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fire
Article
Factors Associated with Structure Loss in the
2013–2018 California Wildfires
Alexandra D. Syphard 1, * and Jon E. Keeley 2,3
1Sage Insurance Holdings LLC, San Francisco, CA 94102, USA
2USGS Western Ecological Research Center, Three Rivers, CA 93271, USA
3Department of Ecology & Evolutionary Biology, University of California, Los Angeles, CA 90095, USA
*Correspondence: asyphard@sageunderwriters.com; Tel.: +1-619-865-9457
Received: 15 August 2019; Accepted: 24 August 2019; Published: 2 September 2019
Abstract:
Tens of thousands of structures and hundreds of human lives have been lost in recent fire
events throughout California. Given the potential for these types of wildfires to continue, the need to
understand why and how structures are being destroyed has taken on a new level of urgency. We
compiled and analyzed an extensive dataset of building inspectors’ reports documenting homeowner
mitigation practices for more than 40,000 wildfire-exposed structures from 2013–2018. Comparing
homes that survived fires to homes that were destroyed, we investigated the role of defensible space
distance, defensive actions, and building structural characteristics, statewide and parsed into three
broad regions. Overall, structural characteristics explained more of a difference between survived
and destroyed structures than defensible space distance. The most consistently important structural
characteristics—having enclosed eaves, vent screens, and multi-pane windows—were those that
potentially prevented wind-born ember penetration into structures, although multi-pane windows are
also known to protect against radiant heat. In the North-Interior part of the state, active firefighting
was the most important reason for structure survival. Overall, the deviance explained for any given
variable was relatively low, suggesting that other factors need to be accounted for to understand the
full spectrum of structure loss contributors. Furthermore, while destroyed homes were preferentially
included in the study, many “fire-safe” structures, having >30 m defensible space or fire-resistant
building materials, were destroyed. Thus, while mitigation may play an important role in structure
survival, additional strategies should be considered to reduce future structure loss.
Keywords:
defensible space; building construction; homeowner mitigation; firefighting; defensive
actions; fire safety
1. Introduction
California has long been recognized for its fire-prone ecosystems and fire-related losses to human
lives and property [
1
]. In the last several years, however, this recognition has turned into bewilderment
and terror as tens of thousands of structures and hundreds of human lives have been lost in fire events
throughout the state [
2
]. Deadly and destructive wildfires have been occurring in other regions across
the globe as well, such as Portugal [
3
], Australia [
4
], and Southern Europe [
5
]. The increased frequency
and magnitude of these fire events have contributed to the recent claim that we are entering a “new
normal” phase of wildfires [
6
]. Most of these catastrophic fires are started by humans, so as populations
steadily increase and people are pushed farther into hazardous wildlands, the problem could get even
worse. Thus, the need to understand why and how structures are being destroyed during wildfires has
taken on a new level of urgency.
Fully understanding why recent California wildfires were so destructive will likely require many
years of research focusing on a range of factors at different scales, from fire behavior and climatology to
Fire 2019,2, 49; doi:10.3390/fire2030049 www.mdpi.com/journal/fire
Fire 2019,2, 49 2 of 15
fire management and land development. Answering questions pertaining to fire behavior will require
different data and methodological approaches, compared to answering the questions related to why
homes were destroyed, although the actual outcome will be a combination of the two.
In California, there has been a long-standing interest in understanding how local and regional
responses are needed to reduce damage from wildfires [
7
,
8
]. In terms of understanding why homes are
destroyed, there is an emerging literature that includes studies focused on local, property-level factors
as well as studies on landscape-scale factors such as vegetation management and fuel characteristics,
fire suppression, topography, and housing development patterns (e.g., [
9
,
10
]). These studies have
significantly advanced our understanding of home safety, but the majority have been conducted
through computer simulations and laboratory experiments, and thus, there remains a need for pre- and
post-fire empirical data to document and validate what happens under actual wildfire conditions [
11
].
Recent fire events have generated more data on structure losses, and the number of empirical studies
is increasing, particularly relative to understanding spatial patterns of structure loss at a landscape
scale [12–15].
In terms of defensible space, the state of California requires fire-exposed homeowners to create a
minimum of 30 m (100 ft) of defensible space around structures, and some localities are beginning
to require at least 60 m (200 ft) in certain circumstances (e.g., [
16
]). Of the few studies that have
empirically tested the relative benefits of defensible space, the authors demonstrated that up to 30 m
(100 ft) of vegetation reduction around a structure can significantly increase the chance of structure
survival (e.g., [
17
–
20
]). However, in these case studies, the most effective distance of defensible space
was much less than regulations require (e.g., [
19
,
21
,
22
]), and other factors, such as housing density,
landscape position, proximity of vegetation to the house, irrigation and water bodies, and building
construction materials, were equally or more important [20,23,24].
