There are various factors affecting flat sales prices. Various characteristics of a flat
play an important role in determining its sales price. In this study, a machine learn-
ing based Bayesian network was built by a restrictive structural learning algorithm
using the data collected from 24 randomly selected cities in Turkey. The data consist
of the sales prices and various characteristics of a flat such as number of bedrooms,
building age, availability of balcony, net area, heating type, mortgageability, num-
ber of bathrooms, seller type, presence in a housing estate area and floor location.
After the model validity check, a sensitivity analysis was performed for the esti-
mated Bayesian network model and related results were provided. Some of these
results indicate that sales prices of flats mostly change depending on the number
of bathrooms available. Additionally, number of bedrooms, net area and floor loca-
tion are also determinative about the sales prices. The lack of significant difference
among the sales prices of flats that are sold by construction companies, housing
estate agents or property owners is another result obtained.