the total effect of flood risk on house prices, we consider the spillover effect which assumes that house prices and/or flood risk of a certain property may have an impact on nearby ones. As discussed previously, the SAC model is used to interpret the impact. Thus, we continue to use the SAC model to adjust the price discount inter- pretation under the spillover effects. The decomposition estimation results are reported in Table 7 where the spillover and total effects of all variables are significant and concur with the fact that sig- nificant coefficients of the spatial weight matrix W in the case of the SAC model as shown in Table 5. Our empirical evidence in Table 7 is consistent with the conclu- sion drawn by Delgado and Florax (2015) in which an appropriate spatial estimator should be used when both spatial sorting and spatial interaction exist in treatment responses. Direct and indirect effects indicate the rate of price discount that houses in the flooded areas were facing. Separating indirect effects from total effects helps to dif- ferentiate between how much the reduction is from being affected by the floods and how much it is caused by staying near inundated areas. Understanding this can aid when formulating a tax policy. The amount accounted for a direct effect could be Table 5   “Move in tandem” test Dependent Variable: Natural logarithm of house prices.t statistics in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. This table illustrates the “move in tandem” test suggested by Gertler et al. (2016). Results show that house prices in both the control and treatment group were increasing before the event. The variable Time is measured by using the listing date of properties Treatment Group Control Group Time 0.002** 0.005*** (2.12) (5.55) Constant -40.722* -87.901*** (-1.76) (-5.09) Observations 612 1,104 ===== PAGE 16 (score 151, numeric-paren 122, table-words 1, var-words 11) ===== 115 1 3 House Prices and Flood Risk Exposure: An Integration of Hedonic… Table 6   Spatial estimation results from alternative spatial models (July 2017—January 2018) Models OLS SEM SAR SLX SDM SDEM SAC Equation (2) Equation (3) Equation (4) Equation (5) Equation (6) Equation (7) Equation (8) Direct effect   D_POST 0.089*** 0.082*** 0.087*** 0.092*** 0.099*** 0.067*** 0.075*** (3.66) (3.90) (3.57) (3.79) (4.01) (3.34) (3.70)   D_TR 0.214*** 0.190*** 0.213*** 0.082* 0.104** 0.029 0.184*** (5.01) (4.51) (5.02) (1.83) (2.21) (0.66) (4.52)   D_POST X D_TR -0.139*** -0.155*** -0.119** -0.116** -0.123** -0.079* -0.091** (-2.62) (-3.43) (-2.23) (-2.19) (-2.31) (-1.84) (-2.08)   CBD (km) -0.129*** -0.195*** -0.139*** -0.112*** -0.127*** -0.138*** -0.191*** (-6.31) (-5.95) (-6.79) (-5.56) (-5.76) (-4.75) (-6.54)   Traffic_congestion (km) 0.067** 0.070* 0.094*** 0.052** 0.076** 0.052 0.163*** (2.52) (1.73) (3.42) (1.97) (2.50) (1.44) (4.42)   Nearest_river (km) -0.041 -0.274*** 0.013 -0.030 -0.044 -0.031 0.067 (-0.81) (-3.79) (0.24) (-0.56) (-0.81) (-0.41) (0.91)   Large_parks (km) -0.144*** -0.372*** -0.177*** -0.105*** -0.128*** -0.322*** -0.483*** (-4.58) (-8.40) (-5.46) (-3.25) (-3.65) (-7.48) (-11.72)   Hospital (1 km) 0.020 0.032 0.026 -0.160*** -0.152*** -0.045 0.117*** (0.49) (0.70) (0.62) (-3.21) (-3.03) (-0.73) (2.70)   School (1 km) 0.314*** 0.370*** 0.332*** 0.355*** 0.365*** 0.372*** 0.355*** (11.73) (10.74) (12.24) (12.45) (12.49) (9.62) (11.03)   Area (m2) 0.006*** 0.005*** 0.006*** 0.006*** 0.006*** 0.004*** 0.004*** (33.09) (29.35) (32.93) (32.98) (32.96) (28.22) (28.67)   Number_of_bedrooms 0.017 0.039*** 0.018 0.022 0.022 0.057*** 