Investing and Property Price Indices
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Traditionally, investing decisions in real estate have been driven by historical property prices, the area’s quality, and proximity to high-value local features like grocery stores, schools, and parks. However, properties within a region can still vary widely, making accurate predictions challenging.
Data science methods can use large data sets that go beyond the traditional data and bring in each property’s individual characteristics. It can factor in property characteristics and demographics (among other things) to create granular sub-market indices. It can go right down to predicting property returns in specific postal districts.
Benefits for the company
Avid investors want to base their decision on high-quality data and insights, which will allow them to make highly accurate predictions for the properties they invest in and make more profitable choices.
Feasability
High
Type of expertise/ AI domain
Advanced Analytics, Regression Models
Internal data required
Historic Property data, Customer Demographic Data
External data possible
Public Data, Demographics Data, Market Information, price/square meter, neighbourhood quality, etc
In accounting for the nontraditional variables, buildings located in the same zip code can have widely disparate outcomes in terms of rental performance. Two buildings that are seemingly identical when evaluated by traditional metrics can ultimately experience very different growth trajectories.