Urban economics, in turn, has shown that housing prices are very much linked to local economic conditions. At the aggregate level, housing prices are positively correlated with economic activity (Aizenman et al. 2016). But at the local level they are influenced by _neighborhood effects,_ or local spillovers whose impact decreases with distance (Goodman 1978, Durlauf 2004, Rossi-Hansberg et al. 2010, Ioannides 2010). The choice of a dwelling to shelter in and a neighborhood to live in are indeed joint decisions, influenced by the characteristics of the dwelling, but also by the social interactions, earnings opportunities and public services the neighborhood gives access to.

Because housing is a good proxy for household welfare, and housing prices are affected by local conditions, the change in the value of dwellings in the immediate aftermath of a disaster can be interpreted as a measure of the welfare loss experienced by their occupants. This interpretation does not require assuming that housing is the only household asset, but only that other assets and liabilities they may hold – such as cash or informal debt – are not affected by the disaster, and especially by its impact on their neighborhoods.

The change in imputed housing prices is estimated through _hedonic pricing functions_ . These are regressions that include among their explanatory variables the characteristics of the dwelling and those of the location it sits in (Balcazar et al. 2017, Silver 2016). The proposed approach pays special attention to neighborhood effects. In the same way as more prosperous neighborhoods are more attractive, eliciting a greater local demand for housing, more severely damaged neighborhoods should experience a greater decline – in relative terms – in the value of their dwellings. The estimation builds on the theoretical underpinnings provided by Grieson and White (1989) and is informed by applications to the United States by Ioannides and Zabel (2003) and to China by Song and Zenou (2012).

The estimated change in imputed housing prices in the aftermath of a disaster can in turn be decomposed into two elements. First is the fall in the value of a dwelling resulting from the physical destruction it suffered; this first element reflects the change in the present value of living conditions and can be linked with the damage concept. Second is the fall stemming from the destruction experienced by the surrounding area; this second element indicates the change in the present value of earnings opportunities and access to services associated with the dwelling, similar in spirit to the loss concept.

The only two requirements to implement the proposed approach are the availability of disaggregated information on imputed housing prices and dwelling characteristics before the disaster, and of geographically disaggregated information on the condition of physical assets in its aftermath. In practice, data on housing prices and characteristics can often be obtained from a representative household survey, and data on the extent of destruction of assets from either a field survey or satellite images.

The first step toward implementing the proposed approach is to use household survey data to estimate a hedonic price function for the value of dwellings before the disaster, with the specification including a neighborhood effect. Second, plausible rules are used to allocate the local destruction rates of physical assets of different types to individual households in the survey. Third, 3