Tag: new jersey towns

  • How Much Water Does a Data Center Actually Use?

    Somewhere right now there is a town meeting where a guy in a folding chair is standing up to say that a data center is going to drink his well dry, poison the aquifer, and leave his grandkids fighting over a garden hose. I am not making fun of the guy. (I have been the guy in the folding chair, about other things.) But before your town votes on one of these buildings, and towns all over this state are voting on them right now, it is worth knowing what the water numbers actually are. Because the real answer is smaller than the panic, bigger than the brochure, and weird in a way nobody at the microphone is saying.

    The question is not fringe. “Data center water usage” is searched about 9,900 times a month in this country, and it is up more than 600 percent in a year. People are not idly curious. A building the size of a small city is going up near their house and they want to know what it drinks.

    The bottle of water claim, taken apart

    The version flying around says every AI prompt you type costs about a bottle of water. That exact question, is it true that one AI prompt uses a bottle of water, gets searched roughly 8,100 times a month. Eight thousand people a month typing a rumor into Google to find out if it is real. That tells you something about where we are.

    Here is the honest version. Cooling a building full of computers takes water, and some of that water evaporates, and evaporated water is gone the way your money at the track is gone. But the per-prompt math behind the bottle claim takes the thirstiest kind of facility, in the hottest kind of place, and spreads it across your one question about a casserole recipe. Most facilities are not that facility. The claim is not a lie exactly. It is a worst case wearing a trench coat, pretending to be the average.

    What the one serious state study found

    Virginia is the data center capital of the world, so Virginia’s own legislative watchdog, JLARC, went and looked at the actual numbers and published them in December 2024. Their finding is the least exciting sentence in this whole fight, which is usually a sign it is the true one: most data centers use about as much water as a large office building.

    Sit with that. Not a reservoir. Not a river. An office building. The kind with a fountain in the lobby that has not worked since 2019.

    Now, the watchdog said most, and most is doing real work in that sentence. A few facilities use far more, and the design is the whole ballgame. A building cooled mostly by air uses very little water. A building cooled by evaporation uses a lot, especially in August. Same company, same logo on the fence, completely different drinker. Which is why the only number that matters for your town is not a national average. It is the number in that project’s permit, the ceiling the state lets them draw, in writing, before anybody breaks ground. If the people pitching the project cannot hand you that number, the meeting is not over, no matter what the clock says.

    One more fact that surprises people: according to the Congressional Research Service, about 97 percent of the water American data centers use comes from municipal systems. It is not a secret well in the woods. It is the same public water system your kitchen tap is on, which is exactly why it is a public question and not a private one.

    The water is not even the biggest bill

    Here is the part that gets lost. The fight is usually about water because water is easy to picture. But the heavier number is electricity. The International Energy Agency estimates a typical AI data center uses about as much electricity as 100,000 homes. One building. A hundred thousand homes. When a town argues about a data center, the water debate fits in a headline. The power debate is the one that shows up later, in grid upgrades and rate cases and a substation nobody budgeted for, long after the ribbon cutting photos fade.

    So no, the building is probably not going to drink your town dry, and yes, it is absolutely going to lean on your grid like it owns the place. Both things can be true. Around here, they usually are.

    What to ask before your town votes

    Ask for the permitted water draw, in gallons, in writing. Ask what kind of cooling it uses, and what it uses on the hottest week of the year, not the average week. Ask where the water comes from and what happens to it after. Ask what the power draw is and who pays for the upgrades when the grid needs them. And ask how many permanent jobs the building actually keeps once the construction crews leave, because that number has a way of shrinking between the announcement and the anniversary.

    None of that makes you anti-progress. It makes you a resident reading the fine print on a building that will outlive everybody on the council that approved it.

    The flat version

    A data center is not a vampire and it is not a toaster. Most of them drink about like an office building, a few drink like a small town, and every single one of them eats electricity like a city. The rumor about the bottle of water is a worst case dressed as an average. The real questions are local, they are in the permit, and they have numbers attached. Get the numbers before the vote, while getting them is still free.

    Related reading on Exit Desk:

    Sources:

    • GridSight, Community Questions (Google Keyword Planner data, refreshed November 1, 2026): “data center water usage” about 9,900 searches per month, up 654 percent year over year; “is it true that one AI prompt uses a bottle of water” about 8,100 searches per month: https://thegridsight.com/community-questions
    • JLARC, Data Centers in Virginia (December 2024): most data centers in Virginia use about as much water as a large office building, though a few use far more; a project’s water permit sets the ceiling.
    • Congressional Research Service, R49057: about 97 percent of US data center water is municipally supplied.
    • International Energy Agency: a typical AI data center uses as much electricity as 100,000 homes.
  • How to Research New Jersey Home Values: Recent Sales, Comparable Homes and Limits

    No website or AI system can reliably predict the future price of every New Jersey home. Buyers and sellers can, however, make a better-supported estimate by examining recent comparable sales, current competition and the property’s material differences.

    This guide explains a research process, not an appraisal or a forecast.

    Start with recent closed sales

    Closed transactions usually provide a stronger foundation than active listings because an asking price is not proof of market value. Look first for recent sales in the same municipality and, where relevant, the same neighborhood, development, school area or property type.

    Prioritize homes with similar:

    • Style, age and finished living area
    • Lot size and location characteristics
    • Bedroom, bathroom and parking configuration
    • Condition, renovations and major systems
    • Flood exposure, association obligations and other recurring costs

    New Jersey Realtors publishes monthly housing-market statistics that can help establish broader county and state trends. Broad statistics should not be treated as the value of a particular home.

    Separate list prices from sold prices

    Active and pending listings help show current competition, but they do not reveal the final price until a transaction closes. Price reductions, concessions and unusual financing can also affect interpretation. Verify important figures with reliable public records or a licensed real-estate professional who has access to current listing data.

    Account for differences honestly

    A nearby sale is not automatically comparable. A renovated kitchen, additional bathroom, superior location or known defect may matter, but a simple dollar adjustment generated by software is not necessarily supported. When the value matters for financing, litigation, taxation or another consequential decision, use an appropriately licensed or qualified professional.

    Understand automated valuation limits

    Automated estimates can be useful as one reference point. They may miss recent renovations, interior condition, unrecorded defects, neighborhood boundaries, unusual lots or rapidly changing inventory. Different services can produce different values because their records, models and update schedules differ.

    A general chatbot does not have guaranteed access to current multiple-listing data and should not be asked to invent comparable sales. If AI is used to organize a table of verified records, preserve the source and date for every entry and check the math independently.

    A practical research sequence

    1. Define the property’s relevant characteristics.
    2. Collect several recent, genuinely comparable closed sales.
    3. Review current competing listings separately.
    4. Note differences without pretending that every adjustment is precise.
    5. Compare the result with broader local market statistics.
    6. Get professional valuation help when the financial consequence warrants it.

    The result should be a documented range with limitations—not a confident prediction of what a home will be worth months from now.