Regarding fire safety in building construction materials, there have been many detailed studies
conducted via carefully designed laboratory experiments [
25
–
27
]; and recent building codes in
California have been designed to reflect these studies. Despite the solid laboratory evidence, few
empirical studies have documented building characteristics associated with structure loss in real
wildfire situations. In one study, Syphard et al. [
23
] found several significant relationships among
building construction materials and structure loss in San Diego County, CA, USA, with window
framing material and number of windowpanes being more protective than roofing or exterior siding
material, and year of construction also being a significant proxy for building characteristics. The
sample size in this study was somewhat limited, however, and other factors like structure density and
vegetation characteristics were found to be equally or more important, depending on the location of
the structure.
In addition to knowing whether certain mitigation actions can be statistically significantly
associated with structure destruction, it is important to understand how often these homeowner ‘best
practices’ actually translate into structure survival. Statistical significance is not a safety guarantee
and does not necessarily translate into probability. While it is important for homeowners to have the
best protection available, it is also important for them to understand the extent to which these actions
tend to result in a positive outcome. Without large datasets of actual structure losses, it has until now
been impossible to know the frequency at which best practices translate into structure survival, and
whether those results are generalizable across different landscapes.
As of now, most guidance on homeowner ‘best practices’ is derived from limited empirical studies
and assumptions based on fire behavior, and thus, the relative efficacy of these practices remains
largely theoretical. Empirical studies on the effects of local homeowner mitigation practices, including
defensible space or building materials, have been mostly in the form of case studies for a selection
of wildfires on specific landscapes (e.g., [
19
,
23
,
28
,
29
]). Although these studies provide insights, we
need a broader understanding across multiple fire events, and thus we need a database that captures
characteristics of structures exposed to many fires across a variety of ecosystems.
Fire 2019,2, 49 3 of 15
The California Department of Forestry and Fire Protection (Cal Fire) began a statewide building
inspection program in the late 1980s that has been continually upgraded and improved over time,
and recent large catastrophic wildfires have added enormously to the amount of data available. The
Cal Fire Damage INSpection Program (DINS) was founded with the goal to collect data on damaged,
destroyed, and unburned structures during and immediately after fire events to assist in the recovery
process, to validate defensible space regulations, and to provide local governments and scientists
information for analyzing why some structures burned and why some survived [
30
]. For all fire events
in the state that involve the damage or destruction of buildings worth $10,000 or more, a team of
trained inspectors visit during and immediately after the wildfire to collect, for all structures exposed
to the fire, a range of information including the extent of damage, defensible space before the fire,
building characteristics, and other items.
Through a public records request, we acquired DINS data for more than 40,000 structures that
survived, were damaged, or were destroyed across all California wildfires from 2013–2018, making
this potentially the largest combined dataset of its sort. Our objective was to summarize these data
statewide and across three broad California regions (San Francisco Bay Area, Northern Interior forests
and foothills, and Southern California) to a develop a more generalized understanding of local-scale
factors characterizing and differentiating destroyed or majorly damaged structures (“destroyed”)
from those that survived or only had minor damage (“survived”) during wildfires. Although other
studies have shown landscape-scale and other spatial factors such as topography, fuels, and housing
arrangement to significantly affect structure loss probability, we focused here exclusively on the
homeowner mitigation practices quantified by the building inspectors to answer:
1. How important was the extent of defensible space in distinguishing destroyed and
survived structures?
2. What structural characteristics of homes were associated with increased susceptibility
to destruction?
3. Did these patterns vary by region?
2. Materials and Methods
2.1. Data and Summary Statistics
The Cal Fire DINS data were collected for all wildfires, of any size, that resulted in structure
damage or destruction. Once building inspection teams arrived at a fire, they recorded information on
every exposed structure, including damaged, destroyed, and unburned homes, valued at a minimum
of $10,000 or greater than 120 square feet (11 square meters), which is the size at which a permit is
required for building. The inspection process occurred by dividing active wildfires into geographical
zones as the fire was burning, then a designated number of two-person teams of trained inspectors
were assigned to the zone and went to the field to record data. Data were collected for surviving
structures in addition to damaged and destroyed structures, and the level of structural damage was
recorded in different percentage classes.
Given that most recent structure losses in California have occurred in three distinct regions of the
state [
2
], with most losses occurring within single fire events, we divided the dataset into three regions
to compare potential regional differences. Thus, we assigned each county with structure loss to either
the “Bay Area”, which included counties surrounding the San Francisco Bay; the “North-Interior”,
which included primarily the northern Sierra Nevada but also other northern coastal and interior
counties; and “Southern CA”, including coastal counties south of San Luis Obispo County (Table 1).
Building inspectors grouped the structures into classes of damage corresponding to unburned;
minor (cosmetic or nonstructural damage); moderate (partial to complete failure of structural building
elements); and destroyed. The vast majority of structures were in either the minor or destroyed
classes (94% in the Bay Area, 99% in the North-Interior, and 95% in Southern CA), so we lumped
Fire 2019,2, 49 4 of 15
unburned with minor and called them “survived,” and lumped moderate with destroyed and called
those “destroyed.”