0.057*** (1.05) (2.77) (1.09) (1.32) (1.38) (4.27) (4.23)   Number_of_bathrooms 0.091*** 0.049*** 0.084*** 0.074*** 0.072*** 0.007 0.014 (5.76) (3.63) (5.30) (4.68) (4.56) (0.56) (1.06) ===== PAGE 17 (score 123, numeric-paren 98, table-words 1, var-words 7) ===== 116 N. Bui et al. 1 3 Table 6   (continued) Models OLS SEM SAR SLX SDM SDEM SAC Equation (2) Equation (3) Equation (4) Equation (5) Equation (6) Equation (7) Equation (8)   Floor_level -0.010*** -0.006*** -0.010*** -0.009*** -0.009*** -0.005*** -0.005*** (-7.01) (-4.86) (-6.63) (-6.52) (-6.34) (-3.83) (-3.97)   FRW (m) 0.003*** 0.002*** 0.002*** 0.002** 0.002** 0.001 0.001* (3.13) (3.02) (2.95) (2.52) (2.53) (1.38) (1.70)   Secured_area 0.172*** 0.143*** 0.166*** 0.156*** 0.156*** 0.104*** 0.116*** (5.26) (5.24) (5.08) (4.83) (4.84) (3.98) (4.37)   Balcony_or_Terrace -0.020 0.022 -0.023 -0.046 -0.046 -0.004 0.007 (-0.47) (0.62) (-0.56) (-1.12) (-1.10) (-0.12) (0.21)   Fiber_Internet -0.147*** -0.112*** -0.146*** -0.127*** -0.126*** -0.099*** -0.109*** (-4.12) (-3.79) (-4.12) (-3.62) (-3.58) (-3.50) (-3.80)   Fully_furnished -0.014 -0.012 -0.008 -0.038 -0.034 -0.011 0.006 (-0.38) (-0.37) (-0.21) (-1.01) (-0.92) (-0.35) (0.19)   Garden 0.050 0.037 0.053 0.054 0.052 0.032 0.033 (1.20) (1.06) (1.27) (1.32) (1.28) (0.98) (0.98)   Swimming_pool -0.314*** -0.286*** -0.294*** -0.226*** -0.228*** -0.135*** -0.185*** (-7.88) (-8.38) (-7.33) (-5.57) (-5.61) (-3.98) (-5.49)   Washing_machine 0.084** 0.094*** 0.081** 0.074** 0.073* 0.074** 0.080*** (2.25) (3.00) (2.17) (1.99) (1.96) (2.48) (2.62) Legal status   1(building permit) 0.001 -0.012 -0.009 -0.015 -0.013 -0.056 -0.042 (0.01) (-0.24) (-0.16) (-0.27) (-0.22) (-1.20) (-0.90)   2(red book) 0.176*** 0.162*** 0.136*** 0.067 0.064 -0.018 0.042 (3.61) (3.95) (2.74) (1.33) (1.28) (-0.45) (1.04)   3 (pink book) 0.186*** 0.114*** 0.174*** 0.155*** 0.155*** 0.051** 0.075*** (6.42) (4.65) (6.01) (5.38) (5.36) (2.17) (3.16) ===== PAGE 18 (score 123, numeric-paren 106, table-words 1, var-words 2) ===== 117 1 3 House Prices and Flood Risk Exposure: An Integration of Hedonic… Table 6   (continued) Models OLS SEM SAR SLX SDM SDEM SAC Equation (2) Equation (3) Equation (4) Equation (5) Equation (6) Equation (7) Equation (8) Direction   1 (north) 0.165*** 0.084* 0.142*** 0.127** 0.123** 0.014 0.028 (3.10) (1.88) (2.66) (2.40) (2.34) (0.34) (0.64)   2 (northeast) 0.094 0.111** 0.073 0.087 0.089 0.053 0.053 (1.44) (2.05) (1.13) (1.35) (1.38) (1.03) (1.01)   3 (east) -0.084 0.032 -0.120** -0.133** -0.133** -0.069 -0.049 (-1.45) (0.67) (-2.04) (-2.27) (-2.28) (-1.46) (-1.04)   4 (southeast) 0.076 0.086** 0.062 0.052 0.052 0.055 0.056 (1.64) (2.20) (1.33) (1.13) (1.13) (1.47) (1.47)   5 (south) 0.108** 0.039 0.100** 0.086** 0.084* 0.003 0.016 (2.50) (1.07) (2.32) (2.00) (1.95) (0.10) (0.46)   6 (southwest) 0.281*** 0.195*** 0.260*** 0.230*** 0.230*** 0.116* 0.126* (3.25) (2.71) (3.01) (2.69) (2.69) (1.70) (1.80)   7 (west) 0.255*** 0.241*** 0.211*** 0.196*** 0.193*** 0.108** 0.130** (3.70) (4.21) (3.02) (2.83) (2.80) (1.96) (2.32)   8 (northwest) 0.119* 0.140** 0.089 0.041 0.039 0.061 0.069 (1.68) (2.36) (1.26) (0.58) (0.56) (1.07) (1.20) Constant 7.911*** 8.894*** 7.906*** 7.848*** 7.906*** 8.435*** 8.568*** (53.84) (42.76) (53.95) (53.91) (53.13) (44.65) (45.53) WY W*   e.Ln_Price 2.415*** 2.581*** 2.581*** (95.11) (63.62) (63.47)   Ln_Price 0.022*** 0.026 0.079*** (3.84) (1.58) (13.76) ===== PAGE 19 (score 45, numeric-paren 25, table-words 7, var-words 2) ===== 118 N. Bui et al. 1 3 Table 6   (continued) Models OLS SEM SAR SLX SDM SDEM SAC Equation (2) Equation (3) Equation (4) Equation (5) Equation (6) Equation (7) Equation (8) WX W*   D_POST -0.431*** -0.715*** -0.039 (-3.52) (-3.29) (-0.24)   D_TR 0.932*** 0.614** 1.708*** (5.65) (2.34) (11.99)   D_POST X D_TR -0.180 0.313 -1.863*** (-0.45) (0.61) (-4.98)   / var(e.lnprice) 0.255*** 0.177*** 0.254*** 0.247*** 0.247*** 0.160*** 0.164*** (15.54) (12.51) (15.61) (15.37) (15.45) (12.01) (12.53)   Pseudo R2 0.647 0.598 0.647 0.658 0.656 0.571 0.599   AIC 3552.2 2692.2 3539.8 3484.4 3484.1 2522.8 2461.1   BIC 3748.3 2894.1 3741.8 3697.9 3703.3 2730.5 2680.4   Log likelihood -1742.1 -1311.1 -1734.9 -1705.2 -1704.0 -1225.4 -1192.6   Chi-squared 4337.1 3981.5 4368.3 4549.2 4547.9 5139.0 5379.5   Wald test of spatial terms 0 0 0 0 0 0   Observations 2,367 2,367 2,367 2,367 2,367 2,367 2,367 Dependent Variable: Natural logarithm of house prices. t statistics in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. This table presents the direct effect of flood risk on house prices. The first column is the standard linear model (OLS) based on Eq. (2). The rest of spatial models are established based on Eqs. (3) to (8). We used the Moran’s I test to show that this model is not suitable for interpreting the effect of flood risk on house prices. The model SAC has the lowest AIC/BIC values and is used for further consideration. The part of the table regarding the spatial weight matrix W shows the available coefficients of the spatial weight matrices depending on which type of models. The formulae in Section 2.2 help understand why W coefficients differ across the models. See Appendix Table A1 for explanations regarding these spatial estimates ===== PAGE 20 (score 25, numeric-paren 0, table-words 9, var-words 7) ===== 119 1 3 House Prices and Flood Risk Exposure: An Integration of Hedonic… attributed to real estate companies that built properties on exiting waterways of the city. This is compensation for harming the environment and affecting people’s lives. On the other hand, tax revenue equal to the indirect effect might be used for inform- ing the residents of flood risk areas, thus raising the awareness of flood risk. Therefore, house prices increase 7.7% from before to after the event among the control group relative to a 1.7% (= 0.077—0.094) decrease in house prices among the treatment group during the same time span. Converting from the logarithm form, the effect of the pluvial flood on house prices was reflected by a discount by 9% of the treatment group compared to the control group from before to after the event. This discount rate could be used as a foundation for proposing an environmental tax (more details are in conclusion). Our results (9% price discount) are consist- ent with results obtained from other studies (e.g. 5.7%—Bin and Polasky (2004), 8.6%—Posey and Rogers (2010), 10.3%—Rabassa and Zoloa (2016), 6.7%—Ram- baldi et al. (2013), and 6.2%—Samarasinghe and Sharp (2010)). Price Recovery After the Flood More analyses are carried out to examine whether houses regain their value. The original dataset used in Table 5 is added with more observations for several months after the flood. Specifically, compared to the original D_POST in Table 5, the new variable DPOST_5m in Table 8 keeps the same three months before the event (value = 0 for the date of listing from 01-Jul-2017 to 30-Sep-2017) and adds two more months after the event (value = 1 for 01-Nov-2017 and 01-Apr-2018). Similarly, DPOST_6m, DPOST_7m, DPOST_8m, DPOST_9m, and DPOST_10m include data 6 months, 7 months, 8 months, 9 months, and 10 months respectively after