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It’s flawed, but not worthless. Here’s how you should think about it.

Starting this month, the tens of millions of Americans who browse the real-estate listings website Zillow will encounter a new type of information.
In addition to disclosing a home’s square footage, school district, and walkability score, Zillow will begin to tell users about its climate risk — the chance that a major weather or climate event will strike in the next 30 years. It will focus on the risk from five types of dangers: floods, wildfires, high winds, heat, and air quality.
The data has the potential to transform how Americans think about buying a home, especially because climate change will likely worsen many of those dangers. About 70% of Americans look at Zillow at some point during the process of buying a home, according to the company.
“Climate risks are now a critical factor in home-buying decisions,” Skylar Olsen, Zillow’s chief economist, said in a statement. “Healthy markets are ones where buyers and sellers have access to all relevant data for their decisions.”
That’s true — if the information is accurate. But can homebuyers actually trust Zillow’s climate risk data? When climate experts have looked closely at the underlying data Zillow uses to assess climate risk, they have walked away unconvinced.
Zillow’s climate risk data comes from First Street Technology, a New York-based company that uses computer models to estimate the risk that weather and climate change pose to homes and buildings. It is far and away the most prominent company focused on modeling the physical risks of climate change. (Although it was initially established as a nonprofit foundation, First Street reorganized as a for-profit company and accepted $46 million in investment earlier this year.)
But few experts believe that tools like First Street’s are capable of actually modeling the dangers of climate change at a property-by-property level. A report from a team of White House scientific advisors concluded last year that these models are of “questionable quality,” and a Bloomberg investigation found that different climate risk models could return wildly different catastrophe estimates for the same property.

Not all of First Street’s data is seen as equally suspect. Its estimates of heat and air pollution risk have generally attracted less criticism from experts. But its estimates of flooding and wildfire risk — which are the most catastrophic events for homeowners — are generally thought to be inadequate at best.
So while Zillow will soon tell you with seeming precision that a certain home has a 1.1% chance of facing a wildfire in the next 30 years, potential homebuyers should take that kind of estimate with “a lot of grains of salt,” Michael Wara, a senior research scholar at the Stanford Woods Institute for the Environment, told me.
Here’s a short guide for how to think through Zillow’s estimates of climate risk.
Neither First Street nor Zillow immediately responded to requests for comment.
Zillow has said that, when the data is available, it will tell users whether a given home has flooded or burned in a wildfire recently. (It will also say whether a home is near a source of air pollution.)
Homebuyers should take that information seriously, Madison Condon, a Boston University School of Law professor who studies climate change and financial markets, told me.
“If the house flooded in the recent past, then that should be a major red flag to you,” she said. Houses that have flooded recently are very likely to flood again, she said. Only 10 states require a home seller to disclose a flood to a potential buyer.
First Street claims that its physics-based models can identify the risk that any individual property will flood. But the ability to determine whether a given house will flood depends on having an intricate knowledge of local infrastructure, including stormwater drains and what exists on other properties, and that data does not seem to exist in anyone’s model at the moment, Condon said.
When Bloomberg compared the output of three different flooding models, including First Street’s, they agreed on results for only 5% of properties.
If you’re worried about a home’s flood risk, then contact the local government and see if you can look at a flood map or even talk to a flood manager, Condon said. Many towns and cities keep flood maps in their records or on their website that are more granular than what First Street is capable of, she said.
“The local flood manager who has walked the property will almost always have a better grasp of flood risk than the big, top-down national model,” she said.
In some cases, Zillow will recommend that a home buyer purchase federal flood insurance. That’s generally not a bad idea, Condon said, even if Zillow reaches that conclusion using national model data that has errors or mistakes.
“It simply is true that way more people should be buying flood insurance than generally think they should,” she said. “So a general overcorrection on that would be good.”
