Leveraging data science to dynamically price residential real estate projects.
easy to implement
The problem of a real estate developer
The developer is not able to accurately determine the value of residential units and loses potential profits when selling them. Once the sale starts, the developer is not able to react quickly enough to changes in the market. Any potential change to the price list usually ends up in the hands of a few sales representatives or project managers. Lacking proper market data, convenient analytical tools, and also enough time, they face a daunting challenge.
The solution to this problem lies in applying unique algorithms that can predict the prices of units much more accurately than a human analyst and initiate an immediate response to market changes.
We collect, unify and verify data from multiple sources and pay close attention to market data as well as competitors, so the developer can react to price fluctuations in real-time.
We consider both the external effects (e.g. competitors’ price fluctuations) and internal effects (e.g. sales targets) to create an introductory price list.
Our algorithms aims to specify what really determines the price – how much the location determines it, the size of the unit, layout, balcony, orientation, and many other parameters.
We continue to measure demand and the market fluctuations. We factor in how long it takes to sell the specific units and the changing internal factors to update the price list as needed.