the flood. The housing market was inactive as Vietnamese people were taking a break for Lunar New Year; hence the disappearance of DPOST_4m. Table 8 shows the recovery of house prices after the flood. Table 8-Panel A repre- sents the direct effect in the SAC model while Table 8-Panel B reports the spillover and total effects. The positive and significant spillover effects indicate house prices are not only affected locally but also by neighbouring inundated areas. Results in Table 8-Panel B help to conclude that the negative effect from the flood event on house prices slowly diminishes as the houses regain their value from month to month. Based on the total effects, houses in the treatment group increased their prices gradually from 4.6% to 5.1% compared to those in the control group, if the duration of after-effect months is extended from 3 to 7–10 months. Figure 4 shows house price changes for the control and treatment groups after the flood event on 30 September 2017. First, we find that house prices (red line) in the treatment group are higher than those (black line) in the control group. This is because houses in the treatment group are close to the city centre, with easy access to the hospital, inside secured areas and are more likely to list with pink book-the certification for both land use rights and house ownership based on the summary statistics by sub-groups in Table 4. Second, in the first four months after the flood event, house prices were reduced in the treatment group and increased in the control ===== PAGE 22 (score 37, numeric-paren 15, table-words 3, var-words 14) ===== 121 1 3 House Prices and Flood Risk Exposure: An Integration of Hedonic… regarding these properties have potential risks of future disputes. Moreover, obtain- ing only a building permit is not enough to significantly increase the price. The views to the west, and southwest were believed to be the best direction to have for a home. The north view looks towards the energetic city centre, while the west and southwest views show the peaceful green reserve of HCMC—the value-of- a-view perspective (Luttik, 2000; Samarasinghe & Sharp, 2008). Possible explanations for the negative sign of the swimming pool variable include the concerns for either hygiene (in shared pools of apartments) and high mainte- nance costs (in villas or townhouses). Vietnamese house buyers realised the incon- venience of a swimming pool and later preferred a playground for their children or an in-house garden for their parents. The sign of the floor level variable also differs from expectation. In contrast to a Westerner’s perspective, Vietnamese like to reside in lower floors for the con- venience of mobility. The structure of Vietnamese households, in general, includes children and elderly parents who would feel safer when they can stay closer to the ground. Unlike other countries, installed fibre connection surprisingly has a negative impact on house prices. Most building contractors in Vietnam work with an internet provider to set up the fibre system. House owners need to connect to a predestined provider; therefore, it restricts their freedom of choice of providers. In the recent years, the freedom of choice has been improved for other new projects. Further research need to be done to validate the outcome of this situation. Although this study adopts the basic DD model by Atreya and Ferreira (2015), we have further developed the DD model by integrating spatial models. In doing so, we can examine the spillover effect, i.e. if residential property prices are affected by