Interactive market model

What Happens to Paris Apartment Prices if Rates Move?

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Transaction price +0.0% Transaction volume +0.0% Rent (long run) +0.0% Price (long run) +0.0%

The left chart shows the long-run steady state, linearized at 2026Q2; all arms are indexed to the latest quarter = 100 (hover the corners for natural units). The right chart shows roughly 1-year responses; curves are anchored at the latest quarter's (2026Q2) observed price and volume, same units as the case study below. Demand shifts with borrowing capacity (exact annuity channel) and unemployment; supply with seller lock-in.

DiPasquale–Wheaton: which quadrant absorbs the shock

Sources: DVF+ (DGFiP / DGALN–Cerema); Banque de France; Eurostat; La Ressource Pro model.

Transaction market: who moved, buyers or sellers

Sources: DVF+ (DGFiP / DGALN–Cerema); Banque de France; Eurostat; La Ressource Pro model.

Case study

Case study: the 2021Q4 → 2023Q4 rate shock

The interactive model above is built to explain shocks to the market, such as the borrowing-capacity shock which took place between 2021Q4 and 2023Q4, when the mortgage rate went up from 1.12% to 3.52%; the unemployment rate remained relatively unchanged (7.5% to 7.6%) in the same time period. Outcome: prices -8.2%, volumes -22.5%. The left chart draws both states through observed stock, rent, price and construction, every arm indexed to 2021Q4 = 100. The right chart shows the estimated demand and supply curves in natural units, anchored on observed transactions (€/m² × thousand sales / quarter); dots mark each quarter's anchored equilibrium.

DiPasquale–Wheaton: where the shock landed

Sources: DVF+ (DGFiP / DGALN–Cerema); Banque de France; Eurostat; La Ressource Pro model.

Transaction market: how the curves moved

Sources: DVF+ (DGFiP / DGALN–Cerema); Banque de France; Eurostat; La Ressource Pro model.

National Cycle, Local Market

Between 2021Q4 and 2023Q4 borrowing capacity fell 19% adjusted for inflation while Paris prices fell 17%, so homes became 3% less affordable. Across 947 French local markets sales fall about 2.05% for each 1% loss of affordability, which implies −6% for Paris against the −22.5% recorded; the rule for France as a whole (0.51%) implies only −1%. The rule describes the typical market, not the differences between markets: in this shock the typical French local market's sales fell about 24% however far its prices moved, so the gap between the rule and Paris's record says little about Paris itself. The national cycle is the first principal component (PCA) of four-quarter real price growth across 949 French commune and property-type markets, 2015Q1 to 2026Q2; it correlates 0.995 with the INSEE-Notaires national index. Paris's loading on it is 1.2. Regressing Paris's real price growth on the national cycle and the rotation between expensive cities and resort markets, with the sampling error of the quarterly median removed, gives an R² of 97%: 54% is the national cycle and 43% the rotation between expensive cities and resort markets; 3% is Paris's own.

What the Rate Shock Did to Paris Sales

Sources: DVF+ (DGFiP / DGALN–Cerema); Banque de France; INSEE; La Ressource Pro model.

How Much of Paris's Price Movement Is National

Sources: DVF+ (DGFiP / DGALN–Cerema); INSEE-Notaires; La Ressource Pro calculations.

Market history & outlook

Paris prices and volumes

Median price €/m², existing apartments. 2026Q2 is a first release: prices are representative (revisions typically within 1%). Statistical projection: 12-quarter drift on ln price, 80% band. Sales per quarter, thousands (Paris intra-muros, apartments). 2026Q2 sales are a first release, typically about 5% understated until late registrations arrive. Seasonal projection: 4-quarter log difference with 8-quarter drift, 80% band.

Prices & forecast

Sources: DVF+ (DGFiP / DGALN–Cerema); Banque de France; Eurostat; La Ressource Pro model.

Transaction volumes

Sources: DVF+ (DGFiP / DGALN–Cerema); Banque de France; Eurostat; La Ressource Pro model.

Market structure

Where the market trades

YoY change by arrondissement (4 quarters to 2026Q2 vs 4 quarters to 2025Q2). Prices: orange = rising, blue-gray = falling; hover for medians and sale counts, zoom in for the smaller areas. Sales: orange = more transactions than a year earlier, blue-gray = fewer. Aggregated medians over the trailing four complete quarters, against the four quarters before them (DVF+ licence). The Bois de Boulogne (16e) and Bois de Vincennes (12e) contain no housing and are omitted from the map. 2026Q2 is a first release: its sales are typically about 5% short until late registrations arrive, so the sales change is understated by about one point.

Go deeper

The full quarterly report

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Methodology & model fit

How well the model tracks reality. The full equations, interpretation and other details are included in the report. Actual values against the level implied by the drivers alone; the France-wide slopes anchor the Paris curves. The demand and supply slopes are estimated (−0.51 / +1.56, national sample); the construction response in the four-quadrant chart is estimated for Paris (+4.39).

Sources: DGFiP / DGALN–Cerema, DVF+ open data; INSEE-Notaires; IGEDD (CGEDD); European Central Bank and Banque de France via DBnomics; Eurostat; INSEE; SDES, Sitadel; ANIL/DGALN, Carte des loyers.