DATA SCIENCE USECASE IN REAL ESTATE
In San Francisco, due to the availability of distinct stay options (rent or host or hostel) and huge competition among rental communities, the price fluctuates every minute. Deciding a price for rental space is a complex task and many companies have lost billions of dollars due to wrong pricing for available space. The creative approach and suggested price feature concept have found Airbnb a unique place in the hotel industry after the sharing economy.
We used a predictive data analysis model. The inbuilt data analysis systems, integrated with intelligent technology, offer constant and profitable price suggestions for every host.
In this use case, to understand the predictive intelligent model that Airbnb built using machine learning technology to suggest prices for hosts in the San Francisco area, we collected past data sets. Based on these historical datasets and parameters affecting the price fluctuations, the model can perform predictive analytics for real-time price suggestions.
Pricing a rental property on online renowned platforms like Airbnb is a challenging task for an owner. And at the same time, customers have to test an offered price with minimum knowledge of an optimal value for the property. Our data intelligence team tried to evaluate possible features for more accurate price predictions for the available place.
Our machine learning based analytical model helped us in understanding changes in the market environment, competitor analysis, internal competitive pricing between host and super host, identifying the optimal location, and their pricing differences.
We use data science and machine learning models to predict and understand price fluctuations and define the right price for the right property.
Our machine learning based analytical model could predict price fluctuation with 65.41%
To read the complete use-case with detailed information on the data sets, technologies, approach, models and procedures followed download usecase.