The types of data collected included features of the property and vegetation, and inspectors also
started to use pre-fire ancillary data, such as assessors’ parcel information, to add details for badly
damaged or destroyed structures. Most data fields were categorical to ensure consistency in recording,
and the teams used phone applications and GPS data to enter information in the field. For this study,
we summarized data for most categories in the inspection report, including distance of defensible
space, roof type, exterior siding, eaves, windowpanes, vent screens, and deck or porch material.
The distance of defensible space around structures was recorded as one of several ordinal
categories, including 0; 0–9 m (0–30 ft); 9–18 m (30–60 ft); 9–30 m (30–100 ft); 18–30 m (60–100 ft); and
>30 m (100 ft). We therefore labeled defensible space into four classes in which 5 m (15 ft) were added
to the lowest number of each class and used as the label. We merged the class 9–30 m (30–100 ft) with
the 18–30 m (60–100 ft) class. Therefore, 0 or 0–9 m were labeled as “5 m”, 9–18 m was labeled “14 m,”
9–30 m or 18–30 m were labeled “22 m,” and >30 m was labeled “35 m.” We also used these numeric
values to calculate average defensible space distances.
In the 2018 fires (including the Camp Fire and Woolsey Fire in the North-Interior and Southern
CA regions, respectively), some new variables were added, including defensive action taken and home
age. For defensive action, the inspectors recorded whether it was firefighters, civilians, or both who
protected the structures during the wildfires, or, they recorded when the information was unknown.
For all years, roof type was most frequently recorded as either “combustible” or “resistant” in the Bay
Area, but it was broken into different material classes in the other two regions, so for each region we
analyzed data according to the most commonly used classification for that variable. Vent screens were
also characterized differently for different fires in which the “screened” class was broken into “fine” or
“mesh >1/8” in some cases, and “unscreened” was referred to as “no” or “none” in some cases. We
lumped these together into “screened” and “unscreened”.
Building data were collected for different occupancy types (e.g., single- and multi-family residences,
outbuildings, commercial buildings, and barns), so we conducted an initial sensitivity analysis using
the full dataset comparing rankings of proportions using all structures versus single-family residential
structures only, and we found similar rankings for most variables. The variables in which the ranking
between single-family residential and other buildings was different were those which would likely
characterize non-residential structures (e.g., buildings having no windowpanes, vents, or eaves).
Therefore, to preserve the integrity of these classes and for a more robust dataset we used all structures
for our analyses in the different regions.
For all variables, there were a substantial number of blank fields where no data were recorded, so
there are unequal numbers of data points in all data categories (Table S1). Therefore, we summarized
and analyzed all data fields based only on the data that were available for those fields. For comparison
purposes we calculated two types of proportions for different perspectives. First, we determined the
proportion of the category in each burn class (i.e., for both survived and destroyed structures, what
proportion belonged to each category of the variable); and second, we determined the proportion of
burn class within each category (i.e., for each category in the variable, what proportion survived or
were destroyed) (Figures S1–S8).
2.2. Analysis
To assess the relative importance of each variable, we developed simple generalized linear
regression models (GLMs) [
31
] using defensible space or building characteristics as single predictor
variables and survived versus destroyed structures as the bivariate dependent variable. For each
model, we used a logit link and specified a binomial response, then calculated and compared the
deviance explained (D
2
), which is analogous to R-squared in linear regression for each variable. For
the statewide analyses of defensive action and structure age, we used the combined data for the
North-Interior and Southern CA regions only. We did not model roof type statewide (i.e., only ran
Fire 2019,2, 49 5 of 15
models for individual regions) because the classification system varied from region to region. For these
regions, we used data from whichever classification was most common in each region (roof type 1 for
North-Interior and Southern CA and roof type 2 for the Bay Area, Table 1). Given the large amount of
missing data in the different explanatory variables, we did not perform multiple regression, as our
objective was to create a relative importance ranking of the variables using only the data available.
Table 1.
Number of destroyed and survived structures from 2013–2018 by county and region in
California. Dash marks indicate no structure outcomes recorded. The bold totals report the sums of
destroyed and survived structures for each region.
