Method Notes

Why this page exists

Every figure on this site — a price per square metre, an occupancy rate, a bed count, an acquisition cost — traces back to a source we can name. This page is where we name them. If you're going to use our research to think about a six- or seven-figure purchase, you deserve to know whether a number came from an official statistical office, a land registry extract, or an asking-price listing that we've adjusted. Spoiler: it's usually a blend of all three, and the blending is the part worth explaining.

The five core sources

Statistik Austria. Austria's national statistical office is our backbone. It publishes median transaction prices by district on an annual cycle, along with population, housing stock, and construction data. When we say "the median apartment price in the Innsbruck-Land district," that's Statistik Austria. The trade-off is cadence: the data is authoritative but arrives roughly a year after the period it describes, so we treat it as the anchor rather than the pulse.

Grundbuch — the Austrian land registry. Every recorded property transaction in Austria passes through the Grundbuch, and the underlying documents are notary-reported. Queries cost roughly €20 to €50 each depending on the extract, which means we can't pull everything — we pull strategically, sampling actual closings in each market to validate what asking prices claim. This is how we know, rather than guess, the gap between what sellers ask and what buyers pay.

ImmoScout24 and Willhaben listing data. Austria's two dominant property portals give us asking prices at volume. Raw asking prices overstate the market, so we validate them against Grundbuch closings. Across our measurement points, asking prices typically run 8–12% above closing prices, and we apply that adjustment when a figure on this site is derived from listings. Where a page shows an asking-price figure without adjustment, it's labelled as asking.

Tourismusverband occupancy data. The regional tourism associations publish overnight-stay and bed-count figures that drive our rental-yield work. The headline number for context: Tyrol recorded 52.4 million overnight stays in 2023 across roughly 80,000 commercial beds. Occupancy is where alpine property economics live or die, so this data feeds directly into the seasonal yield pages.

OeNB interest rate data. The Austrian National Bank publishes the mortgage and reference rates we use in financing examples. When a worked example assumes a borrowing rate, that assumption comes from OeNB series current at the time of writing, not from a lender's marketing rate.

Source	Use	Cadence
Statistik Austria	Median transaction prices by district; housing stock	Annual, ~1 year lag
Grundbuch land registry	Recorded closings, sampled to validate listings (€20–50/query)	Sampled quarterly
ImmoScout24 / Willhaben	Asking prices; adjusted 8–12% toward closing	Continuous collection
Tourismusverband	Overnight stays, bed counts, occupancy	Seasonal + annual
OeNB	Mortgage and reference rates for financing examples	Monthly

The six measurement points

We track six locations in depth rather than skimming forty: Innsbruck, Kitzbühel, Sölden, Obergurgl, Neustift, and Mayrhofen. They were chosen to cover the distinct market types a buyer actually encounters — a year-round city (Innsbruck), a luxury resort with global demand (Kitzbühel), high-volume ski resorts (Sölden, Mayrhofen), a small high-altitude market (Obergurgl), and a valley-floor community with different economics entirely (Neustift). Depth beats breadth: six markets with registry-validated numbers tell you more than thirty markets of unadjusted listings.

How the SVG price map is drawn

The interactive price map on our market pages is generated from a dataset of more than 2,400 listing data points collected across the six measurement points. Each listing is geocoded, deduplicated (the same apartment often appears on both portals, and occasionally twice on the same one under different agents), stripped of outliers using a standard interquartile-range filter, and adjusted toward closing using the 8–12% validation factor. What's left is aggregated into grid cells, and each cell's adjusted median drives the colour and height of its region in the SVG. Sparse cells — where we have fewer than five data points — are deliberately left unshaded rather than interpolated, because a blank patch is more honest than a confident guess.

Refresh cadence

Listing data is collected continuously and re-aggregated monthly. Grundbuch validation samples are pulled quarterly. Statistik Austria and Tourismusverband figures update on their publishers' schedules — annually, with the lag noted above — and OeNB rates are checked monthly and whenever a financing example is republished. Each research page carries a "data as of" note so you can see how fresh its numbers are.

Known limitations

We'd rather state these than have you discover them:

The asking-to-closing gap varies. The 8–12% figure is a range, and it drifts — in hot stretches it narrows, in soft ones it widens, and it behaves differently in Kitzbühel's luxury segment than in Innsbruck's apartment market. Our quarterly Grundbuch samples track this, but the sample sizes are modest, so treat any single adjusted figure as an estimate with a margin, not a measurement.

Small markets are thin. Obergurgl in particular sees few transactions in a year. Medians there can swing on a handful of sales, which is exactly why we leave sparse map cells blank.

Occupancy ≠ your occupancy. Tourismusverband figures cover commercial beds across a whole resort. An individual apartment's occupancy depends on management, pricing, and letting restrictions, and can sit far above or below the regional average.

The lag is real. When a page mixes a current listing-based price with last year's official median, we label both. Read the "data as of" notes before comparing pages.

When we get something wrong anyway — and we do — the fix is logged publicly on our corrections page, with the original figure left visible. Method notes tell you how the numbers are built; corrections tell you where they broke.