If you’re looking at buying a home in a wildfire-prone area, especially in the American West, then you should generally assume that Zillow is underestimating its wildfire risk, Wara, the Stanford researcher, told me.
That’s because computer models that estimate wildfire risk are in a fairly early stage of development and improving rapidly. Even the best academic simulations lack the kind of granular, structure-level data that would allow them to predict a property’s forward-looking wildfire risk.
That is actually a bigger problem for homebuyers than for insurance companies, he said. A home insurance company gets to decide whether to insure a property every year. If it looks at new science and concludes that a given town or structure is too risky, then it can raise its premiums or even simply decline to cover a property at all. (State Farm stopped selling home insurance policies in California last year, partly because of wildfire risk.)
But when homeowners buy a house, their lives and their wealth get locked into that property for 30 years. “Maybe your kids are going to the school district,” he said. It’s much harder to sell a home when you can’t get it covered. “You have an illiquid asset, and it’s a lot harder to move.”
That means First Street’s wildfire risk data should be taken as “absolute minimum estimate,” Wara said. In a wildfire-prone area, “the real risk is most likely much higher” than its models say.
Over the past several years, runaway wildland fires have killed dozens of people or destroyed tens of thousands of homes in Lahaina, Hawaii; Paradise, California; and Marshall, Colorado.
But in those cases, once the fire began incinerating homes, it ceased to be a wildland fire and became a structure-to-structure fire. The fire began to leap from house to house like a book of matches, condemning entire neighborhoods to burn within minutes.
Modern computer models do an especially poor job of simulating that transition — the moment when a wildland fire becomes an urban conflagration, Wara said. Although it only happens in perhaps 0.5% of the most intense fires, those fires are responsible for destroying the most homes.
But “how that happens and how to prevent that is not well understood yet,” he said. “And if they’re not well understood yet from a scientific perspective, that means it’s not in the [First Street] model.”
Nor do the best university wildfire models have good data on every individual property’s structural-level details — such as what material its walls or roof are made of — that would make it susceptible to fire.
When assessing whether your home faces wildfire risk, its structure is very important. But “you have to know what your neighbor’s houses look like, too, within about a 250-yard radius. So that’s your whole neighborhood,” Wara said. “I don’t think anyone has that data.”
A similar principle goes for thinking about flood risk, Condon said. Your home might not flood, she said, but it also matters whether the roads to your house are still driveable or whether the power lines fail. “It’s not particularly useful to have a flood-resilient home if your whole neighborhood gets washed out,” she said.
Experts agree that the most important interventions to discourage wildfire — or, for that matter, floods — have to happen at the community level. Although few communities are doing prescribed burns or fuel reduction programs right now, some are, Wara said.
But because nobody is collecting data about those programs, national risk models like First Street’s would not factor those programs into an area’s wildfire risk, he said. (In the rare case that a government is clearing fuel or doing a prescribed burn around a town, wildfire risk there might actually be lower than Zillow says, Wara added.)
Going forward, figuring out a property’s climate risk — much like pushing for community-level resilience investment — shouldn’t be left up to individuals, Condon said.
The state of California is investing in a public wildfire catastrophe model so that it can figure out which homes and towns face the highest risk. She said that Fannie Mae and Freddie Mac, the federal entities that buy home mortgages, could invest in their own internal climate-risk assessments to build the public’s capacity to understand climate risk.
“I would advocate for this not to be an every-man-for-himself, every-consumer-has-to-make-a-decision situation,” Condon said.
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SoftBank’s error and Nvidia’s scandal suggest data centers may have already peaked as an economic and energy trend.
This is Heatmap Daily, a weekday digest written by our executive editor. Subscribe here.
Covering energy and climate change sometimes means seeing certain stories in a different light than others. And a particularly vivid hue illuminated The Wall Street Journal’s report today that the Japanese conglomerate SoftBank Group is taking a $3.7 billion loss on several gigawatts’ worth of natural gas turbines.
