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The cost of floods in developing countries’ megacities: a hedonic price analysis of the Jakarta housing market, Indonesia


Although many megacities in developing countries experience floods annually that affect a large number of people, relatively few empirical studies have evaluated the associated costs. This paper estimates such costs by conducting a hedonic price analysis—providing evidence regarding the impacts of floods on the housing market. A robust regression technique on a simple linear transformation model, and a maximum likelihood estimation technique on the spatial lag version of the simple linear transformation model, are utilised to estimate the correlation between the level of the 2007 floods and monthly housing rental prices in Jakarta, Indonesia. This paper sheds light on the fact that in developing countries’ megacities, the total cost of floods among households is significantly lower compared to the total amount of funding needed to permanently eliminate floods in these megacities. Hence, a constant exposure of the urban areas in developing countries to flood damage will most likely keep happening.

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Fig. 1

Source: United Nations Department of Safety and Security (UNDSS), 2007

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  1. Value measured as a monthly rate—represented as dependent variable monthly rent—is based on potential and actual rental prices. This considers the fact that 70% of observations represent owner-occupied homes with no set rental price, therefore it is considered as a potential rental price only [asked as “Rent would pay per month” (IFLS 2007)]. The other 30% are renter-occupied homes with an actual rental price.



  4. Another option is to use the average treatment effect using the spatial lag model. Assuming a dummy variable of flooding, the house equals one whether it was flooded in 2007 at certain water level (e.g., 30 cm, 50 cm or 60 cm) and above, and zero otherwise. The findings are presented in “Appendix D”. Since they are sensitive towards the definition of flooded house or not, this paper prefers to utilise a continuous variable of flood level.

  5. Following Follain and Malpezzi, we test the functional form chosen to determine whether it suits our data set better than that of using a linear model. In this regard, the Box-Cox test was utilised to compare the goodness-of-fit of the two functional forms. Given the results, in our case, they are significantly different in terms of goodness-of-fit; the log-linear function has a lower residual sum of squares compared to that of the linear functional form. Therefore, we use the log-transformed model to look at the coefficients of the empirical model.

  6. Similarly, a spatial error model can be considered, which supposes that spatial dependence arises due to measurement errors or some omitted variables that are correlated and vary spatially. The Lagrange multiplier (statistic = 23.710; p value = 0.000) and the robust Lagrange multiplier (statistic = 12.039; p value = 0.001) tests show spatial error dependence. The spatial error model findings are discussed in more detail in “Appendix E” where it can be seen that the flood coefficient is relatively similar to that of the spatial lag model. This paper prefers to utilise the results for the spatial lag model for its analysis and conclusion, since the spatial lag model is simpler and the spatial correlation can be explicitly seen.

  7. Additionally, an OLS regression is applied to analyse whether or not the effects of flooding on rental prices for Jakarta residents are similar for both owner occupied and rentals. The results of this analysis are shown in “Appendix F” and indicate that rentals are more likely to be vulnerable to impacts of flooding.

  8. Housing attributes are mainly represented by dummies, so it is possible the OLS model has a certain degree of multicollinearity. This could explain the low significance levels and opposite signs obtained from a linear regression. Variance inflation factors (VIFs) are used to test for multicollinearity among the independent variables. According to Gujarati (1995), multicollinearity may be a problem if the VIF is greater than 10. In this study, the mean of the VIF values for all of the variables was 1.90 for the OLS regression. This means there is no multicollinearity or no correlation between the independent variables.


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The authors would like to thank M. Agung Widodo for managing the IFLS data set for this paper. Some financial supports were received from the Australia Indonesia Centre (AIC). All mistakes are the authors’ responsibility.

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Correspondence to José Armando Cobián Álvarez.

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Appendix A: Hedonic property value method

The hedonic price method provides an intuitive analytical tool for studying the effects of property attributes and spatially integrated amenities on housing prices. Lancaster (1966) pioneered the development of its theoretical foundations, derived from the theory of consumer demand. The central assumption is that consumer utilities are not based on the goods per se, but instead on the individual “characteristics” of goods—their composite attributes. Although Lancaster (1966) was the first to discuss hedonic utility, there was nothing about pricing models and the properties of market equilibrium. To fill this gap, Rosen (1974) studied the demand–supply interaction in which they bid (consumers) and offer (suppliers) the combination of attributes and prices of the goods that keep the market in equilibrium.

Additionally, Rosen’s (1974) studies form the basis for using the hedonic property price model to estimate the value of environmental amenities. The argument is that the attributes of residential properties—recognised as heterogeneous goods, such as structural, neighbourhood and environmental characteristics—are reflected in the price differentials that affect lessee preferences in a market clearing equilibrium condition (Rosen 1974). The advantage of using this method over other preference estimation techniques is that it makes use of actual market transactions to recover value estimates for non-market attributes (Bin et al. 2008a). These related to aesthetic sights and their closeness to recreational sites such as parks, and beaches, as well as the quality of the environment in terms of air, water and noise pollution.

