with informality. Directly affected households have more insecure tenure arrangements than non-affected ones. Regression analysis shows that they are less likely to hold proof of tenure for the dwellings they rent. Female headed households are more likely to be directly affected than male headed households. Since women often have the double responsibility of provider and caretaker, they may face particular challenges when coping with floods. This will be discussed in more detail in the following sections. Directly affected households are less likely to pay for garbage services – a potential proxy for garbage collection quality . Finally, being directly impacted by floods is not related to housing quality or access to public services. This is in contrast with results found in Accra, Ghana (Erman et al, 2018), where a similar study was carried out.

Indirectly impacted households tend to be located in areas without access to reliable infrastructure. These households are less likely to have drains outside their dwelling and drains in the area tend to be overall rare and of poor quality as measured by data collected by Ramani Huria 2.0.<sup>23</sup> There is also evidence that other services such as water and sanitation are of lower quality among the indirectly affected households compared to the non-affected population. This seems to suggest that indirectly affected households live in areas where heavy rain can more easily affect infrastructure due to poor quality and bad drains. Therefore, improving the quality of infrastructure could mitigate the indirect effects of floods. In addition, indirectly affected households also tend to have a higher number of household members than other households. This is intuitive since having more household members increases the risk of having one of them miss work, school or fall ill due to floods.

Households residing in flood risk areas tend to live closer to work. The travel time of breadwinners of directly affected households is comparatively shorter than non-affected households.<sup>24</sup> They live 13 minutes closer to work. This suggests that households potentially face a trade-off between living close to work and lower flood risk. The contrast is even clearer for households that are defined as frequently exposed to floods. Average travel time for the breadwinners of these households is 17 minutes while it is 43.5 minutes for the rest of the sample – a difference of 61% (see Table 16 in Appendix 2). This result is confirmed in regression analysis (see Table 20 in Appendix 4). Frequently exposed households reside in low elevation, high population density areas close to economic opportunities. But this comes at a great cost.

Surprisingly, flood risk does not seem to affect rent values. Rent values seem to be the same or even higher among households experiencing the floods (directly, indirectly or frequently – see Table 14, Table 15 and Table 16 in Appendix 2). The result is confirmed in regression analysis (Table 22 in Appendix 4). This could be due to a number of factors. Dysfunctional and informal rental markets may cause rent values to not internalize factors that generally affect price – such as poor access to services and flood risk. In addition, many rental contracts in Dar es Salaam are annual, so the family may be unaware of the flood risk upon moving in. In fact, among the households that indicated that their dwelling was located in a flood prone area, 71% stated that they were unaware of the flood risk when they moved in. Observations from focus group discussions indicated that in many areas the situation has worsened with population growth and overcrowding without the necessary upgrading of drainage systems and other infrastructure. Moreover, among households that rent, 74% provided a down payment to access the house, which could make it more difficult to move. Finally, since floods seem to affect different areas on different occasions, it is difficult to predict which areas will be exposed to floods, even with access flood models. Consequently, it will also be difficult for the market to price flood risk exposure correctly.

However, affected households consistently value their own dwelling at a lower price than non-affected households. Households owning their dwelling were asked to estimate how much the dwelling would be worth if someone bought it at the time of the survey. The self-evaluation showed a clear trend. Directly, indirectly and frequently affected households consistently estimated their dwelling to be worth less than the

> 23 The second phase of the Ramani Huria project launched in 2017 as part of TURP.

> 24 See Table 14 with results from simple t-tests analysis in Appendix 2.

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