On the 2nd and 3rd of July, a Datathon was organized by the social landlord Paris Habitat. I did not know Paris Habitat or the Datathon concept, but having experienced the demand for social housing myself, I knew there was a lot of room for improvement in this area.


Our solution centered around the demand for social housing won the third place out of 17 participating groups. We thus won 2000 euros to share. We also obtained the prize of the « Coup de Coeur » de Numa, present on the jury.

No prototypes

But innovative ideas

The main objective of the event is the generation of concept, and not of functional prototype as in a classic hackathon. The Datathon, as explained by Paris Habitat on the site of the event, is based on the use of data collected by Paris Habitat and its environment. "Datasets will gravitate around 5 dimensions: tenants, apartments, residences, neighborhoods, Paris.This information may be in various formats (excel, API, text, reference document, video, images, etc.). " Luckily for me, I was not quite overwhelmed on the subject of Data, because I had a few months ago taken a Coursera, the Andrew Ng's famous course to learn Machine Learning. This understanding, however superficial, proved very useful on a daily basis to collaborate with my fellow Data Scientists.The opportunity to link UX and Data was very interesting to me, especially since I had attended a very disappointing meetup on this subject, leaving a taste of mystery around this domain marriage.


average,waiting timein Paris


waiting timerecord

1/ 10

parisiensare Paris Habitat'stenants

125 000

Paris Habitathousesin Paris

Two days

11h challenge...

On 2 days, from 9h to 19h, with a considerable time left to introduction and conclusion presentations, the challenge would become a race against the clock. Impossible to do science on these data, all that we could do was a confirmation of quick and superficial hypothesis.
Small point on the progress of this extraordinary hackathon:

  • A generalist and, to say the least, heartbreaking introduction to current innovations and the excesses / opportunities involved was made by a DNA journalist
  • The presentation of the mentors was followed by the distribution of participants in three main themes: « Better Living Together », « Improve our relations with tenants » and « Improve our Energy Performance and our Ecological Footprint ». The distribution was based on the answers to the very complete registration form that we had all filled out.
  • Each group of 20-30 participants thus formed began 3 hours of brainstorming / co-creation to bring out questions and affinities.
  • So it’s at 3 pm that our small team of three people formed and isolated themselves to discuss the question we had chosen: the problem of the demand for social housing.

Considering that the rendering was to be done the next day at 15h, and that the premises closed at 21h, it left us 11h to dig our concept. Very short !

Obtaining social housing, or a test of patience

It is common knowledge that the demand for social housing is a long and exhausting process for people in precarious situations. The applicants have no visibility on the processing of their application and regularly go to Paris Habitat or call for news. In Paris, it takes an average of 4 years to get a home, and 30% of requests have more than 5 years of seniority. Often, when housing is allocated, the needs of applicants have changed: a new child, a child gone, work elsewhere, different incomes … Some extreme cases have made the headlines, like that of a woman who received social housing 17 years after her request.

On the Paris Habitat side, the demand for social housing is significant for an heterogeneous housing stock that is too small for certain types of housing. The switchboard and the reception face many calls from insistent individuals who do not understand why their wait is so long. Paris Habitat is one of the few Parisian social landlords and therefore shares with its competitors the processing of applications.

Design Challenge

How could we use the existing Data at Paris Habitat to enable social housing applicants to understand and optimize their demand for social housing?

Our Solution

Our solution is a platform for social housing applicants, free and without registration, which simulates the estimated waiting time according to certain parameters of the request, thanks to the data of Paris Habitat. We called it Locaboost and we think it addresses the main issues related to the demand for social housing right now.

For those who want the short version, here is the support of our 7min pitch:


Housing Applicant Centered

The definition of Stakeholders around the relationship with the tenants was made during our co-creation workshop. We met around an ice-breaker and then submitted some ideas prepared upstream. Finally we collectively made a card sorting by our mentor. Among the participants were social housing tenants, housing experts, Paris Habitat employees, social housing concierges, developers, salespeople and also curious people.

Testimonials and conversations with the various stakeholders of this problem allowed us to inform our design in a relatively accurate way. We did not have the time to consolidate our hypotheses with several interviews. We quickly converged during our debate on the notion of transparency that allows the Data vis-à-vis customers. Applicants have a very strong need for information, or even support on their request for housing. The simplest solution to implement is the provision of data, we focused on this topic.

A full and cross-story story mapping of several typical users allowed to highlight the main points of the demand for social housing.

An analysis of the typical progress of a request, as well as the list of the fields of the form to be completed, combined with the Paris Habitat experts' feedback allowed us to list the parameters with a high impact on the waiting time.

We designed simple screens and after a confrontation with our colleagues and mentors, we validated these elements and created a clickable models, as well as developed a functional prototype.

Wouldn't take much

To improve a lot

The heart of the problem is the lack of transparency on the parameters of the request. Applicants are alone in front of their form and have very different needs, sometimes with emergencies. Sometimes, it’s enough to change a district’s request to significantly reduce their waiting time. Idem for the type of apartment.

Giving visibility automatically and in real-time thanks to the data of Paris Habitat has advantages for both the applicants and for Paris Habitat.

Applicants can:

  • Have better visibility on their estimated waiting time
  • Change to ask them to optimize their waiting time
  • Follow their estimate of waiting time over the months

Paris Habitat is also a winner because:

  • There is a better rotation of the housing stock
  • A reduction in the number of files to be processed and pending
  • An improvement in the satisfaction of the applicants and therefore the image
  • A decline in the refusal rate of a housingup

Stands on recommendation

Our story mapping has highlighted a key point of our product : the retargeting. When a family change occurs, there may be a better matching of housing to operate with, a housing rotation satisfying the current tenant and the new applicant.

Estimating waiting time is the first step in improving a social housing applicant’s experience. But the process would not be complete if we did not give him the keys to allow him to act on his own request and reduce his waiting time. For that, we propose a solution of recommendation, according to the parameters of the request, which suggest similar accommodations with a reduced waiting time. Some people will be interested in these changes to gain access to housing faster while others, who prefer to see their criteria satisfied will know their waiting time in advance.

Story Mapping


Feasible tomorrow ?

Yes !

We propose to integrate this solution with existing tools known to social housing applicants (the SNE directory). It is a demand independent tool, which works anonymously and without registration. It is different from the application form, and is only indicative. The data needed to calculate wait times are data already available within Paris Habitat, as confirmed by an interview with the Director of Information Systems at Paris Habitat.

We have made a schedule and a cost estimate for such a project. The longest phase is of course the creation of the wait time calculation algorithm, requiring the skills of a Data Scientist for several weeks.

La Team

Good Bye

Thank you for reading !

A big thank you to the Paris Habitat teams who organized this very successful event. Congratulations to the mentors who helped us in this race against the clock, and congratulations to other competitors who have successfully challenged the challenge of creating innovative concepts for tenants or Paris Habitat.