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To calculate a useful average from France’s DVF property records, first define the area, period, property type and kind of sale you want to compare. Then group the underlying rows into transactions, handle mixed-property and partial-right sales deliberately, pair each eligible sale’s price with a consistent area measure, and calculate the statistic from sale-level values—not raw rows.
Choose the DVF data source and record its version
The downloadable DVF open dataset and DGFiP’s authenticated “Rechercher des transactions immobilières” comparison service are different routes to transaction information. The open dataset is intended for reuse; the comparison service has its own access conditions and comparison rules. Do not assume service-specific restrictions or outputs apply identically to bulk-data analysis.
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The DVF dataset page on data.gouv.fr describes five years of transaction history and text-file downloads. It says updates are scheduled in April and October, and that later updates can add records to earlier annual files. Record the vintage and the date you downloaded the files so another analyst can identify which version produced your results.
The related geolocated DVF dataset describes fields including id_mutation, date_mutation, numero_disposition, nature_mutation, valeur_fonciere, code_type_local, type_local, surface_reelle_bati, lot Carrez surfaces and surface_terrain. Check the documentation for the particular file vintage before writing code against its schema.
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Coverage is not universal. The dataset description excludes Alsace-Moselle and Mayotte; DGFiP’s comparison service also identifies Bas-Rhin, Haut-Rhin, Moselle and Mayotte as exclusions. Missing records in those areas should not be interpreted as evidence that no property sales occurred.
Define the sales you want to compare
Set the population before removing records. For example, an analysis might cover existing apartments sold in one commune over a specified period, or houses in a defined area. Keep geography, dates, property category and transferred rights consistent. A broad dataset average that mixes unlike sales may be mathematically correct but not useful as a comparison.
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DGFiP’s comparison service returns simple transactions involving properties of the same nature and full ownership. Its online guide says it does not return complex sales, gratuitous transfers or sales involving less than full ownership as comparison terms. Examples of complex sales include a house sold with woodland or several apartments included in one transaction. For a dataset analysis, either exclude such cases under a stated rule or analyze them separately; do not quietly combine them with ordinary whole-property sales.
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A DVF row is not necessarily one sale. A transaction can appear across multiple rows or include several property components, so averaging rows can give a sale with more listed components extra weight. The geolocated schema describes id_mutation as a non-stable identifier used to group rows; it also includes a disposition number. Treat these fields as clues for inspecting transaction groupings, not as a universal deduplication formula that is safe for every vintage and sale type.
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- Inspect candidate groups. Examine rows sharing mutation and disposition information, along with the date, nature of mutation, property type and price fields. Confirm what each row represents in the relevant file documentation.
- Decide what counts as one observation. For a sale-level average, define which grouped records constitute a transaction and how multi-property components are handled. Preserve that rule in your analysis notes.
- Build one sale-level value. Determine which price and area belong together for each eligible observation. Do not sum repeated transaction prices or deduplicate repeated values without checking what the rows represent.
The field description does not establish one safe grouping-and-deduplication expression for all DVF transactions. A defensible cleaning method therefore documents its grouping rule and separates mixed or ambiguous cases rather than applying an unqualified one-line recipe.
Choose a price and area definition that match
Decide whether you need an average transaction total or an average price per square metre; these answer different questions. For price per area, pair each eligible sale’s price with an area field appropriate to the property and available consistently. State how you treat missing or non-comparable surfaces, and exclude a sale from the per-square-metre calculation if you cannot form a defensible ratio.
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Do not treat Carrez area and cadastral useful area as interchangeable. DGFiP defines “surface Carrez” as the private area of a condominium lot described in the notarial deed. “Surface utile” is an area measure based on owner declarations and calculated by cadastral services. The guide notes that the two can disagree. It also cautions that cadastral descriptive information may differ from the property as it stood at the time of sale.
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- SPEAKS YOUR LANGUAGE: Keys clearly labeled in residential mortgage finance terms like Loan Amt, Int, Term, Pmt; this industry-standard calculator is super easy to use on all realty financing matters from finding a loan that works for your client to considering trust deeds investments, or finding remaining balances or balloon payments and more
- CONFIDENTLY AND EASILY SOLVE: Clients' financial questions whether they're buyers, sellers, investors or renters. Increase your perceived professionalism as a new agent, experienced broker or seasoned loan officer. Close more home sales and impress your clients with fast, accurate answers to all their real estate finance questions from PITI Payments to IRR, NPV and Cashflows
- DEDICATED BUYER QUALIFYING KEYS: Enter client's income, debt and expenses to pre-qualify them to only show properties they can afford. Include tax, insurance and mortgage insurance then compare loan options and payment solutions to give your client choices before they make an offer to buy
- FIGURE OUT THE RIGHT LOAN: For your client at the press of a button for jumbo, conventional, FHA/VA, or even 80:10:10 or 80:15:5 combo loans; check to see if ARMs or bi-weekly loans, quarterly payments or if interest-only payments are the answer; giving your client more choices; easily perform what if loan or TVM calculations find loan amount, term, interest or PITI or PI payments
- BECOME AN INVALUABLE RESOURCE: To your clients by reducing their confusion and uncertainty; ensuring they are able to make a purchase offer; knowing they can afford the down payment; and determining which is the right loan for them. Date-math for listings and contracts too. Comes with a protective slide cover, quick reference guide, pocket user's guide, and long-life battery
Calculate the mean and median at sale level
For each selected sale, calculate its price-per-square-metre ratio using the chosen price and surface definitions. The DGFiP comparison service defines its mean as the sum of the selected sales’ price-per-square-metre ratios divided by the number of selected sales. It defines the median by sorting those sale-level ratios: use the middle ratio for an odd number of sales, or the mean of the two middle ratios for an even number.
| Measure | Calculation | What it describes |
|---|---|---|
| Mean total price | Sum of selected transaction totals divided by the number of selected transactions | The average transaction amount, not a price-per-square-metre figure |
| Mean price per square metre | Calculate a ratio for each selected sale, then average the ratios | The arithmetic mean of sale-level ratios used by DGFiP’s comparison service |
| Median price per square metre | Sort sale-level ratios and select the middle value, or average the two middle values for an even sample | The central sale-level ratio in the selected sample |
| Aggregate price divided by aggregate area | Sum selected prices and divide by the sum of their selected areas | An area-weighted ratio; it is not the arithmetic mean of individual sale ratios |
Report the sample size, filters, geography, period, data vintage and download date beside the result. Label the figure clearly as either total price or price per square metre, and name the area field used. This makes the number interpretable and gives readers enough information to understand what was included.
Keep descriptive averages separate from individual valuations
A cleaned sample describes the transactions that meet your rules; it does not by itself establish the value of a particular home. DGFiP describes its comparison application as an aid to estimation and says the returned sales require expertise and comparative analysis. A specific property’s own characteristics matter, so a dataset average should not be presented as a completed valuation.
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Use and share DVF data responsibly
The open-data portal says users must not enable indirect re-identification or indexing of records in external search engines. Avoid publishing combinations of transaction details that could identify individuals. Separately, the authenticated comparison service requires authentication, acceptance of its terms and use within specified personal purposes. Its terms describe an indicative delay of a month before records may become available; that timing applies to the service, not necessarily to each bulk dataset release.
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