exposure.<sup>3</sup> Both variables are used in the analysis to proxy exposure to flood risk by using two binary variables: 1) if the household reports having been directly affected by floods, and 2) if the household perceives the risk of future flood exposure as high or very high. Appendix Table A.1 provides the precise questions and the definitions of the flood exposure variables for each city. Self-reported disaster data have two main advantages. First, they capture actual household experiences and expectations rather than modeled risk estimations. Flood impacts can differ from street to street depending on elevation, housing quality, access to, and topography, etc., and flood models do a generally poor job of capturing localized flood risk, especially in urban areas.<sup>4</sup> Second, in the context where information on flood risk as defined in flood maps is not readily available, up-to-date or well-known to the public, past experiences of flood and the perception of risk can be better predictors of rent values. The use of self-reported proxies for flood risk may therefore increase the accuracy of hedonic models when used in contexts where buyers (or renters) are not fully informed.

In hedonic analyses, house-transaction prices are ideal to define housing value (Bishop et al., 2020). However, they can be difficult to access even in developed countries. Bishop et al. (2020) provide a list of the countries where microdata on housing transactions is available: it includes several European countries, the United States, Australia, Chile, China, Japan, and the Republic of Korea. Examples of studies that use transaction data in middle-income countries are Argentina (Rabassa and Zoloa, 2016) and Indonesia (Alvarez<sup> ́</sup> and Resosudarmo, 2019). Limited data availability explains why, while the hedonic approach to estimate households’ willingness to pay for a change in an environmental amenity is used extensively by researchers, it is difficult to find studies of African countries. Household survey data overcome this limitation since surveys often capture information needed to construct a hedonic pricing model, including housing characteristics, access to services and different determinants of housing costs. The surveys exploited in this study include detailed questions on actual housing costs and rents and an estimate of how much a tenant would pay in rent for the dwelling if rented, as estimated by the respondent. Since only a subset of households are renters, exploiting the “self-estimated” rental values helps increase the sample size. The analysis therefore combines reported rent values and the self-estimated rent values<sup>5</sup> to construct a rent value variable for all four cities. Observations with missing values for both items were excluded. Comparison of the rents, self-estimated rents, and the combined variable distributions are depicted in Appendix Figure B.1. Measurement errors can affect rent and self-estimated rent values. To reduce these errors, outliers were removed and replaced with estimated values.<sup>6</sup> Values are in local currency and transformed to 2017 PPP US$.

The final sample is based on observations without missing values for rents and self-estimated rents. It includes 3,132 households in four cities and four countries: Accra (996 households), Antananarivo (919 households), Dar es Salaam (466 households), and Addis Ababa (751 households). Table 2 presents a summary of the different database characteristics. More detail on the survey methodology and char-

> 3The questions related to flood perception were almost identical for all four cities although for Antananarivo the time horizon was 5 rather than 2 years.

> 4In most studies, flood models or risk area identifications (Beltr ́an et al., 2018) are large surfaces.

> 5The question related to self-estimated rent is “If somebody else wants to rent a dwelling just like this today, how much money would he/she have to pay monthly?”

> 6Outliers are defined as observations that deviate from the mean by three standard deviations or more. Outliers were replaced by estimated rents/self-estimated rents. Rents/self-estimated rents are estimated based on data on expenditure, housing conditions, access to public services, and time to business center. Outliers represent 0.5% to 2% of observations, depending on the city.

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