Robert wants to buy a house in a cozy town called Pave. He found a particular house for sale that suits his needs; a wonderful start for a family of three. The house’s sale price was set at $130,000 but he was not sure how good the price of the house is compared to the other properties for sale within the vicinity. An idea came to his mind in investigating this, and he opens his laptop in a café with a cup of coffee, curious to find out about the house pricing.
All he needs is excel, and an add-in.
Robert found some datasets from Kaggle (you can find out more about it here) about the house pricing around Pave, and he takes the relevant data he needs to conduct this analysis with Excel which are:
Overall Quality Rating (out of 10)
Overall Condition Rating (out of 10)
The Year in which the house was built
The Year in which the house was remodified
There are over 1400 houses within the town and conveniently, each house has been given ratings from their previous resident and the local community. Based on online reviews of the house, he decides to rate the house with overall quality 6 out of 10 and overall condition 5 out of 10. Additionally, the house was built in 1999 and remodified in the following year. This information is important for Robert to conduct his study.
Once the dataset is ready, he uses AiSara to learn the relationship between the house variables with the house sale price of the 1400 houses within Pave. This can help Robert to have a good estimation of whether $130,000 for the house is a good buy or otherwise.
He pauses for a moment to take a sip from his coffee. Eager to see the Sale Price that AiSara predicts for the house, he uses the predict() function and the within a few moments, the price is shown.
How much is the price of the house supposed to be?
It turns out, that the value of the house is around $187,659, more than the sale price that was offered to him, $130,000.
A value for money purchase. A great start for him, his beloved wife and his newly born son in the cozy town of Pave. Robert finishes his coffee, packs his laptop, pays for the coffee and heads back home with excitement to inform his family of the good news.