The Marta Walsh Index

Every Friday we count two things across Arizona’s $2 million-plus market: how many homes are exposed for sale, and how quickly buyers are putting them under contract. The ratio, corrected for the season and compared to the tier’s own recent history, is the Marta Walsh Index. At 100, the luxury market is behaving like its usual self. Above 100, demand is outrunning supply; below, supply is outrunning demand.

City-level market gauges exist and are useful — but no published index isolates the luxury tier, and the tier does not follow the city. In Scottsdale, we measured the two directly: the mass market ($1–2M) and the luxury tier ($2M+) agree about eras (level correlation 0.96) and disagree about moments — their three-month trends point in opposite directions in 31% of months, including May 2020 (mass at twice its normal pace, luxury exactly normal) and late 2022 through 2023 (mass cooling, luxury running 90+ points hotter than the city read implied). A luxury seller navigating by a city index is steering by an instrument that cannot see their market one month in three. This page documents the instrument that can.

The backtest

Construction

Universe. Every ARMLS residential listing with an asking price of $2,000,000 or more, 1998 to present — the same universe as One Shot — refreshed weekly by full-record reconciliation, so late entries and corrections are absorbed rather than frozen. A price-sanity guard excludes data-entry errors.

Demand is the count of listings going under contract in the trailing four weeks — dated to the contract event itself, not to when the status was entered, which keeps the index in event time rather than data-entry time. Supply is the count of listings actively exposed on the measurement Friday. A listing whose sale falls out of escrow is returned to the exposed pool for the interval it was, in fact, back on the market. One structural honesty note: a ratio cannot distinguish demand arriving from supply leaving — the index rises either way. The components below (raw contract counts, active counts, debut vs. returning listings) are published precisely so a reader can make that decomposition for any given week.

Seasonal correction. Weekly absorption is divided by a week-of-year factor estimated from the trailing ten years and refit annually — because the seasonal cycle itself has changed: the spring surge factor grew from roughly 1.0× in the mid-2000s to 1.5× in the 2020s, and a fixed correction would mis-read every recent February as hot.

The scale. The adjusted rate is divided by its own trailing five-year median and multiplied by 100. This choice is deliberate and worth understanding: a fixed $2 million line meant an estate tier in 2010 and includes ordinary Scottsdale homes in 2026, so comparing today’s raw absorption to 2010’s would flatter the present. Anchoring 100 to the tier’s own recent norm makes the index honest across that drift — at the cost that 100 means “normal for the recent era,” not “balanced in some absolute sense.” As a robustness check we also maintain a constant-quality variant whose price threshold floats with a repeat-sales index of the upper market (14,524 same-home sale pairs; that index shows the upper market at 2.25× its 2000 level, with a 37% crash drawdown — notably less total appreciation than the metro overall). The two variants have agreed on every regime call in the backtest.

Discipline rules. The published series is smoothed over four weeks. A weekly move within ±6% is reported as unchanged — the index does not manufacture narrative from noise. The headline is the most recent settled week; the current week is published as provisional, because contract statuses entered around a weekend commonly post the following Monday, and a measured minority of contracts are entered ten or more days late.

Components

Anticipated inventory. Alongside visible supply, we track every parcel that left the market after a failed campaign within the trailing two years and has not returned or sold — currently on the order of 1.7 dark parcels for every active listing. Historical return curves (Kaplan–Meier, with censoring, on 18,704 failed campaigns) say 29% of such homes relist within 13 weeks, 44% within six months, 53% within a year, and about 60% ever. The component is published as a measurement with a historical expectation attached — deliberately not as a forecast, for the reason given under What failed.

Debut vs. returning listings. New listings are split by whether the same parcel had a listing end within the prior two years. A rising share of returns means failed campaigns recycling through the market rather than fresh supply arriving.

Fast-lane share. The percentage of new contracts signed within 14 days of listing — the tier’s most sensitive temperature gauge (21% in 2019, 35% at the 2021–22 peak).

Price changes. The MLS export does not record when a price changed, so this component cannot be backtested; it is measured live, by comparing every active listing’s asking price between consecutive weekly issues — count of cuts, count of increases, and median cut size.

What the index means for a single listing

We linked roughly 22,000 launch campaigns (2005–2024) to the index reading in their launch week and followed them to resolution. The relationship is real — monthly launch-cohort sell rates correlate 0.76 with absolute absorption at launch — but its shape is the finding. Below an index of 60, about one launch in five ever sold, and the survivors gave up a median 26% of ask. Above 250, roughly three in four sold, in a median 50 days, 3.6% under ask. And across the entire wide band between — from 60 to 250, four times the normal range of variation — the sell rate was flat at 46–51%. Within a single stable regime (2012–2019) the index’s whole range of variation was worth about eight points of sell rate; crossing a regime boundary was worth twenty to fifty.

The interpretation we place on this is deliberately conservative: sellers collectively adapt their asking prices to conditions, spending the market’s extra demand on ambition rather than banking it as probability — which is why conditions pay out in achieved price and speed rather than in the odds of selling. Stated as a rule: normal market fluctuation rarely changes a launch’s odds; regime changes change them enormously but arrive perhaps twice a decade; the asking price matters every day. This is why fifty Fridays a year the index’s honest reading is “the outcome is in the seller’s hands,” and why the rare weeks it exits the band carry real information. The evidence linking asks to outcomes is associational — documented in One Shot — and this section inherits that caveat.

What failed, and stays in

Two negative results are part of this methodology and we consider them load-bearing. First, we tested whether the off-market pool predicts future listing volume: a hazard-model forecast built from the pool lost decisively to the naive benchmark of “same quarter last year” (correlation 0.67 vs 0.91) and added no incremental accuracy when combined with it. The pool is therefore published as a nowcast, never a forecast. Second, listing-date seasonality moves speed, not odds: in the current era the best calendar week improves time-to-contract by roughly two weeks while changing the probability of selling by approximately nothing. Claims this index will not make are as much a part of its definition as the ones it does.

Flow leads price — and what this index is not

The MWI measures how fast the market is clearing, not where prices are going. The distinction matters most at turning points. In our own record, tier-wide absorption bottomed in the first quarter of 2009; the tier’s price level — measured by our repeat-sales index — did not bottom until the fourth quarter of 2011, thirty-three months later. Through 2009–2011, absorption ran strong while prices kept falling, because returning buyers were clearing a distressed pipeline that kept refilling; prices turned only when the pipeline exhausted. Housing clears on volume before it clears on price, and any supply-demand gauge is structurally early on price for that reason.

There is also an asymmetry worth stating plainly. Demand cannot hide: a buyer who does not bid does not exist, so when demand withdraws, a ratio index sees it immediately — flow gauges have historically been sharp at tops. Supply can hide: foreclosure pipelines, discouraged owners, and failed campaigns waiting off-market can feed a recovering market for years, which is why flow gauges run early at bottoms. The Anticipated Inventory component exists to shrink exactly that blind spot — it is our estimate of the hidden reservoir — while making no claim to see all of it. Accordingly: this index is not a price forecast, and sustained readings should be interpreted through the components, not the headline alone.

Data and methods notes

The MWI publishes here every Friday. Questions about the construction are welcome — email Marta or call 480-274-5710.