Last month I hosted a twitter space last month in our community @PowerBIPC where @RxDono and @ninafrancez1 shared their experiences on the impact of participating in monthly challenges. On that call, I tasked my self that I was going to give it a try regardless of how busy I was. I took up the #FP20 Data analytics challenge 36. I wasn’t able to meet the deadline for submission but I completed it. It was such an exciting experience for me and I think I might have forgotten the thrill working on projects like this gives me. If you are interested in knowing what a European real estate market analysis covering 5,000 listings across 10 countries, built in Power BI looks like, then keep reading! Before I touched the data, I built the analysis around four questions: - WHERE is the value? (Which markets are premium, which are accessible?) - WHY do prices differ? (What actually drives value: size, amenities, energy rating?) - WHERE should I invest? (Which cities have strong yield, lower competition?) - HOW does the market behave? (Which properties sit unsold, and why?) Those four questions became the four pages of the dashboard. A few things the data revealed that I didn’t expect: - Size alone explains 82% of price variation across all 10 cities. Location matters less than most people think. - Vienna delivers a 5.20% gross yield. Paris delivers 3.60%. The most famous city in the dataset produces the worst return for rental investors. - 44% of all listings sit on the market for 180+ days. Almost half the European property market is stagnant. - Only 2.6% of listings carry an A energy rating, in a market where that gap is worth €774/sqm. I also built the data model from scratch, restructuring the flat dataset into a star schema with a fact table and five dimension tables before a single visual was created.

First Twitter space of the year If you are looking to start a career in data or ready to take your data career seriously this year! Don’t miss this 🙌 Word on the street says I host great spaces🥰