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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →AI-driven data centers can affect nearby home prices, but there is no universal premium or penalty. A September 2026 working paper estimates that announcing a large facility was followed by an average 3% decline in values within three miles and a 7.2% decline within one mile over five years. A separate Realtor.com event study found no measurable sale- or listing-price difference after facilities opened. County-level research, meanwhile, finds positive house-price estimates alongside employment and electricity-price effects. Those results are not contradictory forecasts for one address: they examine different distances, project stages, geographies and measures of value.
If you are buying or selling, treat a national percentage as context rather than a valuation. The practical questions are where the property sits relative to the site, whether the market has already priced an announcement, how large the facility will be, and what noise, traffic, utility and job effects are visible locally.
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What the best current evidence actually says
The most useful way to answer “What’s the impact of living near a data center?” is to separate the studies by the event they measure and the geographic unit they analyze. The following table puts the principal estimates on the same page without treating them as interchangeable.
| Source and date | What it studies | Main result | How to interpret it |
|---|---|---|---|
| Garrison Schlauch and Simon Greenhill, working paper, September 2026 | Matched, stacked event study of housing transactions near newly announced large U.S. data centers | About 3% lower values within three miles over the following five years; 7.2% lower within one mile on average; smaller effects beyond one mile | Announcement-based average estimates, not a forecast for an individual property. Effects are larger for hyperscale and high-capacity sites, urban locations and the first large facility in an area. |
| Glen Morgenstern, Realtor.com Economic Research, August 11, 2026 | Event study of 43 ZIP codes that received a large facility from 2019 through 2025, matched to comparison ZIP codes | No measurable difference in listing or sale prices after activation; nearby prices moved in line with comparable neighborhoods | Measures the period after opening. Sale-price analysis excludes Texas and 11 other non-disclosure states. |
| Fernando E. Alvarez, David Argente, Joyce Chow and Diana Van Patten, NBER Working Paper 35194, May 2026, revised September 2026 | County-level instrumental-variable estimates linking facility growth with economic outcomes | Positive estimates for house prices, employment, establishments, income, wages and other outcomes at different horizons; electricity prices also rise | County averages can improve while a particular home next to a plant does not. The estimates do not isolate a neighboring-property effect. |
| Alex Priest, University of Rochester working-paper abstract | Virginia air-permit and ZIP-code prices using difference-in-differences and event-study methods | Economically small, slightly positive estimates, with no evidence of large negative effects; results are similar for recent AI-facility permits | An abstract from a working paper, not a settled national consensus. |
| Princeton repository abstract, Virginia and Texas | Price and rent effects around larger facilities | Estimates are consistent with a contemporaneous local disamenity rather than a sustained demand boom | The available abstract does not provide enough detail to align its exact estimates with the other designs. |
These are recent working papers and research reports rather than one peer-reviewed consensus. “Announcement,” “permit,” “construction,” “activation” and general “data-center growth” are different treatments. A result measured at the county level cannot be applied directly to a house one mile from a substation, and a ZIP-code result after opening may miss a price change that occurred when the project was announced.
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Why an announcement can matter before a facility opens
The Schlauch–Greenhill paper uses a newly assembled U.S. data-center database and compares transactions near newly announced large facilities with transactions in observably similar areas near facilities announced later. Its central estimate is a 3% average decline within three miles during the five years after announcement, with a 7.2% average decline within one mile. Beyond one mile, the estimated effect is smaller rather than a universal zero boundary.
The timing in that study changes over the sample. Before 2021, prices responded mainly when facilities became operational. For more recently announced projects, the authors find that effects are capitalized mostly at the announcement. A buyer who waits for construction to finish may therefore be looking at a price that already reflects expected traffic, substations, cooling equipment or future expansion.
Scale and context also matter in the paper’s estimates. Hyperscale and high-capacity facilities, urban locations and the first large data center in an area have larger estimated effects. Those categories describe averages across the study sample; they do not prove that every hyperscale site will reduce values or that a small facility is harmless.
Schlauch and Greenhill estimate $20.6 billion in nationwide housing-wealth losses associated with their modeled declines. They also report that annualized U.S. data-center construction spending exceeded $75 billion in July 2026, up 57% year over year and nearly sixfold from July 2022. Both figures are the authors’ estimates and framing in a September 2026 working paper, not official government totals or a bill for any particular neighborhood.
