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It’s a fact house prices are a hot topic in today’s economy. And, while they do seem to be quite random, what if I told you there was a way to predict and analyse them?

Using the data from a Kaggle dataset (and competition), we seek to understand the relationship between the features and the price of a house, along with trying to replicate it. Data was partitioned as a means of training the model.

We modeled 32 linear variables as a logistic function of house price. People with greater of certain variables were more likely to have a higher priced house, while having higher other variables make it lower.

The area of the garage (if there is one) is the biggest factor in determining the price of a house, being 4.5x as important as the average feature.

Lot Area:

Overall Quality:

Ground Living Area:

Year Built:

This graph shows the distribution of house sale prices: