assessments, consistent with the approach proposed by Ceriani et al. (2019). There is also information on physical housing characteristics, including type of construction, floors, water and electricity connections, sanitation and the like.

Information on district characteristics is taken from the 2011 population census, which was conducted roughly at the same time as the NLSS III. The resulting housing stock measure is combined with official figures to estimate the proportion of dwellings destroyed in each district (Government of Nepal 2015).

A detailed description of the variables used in the regression analysis can be found in the annex (table A.1). Their summary statistics are reported there as well (table A.2). Among other insights, these summary statistics show that self-assessed housing rents before the Gorkha earthquakes were higher in the most affected districts (the ones where at least 5 percent of the dwellings have been fully destroyed) than in the rest of the country.

# **6. The hedonic pricing regression**

The hedonic price function in equation (8) is estimated with self-assessed rent as the dependent variable. The alternative would be to use self-assessed housing price, but there are much fewer observations available for this variable. Given the characteristics of the data and the conceptual options available, four specifications are considered (table 1). A first choice is whether to define neighborhood effects at the district level (first two columns) or at the narrower VDC level (last two). A second choice is whether to assume that the impact of neighborhood effects is linear (columns one and three) or quadratic (columns two and four).

The overall significance of the model is strong in all cases. Adjusted R<sup>2</sup> statistics are in the range of 0.58-0.64, highlighting the good fit of the model. The reliability of the estimates is further confirmed by the F-statistics, which are statistically significant at the 5-percent level for all specifications and at the 1-percent level for most of them. As for individual determinants, almost all coefficients on dwelling characteristics and local amenities are statistically significant at the 10-percent level. Most of them are even significant at the 1-percent level.

Reassuringly, the signs and magnitudes of the coefficients are similar across specifications. In all cases, the main determinants of imputed housing prices are the dwelling’s type of foundation and roof, the public services it has access to, and location characteristics. Occupancy indicators, including whether the household rents the dwelling or owns it, and whether it shares it with other households or not, also appear to be strong predictors of self-assessed housing rents.

The coefficients obtained for neighborhood effects are always significant at the 10-percent level and reach the 1-percent level in most cases. The magnitude of the coefficients is larger when neighborhood effects are computed at the VDC level, rather than at the district level. This result is consistent with a decrease of local spillovers with distance. The specifications using a quadratic form reveal that local spillover effects are concave, mattering less in relatively wealthier communities than in poorer areas.

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