I Built a Real Estate Tracker Using Free APIs- Here’s What I Learned

I swear, I will tell you the truth, I did not wake up one day and thought that I will become a PropTech person. However, when I saw that I had looked through Zillow 100 times and still could not figure out why my neighborhood prices were increasing, I reasoned: what would happen to me, though, if I followed this myself?

Thus, I created a local real estate tracker with free APIs during a weekend. No big budget, no coding boot camp education. Only curiosity and certain free tools. This is what actually transpired.

Why I Even Started This

I am in a medium-sized city, and the prices of homes are randomly generated. This week the house costs 320K and the following month it goes up to 345K. I was interested in patterns, not what Zillow claims a place to be worth, but what is actually being sold in my zip code.

Besides, online real estate technology has become strangely affordable. There is no longer a requirement of a real estate license or a degree in data science. It only takes the right APIs and several hours.

The Tools I Used (All Free)

Here’s my stack:

Public Data used by Zillow: They provide no cost CSV downloads with detailed data – inventory, list prices, neighborhood sales. I took the last two years at my region.

Bridge Public Records API: It is a little bit more technical, but their free version provides you with access to property records. Consider the sales history, tax assessments, square footage.

Google Sheets + a Curl: I am neither a developer nor know enough Python to scrape and put the data in a sheet. After that, I have visualized trends in Google Sheets.

What I Built (And How Long it Took)

It was approximately 6 hours over a weekend. Here’s the breakdown:

Hour 1-2: Downloaded the data of Zillow of my city. Cleaned it up due to the fact that some rows were untidy (absence of zip codes, strange structure).

Hour 3-4: Developed a simple Python program to extract the API at Bridge. I selected three neighborhoods that I was interested in.

Hour 5-6: Constructed a rudimentary dashboard with Google Sheets. Nothing elaborate – just line charts of the mean prices over time and a heatmap of the blocks recording the highest sales.

The result? A hyper-local tracker which is updated as I fetch in new information. I can check on which streets are heating, and which are cooling off and where inventory is really moving.

What I Discovered about Technology on Online Real Estate.

It is more accessible than you may believe: You do not have to be a technological genius. Provided you can Google an API and get a tutorial on YouTube, you are 80% of the way. Zillow API documentation is not that unfriendly.

Data Isn’t Perfect: The estimates of Zillow (their so-called Zestimates) are notorious. When I compared them with real sale prices in the public records, some were a bull hit. Others were off by $30K. The tracker assisted me in identifying the gaps.

Patterns Emerge Fast: Within two months of monitoring, I realized that there was something interesting, which is that homes close to a new light rail station sold 12% faster compared to other homes in the neighborhood. Something you would not find scrolling Redfin by chance.

It’s Honestly Addictive: It is when you have your own data and then you start perceiving the market in a different way. You have ceased being a dormant reader. You are trend-setting, trend-reading, you are catching the deals before they trend into the mainstream applications.

Who Should Try This?

This is a weekend worth outing, should you be a tech-curious individual in any way. It’s useful for:

Customers who prefer to follow certain neighborhoods without the use of the Zillow algorithm.
Investors seeking under-priced pockets in their city.
Local real estate trend content writers (editor, were you looking to write this?).
Technical minds who simply enjoy construction and require a project that will be of practical use.

The Catch?

One of them is: maintaining it. APIs do not automatically update your sheets. This will require manually gathering new data or establishing a simple automation (which I have not done yet, but I am planning to do so).

Also, you are restricted by what is publicly available. Want rental data? That’s harder to find for free. Want predictive AI insights? You will require paid tools to do so.

Final Take

Assembling this tracker did not turn me into the expert in real estate immediately. But it provided me with something better, which is my personal perspective on the market. I am not required to believe the black-box algorithm of an app but instead I would be able to see what is really going on in my backyard.

Online real estate technology is no longer a professional only thing. It is to anyone who is willing to spend a weekend studying. And honestly? That’s pretty cool.

In case you are considering it, begin small. Pick one neighborhood. Pull one dataset. See what you find. You might surprise yourself.

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