Introduction

Wildfire damages have increased significantly in the U.S. over the past decade with the factors contributing to this rise including changing climate risks and fire suppression regimes and an increase in housing in wildland areas (Radeloff et al. 2018). Given the number of households living in high-risk areas in the western U.S., and the predicted increase in fire counts and intensities in the coming decades, it is important to understand how households have and have not responded to these changing conditions, particularly as it relates to self-protection of their homes.

While homeowners have a variety of options to adapt to wildfire risk, underinvestment remains high for a variety of reasons (e.g., high costs, information failures, insurance imperfections, and public investment in fire suppression). As a result, governments have adopted a diverse approach to help increase uptake ranging from low-touch programs designed to boost risk disclosure and voluntary investment to command-and-control policies that mandate changes to new and existing residences.

One example of the latter is California’s wildfire building codes. Following the 1980 Panorama Fire, the state mandated that Cal FIRE develop Fire Hazard Severity Zone (FHSZ) maps for the state, which were first released in 1985. These maps classified high fire risk areas into three groups: moderate, high, and extreme (VHFHSZ). The maps also divided the state into three broad areas – federal (FRA), state (SRA), and local (LRA) – based on the responsibly for wild suppression. While some building codes were in place starting in 1985, the most significant policy change occurred in 1992 with the passage of the Bates Bill (AB 337), which made changes to building codes across the state including mandating minimum requirements for defensible space around homes and, for new construction, standards for the fire safety of roofs for SRA areas; it also mapped and provided detailed information to LRAs about VHFHSZ boundaries and recommended that local authorities use the information to mandate policies. Following the Bates Bill, the state made additional changes in 1994, 1995, 1997, 1999, and 2008 with the largest change coming in 1997 with the requirement that Class A roofs be used for all new construction.

Previous research has demonstrated the impact of these policies as it relates to structure vintages and damage during a wildfire event (Baylis and Boomhower, 2021). The authors show that, relative to homes built before 1998, post-1998 homes see their chance of destruction fall by 0.14 percentage points compared to a baseline chance of destruction 0.41. Our paper builds on this research and exams whether these vintage effects – the increased safety afforded homeowners for homes built after the most significant building codes were put into place – have value and thus capitalize into home prices.

Data

The data used in this paper was created from two main sources. First, we obtained data on all single-family home sales and their characteristics for CA via a licensing agreement with CoreLogic. Using these data, we combined them spatially and temporally with publicly available data on the location of SRA and LRA boundaries; data on the boundaries of the FHSZ risk regions in the state; and data on census designations. We limit our sale years to 2013-2020 to focus on the recent period of extreme fire risk in CA. After removing all homes that were destroyed by fire during the study period, the final dataset consists of all single-family home sales from 2013-2020 for homes with a vintage (effective year built) ranging from 1985-2012.

Methods

To examine the impact of wildfire building codes on home prices, we employ a vintage-based quasi-experimental design (event-study DD). We begin by attaching treatment variables to each home based on whether a home is in the SRA or the VHFHSZ area of LRA (our treated areas), and whether a home was built before or after the passage of the Bates Bill in 1992. We then regress the natural log of real home prices on vintage fixed effects and those effects interacted with the SRA and LRA treatment terms. All models include a full set of controls for housing characteristics, sale year-month and census block-group fixed effects. We estimate the DD model using the full data sample and a matched sample based on nearest-neighbor matching to improve balance between the treatment and control samples. Our identifying assumption is that homes in the same sale year-month, block group, and vintage, but outside treated areas serve as good controls (Levinson, 2016).

Results

Results from our preferred matching event-study -DD model reveal that homes with the same vintage but located in the SRA sell for 2.5% premium and those in the LRA sell for a 1.4% premium relative to controls. These effects – of the capitalization of wildfire building standard into home prices – are similar in magnitude those found in Walls et al. (2017) for the capitalization of Energy Star ratings into home prices. Translating these percentage changes into changes in home values shows that the SRA home premium is $15K and the LRA premium is $11K. These values are roughly comparable to the average additional cost of an enhanced roof installed at time of construction cited by Baylis and Boomhower (2021); they are much lower, however, than the cost of a complete retro fit ($62,760).

Conclusions

Our results provide evidence that homeowners recognize and are willing to pay for the various self-protection measures mandated by CA’s wildfire building codes.  

References

Baylis, P. and J. Boomhower. 2021. “Mandated vs. Voluntary Adaptation to Natural Disasters: The Case of U.S. Wildfires.” NBER Working paper 29621.

Levinson, A. 2016. “How Much Energy Do Building Energy Codes Save? Evidence from California Homes.” American Economic Review. 106(10): 2867-2894.

Radeloff, V. C., D. P. Helmers et al. 2018. “Rapid Growth of the U.S. Wildland-Urban Interface Raise Wildfire Risk.” Proceedings of the National Academy of Sciences. 115: 3314-3319.

Walls, M., T. Gerarden, K. Palmer, X. F. Bak. 2017. “Is Energy Efficiency Capitalized into Home Prices? Evidence from Three Cities.” Journal of Environmental Economics and Management. 82(2017): 104-124.