neighbouring house prices and if flood event impact on a certain zone might influence house prices of bordering zones. Since this paper studies the impact of a flood event on house prices and the model does not control for the inundated property directly as done in Atreya and Ferreira (2015); thus, results are different from those of Atreya and Ferreira (2015) in which inundated floodplain properties were discounted by 48% immediately after the flood. We find that the effect of the Table 7   SAC decomposition estimation results (July 2017— January 2018) Dependent Variable: Natural logarithm of house prices. t statistics in parentheses.* p < 0.10, ** p < 0.05, *** p < 0.01. This table focuses on the direct, indirect (spillover) and total effects of the variable D_ POST and the interaction term D_POST × D_TR in the SAC model. These values are used for our final interpretation of flood risk impact on house prices SAC model Direct effect Spillover effect Total effect D_POST 0.075*** 0.003*** 0.077*** (3.70) (3.59) (3.70) D_TR 0.184*** 0.007*** 0.191*** (4.52) (4.35) (4.52) D_POST × D_TR -0.091** -0.003** -0.094** (-2.08) (-2.09) (-2.08) ===== PAGE 23 (score 7, numeric-paren 0, table-words 0, var-words 7) ===== 122 N. Bui et al. 1 3 pluvial flood on house prices among the treatment group experienced a discount by 9% compared to the control group from before to after the event. We also study if houses retain their value after a temporary reduction caused by the flood, which has not been included in Atreya and Ferreira (2015). Residents in HCMC usually experience a typical six-month rainy season, and pluvial flooding events tend to happen in the last three months. Therefore, we select the four months after the flood event as the post period. However, it may raise the concern that the short period – four months after the event- may not be sufficient to isolate the impact of flood risk on house prices due to several plausible alternative explanations from the demand and supply sides of the housing market. For instance, potential buyers might want to postpone purchasing homes in an area affected by flooding due to other reasons such as access, unavailability of labour to do neces- sary repairs, and sellers needing time to repair the affected homes. Moreover, sell- ers who sold homes straight after the flood event could be the liquidity-constrained sellers who needed to sell as quickly as possible. For example, these sellers could be the ones who had committed to purchasing another house and were therefore forced to sell at low prices in a bad market. In our future research, the propensity score matching technique (Papadogeorgou et al., 2019) can be employed to account for selection bias in affected houses in the housing market by using houses with similar characteristics. Conclusion This study sets out to determine the impact of a large pluvial flood event on house prices within the southern area of HCMC, Vietnam. The results of this investigation show that houses exposed to the flood event experienced a significant price discount by 9% after a large flood event on 30 September 2017. The paper extends our knowledge by estimating the impact of a pluvial flood event on house prices in Vietnam. It contributes to the literature by shifting the focus towards the context of developing countries and challenging the issue of data availa- bility. In the past, pluvial floods have received little attention, even though the cumu- lative costs from constantly disrupting the people’s daily travel are relatively high compared to a single large flood. This is the