Region County Number Destroyed Number Survived
Bay Area Contra Costa 1 –
Lake 2588 89
Mendocino 566 32
Monterey 88 4
Napa 1123 587
Santa Clara 29 700
Santa Cruz 6 19
Solano 11 56
Sonoma 6764 470
Yolo 24 88
Total 11,200 2045
North-Interior Amador 1
Butte 19,061 740
Calaveras 936 31
Fresno 10 2
Humboldt 5 –
Inyo 2 –
Lassen 4 1
Madera 16 4
Mariposa 142 20
Mono 58 6
Nevada 63 4
Shasta 1889 260
Siskiyou 339 18
Tehama 26 4
Trinity 142 7
Tuolumne 1 –
Yuba 274 8
Total 22,969 1105
Southern Kern 398 21
Kings 1 –
Los Angeles 1667 339
Orange 38 43
Riverside 53 10
San Diego 246 67
San Luis Obispo 81 7
Santa Barbara 110 42
Ventura 1075 200
Total 3669 729
Because defensible space distance classes can be hypothetically considered as progressively
protective against harm (i.e., that more defensible space is more protective), we used a calculation
common in medical research, the relative risk [
32
], to compare adjacent pairs of shorter and longer
distance classes of defensible space in addition to comparing the protective effect of the shortest versus
longest distance classes (0–30 ft vs. >100 ft). Relative risk is a ratio between proportions of classes
having a good outcome (here, structure survived wildfire) versus proportions of classes having a
bad outcome (here, structure was destroyed) and indicates whether there is either no relationship (a
Fire 2019,2, 49 6 of 15
value of 1) or if the exposed group (structures with shorter distances of defensible space) has either a
significantly higher (values >1) or significantly lower (with values <1) risk of surviving the fire given
the data available.
We also calculated the relative risk for most of the building inspection variables. For those
with more than one independent category, we calculated the relative risk based on the proportion of
survived structures in each category relative to the combined proportion of survived structures in
all other categories. For variables with binary classes of “combustible” or “resistant”, (Table 1), we
calculated the relative risk using the combustible class as the exposure group.
3. Results
From 2013 to 2018, building inspectors examined 41,717 structures, with 37,838 (~90%) damaged
or destroyed by fires in 36 California counties, with the largest number destroyed in Butte County in
the North-Interior Region, followed by the Bay Area, then Southern California (Table 1). Of the total
number of structures inspected, 18% (n =2045) in the Bay Area, 5% (n =1105) in the North-Interior,
and 20% (n =729) in Southern CA survived the fires.
3.1. Defensible Space and Defensive Actions
The relative importance of defensible space, as quantified by deviance explained in the regression
models, was virtually nil statewide, and the only region in which defensible space had a deviance
explained of at least 1% was the Bay Area (Figure 1). Statewide, home survival was associated with
slightly longer average distances of defensible space, and this distinction was more pronounced for
the Bay Area (Figure 2). On the other hand, when averaging mean values of defensible space classes
across survived and destroyed homes, there was a slightly higher mean defensible space distance for
destroyed structures in the North-Interior, and virtually no difference in Southern CA (Figure 2).
Fire 2019, 2, x FOR PEER REVIEW 7 of 17
Figure 1. Deviance explained for building inspection variables statewide in three California regions.
Defensive action and structure age were only available for North-Interior and Southern CA.
Figure 1.
Deviance explained for building inspection variables statewide in three California regions.
Defensive action and structure age were only available for North-Interior and Southern CA.
Fire 2019,2, 49 7 of 15
Fire 2019, 2, x FOR PEER REVIEW 8 of 17
Figure 2. Average distance of defensible space for survived and destroyed structures statewide and
in three California regions.
Except for the comparison between 22 m (75 f) vs. 14 m (45 ft) of defensible space statewide, the
relative risk ratios for the statewide and Bay Area data showed consistently lower relative risk when
comparing classes of longer distance intervals with shorter distance intervals (Table 2). In the North-
Interior, there was a higher relative risk of destruction with more defensible space when comparing
22 m (75 f) vs. 14 m (45 ft), but there was a significantly lower relative risk when comparing 35 m (115
ft) vs. 22 m (75 ft) (Table 2). There were no significant differences in relative risk among any defensible
space distance classes in Southern California (Table 2).
Table 2. Relative risk (RR) among building inspection variables statewide and for three California
regions. A relative risk of 1 indicates no difference between classes; > 1 means the relative risk of
destruction is higher in the first category listed; < 1 means the relative risk of destruction is lower than
in the other classes. Dashes indicate where no data were available for certain categories.
Figure 2.
Average distance of defensible space for survived and destroyed structures statewide and in
three California regions.
Except for the comparison between 22 m (75 f) vs. 14 m (45 ft) of defensible space statewide,
the relative risk ratios for the statewide and Bay Area data showed consistently lower relative risk
when comparing classes of longer distance intervals with shorter distance intervals (Table 2). In the
North-Interior, there was a higher relative risk of destruction with more defensible space when
comparing 22 m (75 f) vs. 14 m (45 ft), but there was a significantly lower relative risk when comparing
35 m (115 ft) vs. 22 m (75 ft) (Table 2). There were no significant differences in relative risk among any
defensible space distance classes in Southern California (Table 2).
