Lowest temperature in Houston on Jun 11, 2026?

Prediction market odds on Kalshi reflect real-money trader consensus and often incorporate meteorological forecasts from the National Weather Service and private weather services. While traditional weather analysts publish deterministic low-temperature predictions, prediction markets aggregate uncertainty and assign probabilities to temperature ranges. Comparing market odds to published forecasts can reveal whether traders expect conditions warmer or cooler than consensus, offering insight into tail-risk pricing and market confidence in the forecast.

On Kalshi, prices reflect that venue's order book, liquidity, and how traders price the outcome right now. On Kalshi, the lowest temperature in Houston on June 11, 2026 is priced as a binary or range-based contract reflecting the probability traders assign to specific temperature thresholds. Prices move as new weather models, seasonal patterns, and atmospheric data influence expectations. Traders buy shares at lower prices if they expect cooler conditions and sell at higher prices if they anticipate warmer lows, with the market price converging toward the eventual observed temperature as the event date nears.

The market resolves on Jun 12, 2026. Resolution is determined by the official lowest temperature recorded in Houston on June 11, 2026, typically sourced from the National Weather Service or a designated meteorological authority. Once the day concludes and the low temperature is finalized, the contract settles based on which outcome bracket or threshold the actual reading falls into, and traders' positions are paid out accordingly.

Market prices will shift as updated weather models become available, particularly high-resolution forecasts issued 7–10 days before June 11. Cold fronts, high-pressure systems, and seasonal climate patterns will influence trader expectations. Real-time atmospheric data, including upper-level wind patterns and soil moisture, can alter overnight low predictions. Additionally, any unusual climate events or anomalies in the weeks leading up to the date may prompt repricing as traders adjust their probability estimates based on evolving meteorological conditions.