Wildfires, Housing Affordability and Climate Change: Virtuous Cycle or Death Spiral?

The basics: How will climate change influence wildfire risk?

Figure 1: State-level expected changes in spatially-averaged burn probability from 2020 to 2060 by climate scenario. States are colored and sized by current burn probability

Projecting future wildfire losses: California, we have a problem

Figure 2: State-level expected changes in single family wildfire loss costs from 2021 to 2050 under RCP8.5, both in $M and percentage terms. States are sized by losses relative to total value.

The risQ Wildfire Score: One number, everything you need to know

Figure 3: The risQ Wildfire Score mapped to counties and overlaid with historical wildfire perimeters over 1984–2020.

Wildfires have become more frequent, but we’ve built more houses in high-risk places

Figure 4: The 25% of US counties with risQ’s highest spatially weighted wildfire probability are analyzed for trends in new Single Family Residential (SFR) construction using the US Census Building Permits database. The share of SFR development has been increasing in high-risk areas in tandem with rapidly increasing average valuations/prices.

Wildfires compound socioeconomic and racial inequality

  • While ~18% of the coterminous US population is Latinx/Hispanic, ~37% (about double the rate) of the total population exposed to the nation’s most extreme wildfire (risQ Wildfire Score = 5) is Latinx. Over 2010 to 2019, that share has significantly increased, from ~32% to ~37%.
  • Native American and Asian populations also have outsized exposure to wildfire risk. For example, Native American share of population exposed to the most extreme risk (risQ Wildfire Score = 5) is more than ~1.6x their share of the coterminous US total population. Asian populations similarly are overrepresented in the most extreme risk locations by ~1.5x.
  • Meanwhile, Hispanic non-white representation in the highest risk locations is lower than the group’s national population share. That share has decreased significantly from 2010 to 2019, in the most extreme wildfire prone locations, from ~63% to ~56%.
Table 1 –Statistic [1a] shows racial proportions of the total population exposed to high, extreme, and the most extreme wildfire risk categories. Statistic [1b] shows the change in 1a from 2010 to 2019 in percentage points. Statistic [2] shows the ratio of [1a] to a given group’s share of the total population of the coterminous US. Demographic data was obtained from the 2019 and 2010 American Community Survey at the census tract level.
Figure 5: risQ Housing Unaffordability Score distributions by risQ Wildfire Score. risQ’s Housing Unaffordability Score is a composite metric based on multiple American Community Survey variables that measure a community’s income relative to its housing costs.

Wildfire Risk and Carbon Transition Risk Go Hand in Hand

Figure 6: Locations with high wildfire risk, defined by a risQ Wildfire Score of 3 or above, are shown in contrast to Scope 1 on-road CO2 emissions in greater Los Angeles, in MTCO2. Suburban sprawl has increasingly pushed socioeconomically vulnerable communities to settle in wildfire-prone locations and in tandem has exacerbated car dependence. CO2 emission estimates are derived from the Vulcan Project 3.0, averaged over the years 2010–2015.
Figure 7: Same as Figure 6 but for the San Francisco Bay Area as well as Sacramento.

Wildfire Risk in the Municipal Debt Market

Figure 8: Over the entire ~$3.9T universe of outstanding municipal debt securities covered by risQ as of May 2021, the distributions of coupon rates by risQ Wildfire Score are plotted. There is no relationship yet between coupon rate and wildfire risk, meaning investors are not getting paid to take on the risk.

Wildfire Risk in the Residential Mortgage-Backed Securities Market

Figure 9: The Government Sponsored Entities (GSEs) mandated to boost home ownership especially for lower and middle income families hold a disproportionate amount of the highest wildfire risk on their books, as compared to the complete wildfire risk distribution of all home loan value — the “loan universe” — originated over 2012–2019. The plot below shows the ratio of holdings per lend-held and GSE-held loan value as a ratio relative to the loan universe. For example, the Ginnie Mae (GNMA) holds ~52% more loan value with a risQ Wildfire Score of 5 on its books compared to the total loan value originated that is scored at a 5.
Figure 10: Total loan value by year from 2012–2019 is inspected for trends that might illustrate increases in securitization preference for lenders; no such discernible systematic change is seen.
Figure 11: 117 anonymized Fannie Mae (FNM / STACR) and Freddie Mac (FHL / CAS) CRT pools are analyzed for wildfire risk. Horizontal bars, color-coded by agency, show the percentage of unpaid mortgage balance. The vertical lines show a median (solid) plus a 95% confidence interval (dashed) on inter-pool spread if loans from the universe were allocated to pools at random, for pools size 10,000.

The ESG Solution: Affordable Urban Density

What can communities and people do to manage their wildfire risk?

  • Prescribed burning near homes in the Wildland Urban Interface (WUI) reduces the probability of future fires reaching the WUI as well as the risk of high-intensity fire in the event a fire does reach the WUI.
  • Prescribed burning on private land on larger parcels with homes can also be implemented to reduce risk.
  • After a century of fire suppression, forest composition and structure has been altered in many places. Fuel treatments that include prescribed burning or wildfire can restore forest structure and remove fuel buildup.

Appendix

Figure S1: The relationship between change in state-averaged Burn Probability (BP) with changes in June-July-August (JJA) temperature (Temp) and relative humidity (RH) is shown. Each point represents a US state (including the District of Columbia) and is colored according to climate scenario (RCP8.5 in red and RCP4.5 in green).
Figure S2: 95% confidence intervals and median estimates of projected change in temperature from 2020 to 2060 from risQ’s probabilistic climate model are shown. Top: RCP8.5. Bottom: RCP4.5.
Figure S3: 95% confidence intervals and median estimates of projected change in relative humidity from 2020 to 2060 from risQ’s probabilistic climate model are shown. Top: RCP8.5. Bottom: RCP4.5.

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We quantify climate risk, carbon transition risk and social impact for US Fixed Income, covering the full municipal bond and MBS universes

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risQ, Inc.

risQ, Inc.

We quantify climate risk, carbon transition risk and social impact for US Fixed Income, covering the full municipal bond and MBS universes

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