Although defensive action was only recorded in the 2018 fires in the North-Interior and Southern
CA regions, it was more important than any other variable for North-Interior, and it was less important
in the Southern California data (Figure 1). Statewide (using these two regions and comparing the
importance to other variables), it had a medium-high relative importance (Figure 1). The relative risk
Fire 2019,2, 49 8 of 15
ratios for both regions showed that civilian, fire department, and both types of defensive actions were
significantly more protective than unknown action (Table 2). In the North-Interior, the fire department
providing defensive action provided better protection than civilian actions, but either both or civilian
defensive actions provided a slightly better relative risk ratio for Southern CA.
Table 2.
Relative risk (RR) among building inspection variables statewide and for three California
regions. A relative risk of 1 indicates no difference between classes; >1 means the relative risk of
destruction is higher in the first category listed; <1 means the relative risk of destruction is lower than
in the other classes. Dashes indicate where no data were available for certain categories.
Variable Statewide Bay Area North-Interior Southern
Defensible Space RR p-Value RR p-Value RR p-Value RR p-Value
14 m (45 ft) vs. 5 m (15 ft) 0.95 0.0001 0.98 0.06 0.97 0.09 0.97 0.24
22 m (75 ft) vs. 14 m (45 ft) 1.08 0.0001 0.98 0.19 1.07 0.003 1.07 0.06
35 m (15 ft) vs. 22 m (75 ft) 0.88 0.0001 0.79 0.0001 0.95 0.0001 0.98 0.61
35 m (15 ft) vs. 5 m (15 ft) 0.91 0.0001 0.76 0.0001 0.98 0.09 1 0.89
Defensive Action
Both vs. others 0.95 0.0001 – – 0.68 0.004 0.69 0.04
Civilian vs. others 1.08 0.0001 – – 0.81 0.0001 0.68 0.04
Fire Department vs. others 0.88 0.0001 – – 0.44 0.0001 0.81 0.03
Unknown vs. defensive action 0.91 0.0001 – – 1.02 0.0001 1.01 0.39
Deck, Porch Material
Composite vs. others 0.85 0.0001 0.93 0.007 0.92 0.03 0.78 0.04
Masonry vs. others 1.002 0.48 1.17 0.0001 0.99 0.03 1 0.78
Wood vs. others 0.98 0.01 1 0.6 1.01 0.002 0.97 0.27
None 1.01 0.10 0.35 0.0001 1 0.24 1.02 0.25
Roof Type
Asphalt vs. others 1.05 0.0001 – – 1.03 0.0001 1.02 0.4
Concrete vs. others 0.89 0.0007 – – 0.94 0.05 0.82 0.04
Metal vs. others 0.97 0.0001 – – 0.98 0.001 1.04 0.14
Tile vs. others 0.88 0.0001 – – 0.89 0.0001 0.97 0.25
Wood vs. others 1 0.84 0.99 0.96 1.06 0.38
Combustible vs. resistant – – 1 0.75 – – – –
Eaves
Enclosed vs. others 0.79 0.0001 0.88 0.0001 0.95 0.0001 0.83 0.0001
None vs. others 1.06 0.0001 0.49 0.0001 1.02 0.004 1.35 0.0001
Unenclosed vs. others 1.04 0.0001 1.15 0.0001 1.5 0.0001 0.99 0.86
Vent Screen
Screened vs. unscreened 0.94 0.0001 0.76 0.0001 0.97 0.0001 0.95 0.23
Exterior Siding
Combustible vs. resistant 1.05 0.0001 1.03 0.0002 1.04 0.0001 1.07 0.0001
Window Panes
Multi vs. others 0.94 0.0001 0.94 0.0001 0.97 0.0001 0.74 0.0001
None vs. others 1.01 0.12 0.25 0.0001 0.98 0.04 1.14 0.01
Unenclosed vs. others 1.06 0.0001 1.05 0.0001 1.02 0.0001 1.12 0.0001
3.2. Building Inspection Characteristics
Home construction materials explained a substantial amount of variation in housing losses
statewide and across regions (Figure 1). Overall, eaves consistently explained more than any other
structural parameters, and having enclosed eaves versus no eaves or unenclosed eaves had a highly
significant protective effect as seen in the relative risk ratios (Table 2). The structural variable with
the second highest deviance explained across all regions was windowpanes (Figure 1), although
statewide this variable was ranked slightly lower than vent screens, and vent screens were also nearly
as important as windowpanes in Southern California (Figure 1). The relative risk of having single pane
windows was consistently and significantly higher than having multiple pane windows statewide
and across all areas (Table 2). Structures that had no windows were not significantly different in
relative risk compared to structures with windows statewide, but they had a lower relative risk than
structures with windowpanes in the Bay Area and North-Interior, and this was reversed in Southern
CA (Table 2). There was a consistent and significantly lower relative risk for structures with screened
versus unscreened vents across the state and regions (Table 2).
