Analytics driven approach to revenue augumentation

1. aravind.gaddala@gmail.com
Analytics Driven Approach to Revenue Augmentation for Cities
There is an increasing focus on digitizing citizen services through various e-governance initiatives. These initiatives are
generating data at a tremendous scale. This data is enabling City administrators to monitor, track, and make better
decisions. But, this data is siloed with various departments and agencies, with various levels of accuracy and
consistency. There are initiatives underway through DataSmart Cities to build an enabling ecosystem supported by a
robust system of data acting as a backbone. With the increasing sophistication of algorithms, machine learning
capabilities, decreasing cost of storage and processing, there are unique opportunities to use analytics to increase
Revenue, plug leakages, and deliver better citizen services.
This note lays down the context and applicability of an analytics-driven approach to increase revenues for the
Government. Property tax as a revenue source has been chosen to illustrate the possibilities, while a similar
approach can be considered for other revenue sources as well.
Revenue Sources for Cities
The main sources of revenues for cities can be boxed under the following categories:
Revenue Category Sources of Revenue
Tax Revenue(Own Tax) Property Tax, Advertisement Tax, Octroi, Trade Licenses
Non Tax Revenue (Own Non Tax) User charges, Municipal fees, Lease Amounts
Other receipts Sundry receipts, Fees, Fines, Miscellaneous Sales
Assigned shared revenue Entertainment tax, Motor vehicle tax, Surcharge on stamp duty
Grants and Loans from Central and
State Governments
HUDCO, LIC,Municipal Bonds, Grants from State and Central
governments
Figure below shows the components of municipal revenue for 2007-08. Tax revenue(Own Tax) and non-tax (Own Non
tax revenue) comprise 52.94% of total municipal revenue
Property tax is the most important component of Tax revenue and comprises on an average 50-60% of tax revenue.
The table below presents the property tax collection as a ratio of GDP of India and other developed and developing
countries. Given the importance of property tax to the revenues of a city, it spotlights the scope to improve collection
and plug leakages:

2. aravind.gaddala@gmail.com
%age of Property Tax to GDP Countries
2.12 OECD
0.60 Developing
0.68 Transitional
0.16 - 0.24 India
Based on past research, the most crucial reason for the low property tax collections is the poor coverage of the tax.
Poor coverage is due to (1) Wide-ranging exemptions (2) Lack of up-to-date registry of land and properties by
municipal bodies. A significant problem constraining reforms is the absence of a comprehensive digitized record of
features. Clear assignment of ownership is crucial to levy the tax. Most cities do not have an up to date and accurate
register of property ownership and past payments made.
Data based approach to increase Revenue
Governments have data and records for services provided by them to citizens. But, each department uses different
taxonomies, data formats, data structures for storing nearly the same information. This problem is compounded by
missing and/or inaccurate data. Hence, they lack a unique view of the citizen. As an example, consider three different
agencies in Bangalore.
Agency Data Available
BESCOM-Electricity Unique Account ID, Name, Address, Consumption, Payment History, Address,
Telephone number, Type of connection( Residential, Industrial)
BBMP-Property Tax Unique Property Tax ID,Name,, Address, Type of property ( Residential, Commercial,
Mixed), Usage( Self occupied, Rented), Property Details ( land area in number of floors,
measurement), Past Payments, Telephone number
BWSSB-Water
Supply
Consumer ID, Name, Address, Consumption Details, Consumer Type, Payment History,
Telephone number
These agencies have the opportunity to connect this trail of transactional records to derive insights from this Data.
Here are a few innovative ways of operationalizing analytical insights to increase Property tax revenue and reduce
revenue leakage:
● Searching and matching of data from electricity bills, water bills with property tax records through fuzzy-matching
of names and addresses or any other non PII can identify citizens who have not registered their properties. On ground
verification surveys can then be conducted for all such identified properties to bring them under tax assessment.
● An analytics-driven approach can identify patterns and relationships between historical electricity consumption
and property tax payment records to unearth possible fraud in the declaration of property. Example: Unusually high
electricity consumption could point to the operation of trades or industrial units, though the property is declared as a
residential unit.
● Matching social security and pension disbursal data from the state employee welfare department with property tax
exemptions to validate the exemption category (handicapped, widowed etc;).
Actionable Steps
● Setup pilot programs in cities
● Ensure there is a mechanism for data privacy
● Building transparency around data usage and interpretation
● Model to test, learn and iterate with data sets to progressively improve outcomes

3. aravind.gaddala@gmail.com