Bernstein, Gustafson, and Lewis (2019): Disaster on the Horizon

Why this paper is on the syllabus

This paper is the sea-level-rise analogue to the classic hedonic housing papers we study earlier in the semester. It asks whether a long-run climate risk that is not expected to cause major local damage for decades is already capitalized into current house prices.

That question connects directly to several themes of the course. It links discounting, beliefs, and asset pricing inside a single market that students already understand reasonably well, and it provides one of the cleanest pieces of evidence that forward-looking capitalization of climate risk is not limited to specialized financial instruments. It shows up in the ordinary residential real estate market.

The question

Do homes exposed to future sea level rise sell at a discount today relative to otherwise comparable unexposed homes in the same local market?

The paper is not a simple question about current damage. Many of the exposed properties in its sample are not expected to flood in any given year for decades, and some not for close to a century. The relevant question is therefore whether buyers are capitalizing a long-horizon climate risk into current sale prices, rather than whether they are reacting to realized flood events.

Why this is complicated

Coastal housing is a useful but difficult setting because the coast provides both an amenity and a risk. A house near the ocean may have a valuable view, beach access, and higher demand precisely because of its coastal location. At the same time, being low-lying and close to the water makes the house more exposed to long-run sea level rise and to the associated tail risks.

The central empirical challenge is therefore to separate the positive capitalization of coastal amenities from the negative capitalization of future flood risk. A simple comparison of inland homes to beachfront homes would confound these two margins and would not identify the risk discount of interest.

Data and setting

The authors use detailed transaction-level housing data together with geographic information on property locations, elevations, and projected sea level rise exposure. For each property, they determine whether the structure would be inundated under a defined sea level rise scenario drawn from standard projections, and they record a rich set of hedonic characteristics.

The sample is restricted to properties close to the coast, and the core comparisons are made within narrowly defined local markets, typically using zip-code or finer geographic fixed effects. That sample restriction is important. The identifying variation is not “coastal versus inland.” It is closer to “two properties in the same local coastal market, with similar observable features and comparable distance to the waterfront, but different projected inundation exposure because one sits at a higher elevation than the other.”

What the authors do

The main regression compares sale prices of exposed and unexposed homes while conditioning on a rich set of controls:

1. distance to the coast,
2. zip-code or finer local fixed effects,
3. time-of-sale fixed effects at the month level, and
4. hedonic property characteristics such as living area, age, number of bedrooms, and number of bathrooms.

In economic terms, the authors compare properties that are similar in their local market, in their sale timing, and in the hedonic attributes that determine baseline value, and that differ primarily in projected exposure to future inundation.

The paper then pushes the design in three useful ways. It examines the rental market as a contrast to the sale market. It studies how the estimated discount evolves over time. And it asks whether the discount varies with buyer sophistication and with local climate concern, both of which proxy for the information inputs that a forward-looking capitalization story predicts should matter.

How the empirical strategy works

The strategy leans on the fact that two homes with very similar distance to the coast can differ meaningfully in elevation and therefore in projected long-run flood exposure. That comparison is powerful because it holds fixed much of what buyers like about coastal housing, including the view, the access, and the local amenity value, and isolates variation in exposure that arises from topography rather than from coastal proximity.

The heterogeneity analysis by ownership type is particularly informative. Non-owner-occupied properties are disproportionately purchased by investors and by buyers who are plausibly more attentive to long-run asset value than owner-occupiers making consumption-driven housing choices. If the exposure discount is larger among non-owner-occupied properties, that pattern is consistent with better-informed or more return-sensitive buyers pushing prices to reflect long-run climate risk more aggressively than less-sophisticated buyers would.

The rent test complements the sale-price analysis in a theoretically sharp way. If sea level rise is primarily a long-run ownership risk and not an imminent service-quality issue, it should be capitalized into sale prices, which reflect claims on the full future stream of housing services and resale values, but not into current rents, which reflect compensation for current housing services. Finding a price discount but no rent discount would therefore be strong evidence that the effect is about long-run asset pricing rather than about current livability.

Main findings

The headline result is that homes exposed to projected sea level rise sell for approximately 7 percent less than observably equivalent unexposed homes that are equally close to the coast. That exposure discount is the paper’s core number and is robust across a range of specifications that vary the geographic fixed effects, the control set, and the exposure definition.

A set of supporting results makes the interpretation especially convincing.

First, the discount grows over time across the sample period. That pattern is consistent with buyers learning more about climate risk, with the salience of sea level rise rising in public discussion, or both.

Second, the discount is concentrated among more sophisticated buyers. In particular, it is larger and more precisely estimated among non-owner-occupied properties, which are disproportionately held by investors. That heterogeneity fits a forward-looking information story rather than a mechanical amenity story.

Third, the discount is stronger in communities with greater measured climate concern, as proxied by survey-based beliefs about climate change. Beliefs therefore appear to be part of the pricing mechanism, not just a background attitude.

Fourth, and especially importantly, the paper finds no comparable effect in rental rates. The rent null is central to the interpretation. If current flood damages or current livability were driving the pattern, rents should move as well. Instead, the evidence looks like a long-horizon asset-pricing effect that shows up in ownership claims but not in the price of current housing services.

Finally, the paper finds a discount even for properties that are not projected to flood in any given year for close to a century. That is precisely the piece of evidence that connects the paper to the discounting lectures. Very delayed expected damages still move current prices.

Why the paper matters

The broad lesson is that residential real estate markets can be forward-looking over long horizons. Buyers do not need to wait for frequent realized flooding before adjusting prices. If future resale values will be lower because later buyers will face greater exposure, then current buyers rationally pay less today, and that capitalization is observed in the price data.

That result positions the paper as a bridge between hedonic pricing and climate finance. The asset is a house, but the pricing economics is close to that of a long-duration claim on an uncertain future cash flow stream. A future stream of expected damages changes today’s asset value, just as it does for the municipal bonds studied by Painter or the leasehold properties studied by Giglio, Maggiori, and Stroebel.

The paper is also important because it shows that beliefs matter. The exposure discount is not uniform across buyers or locations. It is stronger where the underlying information or concern is stronger. That is useful evidence that price formation in this setting depends on who is active in the market and on what they believe about long-run climate trajectories, not only on physical exposure measured in feet of elevation.

What to focus on when you read

On a first pass, focus on four things.

First, understand the core comparison. The relevant contrast is between exposed and unexposed coastal homes that are otherwise very similar in location, timing, and hedonic characteristics, not between coastal and inland homes.

Second, understand why the rent result matters. The absence of a rent effect strengthens the interpretation that the paper is about long-run ownership value rather than current housing services.

Third, understand the sophistication result. The fact that the discount is driven disproportionately by more informed or investment-oriented buyers connects the paper to information-based asset pricing and helps rule out simple amenity explanations.

Fourth, keep the timing in mind. The paper is not about homes that are flooding today. It is about how distant, low-probability future damages are capitalized into current prices, and that is what makes it so directly relevant for the discounting and climate-finance themes of this part of the course.

Terms to know

• Capitalized into prices: reflected in current asset values through the forward-looking pricing decisions of buyers and sellers.
• Sea level rise exposure: the vulnerability of a property to future inundation under a specified sea level rise scenario.
• Hedonic comparison: comparing prices of observably similar properties that differ in a specific attribute of interest, in order to identify the implicit price of that attribute.
• Non-owner-occupied property: a property that is not occupied by its owner, typically held as an investment or rental.
• Rental rate: the current market price of housing services, as distinct from the sale price of the underlying asset.