Drivers of Property Damage in the Los Angeles Fires

by Marco Giacoletti and Davide Proserpio

In this post, we explore potential predictors of property-level damage among individual properties affected by the Palisades and Eaton fires. Our analysis focuses on two key factors.

First, we examine fire risk exposure estimates that were available prior to the disaster. Specifically, we use data from FEMA’s National Risk Index (NRI), reported at the Census tract level as of March 2023. Following the approach used in a previous post, we convert the annual probabilities of a large fire from the NRI into 10-year probabilities to estimate the likelihood of a major fire over the next decade.¹ These estimates represent the publicly available information on fire risk before the Palisades and Eaton fires, which likely shaped both the level of concern among property owners and the countermeasures they took.

Second, we consider the age of the property. Newer structures are generally expected to use better materials and construction technologies, making them more resilient to fire exposure and other risks. In addition, building codes have become increasingly stringent over time. Notably, in 2008, Los Angeles County introduced new regulations for buildings located in Fire Hazard Severity Zones (FHSZs) and Wildland-Urban Interface Fire Areas, requiring fire-resistant construction for new developments and additions. We test whether more recent construction, reflecting advancements in building standards as well as stricter regulations, leads to higher fire resilience.

Overview

Overall, we find that a non-negligible share of the properties affected by the fires were located in areas with very low ex-ante estimated fire risk probabilities.

Interestingly, conditional on being within the fire perimeters, the properties that ex-ante were categorized as low-risk are more likely to have been heavily damaged compared to ex-ante high-risk properties. Although this may appear counterintuitive at first, it may instead reflect that properties in low-risk areas were less prepared to handle a fire event.

We also find that properties within the fire perimeters tend to be relatively old on average, which complicates efforts to isolate the effects of the 2008 fire-resistant building regulations, given that relatively few homes have been constructed in the past 15 years. Nonetheless, we find that homes built within the last 20-30 years proved to be more resilient compared to older buildings.

Ex-Ante Estimated Risk Exposure and Damage

Our analysis focuses on single-family properties, geolocated by CALFIRE as part of its ongoing assessment of property damage,² and located within the fire perimeters, as defined by shapefiles from the Wildland Fire Interagency Geospatial Services. Using geolocation data, we merge CALFIRE’s damage assessments with parcel-level tax assessment records, allowing us to link each property to its characteristics, including year built. After completing the merge and excluding properties labeled as “inaccessible” by CALFIRE (for which damage information is unavailable) our final sample includes 7,174 single-family homes affected by the Palisades fire and 7,582 affected by the Eaton fire.

First, focusing on NRI fire risk exposures, an important finding is that a non-negligible share of affected properties were located in Census tracts with very low estimated fire probabilities. As shown in the pie chart below, the majority of properties within the fire perimeters were in high-risk areas, defined as Census tracts with 10-year fire probabilities exceeding 10%. However, more than 17% of properties within the fire perimeters were in areas where the estimated 10-year fire probability was below 0.25%.

Of course, this finding does not imply that the probabilities estimated by the NRI are biased. The January fires were extreme events that caused damage deep within urban areas, well beyond what might typically be expected. However, the results do highlight that a substantial share of affected homeowners likely perceived fire risk as a remote possibility, and may therefore have been underprepared for such an event.

Consistent with this observation, the figure below reveals a striking pattern in the extent of damage across areas with different ex-ante fire risk estimates. Properties located in low-risk areas were more likely to suffer severe damage. Specifically, the share of properties experiencing damage that destroyed 50% or more of the structure is 84% in low-risk areas (10-year fire probability < 0.25%), compared to 74% in areas with intermediate risk (0.25%–10%), and 69% in high-risk areas (fire probability > 10%).

Property Age and Damage

We then examine the relationship between property age and fire damage. To begin with, it is important to note that the stock of properties impacted by the fires is relatively old. As shown in the pie chart below, among properties with available year-built data, 77% were constructed before 1980, and only 4% were built after the introduction of stricter fire-resistant building regulations in 2008. While some older homes may have been renovated or expanded in ways that improved their fire resilience, it is reasonable to expect a substantial difference in construction quality and regulatory compliance between homes built 30–40 years ago and those built more recently.

Another important consideration is that the spatial distribution of older and newer properties is not random. As the figures below illustrate, particularly for the Palisades fire, newer construction tends to be clustered in specific areas, which may influence observed patterns of damage and resilience.

Thus, to formally assess the relationship between property age and fire damage, we estimate a simple linear regression model that compares the likelihood of a house experiencing more than 50% structural damage, depending on whether it was built before or after a given cutoff year, and after controlling for property characteristics and location.³

More specifically, to test the effects of property age, we include in the regression a dummy variable equal to one for properties built before a specified cutoff year. The coefficient on this indicator captures the difference in the likelihood of experiencing more than 50% structural damage between properties built before and after the chosen year. If newer homes are more resilient, we would expect this coefficient to be positive and statistically significant.

To control for property characteristics we include dummies for number of bedrooms, bathrooms, and stories, as well as the log of property size (living square feet). To account for micro-location effects, we divide the fire-affected areas into 100-meter grid cells and include fixed effects for each cell. This approach allows us to compare properties of different ages with similar characteristics and within very small geographic areas. This approach helps isolate the specific effect of construction year from other confounding factors.

The figure below presents the estimated coefficients for different choices of the property age dummy, using cutoff years of 1980, 1990, 2000, and 2008, respectively. Along with the point estimates, we also display 95% confidence intervals.

The point estimate for the 2008 cutoff is approximately 3%. This implies that houses built before 2008 are 3% more likely to experience structural damage exceeding 50%, compared to those built afterward. However, this difference is not statistically distinguishable from zero. The fact that the estimate is imprecise should not be taken as evidence that the 2008 building regulations were ineffective. Rather, it reflects the limited number of homes within the fire perimeters that were built after 2008, as discussed above. This reduces the precision of the estimate.

Indeed, we find larger and statistically significant estimates when using 2000 as the cutoff year, which gives us a larger sample of homes built after the cutoff. For homes built before 2000, the likelihood of experiencing structural damage of 50% or more is 5.5 percentage points higher than for those built after 2000. Results are similar when using 1990 as the cutoff year. This difference is not negligible, given that, on average, 73% of properties in the perimeters suffered damage exceeding 50%. Thus, the estimated effect represents a relative reduction of approximately 7.5% (5.5/73). This evidence is consistent with the notion that more recent construction offers greater resilience to fire damage.

When we shift the cutoff year to 1980 or 1970, we continue to find that more recently built properties are less likely to suffer structural damage exceeding 50%. However, the estimated difference declines to 3% for the 1980 cutoff and becomes small and statistically insignificant for 1970. This pattern is sensible: as the cutoff year moves further back, the group of homes built after the cutoff increasingly includes old structures that could not have greatly benefited from improvements in materials and the introduction of stricter building codes.

1. See our previous post for a discussion of the methodology.

2. We obtained this information from https://www.fire.ca.gov/, which provides the geolocation and the type of damage for each property affected by wildfires.

3. We estimate:

where the dependent variable is a dummy equal to one if affected property i in micro-location l suffered damage exceeding 50% of the structure, the main variable of interest is a dummy equal to one if the property was built after year Y. The specification includes a vector of property characteristics, and micro-location fixed effects.