How these numbers are built

What we measure

Asking prices, not sale prices. We publish the figure written in the listing, not the one at which the property will change hands: between the ask and the closing there is a negotiation, and in Italy the ask is almost always higher.

How we collect

A program reads the public listings in the neighborhoods we follow and every week calculates the median price per square meter for each neighborhood.

Why the median and not the average

The median is the middle value: half of the listings are above it, half below. The average is not like that: a single outlier penthouse would move it by hundreds of euros without the market having moved an inch.

Filters and de-duplication: what is counted and what is left out

The neighborhoods are always the same, and so are the filters: residential listings of 50 m² or more are in; bare ownership, timeshares, auctions declared in the title and anything under €2,000/m² are out (at that level, in these neighborhoods, it is a typo or a garage, not a home). A few non-residential listings may slip through when the text doesn't declare them.

The same property is often published by several agencies, and sometimes attributed to two neighboring areas. When size and price match within the same city we count it once: on the current sample that is about 13% of listings.

The week

Each week runs from Monday to Sunday; its value is the median of the readings taken on those days, compared with the previous week. If no data was collected in a week, that week is left blank — we don't estimate it and we don't hide it: in the charts it is a gap, and you can see it.

Why we don't publish “new” listings after a pause

What appeared while we weren't looking would show up all at once on the day we resumed collecting data: it would be false data, not late data. Better to leave it out.

The comparison with the Italian Revenue Agency

Next to the asking price we put the value from OMI (the Italian Revenue Agency's real estate market observatory), built on recorded sales. The gap between the two numbers is not the discount you will get: they are two different measures, today's demand against contracts signed in the past.

How we choose the OMI reference value, and what the comparison is for

We take the midpoint of the OMI range for “abitazioni civili” (standard residential dwellings) in normal condition; where a neighborhood we monitor crosses two OMI micro-zones we average them (we say so in each city's table).

The OMI value is an average of contracts on properties of different quality and condition from the one you are looking at: the comparison helps you see where the distance between what is asked and what was paid is wider than usual, not calculate an exact discount.

What these numbers cannot tell you

• What your property is worth. A neighborhood median knows nothing about floor, exposure, condition or view: on a single property it can be far off in either direction.
• How long it takes to sell. We don't know when listings were published, only the day they first appeared in our data: an “average time to sell” derived from here would be an estimate dressed up as a measurement, and we prefer not to publish it.
• Whether a price cut is recent. We see that the price has dropped since the first sighting, not when it happened.
• The market beyond the monitored neighborhoods. The neighborhoods are chosen, not exhaustive: they are not “Milan” and “Florence”, they are those neighborhoods.

Small samples

With fewer than five properties in a size band we don't publish the median, only the count. When a city's history is still short we say so at the top of the page: over a few weeks, a change says more about noise than about the market.

What we don't publish

No individual listings: no titles, no addresses, no links, no photos. Only neighborhood aggregates.

How often it is updated

Data collected weekly, with the site republished by hand at each update. The date of the latest data is at the top of every page.