A principal tax collecting agency in the U.S. state was in charge of administering the state's tax laws and collection of state taxes for nearly 40 programs including 1.8% of Transient Lodging rent.
Without adequate data about the transient lodging facilities (revenue, number of rooms, exact locations, etc.) the tax collecting authority faced a challenge in monitoring whether the Transient Lodging facilities are paying tax to the government as per the occupancy and required standards.
The Department of revenue was suspecting the lodging facilities to be forging the occupancy rate by altering the number of rented spaces booked ( occupancy rate is calculated as the ratio of rented or used space to the total amount of available space).
The pressing need to aggregate information for these different categories got them to Xtract.io and they decided to implement it for their business.
To get the daily occupancy rate of the lodging facilities
To validate the occupancy of the lodging facilities throughout the state on a daily basis
Cross-check whether the taxes paid as per the occupancy rate is the same as the bookings made across online platforms
Monthly report creation for audit purposes
The complexity involved in aggregating this data was multi-fold. Here’s a quick overview of the expertise they required from Xtract.io
A fully automated approach was required to find out the occupancy rate of the lodging facilities
The data on occupancy was required to be aggregated from 10+ third-party websites periodically (roughly 4 times a day)
Site-specific bots were required to crawl multiple websites
The data experts at Xtract.io analyzed the challenges and implemented a step-by-step solution.
A custom-built platform
Data aggregation from multiple websites/channels
Data delivery and reports + analysis
Xtract.io helped the Department of Revenue identify the tax evaders by calculating the occupancy rates. The results are quantified and are as follows.
The solution covered more than 95% of all transient lodging facilities of the State
Tax evaders were identified based on variation in the tax paid and occupancy data
40% Increase in tax collection