Seasonality in Sunbelt retail: Austin, Houston, Phoenix and Miami compared

“Visits are down 9% since March” is one of the most common sentences in a retail operations review, and one of the least informative. In much of the Sunbelt, a spring-to-summer decline is not a signal about your store. It is the calendar.

The four US metros in MapDove’s demo dataset (Austin, Houston, Phoenix and Miami) all sit in the warm southern belt, but they do not share a seasonal shape. Treating them as one “Sunbelt” pattern is a quick way to misread a portfolio.

A note on data: everything below refers to MapDove’s demo dataset, which is modeled from neighborhood-level parameters, not from a device panel. The seasonal parameters are deliberately chosen to reflect well-known regional patterns, which makes the dataset a useful sandbox for practicing the analysis.

Four cities, four calendars

Each city in the demo data has a seasonal amplitude (how far traffic swings above and below its annual baseline) and a peak day of the year:

City	Amplitude	Seasonal peak	What drives it
Austin	±6%	~late April	Mild spring, festival season, university calendar
Houston	±5%	~early April	Humid summers, relatively flat year
Phoenix	±12%	~mid-February	Winter visitors; deep summer heat dip
Miami	±12%	~early February	Winter high season, tourism

Two clusters jump out. Austin and Houston are gentle: a mid-single-digit swing with a spring crest. Phoenix and Miami are pronounced: roughly double the amplitude, peaking in winter, with a trough in late summer.

The practical consequence: a Phoenix store that is 20% below its February level in August may be performing perfectly normally. An Austin store showing the same drop has a problem worth investigating.

Holiday effects sit on top of the season

Layered on the smooth seasonal curve are a handful of sharp calendar effects that hit all four markets:

• Black Friday spike. A one-day surge concentrated in apparel, department stores and big-box categories.
• Holiday peak around December 20. A broad lift across most retail categories through the last shopping week before Christmas.
• Closures on Thanksgiving and Christmas Day. Visits near zero for most store categories.

These matter more than their size suggests. A single closure day removes about 3% of a month’s traffic. If Thanksgiving falls in a different week relative to your comparison window, a naive week-over-week or month-over-month number can swing several points for no operational reason.

Day-of-week mix matters too. A month with five Saturdays will outperform a month with four for most consumer categories, all else equal.

Why year-over-year isn’t automatically clean

YoY is the standard fix for seasonality, and it removes the bulk of it. But it leaves three distortions:

1. Calendar drift. Holidays tied to weekdays (Thanksgiving, Black Friday) move against fixed dates. November YoY can look strong or weak depending on where the Thursday falls.
2. Weekday composition. The same calendar month has a different mix of weekdays each year.
3. Shifting base. If the prior year was itself unusual (an extended closure, a remodel, a new competitor nearby), YoY inherits that noise.

A practical deseasonalization workflow

You don’t need a time-series PhD to get to an honest comparison. This is the routine we recommend:

1. Compare like with like on the calendar

Align comparison windows by weekday, not by date: compare the 52 weeks ending on a Sunday against the 52 weeks ending on the matching Sunday a year earlier. For holiday periods, compare “Thanksgiving week” with “Thanksgiving week,” not “November 22–28” with “November 22–28.”

2. Strip the holiday days

Exclude closure days and the Black Friday spike from routine trend analysis, then report them separately. They are events, not trend.

3. Benchmark against the category in the same city

The fastest way to neutralize seasonality is to divide it out. MapDove’s visit series for every place ships with a benchmark: the average per-place visits for the same category in the same city, at the same granularity. If your Phoenix FreshCart Market location is down 18% from February but the Phoenix grocery benchmark is down 19%, your store is slightly outperforming its market.

An index such as store visits / category benchmark × 100 is seasonally adjusted by construction, as long as your comparison set lives in the same city. Pull it from the place view or via /places/:id/visits.

4. Never pool cities without adjusting

When you roll a Sunbelt portfolio into a single number, a heavy Phoenix and Miami weighting will make the whole network look seasonal. Index each store to its own city benchmark first, then aggregate the indices.

What this means for site selection

Seasonality is not just a reporting headache; it affects underwriting.

• Peak-to-trough swing sets staffing and inventory risk. A ±12% market requires more operating flexibility than a ±5% one.
• Annualize before you compare. A site evaluated from three winter months in Miami will look systematically stronger than one evaluated from three summer months in Houston. Use the full year of history, which the Pro plan provides; the Free plan’s 90-day window is fine for exploring, but it is not enough to underwrite a seasonal market.
• Forecasts should be annual. The Huff forecast produces annualized expected visits; resist the temptation to sanity-check it against a single peak month.

Takeaway

Before you react to a decline in a Sunbelt store, ask which calendar it lives on. Austin and Houston swing gently and peak in spring; Phoenix and Miami swing twice as hard and peak in winter. Align windows by weekday, pull holiday days out of trend, and index every store to its own city-and-category benchmark. Do those three things and most “seasonal mysteries” resolve themselves. The methodology docs cover how the benchmark is computed.