NAR’s Data Center Report: The Commercial Finding Was the Clear One

The National Association of REALTORS® released its 2026 Data Center Impact Report on September 8, 2026, combining national, state and county-level data analysis with a survey of agents who are REALTORS® working in data center markets. The association states the headline finding in the negative: data centers’ impact on real estate is dramatically different depending on the local market. Thirty-eight percent of survey respondents reported a data center already in place or in development in their market. Among them, perceptions of the residential impact were mixed, with 25 percent seeing a positive effect on nearby home values and 22 percent a negative one. The commercial picture was more clearly positive, with 50 percent reporting increased nearby commercial property values and 42 percent reporting increased demand for nearby commercial space, particularly industrial properties and land. Asked what clients actually raise, respondents named energy costs (61 percent) and water use (56 percent). Those are survey responses from agents, which NAR presents as perceptions, not as measured transaction outcomes, and the distinction matters for anyone who wants to use them.

The county analysis is the part that constrains how far any of this travels. NAR maps no data centers in 92 percent of US counties and 10 or more in only 1 percent. The 10 counties with the most facilities hold about 42 percent of them nationwide, and Loudoun County, Virginia alone accounts for about 14 percent; Loudoun and Prince William together account for about 19 percent. NAR names the other major clusters as Silicon Valley (5 percent), central Ohio including Franklin and Licking counties (5 percent), the Phoenix area (4 percent) and central Washington (4 percent). NAR’s own county data file records Loudoun at 213 mapped data center locations in 2026 against 199 in 2025, with 28,218,432 square feet of data center building area and 30,435,065 square feet of campus area; that single-county building figure is larger than many entire suburban office markets, and the year-over-year move computes to about 7 percent growth, PSV’s arithmetic on NAR’s posted file. On the demographic side NAR reports a median home value of $431,750 in counties with 10 or more data centers against $174,500 in counties with none, decade home value growth of 95 percent against 64 percent, median household income of about $89,000 against $64,000, employment growth of about 16 percent from 2014 to 2024 against 2 percent, and residential electricity rates up 21.4 percent from 2020 to 2024 against 15.7 percent elsewhere. NAR cautions directly that correlation is not causation, and that these counties were already high-income, highly educated technology hubs before the recent surge. NAR Chief Economist Lawrence Yun is quoted saying there is no single data center effect and that the number of data centers alone does not tell us what will happen to home values, jobs or utility costs. NAR also states it does not currently have an official policy position on data centers.

Almost every outlet led this report with housing, and that is the least decisive thing in it. A 25 to 22 split on nearby home values is close to no signal at all, which is precisely NAR’s point. The commercial answers were the ones that separated: half of surveyed agents in these markets reported higher nearby commercial property values, and 42 percent reported higher demand for nearby commercial space, with industrial property and land called out by name. For a CRE operator that is the useful sentence in the report, and it points at the two product types where a data center cluster plausibly changes the bid: land, because a hyperscaler buying acreage resets what neighbouring ground is worth, and industrial, because the construction and operations tail wants space near the campus. What the report does not supply is a magnitude. It does not report how much values moved, over what period, or on what transactions, because it did not measure transactions; it asked agents what they observed. Treat 50 percent as evidence that the effect is commonly perceived where these facilities exist, not as an input to a pro forma.

The concentration data is the discipline. With 92 percent of counties hosting none and 1 percent hosting 10 or more, there is no national data center trade to put on. The question is always about a specific parcel’s distance to a specific campus, and the answer in Loudoun, where NAR counts 213 locations and 28.2 million square feet of building area, has almost nothing in common with the answer in a county absorbing its first facility. The second half of the operator read is the cost side, and it is the half the value figures obscure. NAR’s residential electricity rate gap, 21.4 percent against 15.7 percent over 2020 to 2024, is a utility-cost divergence in the counties with the heaviest concentration, and energy costs were the top client concern at 61 percent with water use second at 56 percent. For an owner near a cluster those show up in recoveries, in gross-to-net, and in how an entitlement hearing goes, well before they show up in an exit cap. A report that says commercial values are perceived to rise while measured electricity rates rise faster is not a contradiction, it is the whole underwriting problem in one page: the asset may be worth more and cost more to run, and only one of those two is actually measured here.

The artifact worth building is a proximity file, one row per asset a firm owns or is bidding, refreshed on a schedule rather than assembled during diligence. Inputs: NAR’s posted county data file, which carries per-county facility counts for 2026 and 2025, building square footage, campus square footage and the year-over-year change; the asset’s county and its distance to the nearest mapped campus; the local utility’s filed rate cases and tariff schedules; the county’s permit and rezoning docket for pending facilities; and the lease file, specifically which operating costs are recoverable and which are capped. Output: columns that decide something. The county’s facility count and its one-year change, so a market absorbing its first three facilities is never read through Loudoun’s numbers. Distance bands to the nearest campus and to the nearest announced-but-unbuilt one. The share of the asset’s operating expense exposed to electricity and water, and how much of it is recoverable under the actual leases. The pending-application count, because the entitlement fight is the leading indicator, not the ribbon cutting. And a plain flag for which figures are measured and which are perceived, because this report mixes both and a file that blurs them will mislead whoever reads it next quarter.

The reviewer is the asset manager for held assets and the acquisitions lead for live bids, and the approval gate is narrow: no proximity figure enters an investment memo until someone has named its source and its type, a measured county statistic or a survey perception. An assistant does the volume work well here, and this brief is an example of the shape: pulling a 49-page county table out of a posted PDF, isolating one county’s row, computing a year-over-year change, and reconciling a stated 14 percent share against a raw facility count. It should not be the thing that decides whether proximity to a campus is a premium or a discount for a given asset, because the published record does not answer that and no amount of processing will make it. PSV ran no valuation model on this report, tested no product, and promises no pricing, cost or return outcome. Every figure above is NAR’s except the Loudoun growth rate, which is PSV’s arithmetic on NAR’s file and is stated as such.