Net Realizable Value

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Valuation Method for Aging Tax Debts 2006 MSATA Presentation By: Jeff Scott, Executive Director of Compliance Enforcement Andrew Kapp, Accounts Receivable Manager Net Realizable Value Kansas Department of Revenue

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Kansas Department of Revenue. Valuation Method for Aging Tax Debts 2006 MSATA Presentation By: Jeff Scott, Executive Director of Compliance Enforcement Andrew Kapp, Accounts Receivable Manager. Net Realizable Value. Net Realizable Value (NRV) . What is NRV? - PowerPoint PPT Presentation

Transcript of Net Realizable Value

Page 1: Net Realizable Value

Valuation Method for Aging Tax Debts

2006 MSATA Presentation By:Jeff Scott, Executive Director of Compliance Enforcement

Andrew Kapp, Accounts Receivable Manager

Net Realizable Value

Kansas Department of Revenue

Page 2: Net Realizable Value

What is NRV?• Simply, NRV is predictive statistics.

– A statistical probability of debt recovery based on each debt’s unique combination of variables or characteristics.

• Probabilities are used to determine a realizable realizable valuevalue of an inventory over a defined period of time.

• NetNet... bankruptcy and bad debts are not included.

Net Realizable Value Net Realizable Value (NRV) (NRV)

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• Building Valuation Models– KDOR’s Approach– Valuation Flow & Coding Scales– Valuation Example

• Application Technique– Report Examples– Other Uses For Models

• Discussion & Questions

Topic Outline Topic Outline

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NRV ConsiderationsNRV Considerations• Method

– Judgmental or Empirical (Statistical)

– Continuous or Incremental

• GAAP (Reporting)• Model Application

– Data Warehouse– Reporting Software

• Valuation Components– Tax Type– Age – Amount– Filing Frequency– Debt Source– Physical Location– Customer Profile– Business Type– Etc...

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KDOR’s NRVKDOR’s NRV• Method

– Judgmental or Empirical (Statistical)

– Continuous or Incremental

• GAAP (Reporting)• Model Application

– Data Warehouse– Reporting Software

• Valuation Components– Tax Type– Age – Amount– Filing Frequency– Debt Source– Physical Location– Customer Profile– Business Type– Etc...

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Building Probability Models• Five years of historical data is extracted from

KDOR’s collection system. • Extracted data is sent through a custom analyst

process (data warehouse environment). • Each debt is coded according to it’s components. • Debts are compared to collection(s).• File is created describing incremental collections of

each unique combination of debt components.

Debt Valuation Debt Valuation Modeling Process Modeling Process

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Process Illustration

Modeling Process Modeling Process

DataExtracted

FromSy stem

FileTransf erred

to DataWarehouse

DataComponents

Analy zed

Component 1:Tax Ty pe

Component 2:Collection Age

Component 3:Collection Amt

Component 4:Debt Source

ValuationOutput File

Created

Analy sis RulesAssigned

Partial recovery is included.

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Process Illustration

Modeling Process Modeling Process Tax Type

Individual IncomeEmployers' WithholdingRetailers' SalesCorporate Income

Debt AmtBetween $5 and $50Between $50 and $100Between $101 and $200Between $200 and $300

Debt AgeMore than 0 days More than 30 days More than 60 days More than 90 days

Debt SourceAssessed Tax Liability Non-Assessed Liability

ID Tax Type Debt Amt Debt Age Assessed1 Retailers' Sales $140 24 N2 Individual Income $194 72 N3 Employers' Withholding $100 40 Y

Valuation File

Historical Debt File

Debt Components

Valuation File

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Valuation File - Report View

Valuation File Valuation File

Tax Type Debt Amt Debt AgeDebt

SourceCumulative Recovery

Incremental Recovery

Individual Income Between $5 and $50 More than 0 days N 84% 21%More than 30 days N 63% 21%More than 60 days N 42% 16%More than 90 days N 26% 26%

Between $5 and $50 More than 0 days Y 44% 21%More than 30 days Y 23% 17%More than 60 days Y 6% 5%More than 90 days Y 1% 1%

Individual Income Between $50 and $100 More than 0 days N 91% 14%More than 30 days N 77% 31%More than 60 days N 46% 24%More than 90 days N 22% 22%

Between $50 and $100 More than 0 days Y 32% 14%More than 30 days Y 18% 7%More than 60 days Y 11% 5%More than 90 days Y 6% 6%

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Alternative Use of Valuation File

Valuation File Valuation File

Tax Type Debt Amt Debt AgeDebt

SourceCumulative Recovery

Incremental Recovery

Individual Income Between $5 and $50 More than 0 days N 84% 21%More than 30 days N 63% 21%More than 60 days N 42% 16%More than 90 days N 26% 26%

Between $5 and $50 More than 0 days Y 44% 21%More than 30 days Y 23% 17%More than 60 days Y 6% 5%More than 90 days Y 1% 1%

