finding a new home is hard.. not only do searching WHERE you want to live.. and IF you can find work there.. but also
- will I have space for my dogs
- how many neighbours will I have
- is it noisy? train lines? aircraft noise?
- can I immediately live there or do I have to renovate?
- can I walk my dogs? busy roads nearby? will there be water?
- and so on… a LOT of factors that define happiness. and only so many can be asked during a visit. Also.. the internet has a LOT of websites trying to sell you property.. but which ones are worth investigating?
enter GPT-ImmoRadar
it started as an idea.. what if AI could understand what I want and help me create a shortlist of objects, that I want to investigate.
the basis is an email search agent from the mortgage agency / broker. it is then enriched by geodata, water, neighbourhood, water, noise, etc.. data that I can publicly find. furthermore a browser plugin pulls data from the expose, like price, condition, energy label, etc.
this is not AI doing my job, this is interaction between GPT and a human, massively improving my capabilities, saving me a lot of time and.. honestly.. I haven’t had so much fun with a self-hosted project in years. oh yea.. it is self-hosted, I don’t think anyone would like to do this and not make money with it. I don’t care. download the code.. adjust the scoring algorithm for your personal situation, find the house of your dreams.
I was afraid if AI. Now, I am still (or even more) afraid.. but I have seen what a powerful engineering tool it can be.
Not by asking ChatGPT “build me an app” once (I admit, thats how it started) but through hundreds of iterations: requirements, implementation, production tests, failures, debugging and architectural changes we have now arrived here:
AI did not remove the engineering work. It dramatically increased what one person could build.
interaction with the server via telegram and browser
in the end I can create a top-3 list that I can investigate deeper.. ask for the exact address, rescore land, water and noise levels.. contact the broker and plan a visit.
the “workflow” is like this:
Property alerts
?
Email parser
?
SQLite ? Geo / Water / Noise
?
Telegram
?
Human + Browser + Tampermonkey
?
Browser enrichment
?
Explainable scoring
the “scoring” is based on my preferences:
For example, a good house might score like this:
Price + condition 19 / 25
Plot 16 / 20
Neighbours 17 / 25
Quiet 15 / 15
Water 8 / 10
Living area 4 / 5
Energy label "A" +2 <- bonus
--------------------------------
Total 81 / 100 -> very interesting
Energy is deliberately shown as an adjustment rather than as a fixed category worth a certain number of points. A good label can add points; a poor label can subtract them.
A different house can look cheap at first but still rank much lower once condition and energy are taken into account:
Purchase price 130,000 EUR
Condition surcharge +140,000 EUR (major renovation)
Effective price 270,000 EUR
Price + condition 9 / 25
Plot 11 / 20
Neighbours 6 / 25
Quiet 12 / 15
Water 7 / 10
Living area 3 / 5
Energy label "H" -8 <- penalty
--------------------------------
Total 40 / 100 -> archive
In the current model, for example, energy label A gives +2 points, while label H deducts 8 points. The detailed energy adjustments are configurable and documented with the rest of the scoring model.
source code is public
I don’t like proprietary stuff.. also I thing GPT and immowelt/immobilienscout earn enough. this is just me using what is out there so.. use it. find your dream.
