"Traditional rent statistics answer a deceptively simple question: 'What is the average rent per square foot among leases signed this quarter?' For decades, rent averages have been the best measure of a rental market, shaping how economists, investors, lenders, and policymakers understand commercial, retail, and industrial markets. From how cities set property taxes to how lenders underwrite loans and how companies decide where to expand or sign leases, these figures influence major decisions."
"A more illuminating approach - comparing like with like in similar locations, and accounting for the concessions for tenants in lease terms - paints a different picture in Dallas. At best, office rents have stalled. That difference matters: when business leaders and policymakers rely on headline averages that make the market appear healthier than it really is, they may make decisions based on a distorted picture of demand."
"While rent averages can provide a rough guide to the market in regular times, they often fall short - particularly during unprecedented times. In the years following COVID‑19, they were misleading. This isn't just a quirk in Dallas. It's a nationwide measurement problem."
Traditional rent statistics measure average rent per square foot among leases signed in a given period, but this metric fails to reflect actual market conditions. In Dallas, headline data suggested rising office rents, yet when accounting for tenant concessions and comparing similar properties in comparable locations, rents have actually stalled. This measurement gap distorts decisions by mayors, lenders, and corporate real estate teams nationwide. The standard metric particularly fails during unprecedented times like the post-COVID period. Headline averages make markets appear healthier than reality, causing business leaders to make expansion and leasing decisions based on incomplete information that doesn't reflect true market weakness or actual tenant costs.
#commercial-real-estate-metrics #office-market-analysis #tenant-concessions #market-data-distortion #post-covid-real-estate
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