Wildfire and house prices: A synthetic control case study of Altadena (Jan 2025)

TL;DR

This paper applies the Synthetic Control Method to quantify the causal impact of the January 2025 wildfire on Altadena's housing prices, revealing a significant negative effect over six months.

Contribution

It demonstrates the use of SCM to estimate disaster impacts on housing prices, providing a novel case study for wildfire economic effects.

Findings

Average monthly house price loss of $32,125 post-wildfire

Statistically significant impact at 10% level based on RMSPE ratio

Effect not significant when measured by post-treatment gap

Abstract

This study uses the Synthetic Control Method (SCM) to estimate the causal impact of a January 2025 wildfire on housing prices in Altadena, California. We construct a 'synthetic' Altadena from a weighted average of peer cities to serve as a counterfactual; this approach assumes no spillover effects on the donor pool. The results reveal a substantial negative price effect that intensifies over time. Over the six months following the event, we estimate an average monthly loss of $32,125. The statistical evidence for this effect is nuanced. Based on the robust post-to-pre-treatment RMSPE ratio, the result is statistically significant at the 10% level (p = 0.0508). In contrast, the effect is not statistically significant when measured by the average post-treatment gap (p = 0.3220). This analysis highlights the significant financial risks faced by communities in fire-prone regions and demonstrates SCM's effectiveness in evaluating disaster-related economic damages.