Fire 2019,2, 49 9 of 15
Aside from eaves, windowpanes, and vent screens, the importance and relative risk of structural
parameters associated with structure survival varied across the state and regions. Statewide and
in the Bay Area, fire-resistant exterior siding material and deck or porch material were nearly as
important as windowpanes (Figure 1), with consistently lower relative risk ratios for fire-resistant siding
material (Table 2). In terms of deck or porch material, the most consistently significant effect was the
significantly lower relative risk of having composite decking material versus other materials (Table 2).
Although roofing material did not explain substantial variation in any of the regions (Figure 1), for the
North-Interior and Southern CA regions, where the material types were broken out, concrete and tile
both had lower relative risk ratios, although tile was not significant for Southern CA (Table 2). In the
North-Interior, metal roofs also had slightly lower significant relative risk (Table 2).
Although structure age, a proxy for all building construction materials, was only recorded for the
North-Interior and Southern CA regions, it did not explain substantial variation in structure survival
relative to individual building characteristics (Figure 1). On average, however, older homes were
consistently more likely to be destroyed than younger homes (Figure 3).
Fire 2019, 2, x FOR PEER REVIEW 11 of 17
Figure 3. Mean age of structure for survived and destroyed homes in two California regions. The
statewide calculations are based on combined totals of both regions (i.e., the Bay Area did not include
this variable).
4. Discussion
In terms of mitigation practices for protecting homes against wildfire, perhaps the most widely
recognized and regarded action that homeowners can take is to create defensible space around
structures [20,33]. In fact, defensible space and “hardening homes” via building construction
practices or structure retrofits, collectively referred to as the home ignition zone (HIZ), have often
been considered the primary factors that matter in terms of structures surviving wildfire [34,35].
Despite the widespread advocacy of these practices, there has been little empirical study of their
effectiveness under actual wildfires, and there is still debate on how much defensible space is critical
to home survival despite the regulated distance of 30 m (100 ft).
In this study based on more than 40 k records of structures exposed to wildfires from 2013 to
2018, we found that, overall, defensible space distance explained very little variation in home survival
and that structural characteristics were generally more important. Although the relative importance
and relative risk ratios of different factors recorded by building inspectors varied slightly from region
to region, there were also general similarities, particularly in that structure survival was highest when
homes had enclosed or no eaves; multiple-pane windows, and screened vents.
Figure 3.
Mean age of structure for survived and destroyed homes in two California regions. The
statewide calculations are based on combined totals of both regions (i.e., the Bay Area did not include
this variable).
Fire 2019,2, 49 10 of 15
4. Discussion
In terms of mitigation practices for protecting homes against wildfire, perhaps the most widely
recognized and regarded action that homeowners can take is to create defensible space around
structures [
20
,
33
]. In fact, defensible space and “hardening homes” via building construction practices
or structure retrofits, collectively referred to as the home ignition zone (HIZ), have often been considered
the primary factors that matter in terms of structures surviving wildfire [
34
,
35
]. Despite the widespread
advocacy of these practices, there has been little empirical study of their effectiveness under actual
wildfires, and there is still debate on how much defensible space is critical to home survival despite the
regulated distance of 30 m (100 ft).
In this study based on more than 40 k records of structures exposed to wildfires from 2013 to 2018,
we found that, overall, defensible space distance explained very little variation in home survival and
that structural characteristics were generally more important. Although the relative importance and
relative risk ratios of different factors recorded by building inspectors varied slightly from region to
region, there were also general similarities, particularly in that structure survival was highest when
homes had enclosed or no eaves; multiple-pane windows, and screened vents.
The only region in which defensible space distance explained at least 1% variation in structure
survival was the Bay Area, where survived structures had an average of 9.7 m (~32 ft) of defensible
space versus 7.4 m (~24 ft) for destroyed structures. Although there were significant differences
in relative risk between most pairs of distance classes of defensible space statewide and for the
North-Interior, there were some conflicting patterns in the Bay Area and North-Interior, and there was
no significant effect of defensible space distance for any comparison in Southern California. The other
surprising finding was that, of the structures that did have more than 30 m of defensible space, the
vast majority were destroyed in these fires (Figures S1–S8). This of course reflects the large proportion
of destroyed structures in the dataset, but it also suggests that structures with greater amounts of
defensible space are often still vulnerable.
One potential explanation for the limited importance of defensible space in these data may be that
the defensible space distance classes were defined rather broadly, too broad to discern critical details
that may have a much bigger impact. Of the few studies quantifying the most effective distance of
defensible space for making a significant difference in structure survival probability,
Syphard et al.
and
Miner [
19
,
21
] both found the optimum distance to be much shorter than the required 30 m, with the
ideal range between 5–22 m. Distances longer than that provided no additional significant protection.
