in the spirit of microsimulations, these rules allow predicting new imputed housing prices for the damaged dwellings. Fourth, the predicted average value of all dwellings in each geographic area is computed. And fifth, plugging these new neighborhood effects in the hedonic price function allows predicting new imputed housing prices for all dwellings. Steps four and five are then repeated until imputed housing prices converge to stable values.

The proposed approach is illustrated in the case of Nepal, a country with a high vulnerability to natural disasters (World Bank 2022). The focus is on the cost of the 2015 Gorkha earthquakes. At the aggregate level, this exercise yields a disaster cost similar in magnitude to the one jointly reported by the Post-Disaster Needs Assessment (PDNA) conducted in the months that followed (National Planning Commission 2015). At a more disaggregated level, however, the estimated disaster cost is significantly higher in the most severely affected districts, and lower elsewhere. The main reason for the discrepancy with the official assessment stems from the strength of neighborhood effects in the worst-hit localities.

# **2. Relationship to the literature**

The focus on the value of dwellings marks a departure from previous approaches to assess the cost of disasters, which have tended to rely on other _indicators_ . Simplifying, those other indicators include national accounts, field surveys of physical assets, and satellite images.

Before-and-after comparisons of national accounts data on aggregate output and its components provide a straightforward way to assess the impact of disasters in developing countries. Given the large number of events occurring worldwide in every single year, the number of observations available to conduct comparisons of this sort is substantial. Large samples, in turn, allow to qualify impacts depending on the type of disaster, its magnitude and the country’s development level (Loayza et al. 2012). Some studies have even focused on narrower sets of disasters, such as storms (Ishizawa and Miranda 2019), hurricanes (Strobl 2012), and earthquakes (Webb et al. 2002).

Studies based on this approach have shown that severe disasters trigger significant contractions in output and trade (Gassebner et al. 2010). However, their estimates are quite sensitive to assumptions, especially because observed income in the years following a disaster may be boosted by reconstruction-related investments. Moreover, with few exceptions (Kashiwagi et al. 2021), estimates based on national accounts fail to provide a breakout of the cost by geographic areas, which is critically important to target support. Importantly, waiting for the release of reliable national account data may not be practical when quick estimates are needed to guide assistance and reconstruction efforts on the ground.

Rapid field surveys like those conducted in the context of PDNAs address some of these concerns. Field surveys typically focus on the geographic areas most affected by disasters. Teams on the ground take stock of existing public and private assets, assess their condition in the aftermath of the disaster, and collect qualitative input from local economic agents. The result is a rich information set that is used to forecast the impact of the disaster on local economic activity and 4