Global Housing Purchase Data

By the_data_guy · November 18, 2025

This data shows world wide trend of housing purchases, loan, house sizes, demographics etc..

I've analyzed what percentage of property prices are financed through loans across different property types. A visualization and detailed breakdown have been generated showing the average loan-to-price ratio for each property category.

I've analyzed the average price per square foot across different countries in your dataset. A bar chart has been created showing how prices compare between countries, making it easy to see which locations have the highest and lowest property values per square foot.

I've analyzed which property types generate the highest total revenue across all markets. While there was a minor technical issue with the detailed breakdown, I was able to create a comprehensive visualization showing the top 10 property types by total revenue.

Yes, newer properties do command slightly higher prices per square foot than older ones, but the difference is minimal. Properties built within the last 10 years average $380.29 per square foot, while properties over 40 years old average $378.61 per square foot—a difference of just $1.68 or 0.4%.

I've identified the cities with the best price-to-salary ratios for buyers. A lower ratio means better affordability - it shows how many times a buyer's annual salary equals the property price. The analysis successfully created a visual comparison of the top 10 most affordable cities, along with detailed data showing average property prices, buyer salaries, and the number of properties analyzed in each city.

Furnishing status has virtually no impact on customer satisfaction scores. All three categories—Fully-Furnished, Semi-Furnished, and Unfurnished properties—show the same average satisfaction score of 5.5 out of 10.

I've analyzed which customer segments face the highest financial stress based on their EMI-to-income ratios. While the detailed breakdown encountered a technical issue, I was able to generate two important visualizations that show the patterns clearly.

The analysis reveals that connectivity score has virtually no overall relationship with property prices across cities, with a correlation of just 0.002. This suggests that connectivity alone doesn't significantly drive property prices when looking at the data as a whole.

The property size range of 1500-2000 square feet shows the highest purchase decision rate at 26.34%. This means that roughly 1 in 4 properties in this size range resulted in a purchase decision.

I've identified the cities where buyers have the highest monthly expense-to-salary ratios. A visualization showing the top 10 cities has been created, displaying which locations have buyers spending the largest percentage of their income on monthly expenses.

No, the number of previous owners does NOT negatively impact property prices. The analysis shows virtually no relationship between these factors.