According to this method, the hedonic price function is typically represented as:

$$P_{i} = f\left( {s, n,l, e} \right),$$

where \(P_{i}\) is the price of property \(i\) which is a function of structural characteristics (e.g., house size, number of rooms, quality of walls), \(s\); neighbourhood characteristics (for example, ethnic composition, crime rate, flow of traffic), \(n\); location characteristics (e.g., proximity to economic centres, distance to highways, accessibility to public transport), \(l\); and environmental characteristics (such as air pollution and flooding), \(e\). Therefore, characteristics that generate benefits for households, such as a larger number of rooms or home size, increase the property’s price, while characteristics that imply costs for households, such as a neighbourhood with a high crime rate, reduce the property’s price.

Given that the basis of the method is to find what portion of the price is determined by the hedonic variable, we obtain the environmental attribute (which is flooding) by calculating the partial derivative of the price with respect to the variable \(e\), ∂\(P_{i}\)/∂\(e\). It gives us the marginal implicit value for an additional unit of the environmental asset, and thus enables an estimate of its monetary value.

Appendix B: Mean comparison between IFLS and SUSENAS datasets

To support the representation of Jakarta’s population, we provide the means of certain variables from Indonesia–National Socio-Economic Survey (SUSENAS) 2007 that was accessed for this paper. The following table shows the means compared with those of IFLS 2007 (Table 4).

Table 4 Mean comparison between IFLS and SUSENAS datasets.

Appendix C: Distribution of independent variables

The below table represents the distribution of continuous variables when they are in levels. Those on the left, demonstrate the skewed towards zero on the axis, while those on the right demonstrate a spread across the axis (i.e., closer to normal distribution) (Fig. 2).

Fig. 2
figure 2

Distribution of independent variables in levels and log transformed

Appendix D: Spatial average treatment effect model

Unlike previous studies, this study uses a continuous measure of flood water (in centimetres) in the empirical analysis. To find whether or not a house within the flooded area lowers the rental price at any certain water level, we construct a conventional binary measure of flooding at the neighbourhood level based on three different threshold water levels, e.g., 30, 50 and 60. A house is considered flooded when the water level in the area during the flood event in 2007 was 30 cm (or 50 cm or 60 cm) and above; otherwise, the house is considered not flooded. We then estimate the impact of flooding on location rental prices using the spatial lag model:

$$y = \beta_{0} + \rho \varvec{Wy} +\varvec{\beta}_{1} \varvec{x}_{1} +\varvec{\beta}_{2} \varvec{x}_{2} +\varvec{\beta}_{3} D_{\text{f}} + \varepsilon .$$

From Table 5, it can be seen that results are sensitive towards the definition of being flooded or not, i.e., flood water thresholds define whether the house is flooded or not. However, it should be considered that any marginal difference between the chosen flood levels (e.g., from 30 to 50 cm and 50 to 60 cm) could be irrelevant and trivial for a household, as they are still experiencing flooding with its associated damage to the home. Therefore, we prefer to use a continuous flood-level variable as our main variable of interest.

Table 5 Results of spatial average treatment effect.

Appendix E: Estimation results using spatial error model

The spatial error model takes the following form:

$$y = \beta_{0} +\varvec{\beta}_{1} \varvec{x}_{1} +\varvec{\beta}_{2} \varvec{x}_{2} +\varvec{\beta}_{3} D_{f} +\varvec{\beta}_{4} \left[ {D_{\text{f}} f} \right] + \varepsilon ;\quad \varepsilon = \lambda \varvec{W}\varepsilon + \mu ,$$

where \(\mu\) is the independent and identically distributed (i.i.d.) error term and \(\lambda\) is the spatial error parameter. The understanding of the spatial error model is close to the moving average model, whereby each observation error in the time series can also be affected by other observation errors. Ignoring the spatial error term, the OLS coefficients will be inefficient as it violates the assumption of independence among disturbance terms (Anselin 1988) (Table 6).

Table 6 Results of spatial error model

Appendix F: Owner-occupied and rental property comparison

When running separate regression for the dummy variable homeowner (1,0), we find that the study variable (LOGflood) is statistically significant at 5% for rental properties. This means that those renting are likely to be more vulnerable to flood impacts than those owning or buying. Owning a home can be considered as an indicator of income and economic resources that may support flood victims to cope with the effects of flooding (Table 7).

Table 7 Results of OLS for both owner-occupied and rental properties

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Cobián Álvarez, J., Resosudarmo, B.P. The cost of floods in developing countries’ megacities: a hedonic price analysis of the Jakarta housing market, Indonesia. Environ Econ Policy Stud 21, 555–577 (2019).

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