Price fixing by algorithm is still price fixing

Original Article ↗ Hacker News Discussion ↗

US antitrust enforcers are warning that landlords can’t evade price‑fixing laws by outsourcing rent decisions to shared pricing algorithms such as RealPage’s tools. Commenters debate where the legal line lies between using market data to set prices and colluding via a common intermediary, drawing parallels to insurance, salaries, and other algorithmically priced markets. The thread widens into housing policy, with arguments over rent control, zoning, land value taxes, and whether stronger regulation or more supply is the only durable fix for soaring rents.

Scope of FTC Position on Algorithmic Price-Fixing

  • Core idea: using a shared pricing algorithm across competitors can be illegal price‑fixing, even if each landlord “retains discretion” or sometimes deviates.
  • Key distinction drawn in the thread:
    • Legal: independently using public data (Zillow/KBB‑style) to inform your own pricing.
    • Potentially illegal: many competitors agreeing (explicitly or via contract) to follow the same third‑party algorithm, especially when deviations are discouraged, monitored, or penalized.
  • Commenters highlight RealPage/YieldStar: landlords feed non‑public data (actual leases, occupancy, inventory) into a common system that outputs specific rents and encourages landlords not to negotiate, sometimes even to tolerate vacancies.

Does It Actually Raise Rents?

  • Some argue these systems let landlords act like a cartel in an inelastic market (housing), pushing rents above what competition would yield, especially when a large share of units in a city use the same tool.
  • Others are skeptical, pointing to:
    • Rent trends roughly tracking inflation and interest rates.
    • Vacancies and cash‑flow limits that make high vacancies risky.
    • Countervailing forces like people moving away, doubling up, or becoming homeless.
  • Several emphasize: under antitrust law, the agreement to coordinate pricing can be illegal even if the scheme is imperfect or fails.

Comparison to Other Markets (Cars, Insurance, Wages)

  • Analogies raised:
    • Gas station managers matching nearby posted prices (generally seen as legal, absent an agreement).
    • Auto insurers whose quotes cluster closely; some think that’s cartel‑like, others note heavy state rate regulation and public SERFF filings.
    • Compensation consultants and SaaS tools that aggregate salary data and recommend ranges; some wonder if this is similar collusion, others say it’s only illegal if there is an agreement to use shared recommendations as a floor/ceiling.

Housing Policy, Rent Control, and Structural Fixes

  • Many argue the real solution to high rents is more housing supply (zoning reform, public or social housing), not just enforcement on algorithms.
  • Others push for:
    • “Rent stabilization” tools: multi‑year leases, caps tied to inflation, long notice periods.
    • Taxes on vacant units or multiple homes, or land‑value taxes, to reduce speculation and forced vacancies.
  • There is disagreement over rent control:
    • Critics: say it reliably reduces supply and worsens quality.
    • Supporters: see it as necessary protection in structurally inelastic, underbuilt markets.

Ethics, AI, and Information Asymmetry

  • Several see this as “collusion‑as‑a‑service”: AI/algorithms used to launder cartel behavior and remove human empathy from pricing.
  • Others note information asymmetry: landlords gain pooled, non‑public data, while renters lack an equivalent “rent‑minimizer” tool.