Applying Machine Learning Methods for Housing Price Prediction

Real estate market was and still is one of the most attention-centered industries in global economy. In the era of constant change, where prices of properties are growing faster than salaries, people are looking for the smart way how to get the most for less. This approach is applied in many areas of life. To understand drivers of price is crucial for many industries and real estate is not an exception. Applying different methods might bring better understanding for underlying factors of value behind housing. With the diversification of economy services and products we can also observe disruptive concepts in this field. One of them is definitely Airbnb - as a flagship of sharing economy. In our research we are aiming to apply most appropriate machine learning techniques to comprehend features and value drivers behind the price of Airbnb listings in Los Angeles. The study is focused on exploratory data analysis, feature engineering and predictive modeling with the utilization of regression methods.