Furthermore, these and other studies have shown that more nuanced characteristics of landscaping are
most critical for structure protection, including vegetation touching the structure or trees overhanging
the roof [
36
]. The arrangement of vegetation and irrigation are also important factors not accounted
for [
20
]. In fact, despite defensible space traditionally being divided into zones, with the first being from
0–9 m (30 ft) from the structure, newer recommendations are beginning to isolate and focus heavily on
the first zone being from 0–1.5 m (5 ft) [37], which may be the most critical zone to account for.
Most structures are lost in wildfires that are burning under severe weather and wind conditions [
2
],
such that burning embers are capable of crossing large, multi-lane freeways and have been reported
to blow as far as 1–2 km ahead of a fire front [
2
,
25
]. Therefore, one of the primary reasons for the
importance of vegetation modification directly adjacent to homes as opposed to longer distances, is
that homes are generally not ignited by the fire front but more often by wind-driven embers landing
on combustible fuels in or on the house [
17
,
29
,
38
]. Material closest to the house is thus the most likely
to cause a proximate spark that can penetrate the structure. To this point, irrigating vegetation and
removing dead plant material to reduce ignitability may be as or more important than fuel volume,
which is a finding borne out by recent research [
24
]. While defensible space distances <30 m may be
sufficient for increasing structure survival probability, another important reason for requiring 30 m
(100 ft) is firefighter safety and providing a zone of protection [
39
]. Finally, while the inspectors
recorded defensible space distances, part of the definition of defensible space in California revolves
Fire 2019,2, 49 11 of 15
around the horizontal and vertical spacing of fuels; thus, if these factors matter as much or more than
distance, they could not be accounted for here.
The nature of building loss via ember flow factors such as exterior siding or roof material were
much less important than exposed eaves, vents, or windows. This again is likely due to the extreme
weather condition characteristics of destructive wildfires. That is, the fire-resistance of materials such as
roofs or siding, i.e., preventing them from catching fire, was less important than building characteristics
that provided gaps in the structure that could allow penetration of wind-borne burning debris. These
results suggest that one of the potentially most effective methods of protecting homes from wildfire
destruction would be to perform simple building retrofits, such as placing fine mesh screens over
vents and coverings other openings in the structures, such as gaps in roofs, and enclosing structure
eaves. Specific recommendations for these types of retrofits are easily found online, e.g., [
40
], and
suggest that improving the fire safety of structures does not necessarily require expensive replacement
of construction materials but rather careful attention to structure details.
The previous post-fire study of the role of construction materials in structure survival also found
that windows, particularly framing material and panes, were more important than roof or siding
material, although the methods and overall suite of variables differed in that study [
23
]. In the case of
windows, they can, like other parts of the structure, provide an easy entry point for firebrands [
26
].
Additionally, however, they are also vulnerable to radiant heat, and multi-pane windows can withstand
much higher levels of thermal exposure than single-pane windows [
41
]. Although not recorded here,
the type of glass used in the window is also important for resistance to cracking [26].
Although individual structural characteristics were highly influential in this study, structure
age did not explain a lot by itself, which may mean that, at a broad scale, it does not necessarily
serve well as a proxy for the building characteristics most likely to protect homes. On the other
hand,
Syphard et al. [23]
found that structure age did correlate with both building characteristics and
structure survival, but that study was only conducted in San Diego County, where building codes
had already been updated several times in response to wildfires in the regions. Although the state of
California has also recently adopted strict building codes for wildfires [
42
], those codes only apply
to new housing, so the effects may not have been seen yet. Further analysis might be warranted to
compare structural characteristics and outcomes as a function of date of code enforcement.
Another consideration is that, despite the importance of structure age in the San Diego study, that
study also determined that building location and arrangement were more important in predicting
structure loss than structure age, building materials, or defensible space. The effect of structure age
was primarily important in higher-density neighborhoods where structure loss was overall less likely.
Thus, the role of housing arrangement and location, found to be the most important predictors of
structure loss in several California studies [
13
–
15
] and nationwide [
43
] should ultimately be factored
into discussions of reducing future fire risk; and this looks to be a challenge given trends of rapid
ongoing development in the wildland–urban interface [44].
One of the reasons that housing arrangement and location are such strong predictors of structure
loss may be structure accessibility by firefighters, who must divide manpower and resources to
reach communities located in dispersed or remote locations [
45
,
46
]. The role of defensive actions in
determining the extent and location of structure survival has been historically difficult to quantify,
mostly because data are sparse, but also because defining suppression effectiveness is an inherently
difficult task [
47
]. In the North-Interior region, defensive action explained more than any other factor in
structure survival, although it was less important than building characteristics in Southern California.
Even given the high importance of defensive action in the North-Interior, the total number of structures
with unknown defensive action was substantial, and the proportion of unknown actions was even
larger in Southern California. Thus, while these results suggest that defensive actions may be one of
the most important and overlooked factors in structure survival, it remains difficult to make definitive
conclusions. Given that building inspectors have just started collecting this information, it is important
to recognize this is an on-going process of increasing our knowledge base as more data are collected.
