ImmoDash

Why I built ImmoDash

I grew up in Mumbai, one of the most crowded cities in the world. Moving to Germany taught me that the same question follows people on small incomes everywhere: how much of what I earn will the roof take?

Background: rent per 100 m square in Berlin, Zensus 2022. Stronger blue is dearer.

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ImmoDash in 50 seconds

One question, answered with open data: is the rent you are offered fair? The short film shows what the site does, from the trend in 37 cities to the 12-month outlook.

Mumbai's skyline at sunset seen from the beach, with people walking on the wet sand in the foreground
Mumbai at sunset, seen from the beach. Photo: Vyacheslav Argenberg, CC BY 4.0, via Wikimedia Commons.

Growing up in Mumbai

Mumbai has close to 20 million inhabitants[17]. In the 2011 census, 41.8% of the people in Greater Mumbai, about 5.2 million, lived in slums[18].

Growing up there, I saw how housing shapes the lives of people on small incomes: where they can live, and how little is left at the end of the month once the roof is paid for. Housing is not one expense among many. For a low-income household it is the expense that decides the others.

Then Germany

In November 2024 I moved to Bielefeld to study for an M.Sc. in Data Science, and went through the German flat search myself as a student.

Germany is a far richer country, yet the pattern is familiar. On average, households spend 24.1% of their disposable income on housing. For people at risk of poverty it is 43.7%[19]. More than a third of them, 35.0%, spend over 40% of their income on housing, against 11.2% of the whole population[20].

In the cities it is sharper still. A study of Germany's 77 big cities found that almost 13% of tenant households have less than the subsistence minimum left after paying rent[21]. And a rent above 30% of net income is generally seen as problematic, because too little remains for everything else[1].

Why data, and why open data

Renters usually have one number to go on: the price in the listing. I wanted to put that number in context, using only sources anyone can check.
  1. 01

    Is this rent normal?

    Compare a new lease with what people already pay and with the trend in 37 cities.

    The story
  2. 02

    Can I afford it?

    Check a flat against the 30% rule, or find every district where a budget works.

    Affordability
  3. 03

    Where is it hardest?

    See where few flats stand empty while the population grows, in all 400 districts.

    Supply & demand
  4. 04

    Can I trust it?

    Every number comes from official or academic data, and every explanation links to a published source.

    How it works

About me

I'm Zoeb Ali Khan, currently pursuing an M.Sc. in Data Science at Universität Bielefeld. I built ImmoDash end to end: the data pipeline, the models, the API and this website. It is an independent project and not affiliated with any of the data providers.