acteristics for Accra, Dar es Salaam, and Antananarivo can be find in Erman et al. (2020), Erman et al. (2019), and WB (2017) respectively.

TABLE 2: Databases descriptions

|**City**|**Database**|**Area**|**Date**|**Flood/s**|**HH selection method**|
|---|---|---|---|---|---|
|Accra|Disaster-Poverty<br>household survey|9 neighborhoods<br>considered slum areas<br>in the Odaw basin area|May-Jun/2017|2015 food|4 strata: combination of high/low<br>risk of food and high/low level of poverty.<br>Informal settlements.<br>Not representative at the citylevel.|
|Antananarivo|Urban Living<br>Standards|Greater Antananarivo|Oct-Dec/2017|2015 food<br>“Chedza food”|2 strata: risky and no risky areas, where<br>risky are EAs affected by Chedza.<br>Representative at the citylevel.|
|Dar es Salaam|Disaster-Poverty<br>household survey|Dar es Salam|Nov/2017,<br>Sep/2018|Most recent food,<br>2018 food|3 levels of food risk (no risk, low risk and<br>high risk). Representative at the citylevel.|
|Addis Ababa|Disaster-Poverty<br>household survey|Addis Ababa|May-Jun/2017|Most recent food|Representative at the city level.|

# **3.2 Accessibility to jobs**

In an efficient housing market, property prices rise with increased access to jobs and amenities (Ahlfeldt, 2013), and in choosing where to live people make a trade-off between low housing value and proximity to jobs. To understand rents, we need to understand access to economic opportunities and how it influences housing markets. However, there are two significant data constraints, common to developing country cities which make it difficult to capture access to economic opportunities, particularly for SSA cities: 1) lack of information on job locations, and 2) lack of information on transit routes and travel times (Peralta-Quiros et al., 2019). This study captures access to economic opportunities using the spatial density of employment developed in Barzin et al. (2022). This measure is used to proxy for job density. It was created using a machine learning based algorithm, and data from OpenStreetMap (OSM) and Google Earth Engine (GEE). OSM data provide information on the location of different types of public and private services and institutions (hospitals, pharmacies, banks, schools, etc.), amenities (rivers, parks, etc.), and infrastructure (roads, airports, etc.). GEE data were used to add information on population density, intensity of night lights, and land cover. This is a novel way to estimate job density. The main advantage of this proxy is that it can be calculated for any city which has accurate and available OSM and GEE data and where data on economic activity are scarce or missing. It allows comparison of economic activity across cities, based on a unique measure. The job density proxy measures relative job density where a low value denotes lower job density and a high value higher job density. The values themselves cannot be interpreted directly. The maps of the four cities analyzed here, and with the spatial distribution of jobs are provided in Appendix Figures D.1 to D.4.

# **3.3 Descriptive statistics**

Thirteen percent of households in our sample had been affected directly by at least one flood event: 44 percent in Accra, 9 percent in Antananarivo, 20 percent in Dar es Salaam, and 8 percent of households in Addis Ababa. In Accra and Dar es Salaam, the proportion of households that perceived risk of future 8