Fire 2019,2, 49 12 of 15
5. Dataset and Limitations
Given the enormous number of structures lost in California in recent years, the dataset compiled
for this study may represent the largest existing source of information on homeowner mitigation
practices associated with structure loss. Other large databases and studies of house loss have been
developed in other countries, however, where wildfires result in substantial losses in structures and
human life; much of this work has been conducted in Australia, a country with a long history of
destructive wildfires with substantial structure losses [
48
], and human fatalities [
49
]. This ongoing data
collection process, especially if more exposed but unburned homes are included, will be important for
continued understanding of structure loss and identifying the most effective strategies for prevention.
Despite the unprecedented opportunity the DINS data have provided for this broad-scale analysis
of structure loss, there are nevertheless uncertainties and limitations within the data, and Cal Fire is
working to improve the collection process on an ongoing basis [30].
The primary limitation is, as we discussed previously, that defensible space was presented
uni-dimensionally as a function of distance categories and thus excluded other relevant factors such as
vegetation spacing, height, type, age, moisture content, or composition. Nevertheless, given the broad
scale of the data and similar conclusions for all study areas, these additional vegetation characteristics
do not appear to be biased in one direction or the other; thus, our conclusions about distance classes
are likely robust.
Another limitation of the dataset is the potential uncertainty inherent in recording building
characteristics after a wildfire for homes that have been badly burned with materials largely consumed
in the fire. This likely explains the missing data seen throughout the records. Cal Fire is aware of this
and is beginning to combine their reports with pre-fire information from county assessors’ offices [
30
];
however, the extent to which pre-fire data may have been incorporated in the reports used for this
study is unclear.
Finally, as mentioned previously, this study only focused on the relative importance of the
local-scale factors reported by the building inspectors, and full understanding of structure loss will
need to include additional factors. Ongoing research will account for a fuller range of landscape-scale
factors as well as information on fire behavior and spatial patterns.
6. Conclusions
We have explored the factors correlated with structure loss and survival during a recent five-year
period in California. In most regions home structural characteristics are far more important in
determining home survival than defensible space. Statewide, the most critical factor was eave
construction. Windowpanes were also widely important in the state. Exterior siding was an important
structural characteristic in the Bay Area, but vent screens were much more important in southern
California. The likely explanation for why structure characteristics play a greater role than defensible
space is that most homes burn by embers, which often come from long distances; and the impact of the
ember cast is not likely affected by distance of defensible space. Whether or not the embers ignite is
largely a function of structure.
Given that the primary role of building inspectors is to assess building damage, most structures
in the data were destroyed. As such, one of the striking outcomes of this study is the finding that many
of these destroyed structures could be characterized as “fire-safe,” such as having >30 m defensible
space or fire-resistant building materials. While the number of structures lost in these fire events was
unprecedented in California history, structure loss during severe fire-weather and wind conditions
similar to some of the fires represented here has occurred for decades in the state
2
. Therefore, it may
be safe to assume that these data are broadly representative.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2571-6255/2/3/49/s1,
Figure S1: Proportion of defensible space distance classes for survived and destroyed structures (a) and proportion
of survived and destroyed structures within defensible space distance classes (b) for three California regions,
Figure S2: Figure S2: Proportion of defensible action type for survived and destroyed structures (a) and proportion
Fire 2019,2, 49 13 of 15
of survived and destroyed structures within defensive action types (b) for two California regions, Figure S3:
Proportion of deck material type for survived and destroyed structures (a) and proportion of survived and
destroyed structures within deck material type classes (b) for three California regions, Figure S4: Proportion of
roof material type for survived and destroyed structures (a) and proportion of survived and destroyed structures
within roof material type classes (b) for two California regions, Figure S5: Proportion of eave type for survived
and destroyed structures (a) and proportion of survived and destroyed structures within eave type classes (b) for
three California regions, Figure S6: Proportion of Exterior siding classes for survived and destroyed structures (a)
and proportion of survived and destroyed structures within exterior siding classes (b) for three California regions,
Figure S7: Proportion of vent screen classes for survived and destroyed structures (a) and proportion of survived
and destroyed structures within vent screen classes (b) for three California regions, Figure S8: Proportion of
windowpane type for survived and destroyed structures (a) and proportion of survived and destroyed structures
within windowpane type (b) for three California regions. Table S1: Number or average value of destroyed and
survived structures within building inspection classes for three California regions.
Author Contributions:
Conceptualization, A.D.S. and J.E.K.; methodology, A.D.S. and J.E.K.; formal analysis,
A.D.S.; data curation, A.D.S.; writing—original draft preparation, A.D.S.; writing—review and editing, J.E.K.
Funding: This research received no external funding.
Acknowledgments: The US government does not endorse any product mentioned in this manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
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