reason why we choose pluvial floods to be the main focus of the present study. The methodology of the integrated hedonic model and spatial economic approach into a single analytical framework is the first attempt to be applied to estimate the economic impact of pluvial floods, and it can be applied extensively. The findings of the study provide insights to design effective insurance policies. The evidence from this study can be used as a reference for policymakers to design a flood insurance scheme. Without sufficient capital to properly repair the houses after the flood, owners can become even more vulnerable. The scheme can help financially-difficult people in high-risk zones. The premium price could also act as a source of risk information for both owners and sellers. Blocking the existing channels with new residential areas led to frequent flooding. Now being constantly under the threat of inundation, even when it is not raining, the ===== PAGE 24 (score 97, numeric-paren 82, table-words 2, var-words 2) ===== 123 1 3 House Prices and Flood Risk Exposure: An Integration of Hedonic… Table 8   Price recovery after the flood (SAC estimation results) 5 months 6 months 7 months 8 months 9 months 10 months Panel A – Direct effect   D_TR = 1 0.324*** 0.306*** 0.300*** 0.323*** 0.320*** 0.319*** (12.90) (12.54) (12.18) (13.87) (13.96) (13.98)   D_POST_5m = 1 0.031* (1.68)   D_TR = 1 X D_POST_5m = 1 0.030 (1.28)   D_POST_6m = 1 0.031* (1.68)   D_TR = 1 X D_POST_6m = 1 0.036 (1.59)   D_POST_7m = 1 0.035* (1.89)   D_TR = 1 X D_POST_7m = 1 0.045** (1.96)   D_POST_8m = 1 0.042** (2.30)   D_TR = 1 X D_POST_8m = 1 0.051** (2.25)   D_POST_9m = 1 0.045** (2.46)   D_TR = 1 X D_POST_9m = 1 0.050** (2.25)   D_POST_10m = 1 0.045** (2.50)   D_TR = 1X D_POST_10m = 1 0.050** (2.23) W*   Ln_Price 0.045*** 0.049*** 0.051*** 0.056*** 0.057*** 0.057*** (15.43) (18.82) (19.95) (23.88) (24.75) (24.85)   e.Ln_Price 3.170*** 3.434*** 3.616*** 4.940*** 5.336*** 5.376*** (55.00) (96.01) (79.90) (52.30) (58.82) (60.17)   / var(e.Ln_Price) 0.084*** 0.085*** 0.087*** 0.086*** 0.086*** 0.086*** (43.47) (46.84) (49.24) (53.10) (55.29) (55.59)   Pseudo R2 0.747 0.751 0.752 0.764 0.768 0.770   Log likelihood -688.1 -823.9 -965.2 -1082.7 -1171.9 -1173.9   Chi-squared 30,498.7 43,989.7 48,711.4 105,231.3 130,008.8 137,067.8   Wald test of spatial terms 0 0 0 0 0 0 Panel B – Spillover and total effects Spillover effect   D_TR 0.007*** 0.008*** 0.007*** 0.008*** 0.008*** 0.008*** (10.37) (10.98) (11.14) (12.69) (12.83) (12.81) ===== PAGE 25 (score 73, numeric-paren 57, table-words 3, var-words 4) ===== 124 N. Bui et al. 1 3 city is considering a four-billion-USD flood prevention project (Vietnamnet, 2018). Housing projects built on top the channels should be taxed based on the converted land area to fund flood protection infrastructure. Another important practical implication is that estimates of price discount due to flood risk could be used to justify flood prevention policies by the government. Many flood protection measures in HCMC are criticised as to their cost-effective- ness. For example, the city hired an expensive pump for removing surplus water on Nguyen Huu Canh Street (Huu, 2018). Therefore, further research around the costs and benefits of different flood protection measures is worthy of being investigated. Dependent Variable: Natural logarithm of house prices. t statistics in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. ~ Nm denotes the corresponding month, here, N = [5, 6, 7, 8, and 9]. The model includes other control variables, such