For the Journal, the big news is that SoftBank and its billionaire founder, Masayoshi Son, are failing to capitalize on the artificial intelligence boom. But at Heatmap, we’re thinking about the turbines.
The data center boom and the return of electricity load growth has depleted the world’s supply of natural gas turbines. SoftBank had purchased reservations for about 13 gigawatts of turbines, according to the Journal. That’s a staggering amount of electricity — it could meet New York City’s maximum power draw on the hottest part of a summer’s day. The conglomerate now seems set to dump reservations for about four gigawatts’ worth. Keeping a reservation, it turns out, really is harder than making it.
Most of those rotors were reportedly from GE Vernova, one of the world’s largest producers of gas turbines. GE Vernova’s order list for gas turbines has been filled for nearly two years and the waiting list now stretches into 2031. That has led data center developers, hungry to get power as soon as possible, to reach for ever dirtier forms of electricity generation, including diesel generators and even old jet engines.
On that front, SoftBank’s failure to sell the turbines to a higher bidder suggests (albeit weakly) that demand for energy-producing assets might not be as strong as the AI boom’s backers once thought. SoftBank was already set to send about 9 gigawatts of turbines to the enormous Trump-backed OpenAI data center in Pike County, Ohio. The roughly remaining four gigawatts will be released back their manufacturers. (One thing this news can’t quite tell us, alas, is what type of turbine set-up will be used in the Pike County facility, which will be the world’s largest gas-fired power plant if it is build to completion. Recent public filings have suggested the facility isn’t planning on using any turbines in the more efficient combined-cycle configuration, which would mean the plant’s carbon intensity will be worse than most other new natural gas plants. But we’d love to hear any tips on this front.)
To zoom out for a second, I’m struck by how the AI bubble has been replaced by an AI muddle. As I wrote this, news emerged that Nvidia is preparing to fire its CEO, Jensen Huang, for his affair with a Chinese woman who is alleged to be a spy. (He had at least one more affair with another Nvidia executive, according to The New York Times.) Under Huang’s leadership, Nvidia has invested in a sprawling and sometimes circular network of energy-related companies, including Emerald AI, a startup that helps data centers flex their electricity consumption, and Redwood Materials, a battery recycler and producer led by the former Tesla executive J.B. Straubel. Huang has used Nvidia’s enormous balance sheet to buy down the risk of other AI and AI-related companies, propelling the overall AI trade to become the biggest story in American markets.
Now that balance sheet is in peril: Nvidia’s stock is already down 11% over the past five days, and it has fallen another 8% in after-hours trading since news broke of Huang’s ouster. If the chip designer’s financial network began to fray — or even if the next CEO had a lower risk appetite than Huang — then its demise could impact a much larger set of firms.
Even before the Huang scandal broke, companies at the center of the AI trade were showing signs of weakness. Alphabet, Meta, and Microsoft are all down on the month. Nvidia was already lagging by about 10% on the month before its hebdomas horribilis began, too. The memory and chip makers Micron and Applied Materials are both down more than 25% in about the same period, and GE Vernova and Oklo have fallen nearly 20%. Although the data center company Coreweave rebounded on an earnings beat today, it’s still down 24% on the month and nearly 50% since May.
The market rout isn’t total — Anthropic seems likely to enjoy one of the largest IPOs ever in the next week (although perhaps not with quite as large a valuation as its investors once imagined). And the underlying technology of AI seems to be getting worryingly better all the time. But even though the politics of data centers and AI seem set to dominate the new Washington, I’m wondering if data centers as a mainstream economic trend might have already peaked. Americans voted for a more subdued AI boom in the midterms. Now it seems like they might get it ahead of schedule.
Lunar Energy’s latest AI-powered system aims to integrate EVs into the automated demand response mix.
In theory, it’s straightforward to assemble the smart energy home: Solar panels, home backup batteries, and electric vehicles, plus smart thermostats, heat pumps, and water heaters — all the individual pieces can be had for the homeowner who can afford the up-front cost. The challenge is getting all those pieces to talk to one another.
