How to organize rental data from PDFs and spreadsheets into a centralized database by detecting errors and flagging them before they are input into the database.
How to use metrics like property reviews, footfalls, and nearby amenities data to find correlations with property prices.
How to use historical data and trending patterns to forecast rental yield, occupancy rate, and market value.
How to develop a platform for comparative analysis of different properties.
JMI Implementation
Assessed the client’s data preparation processes that relied on legacy PDF, spreadsheet, and CSV input formats. Used Bayesian frequency models to detect anomalies and discrepancies in input data.
Acquired unstructured data from third-party sources like foot traffic, visitors, nearby amenities, and reviews that are likely to be associated with determining the price of the property.
Analyzed historical data using statistical algorithms and ML methodologies to understand the rental yield, price appreciation, and occupancy rate.
Developed a sentiment indicator tool and publicly available data (Zillow) and modeled property price and rental value on a one- to ten-year time scale.
Developed an interactive web platform that delivers comparable analyses using a variety of different user inputs.
Results
The data accuracy improvements led to a 12% decrease in time spent on manual corrections.
The client has been able to scale up the client offerings by more than 15% in a six-month timeframe.