Plotting Sweden’s real estate prices on a heatmap with and Cloudflare


A heat map of apartment prices in central Stockholm

Disclaimer: I will be linking to sites in Swedish. A translation extension might be handy!

I, like a lot of people in Stockholm, need to buy an apartment. The rental situation is bad. Getting a “first-hand contract” is hard. I have friends who have even had to settle for temporary “third-hand” contracts!

With finding a rental unit out of the picture, buying is the only option. Buying an apartment in an inflated market during big increases in mortgage rates is not fun position to be in. So what should a data person such as myself do to identify which areas in Stockholm are reasonably priced? Plot all the data points on a heatmap! Which is what I set out to do.

My side project bostadsbussen scrapes user entered real estate listings from hemnet and archives them. You can read about the tech behind it in my previous blog post.

All right, we have a place to host a heatmap. First we need to get the data!

hemnet Picture of the closing prices page on

Luckily the data is out there on the internet! provides the closing prices for most of their listings. The problem is that they only return max 2500 results per search query. So we need to craft some queries to extract all the 1 million+ results on their site. It was as simple as limiting the search queries by different parameters until the result was lower than 2500. Then extracting the data from each listing was easy.

I was also very mindful of not putting unnecessary load on their servers.
I chose to not parallelize the scraping.
Getting all the listings took a week in real time.

Cool! Now we have a big JSON array with a million properties. Now I want to visualize this on an interactive map! And share it with the internet!

My first thought was to spin up a dashboarding solution like metabase or superset on a rented VM. They are both great tools and it would have been a great option. But a rented VM that can handle bursty traffic could be quite expensive. I also don’t want to deal with autoscaling stuff like kubernetes without getting paid 🤓

So I would need to build the visualization myself to get around renting a VM. I found which is great for displaying large amounts of data on a map. Perfect!

We also need some map tiles that we can overlay the visualization from on. Mapbox has an excellent free tier where the first 50000 views per month doesn’t cost anything. I doubt I will ever get more traffic than that.

map The heat map of southern Sweden (where all the reasonable people live (sorry Norrland))

OK, with this built locally on my machine I had a pretty cool visualization. I spent an hour dragging the map around Sweden to see if my pre-conceived notions about expensive areas was true. It was! (The Östermalm area in Stockholm is really expensive)

It works locally, now we need to host it! I chose Cloudflare Pages for this. But it’s not really a visualization if there is no data to visualize.

This leads us to the problem of getting the data to the user.

My JSON array was 25MB compressed with gzip (125MB uncompressed). Hosting it on an object storage like GCS would cost nothing storage wise. The big problem would be the egress fees. GCS charges $0.12 per GB. If I got lucky (or unlucky) and had 10000 people download the data, I would be looking at $30 in just egress fees. Not good for a product with zero revenue!

Luckily Cloudflare’s object storage R2 has 0 egress fees. Zero! Now I could use that share the data to the user with a simple GET request.

I ran into some CORS problems for the public bucket but that was easily solved with this guide.

<?xml version="1.0" encoding="UTF-8"?>
<CORSConfiguration xmlns="">

With the above allow all CORS, the data could be shared with everyone on internet. I don’t have to be worried about waking up to a huge cloud bill, since it all costs zero! An additional benefit was that I could provide the full json data. So other interested parties don’t need to hit servers and instead just download that file!

The map is available on (In Swedish!)

My next steps is to include some line charts for analysis and also make sure the json blob is updated with new data everyday. Kind of like a serverless dashboard!

I’m also reaching the end of my travel sabbatical (trekking in Nepal was a highlight!). So I’m looking for a Data Engineering or Infrastructure job. Based in Stockholm or EU remote. Here’s my resume

Shoot me an email at [email protected] if you want to talk 🤓

Stringing together several free tiers to host an application with zero cost using, Litestream and Cloudflare


The image was generated by putting the blog post title into DALL-E 2. Quite fitting!

I have a side project called bostadsbussen. It scrapes property listings for the Swedish real estate market. The site needs to persist data in the form of user accounts, property data and images.

At the time of writing this post I am currently on a sabbatical. With my income at 0, I want to keep the cost of hosting my side project as low as possible. We will be traveling a few months in Asia, so hosting the site on the home servers is also out of the question.

That leaves us with the cloud ☁️

There are cloud offerings like Firebase which are a great place to host a side project. But I want to avoid the vendor lock-in and have the option to move the entire application to my own server in the future. So this post will skip examining Firebase et al.

Renting a VPS (Virtual Private Server), is a good and cheap option with no lock-in. They usually cost around $5/month for a 1GB RAM and a shared CPU. But what if we want to do it even cheaper?

What if we could do it for free?

Enter They provide a free 256MB instance that you can spin up with a valid Dockerfile and fly deploy. Great developer experience!

❯ fly deploy
==> Verifying app config
--> Verified app config
==> Building image
Remote builder fly-builder-spring-snow-7814 ready
==> Creating build context
--> Creating build context done
==> Building image with Docker
--> Building image done
==> Pushing image to fly
==> Creating release
--> release v8 created

And we have released our application on!

All right, we got our free server, what should we do about persisting data? If we store data on the instance and if it crashes we lose everything! The common choice would be to spin up a separate database server and use that for storing our data.

But with the introduction of Litestream, we don’t need to! Litestream will replicate the changes to an SQLite database to an object storage. Litestream will also restore the database when the server restarts. No dedicated database service needed! Michael Lynch has written a great blog post on this.

When it comes to cloud storage all providers are very cheap for running Litestream. So it comes down to developer preference. I chose Cloudflare R2 because of their free tier.


Getting Litestream to communicate with R2 is quite simple:

# The litestream config

  - path: /pb_data/data.db
      - type: s3
        endpoint: ${R2_URL}
        path: ${R2_DATA_PATH}
        bucket: ${R2_BUCKET}
        access-key-id: ${R2_ACCESS_KEY}
        secret-access-key: ${R2_SECRET_KEY}
# The script that restores and then continously replicates the data

echo "Restore db if exists"
litestream restore -if-replica-exists /pb_data/data.db
echo "Restored successfully"

echo "replicate!"
exec litestream replicate -exec "/pocketbase serve --http"

Now we need a backend to host on the server. I have been very productive with PocketBase. It is a go framework with several great features. Like user authentication, an admin panel, an extendable API and a JS SDK for connecting it to the frontend. Best part it uses SQLite as the database, so we can use Litestream for our replication 🎉!

We also need a frontend. I’ll admit I’m not very good at the frontend stuff, I built one with React! It was quite enjoyable. For hosting a React app there are several free options. Like Vercel, Netlify and Render. But I chose Cloudflare Pages. I don’t see much difference between the mentioned alternatives. Since I’m already using Cloudflare’s other services (DNS, R2) the choice was easy. (And I’m lazy).

The last thing I have in my application is the scraping part. Loading 100s of images concurrently and moving them to an object storage is quite memory intensive. At least a 256MB instance can’t handle it! I offloaded the scraping part to Google Cloud Run. It scales to zero, and will only run when it gets a scraping request. It stores images in a bucket and returns the scraped data to the PocketBase backend. It of course also has a free tier that I use! 🤓

And here is a diagram of the architecture. Generated with Diagrams as Code.


That’s it! Hope you enjoyed the post.

Check out for a lab for setting up this architecture

Disclaimer: I paid $10/year for the domain.