Why an opening may show no measurable price penalty
Realtor.com Economic Research asks a different question: what happened to prices after large facilities activated? Its report, dated August 11, 2026, covers 43 ZIP codes receiving a facility between 2019 and 2025. Each was matched to comparison ZIP codes using pre-activation median sale price and population density; same-metro comparisons were used when possible, with state-level matches for five smaller or rural markets.
In that design, listing and sale prices near an activated facility rose in line with comparable neighborhoods. The report finds no measurable difference in the years after activation. Its “large facility” definition is at least 50 megawatts of selected power capacity, and its inventory was extracted June 30, 2026. The sale-price portion excludes Texas and 11 other states where disclosure rules prevent comparable transaction analysis.
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A null result after activation does not disprove an earlier announcement effect. It can mean that buyers and sellers adjusted prices when the project was disclosed, that the selected ZIP codes absorbed the facility without a detectable change, or that other local price forces were larger than the facility signal. It also does not describe a site that has not yet been announced, a project that is materially larger than the sample or a street immediately beside industrial equipment.
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What county-level growth can—and cannot—tell a homeowner
NBER Working Paper 35194 by Alvarez, Argente, Chow and Van Patten uses a shift–share instrumental-variable approach to link facility-level data with county outcomes. The University of Chicago Becker Friedman Institute summary reports positive instrumental-variable estimates for total employment, data-processing employment, construction employment, establishments, house prices, electricity prices, tax returns, adjusted gross income and wages at different horizons. Annual payroll responds less robustly.
Those results describe a county’s combined economy. A county can gain well-paid jobs and a larger tax base while a subdivision beside a loud cooling plant loses some appeal. Conversely, a nearby house can benefit from stronger demand even if countywide electricity costs rise. The paper’s unit of analysis and identification strategy do not establish the price change for each neighboring parcel.
Electricity is an important local channel. Data centers require large, reliable loads, and the county-level estimates associate facility growth with higher electricity prices. The size and timing of a bill change depend on the utility, rate design, infrastructure investments and who bears those costs. Do not convert the county estimate into a guaranteed monthly increase for a particular account without checking the local tariff and utility filings.
Local studies show why geography matters
Alex Priest’s Virginia analysis combines air-permit information with ZIP-code home prices. The working-paper abstract describes economically small, slightly positive estimates and no evidence of large negative effects, including similar results for recent permits tied to AI facilities. Because only the abstract is available here, it should be treated as a useful local datapoint, not proof that all Virginia markets—or the country—behave the same way.
A Princeton repository abstract covering Virginia and Texas reports price and rent effects for larger facilities that are consistent with a contemporaneous local disamenity channel rather than a sustained local demand boom. “Disamenity” can include industrial appearance, noise, heavy construction or traffic. The abstract does not provide enough detail to compare its exact estimates with the announcement and activation studies.
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Virginia interviews summarized by the state’s Joint Legislative Audit and Review Commission identify noise as a concern near residential areas. Interviewees generally did not observe home-value declines or slower sales in the areas they discussed. Those interviews are descriptive observations, not a national causal estimate, and they may not capture future projects with different equipment, setbacks or operating schedules.
The market is expanding into more residentially sparse areas
Realtor.com reports that the share of U.S. home sales within five miles of a large data center increased from 0.67% in 2018 to 1.5% in 2026. The report attributes the change to facilities opening in new areas rather than more sales activity around existing facilities. It projects that more than 2% of home sales will be within five miles of an active large facility by the end of 2027; that is a projection, not an observed result.
The typical setting is also changing. The median large facility opening in 2026 was in an area with 32 residential housing units per square mile, compared with 116 in 2017. Lower density can reduce the number of directly affected homes, but it can also mean fewer nearby comparable sales and less familiar local experience for appraisers and buyers.
National exposure remains uneven. NAR’s September 2026 Data Center Impact coverage says 92% of U.S. counties had no mapped data centers. Loudoun and Prince William counties in Northern Virginia together accounted for about 19% of mapped U.S. facilities. In NAR’s county-level associations, greater data-center concentration coincides with higher home values, income and long-term job growth, but the report cautions that association does not establish causation. In its agent survey, 25% perceived a positive effect on nearby home values and 22% perceived a negative one; perceptions are not transaction estimates.
“Higher home values or faster price appreciation do not necessarily mean that data centers caused home values to increase.”
That distinction matters because technology employers, existing population growth, transit investments and land-use policy can attract both a data center and appreciating homes.
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How to evaluate a specific property
Use the following process before treating a proposed or operating facility as a reason to change your offer.
