July 13, 2023

10 min read

Parcl Labs Price Feed White Paper - France

Executive Summary

  • The Parcl Labs Price Feed (PLPF) for France is an indicator that tracks daily price changes in residential real estate across Paris and the Ile de France region. It utilizes a simple metric: price per square meter in Euros.
  • Existing data sources often fail to provide complete and timely information. In rare cases where data is available, such as with the Paris SQM index, there is a delay in its publication.
  • To overcome these limitations, we have implemented an enterprise-level ETL process. This process involves ingesting, cleaning, and transforming millions of individual data points, leveraging spatial data science to generate price estimates for different geographic levels.
  • To further enhance the accuracy and relevance of our price feed, we employ a range of smoothing time series techniques. These techniques consider historical government data sources and more up-to-date information, ensuring alignment with the gold standard outlined in our Parcl Labs Price Feed White Paper.
  • The PLPF for France empowers users to make better-informed decisions.


The real estate market in France faces similar challenges as its American counterpart, with fragmented and obscure data. Timely information, such as property listings, is often incomplete and scattered. Additionally, crucial details about properties, like their precise locations, are not easily accessible. Official data sources also fall short in providing timely and comprehensive market insights, with a lag of 4 to 8 months from closing to publication, along with incomplete property details. This information gap leaves the general public and customers relying on scattered and incomplete data to make informed decisions.

Even when real estate information is available, such as with the Paris SQM index developed by Compass, it suffers from a two-week lag and is derived from a privately owned database that requires payment for access, with unclear rules on how to obtain it. Further, their data cleaning and index construction considers observations that can potentially bias the price per square meter in Paris.

Today, we are proud to announce that Parcl Labs continues its mission of providing high-quality information to our customers and users with the introduction of Parcl Labs Price Feeds for the City of Paris and the Ile-de-France region. Our price feeds are the first of their kind, offering real-time indicators that track the evolution of real estate prices using the widely recognized metric of price per square meter.


The segmentation and incompleteness of data pose significant challenges in creating reliable price feeds in France. To address these challenges, we rely on historical data published by the Federal Government of France, along with more timely information from real estate property listings. We focus on residential properties, including condos, apartments, single-family homes, and others, allowing us to combine historical trends with more up-to-date data.

Similar to the Parcl Labs Price Feed, we employ a rigorous process to create a top-notch data warehouse for the development of our Price Feeds. This process involves:

  • cleaning, deduplicating, and standardizing information to ensure the highest quality records for each property.
  • In the case of historical sales, we validate the accuracy of addresses using a third-party source to prevent duplicate data entries and ensure data integrity.
  • Once the data is cleaned, standardized, and processed, we leverage state-of-the-art geographic information systems technology to assign properties to multiple types of markets. This enables us to select specific information for desired markets and facilitates tailored analysis at various geographical levels, such as arrondissements.
Figure 1. Map displaying data points used to calculate the Parcl Labs Paris Price Feed

Once the data standardization process is completed we have a unique state of the art database to build scalable and timely price feeds for any desired level of geography. This is a necessary step before we can build the most reliable and timely price feed for residential real estate. And while we are launching just in two markets today, we have the capability to replicate this process for arrondissements, communes, departments, cities, etc.


Just as we did with the Parcl Labs Price Feed white paper we took our data and filtered outliers and anomalous operations. To address the sparsity of real estate transactions we followed the same approach of our original price feeds and constructed a dynamic back-propagation window based on volatility of transactions. This simply looks at how many transactions in a given period of time are available in each market before deciding how far back in time we are going to look to create a sample space. The window selected changed by market to reflect local idiosyncrasies and to capture the relevant volume of data.

Once we have collected sufficient and relevant information for each type of time series, we select observations within the 25th and 75th percentile of the price distribution. This ensures the representativeness and robustness of our approach, considering that real estate distribution tends to be heavily skewed, requiring the filtering of outliers.

Figure 2. Distribution of Sales in Paris in 2022 as Measured in Price per Square Meters in Euros

After this initial estimate is conducted for both timely sources as well as historical data we use a weighted average with a set of decaying geometric weights for the periods where there is overlap between both series. This allows us to obtain a more representative sample of the real estate market and incorporate longer term trends into our timely information. The last step is to perform a final smoothing process using a moving average that is tailored to each market to filter out anomalies on price fluctuations.

To guarantee the consistency and reliability of our data we conduct tailored testing to each one of the markets available in our API before publishing a data update. This testing takes into consideration abnormal behavior in the different data sources that compose our database, the local market idiosyncrasies that explain volatility in volume and prices, as well as a geographic factor that further adjusts the volatility of our series. This results in a time series that rigorously tests for any sudden movements on the price per square foot.

As an additional quality control metric we also calculated the Pearson correlation coefficient for the Parcl Labs Price Feed series and the Paris SQM from September 1 2017 to June 30th 2023 and found a correlation of 0.97. The strong relationship between both metrics is another proof that daily data that is robust and easily accessible is possible.

Figure 3. Correlation Between Parcl Labs Price Feed Paris and Paris SQM


The fragmentation and lack of transparency of real estate data in the French market has prevented users and consumers from making informed decisions regarding residential real estate. Incomplete and siloed information has been the norm for a long time.

Today the Parcl Labs Price Feed (PLPF) for France revolutionizes the way we access real estate data in the French market. By meticulously tracking daily price changes in residential properties across Paris and the Ile de France region the PLPF empowers users with timely and comprehensive information like never before.

With our user-friendly API, the Parcl Labs Price Feed for France puts the power of informed decision-making directly into the hands of our users. Get ready to make confident real estate choices like never before!

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