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To calculate a price per square meter from French DVF data, divide the relevant transaction value by the matching property area: price per m² (€) = transaction value (€) ÷ area (m²). The arithmetic is straightforward; the key is choosing an area field and transaction scope that describe the same property. Label the area convention and filters, because DVF records completed transactions—not asking prices or automatic valuations.
What DVF measures—and what your result means
DVF, or Demandes de valeurs foncières, is produced by France’s Direction générale des finances publiques (DGFiP) from notarial deeds and cadastral information. Its records concern property transactions, not asking prices. The DGFiP dataset page describes a five-year transaction-history window; the available records and coverage should be checked against the release you use.
A calculated ratio is an observed transaction value per unit of the chosen area for a defined sale or sample. It is not, by itself, a like-for-like market valuation: property type, condition, date, location, area convention, and bundled sales can all affect the result.
Choose the area field that matches your question
The geolocated DVF dataset distinguishes built area, land area, and lot-level Carrez area. Those fields are not interchangeable. The geolocated dataset documentation lists surface_reelle_bati, surface_terrain, and up to five lot-level fields such as lot_1_surface_carrez.
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surface_reelle_bati: real built area. Use it when your comparison is explicitly based on built area.- Lot-level Carrez fields: area figures associated with individual lots. They may be incomplete; the government explorer says these values occur sporadically and have no legal value.
surface_terrain: land area. Do not use it as the dwelling-area denominator when calculating a home’s price per built square meter.
The government DVF explorer FAQ says the area displayed by the explorer is real built area, not Carrez area. State the field and convention you use, and do not combine figures based on different area definitions as though they were equivalent.
Calculate a ratio for one eligible sale
- Define the comparison. For example, specify apartment sales in a named commune over a stated period. Keep apartments, houses, and non-built land separate unless your analysis explicitly addresses their differences.
- Select a dataset and release. Raw DGFiP files, the geolocated derivative, and Cerema’s DVF+ structure are related but distinct formats. Record which one you downloaded, its release date, and the transaction years included. The Cerema DVF+ documentation describes its open-data structure.
- Filter by transaction date and property type. Keep only records that meet your stated geography, period, and property-type criteria.
- Inspect the mutation. Use the mutation identifier to inspect associated rows and property components before assigning a transaction value and area to a sale.
- Choose one matching area field. For a built-area calculation, use the selected built-area convention consistently; do not substitute land area.
- Calculate the ratio. Divide the relevant transaction value in euros by the matching area in square meters. Exclude missing or non-positive values and areas, and state that exclusion rule.
Example: if a relevant transaction value is €240,000 and the matching built area is 80 m², the ratio is €3,000/m² (€240,000 ÷ 80 m²). This example illustrates the arithmetic, not a DVF market statistic.
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Handle multi-row and bundled mutations carefully
DVF is organized around mutations, and a mutation can involve multiple rows, lots, or property components. A row is not necessarily a complete property sale, and the full mutation value should not automatically be assigned to each property represented in its rows. The mutation identifier helps you inspect grouping; it does not provide a universal price-allocation rule.
Calculate a single-property ratio only when the transaction value and area can be matched at the same scope. If a mutation bundles properties and you cannot make that match, exclude it from a single-property comparison or analyze it separately. The DVF+ documentation and geolocated dataset documentation describe the data structure, but do not establish one allocation method suitable for every mixed transaction.
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Summarize comparable sales for a local estimate
For a neighborhood or commune summary, first define a consistent sample, then report both the number of transactions and the statistic you calculated. A median can help show the middle observed ratio without being dominated as strongly by extreme values as a mean, but neither statistic controls for differences in property characteristics.
- Keep property type, period, and geography consistent.
- Use the same area convention for every sale in the sample.
- Make the treatment of bundled mutations, missing areas, and non-positive values explicit.
- Call the result a median or mean of the selected transaction ratios, as applicable; do not present it as an official DVF formula or appraisal.
Check coverage, update timing, and access
The DGFiP dataset description states that DVF covers the previous five years in metropolitan France and overseas territories, with exceptions for Alsace, Moselle, and Mayotte. It also says the dataset is updated in April and October, and later releases may add transactions to earlier mutation years. These details are release-sensitive, so identify the file’s release date and the transaction period used rather than treating coverage as fixed.
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You can obtain raw files from the official DGFiP dataset page, use the geolocated derivative for normalized columns and parcel coordinates, or consult Cerema’s DVF+ open-data documentation. The public government DVF explorer provides a visualization and filtering interface; a government explorer onboarding page also introduces the tool.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to include when you publish the figure
A transparent report lets readers understand exactly what the ratio represents. Include:
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- the area field and area convention;
- the property type and geographic boundary;
- the mutation period and dataset release date;
- whether the figure is for an individual sale, a median, or a mean;
- the number of transactions and relevant inclusion or exclusion rules;
- how you treated mutations containing multiple properties or components.
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