as, location, structure, neighbourhood, facilities, direction and legal status Table 8   (continued) 5 months 6 months 7 months 8 months 9 months 10 months   ­D_POST_Nm~ 0.001* 0.001* 0.001* 0.001** 0.001** 0.001** (1.67) (1.67) (1.88) (2.29) (2.45) (2.49)   D_TR × D_POST_Nm 0.001 0.001 0.001* 0.001** 0.001** 0.001** (1.27) (1.59) (1.96) (2.24) (2.24) (2.23) Total effect   D_TR 0.331*** 0.314*** 0.308*** 0.331*** 0.328*** 0.327*** (12.93) (12.56) (12.21) (13.90) (13.99) (14.01)   D_POST_Nm 0.032* 0.032* 0.036* 0.043** 0.046** 0.046** (1.68) (1.68) (1.89) (2.30) (2.46) (2.50)   D_TR × D_POST_Nm 0.030 0.037 0.046* 0.052** 0.051** 0.051** (1.28) (1.59) (1.96) (2.25) (2.25) (2.23)   Observations 3,780 4,389 4,850 5,644 6,117 6,183 8.0 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 ** ** ** ** ** ** * ** 4 months 10 months 9 months 8 months 30 September 2017 Flood event s e cir p e s u o h f o m h tir a g olla r u t a n control group treatment group 7 months ** * Fig. 4   House prices recovery after the flood event on 30 September 2017. Notes: **5% level of signifi- cance; *10% level of significance ===== PAGE 26 (score 40, numeric-paren 1, table-words 17, var-words 2) ===== 125 1 3 House Prices and Flood Risk Exposure: An Integration of Hedonic… Appendix Table 9   Auxiliary tests We perform further test for the hypotheses that the SAR or SEM model can be nested in the SAC model (see Fig. 1). First, if λ = 0, the SAC model can be reduced to SAR. Second, if ρ = 0, the SAC model can be replaced with SEM. Wald tests of spatial terms together with these LR tests show that the hypotheses of the simplification of SAC into either SAR or SEM must be rejected Panel A: Results of Moran’s I test for house prices   Ho: error is i.i.d     Moran’s I 0.111     Chi2(1) 6404.82     P-value 0.00 Panel B – Spatial Autocorrelation Tests   Ho: Error has no spatial autocorrelation     GLOBAL Moran MI = 0.6590 Z(226.008)     P-Value 0.00     GLOBAL Geary GC = 0.4110 Z(-18.983)     P-Value 0.00     GLOBAL Getis-Ords GO = -0.2925 Z(-6.3e + 04)     P-Value 0.00     Moran MI Error Test = 475.9766 Z(1.6e + 05)     P-Value 0.00     LM Error (Burridge):Chi2(1) 2.07e + 04     P-Value 0.00     LM Error (Robust):Chi2(1) 2.01e + 04     P-Value 0.00   Ho: Spatial lagged dependent variable has no spatial autocorrelation     LM Lag (Anselin):Chi2(1) 2937.9678     P-Value 0.00     LM Lag (Robust): Chi2(1) 2341.0011     P-Value 0.00   Ho: No General Spatial Autocorrelation     LM SAC (LMErr + LMLag_R): Chi2(2) 2.31e + 04     P-Value 0.00     LM SAC (LMLag + LMErr_R): Chi2(2) 2.31e + 04     P-Value 0.00 Notes: To enhance the validity of the Moran’s I test, we used LM tests and robust LM tests. Both tests indicate that the hypotheses of no spatially lagged dependent variable and no spatially auto-correlated error term must be rejected with the significance level of 1% Panel C– Postestimation for the SAC model—LR tests   LR test for SAR (λ = 0): Chi2(1) 40.167   P-Value 0.00   LR test for SEM (ρ = 0): Chi2(1) 72.410   P-Value 0.00   LR Test SAC vs. OLS (ρ + λ = 0): Chi2(2) 102.026   P-Value 0.00 ===== PAGE 27 (score 5, numeric-paren 0, table-words 1, var-words 3) ===== 126 N. Bui et al. 1 3 Table 9, Figs. 5, 6 Fig. 5   Map of houses on arterial road. Notes: This study uses only the houses located on arterial roads within a radius of one kilometre. Each of these roads is at least 1 km different from the others. If the arte- rial road is flooded, there is no chance that the small roads connect to it can stay dry since arterial roads are built higher than smaller roads connected to them