Climate Change, Weather Risk, and Residential Home Prices–Part 1

How do we know what we know?

High water risin’, the shacks are slidin’ down.

Folks lose their possessions and folks are leaving town.

- Bob Dylan, High Water (2001)

It is clear that we should be concerned about how the housing market is responding to climate change. Bob Dylan sang about it as far back as 2001, and Sean Becketti from Freddie Mac noted the following in 2016:

“It is only a matter of time before sea-level rise and storm surge become so unbearable along the coast that people will leave, ditching their mortgages and potentially triggering another housing meltdown — except this time, it would be unlikely that these housing prices would ever recover.”

-The New York Times, 11/24/2016.

But it is much less clear if the average home buyer is concerned (yet). Recent research from Redfin demonstrates that, counterintuitively, migration is strongest into some of the highest climate risk regions. Additionally, the Wall Street Journal profiled strong demand for homes in fire-prone Napa Valley. So the titans of rock and roll and the lead thinkers of the mortgage industry are worried, but the average buyer’s behavior seems to indicate little to no understanding of these risks. This article is the first in a two-part series that attempts to explain the current state of research into how climate is impacting housing. In this first part, I focus on the question “How do we know what we know?” In the follow-up, I’ll summarize what we actually know.

What are the Major Challenges?

Housing is diverse, and housing demand is rapidly changing.
It is conceptually complex to figure out what houses are worth. Things like cars are just the sum of their parts. You can take them with you wherever you go, so the face value of a car is its total value. By contrast, a house is fixed in place. Valuing a home involves understanding how consumers value a bundle of goods that includes not only the home itself but also the amenities (or disamenities) offered by the area around the house. Things like school quality, crime rates, walkability to bars and restaurants, and flood risk are all part of a home’s value. Estimating how things like the weather impact the home’s value is therefore complex because the home itself and its surroundings influence each other. Consider that homes on the Gulf Coast tend to be built on stilts to avoid floodwaters. Adaptation measures like these exist most often in areas with a history of flood risk. Therefore, a region with more historical flood risk is likely to be more resilient to increases in future flood risk. We can’t compare flooded homes with large levee improvements in a post-Katrina New Orleans to flooded homes in nearby areas without major levee improvements like Biloxi.

Remote work has completely changed the game.
As remote work becomes more common, we are likely to see a combination of risk- and amenity-loving consumers moving towards coasts and woodland areas. The areas that are exposed to the highest levels of climate change-related risks. The migration towards risky areas is happening simultaneously while climate-risk-averse buyers are moving away from those same amenities. In fact, the local beliefs in climate change are a significant factor driving its impact on home sale prices. In this increasingly complex landscape of real estate, it is no wonder a hard-working professional real estate appraiser can earn well over six figures. The market value of a home is the complex interaction of the home’s size, construction quality, local schools, crime rates, climate change, and animal spirits all at once.

Good data on housing is hard to get.
For most of the world, housing data are limited at best. Datasets exist for the US, Australia, the UK, and a few other parts of Europe, but for most of the rest of the world, they are an unknown quantity. Quality and geographically explicit housing data are most readily available in the United States, and thus most studies focus on the US housing market. In the US, housing is big business, and there are multiple substantial and very successful companies dedicated to collecting, cleaning, and reselling information about the US housing stock. In addition, there is a lot of money in the housing market and, thus, strong demand for good analytics. For example, Freddie Mac forecasts USD $1.9 trillion in new purchase originations in 2021 alone. Unfortunately, all this demand means that there is money to be made, and thus these data are not free. Some companies like Zillow and Corelogic offer their data to university-affiliated researchers for free, but these data are not perfect and only available to academics. Further, even after obtaining relevant data, researchers could be prohibited by copyright law from publishing their data along with their results, so new researchers cannot challenge the findings of previous studies.

How Do We Measure Impacts? Events versus Boundaries

In this short essay I focus my attention on flood risk, as it is the most studied of the existing climate perils for real estate. I classify the existing studies on flood risk into two broad types: event-based studies (hurricanes and floods damaging things) and studies exploiting existing floodplain-related boundaries that impact the sale of a home (these are “information effects”, i.e. “My flood insurance is how much?”). The first can be thought of as measuring the response to actual weather events, and the latter as measuring the impact of information about the probability of weather events in the future. Event-based is the larger of the two categories, with Hurricanes Sandy and Harvey representing the two most studied events.

Event-based studies examine the impact of significant weather events on the price of the local housing stock.
These studies compare homes at the edge of the affected region that survive the event unscathed with homes inside the affected area that were damaged. Think of a randomized control trial (RCT) experimental design. By happenstance or accident, homes really close to the border of a disaster ended up there nearly at random. For example, when a hurricane hits the coast, it typically doesn’t destroy the entire region. Some homes will survive untouched, some will be damaged, some destroyed. Using information about price changes for similar damaged and undamaged homes, or homes in impacted and nearby “near miss” areas, the author can estimate the impact of the weather event on home prices confidently. A recent paper by Francesc Ortega and Suleyman Taspinar from CUNY Queens on the impact of Hurricane Sandy on New York real estate is an excellent example of this type of study. Each study of this type is then uniquely responsible for justifying the external validity of its estimates to as-of-yet-unrealized disasters. This validation is often a daunting task and is a significant weakness of this type of study.

Floodplain boundary-based studies exploit the fact that artificial legal boundaries often define homeowners’ amount of, and types of information about, risk(s) to which a particular property is exposed.
For example, most often, the only information about flood risk that’s available to consumers comes in the form of a written warning from their lender that the buyer must purchase flood insurance as collateral for securing their loan. In the US, this requirement is determined almost entirely by whether the home is located inside a FEMA-designated special flood hazard area (SFHA). Hino and Burke (2020) exploit changes in these SFHAs over time to study the impact on home prices for homes that were moved from outside to inside these boundaries after those boundaries were revised. Other studies, like Keys and Moulder (2020), abstract from floodplains and use census tracts with associated measures of high or low Sea Level Rise risk to each census tract and examine the distribution of home prices within each area.

Concluding Thoughts

I have laid the foundation here for an understanding of how researchers approach the questions of flood risk, climate change, and the housing market. There are certainly studies that do not fall into my broadly defined dichotomy. But these two methods describe the most common techniques at a level sufficient to support a dinner-party conversation about how exactly economics approaches this question. In Part 2 I will dive deeper into the results of these studies — attempting to parse what we actually know into a useful rule of thumb that can be used to approximate the likely impact of climate-change-related risks on the housing market in general.

Ryan Vaughn, PhD is a Technical Product Manager at Jupiter Intelligence. Learn more about Jupiter at jupiterintel.com.