Bay Area-based Lunar Energy is one of the tech firms that’s been trying to crack this problem. Today, it announced an upgraded version of its artificial intelligence-powered home energy control system, which not only integrates all those separate systems but also responds tactically to energy markets. CEO Kunal Girotra told me Lunar AI saves its users money by monitoring their home 24/7 and making decisions to charge, not to charge, or discharge so that they save the most money.
“Utilities are all moving to almost-real-time changes of rates,” he said. “You need software to handle that for you. I think that the race is on for who has the best AI machine learning algorithms to save the most amount of money. And so far, we seem to have a head start.”
Lunar’s first-generation system focused on generation, storage, and control. In other words, it connected elements like a home’s rooftop solar panels with the backup battery the residents might keep in the garage, using the AI to decide when to meet the home’s real-time energy needs with solar versus grid energy versus stored power. What it didn’t have was the ability to work with electric vehicles to manage charging and use the car’s battery as a bonus source of energy.
To fix this, Lunar has built its own bidirectional EV charger — one that can charge up the battery or discharge the car’s battery for a secondary use, like backing up the home’s power supply. Girotra, formerly the head of Tesla Energy, told me the problem with that functionality to this point has been the connection point between the car and the home electrical system. Carmakers and other companies in the space didn’t have the deep experience with electrical certifications to make the process seamless, he tells me. That led to setups like the original Ford Charge Station Pro, which required several different wall-mounted electrical boxes to connect the F-150 Lightning EV to the home.
Lunar’s charger is built to connect to its customers’ existing home Lunar energy system, which includes a microgrid interconnect device they call Lunar Bridge that islands the home in case of a power outage on the grid. In that way, the power stored in the EV battery is ready to back up the home in case of a blackout without any additional work — or to be optimized as part of a fully smart home that’s making money as part of a virtual power plant.
The company’s setup allows for another fun feature: “slow” charging that’s not slow at all, but actually two or three times as fast as the Level 2 charging most EV owners use to recharge their batteries overnight. Electric cars in the U.S. can typically take on only about 11 kilowatts of AC power from one’s home because of the limitations of their on-board hardware. Because Lunar’s system is fully DC, however, Girotra said the company decided to build a DC home charger, one that — like a Tesla Supercharger or other DC fast chargers out in the world — isn’t limited by that power cap. Lunar said it could use a combination of power from the grid, home solar, or home batteries to charge an EV at up to 25 kilowatts. That’s enough to recharge an EV in three to four hours rather than six to eight.
“If the standards get high, and if everybody sees cars can get charged in three hours, then all of a sudden people are like, ‘Wow, this is how I want it,’” he said. “Especially for 25% of the users who are like me, who always are running low on charge and they don’t mind a boost every now and then.”
Some of the other improvements in the second-gen system are more incremental. An inverter that’s 40% more powerful helps Lunar get its system up to enterprise scale. That’s crucial, as the world moves toward a place where more homes and businesses employ battery backup systems. Lunar’s new batteries come in modular 5 kilowatt-hour blocks ranging from 10 kilowatt-hours to 30 kilowatt-hours in a single unit and scale up to 300 kilowatt-hours for home or small commercial use; there’s also new pad-mounted configurations with capacity up to 1 megawatt-hour.
Batteries, though, are the simple part. Everyone from EV makers like Rivian to utilities like Octopus Energy is racing to make a person’s or a business’s batteries and solar panels and electric cars work for them, using the power of AI to account for moment-to-moment changes in energy prices and supply. Lunar’s new tech makes the case that the most important element is a single brain to integrate every last smart, electrified thing in your house.
The fundraise comes on the heels of a $50 million grant from the Department of Energy.