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- Measure more than a straight-line radius. Record the distance to the parcel, access road, substations, generators, cooling equipment and transmission corridors. Start with one mile, one to three miles and farther away because those bands appear in the announcement evidence, but do not assume a hard cutoff. Topography, walls, vegetation and road placement can matter as much as the map distance.
- Identify the project stage. Search the city or county planning portal for zoning cases, site plans, conditional-use approvals, building permits and utility filings. Write down the public announcement date, permit date, construction schedule, expected energization and any expansion phases. A project can be priced at announcement even if operation is years away.
- Confirm the facility’s scale and use. Find the planned power capacity, whether the site is hyperscale, the number of buildings and the location of backup generation and cooling systems. A 50-megawatt threshold is the definition used in Realtor.com’s large-facility inventory; it is not a universal legal or neighborhood-impact threshold.
- Visit at different times. Listen during daytime construction, evening, overnight and weekend periods. Note truck routes, idling, lighting, generator tests and the sound of cooling equipment. Noise concerns reported in Virginia justify an on-site check, but an informal visit does not establish code compliance or predict resale value. A consumer sound-level meter can document readings for your own comparison; no specific model or standardized protocol is established here, so do not treat a reading as a legal finding.
- Check utility and infrastructure documents. Review the utility’s rate cases, planned substations, transmission work, water-supply plans and public meeting minutes. Ask which costs are assigned to the facility, spread across ratepayers or funded through taxes. The county-level evidence links data-center growth with higher electricity prices, but your bill depends on local rules.
- Build a genuinely local comparable set. Compare recent closed sales and current listings with similar age, size, condition, school assignment, lot, view and distance from the site. Separate homes already exposed to the announcement from homes outside the likely impact area. A county median or national 3% figure is not a substitute for matched local comparables.
- Ask how the market is behaving now. Agents can report days on market, inspection objections and buyer questions, but treat survey opinions as anecdotal. Look for repeated patterns across several transactions rather than one seller’s explanation.
- Stress-test your holding period. If you may sell during construction, announcement-related uncertainty could matter more than the eventual operating phase. If you plan to hold for decades, weigh possible employment and tax-base gains against enduring noise, traffic, utility or visual changes.
Questions buyers should put in writing
- What approvals are final, and which are still appealable?
- Is the site approved for one building or a campus with later phases?
- Where will substations, transmission lines, cooling towers, generators and fuel storage sit?
- What are the permitted construction hours and expected truck routes?
- Has the utility disclosed rate, water-use or infrastructure changes?
- Are there recorded easements, development agreements or tax incentives affecting the parcel?
- Have comparable homes near the site sold since the announcement, and how do their prices compare with matched homes farther away?
Request answers and documents rather than relying on a broad statement that the project will create jobs or that it is “too far away to matter.”
What sellers and appraisers should disclose carefully
Sellers should describe known, material facts according to their state’s disclosure rules, including visible construction, recorded notices, unusual noise or access changes. Do not promise that a data center will raise values because county research finds positive house-price estimates, and do not claim a fixed percentage loss because one working paper reports an average near newly announced sites.
Appraisers and buyers should identify the relevant event date and comparison group. A sale after activation may be informative about operating conditions but not about the announcement shock. A sale before construction may include a discount or premium for expectations that later changed. Explain which homes are inside the same sight, sound and traffic environment rather than simply using a ZIP-code average.
How to read a local price change without jumping to causation
Suppose prices rise after a facility is announced. That could reflect new jobs, a broader regional boom, scarce housing, a school-boundary change or anticipation of tax-funded improvements. Suppose prices fall. Possible explanations include construction nuisance, industrial land-use concerns, higher utility costs, a recession or a separate employer leaving. The NAR warning about correlation applies in both directions.
Look for a credible comparison: similar homes outside the facility’s likely exposure area, sold over the same period, with the same local demand conditions. Then check whether the difference appears at announcement, permitting, construction or activation. No single before-and-after chart can separate those events reliably.
Bottom line for a home decision
AI data centers are large enough to change land use, infrastructure and local labor markets, so dismissing them as irrelevant is unwise. It is equally unsound to subtract 3% or 7.2% from a listing price automatically. The strongest negative estimate is localized and announcement-based; the strongest no-effect result is activation-based; county-level work finds broader gains and higher electricity prices; local studies show small positive or disamenity patterns depending on place and design.
For a particular home, make the decision from the project timeline, distance and physical exposure, facility scale, utility documents and matched sales. Visit the property when the equipment or construction is operating, obtain professional legal or inspection advice where needed, and price the home against truly comparable local transactions. That process recognizes what the evidence can establish—and what it cannot.
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