Almost all lithium-ion batteries on the market rely on graphite for their anodes, more than 90% of which is processed in China. That concentration has created a chokepoint in the Pentagon’s supply chain as the military increasingly turns to batteries to help its transportation network operate more quietly and reduce reliance on complex refueling logistics.
So when Congress passed the latest National Defense Authorization Act at the end of 2025, barring Chinese-linked battery materials from military use starting in 2028, Coreshell saw an opening. While the California-based silicon-anode battery startup had previously focused on automakers as its beachhead customers, it’s now shipping its first battery cells to the military for use on Navy ships and Army ground vehicles. On Wednesday, Coreshell announced a $30 million Series B to scale up manufacturing operations to meet the needs of this growing customer base.
“We’re entering a new stage where we’re no longer, I would say, demand limited,” Coreshell’s CEO Jonathan Tan told me. “This year we’ve had to put on second and third shifts, work nights and weekends, just to get the product out the door.”
Coreshell sets itself apart by going a step further than the NDAA requires. The law doesn’t make suppliers trace battery materials all the way back to the mine, which would be exceedingly difficult given China’s dominance across the battery supply chain. But Coreshell has nevertheless eliminated Chinese sourcing from its anodes by replacing graphite with silicon mined and refined in the U.S. by Ferroglobe, one of its Series A investors.
Ferroglobe and global investment firm Access Industries led the latest round, with defense contractor ADS also joining as a new strategic investor. The financing comes on the heels of a $50 million Department of Energy grant that Coreshell secured in August as part of a program to strengthen the domestic battery supply chain. That funding will support the company’s first commercial-scale facility, a 2-gigawatt-hour factory.
That’s about 20 times the size of the 100-ish-megawatt-hour plant the startup had originally envisioned, as the DOE grant has allowed Coreshell to “expand much further than we were originally expecting,” according to Tan. The company is now evaluating several potential U.S. sites and expects to announce its selection early next year. For now, it relies on unnamed contract manufactures, along with its small pilot line in San Leandro, to produce its cells. But once the gigafactory gets up and running, the startup plans to bring electrode production in house by 2028, followed by full cell assembly in 2029.
Building this plant will ultimately require significant additional capital. But Tan believes this latest vote of confidence from the federal government will help Coreshell to diversify its capital stack as it continues to fundraise. “That’s one of the great things about having such a notable DOE award is that it also allows us to attract multiple forms of capital,” he told me.
Silicon has long been recognized as a promising alternative material for battery anodes, as it can store far more energy than graphite. The catch is that it tends to swell during charging, causing it to break down quickly and ultimately leading to a much shorter cycle life. Coreshell says it’s extended its anode’s lifespan by giving it a proprietary coating, which Tan told me back in April essentially means “painting the inside of your battery” with an ultra-thin protective layer.
Other companies, such as Sila Nanotechnologies and Group14 Technologies, are also working to commercialize silicon anodes. But Coreshell says many of its competitors rely on more highly engineered, expensive forms of silicon, such as silane gas. By contrast, Coreshell uses metallurgical silicon, a cheap, raw commodity already widely used in aluminum alloys. That, Tan argues, gives the company a cost advantage not only over its competitors, but potentially Chinese graphite too.
Coreshell hasn’t yet demonstrated that its cells can meet the performance demands of the automotive industry, however. When the company raised its Series A in early 2025, it said it expected its automotive cells to exceed 1,000 charge cycles while retaining 80% of their original capacity. But Tan told me many defense applications don’t require such demanding performance targets, as Navy ships and ground vehicles are “not intending to have as many miles or missions or use cases on that same battery” as a consumer EV would.
That doesn’t mean the company is abandoning its automotive ambitions. As Tan pointed out, electric vehicles remain by far the largest source of global and domestic battery demand. “It’s natural for us to obviously utilize the security supply chain first,” he told me. “That allowed us to really get to commercialization, revenue, and production faster, because automotive qualification is this longer journey. But ultimately, we want to do both of those and more.”