Individual Income Between $50 and $100 More than 0 days N 91% 14%More than 30 days N 77% 31%More than 60 days N 46% 24%More than 90 days N 22% 22%

Between $50 and $100 More than 0 days Y 32% 14%More than 30 days Y 18% 7%More than 60 days Y 11% 5%More than 90 days Y 6% 6%

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Application of Valuation File

Valuation of Debt Valuation of Debt

CurrentDebt

Extract

Current DebtComponents

"Coded"

Component 1:Tax Ty pe

Component 2:Collection Age

Component 3:Collection Amt

Component 4:Debt Source

ComponentsMatched to

Valuation File,Valuation %assigned

DataConsolidatedPer ReportingRequirements

Reporting

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Reporting Examples Reporting Examples

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Reporting Examples Reporting Examples

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Other Report Uses• Debt risk scoring• Strategy effectiveness over time• Taxpayer typology research• Employee performance• Test new collection strategies• Regional analysis• Agency performance benchmarking

Report Utility Report Utility

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Report Utility Report Utility 18 Month NRV File Comparison

Retailers' Sales Tax                 

  January 2005   June 2006   % Change

Age in Days Debt Assess   Debt Assess   Debt Assess

<30 66.4% 13.3%   73.8% 38.4%   11.2% 189.0%

<60 55.7% 11.1%   65.2% 31.1%   17.0% 179.3%

<90 47.9% 9.6%   57.4% 25.4%   19.8% 164.8%

<120 43.4% 8.7%   48.5% 18.7%   11.9% 115.7%

<240 40.6% 8.1%   40.7% 13.9%   0.4% 71.8%

<360 31.3% 6.3%   30.5% 8.5%   -2.7% 36.1%

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Report Utility Report Utility

January 2005 to Current NRV

0.0%10.0%20.0%30.0%40.0%50.0%60.0%70.0%80.0%

<30 <60 <90 <120 <240 <360

Age in Days

Cum

ulat

ive

Rec

iver

y

2005 Debt 2005 Assess Current Debt Current Assess

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Questions and Discussion

Contact Information:Jeff Scott -

(785)[email protected]

Andrew Kapp -(785)[email protected]

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ACM data snapshot

Business assessments byTAP

Income tax set up returnsby TAP

AR Data(Data SourcesCombined and

Coded)

Collection report datasnapshot

Recov ery Data(Data Sources

Combined)

NEW warrant andbankruptcy totals

NEW uncollectable andpriv ate collection totals

Legacy Tax Ty pe Entries

ATP NRVValuation

Legacy NRVValuation

Monthly historical totals

Monthly historical aging

Reporting month details

Reporting Month aging

DataConsolidation

AR at aglance-Page 5

ACM AgingAnaly sis -

Page 3

All Tax Ty peOv erv iew-

Page 4

Tax Ty peSummaries-Page 6-19

Recov ery byf iling y ear -

Page 2

NRV by f ilingy ear -Page 2

PC totals,NRV deduct,

BK total,UC total -

Page 2

Recov ereddebt andassess -Page 2

New debt,New assess-

Page 2

Beginning Inv entory $

Beginning ofmonth

inv entory -Page 2

Endinginv entory $-

Page 2

Reportpage or

page entry

Manualdata

captureSQL Data Monthly

dataset

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Size = 3,000Rows

EXTRACT (SQL)All New Debt

Ref errals Betweenmm/dd/y y y y

and mm/dd/y y y y

EXTRACT (SQL)All Credits and

Debits Applied toDebt Ref erred

Between mm/dd/y y y y and mm/dd/

yyyy

Collection Sy stem (ACM)

Codes Assigned to RowsBased on Original Debt

Amount and Debt Age atRow Transaction Date

Row Thresholds Limitedto Maximum of 100%

Recov ery . Debt Remov edf rom Cumulativ e Count

(Denominator) at 100%.

RegressionAnalysis

Starts

This PositionRepresentsCombinedRelational

and Coding(SPSS).

DataLoaded to

DataWarehouse

Codes Assigned to Rowsof Debts Without FinancialTransactions - Based onOriginal Debt and Age.

(Age based on inactiv e ,write of f , or current

date.)

Codes are Joined by TaxTy pe, Age, and AmountCodes. Group f unction

on Codes, Sum onCumulativ e Collection and

Count of Debts.

REPORTFormatted Report

Describing Tax Ty pe,Debt Age, and Debt

Amount

Simple Accounts Receivable Recovery Regression(3 Column)

Codes limitvariables byassigning a

"standard" tier toeach debt and

outcome scenario

Size = 3.5Million Rows

Relational Procedure -Cumulativ e Association of

Debts and FinancialEntries Ov er Time (1 row

represents cumulateactions @ each

transaction date.)

Size = 7.5Million Rows

Data Importedto Sub-Sy stem

Compare Perf ormed inautomated procedures

(SPSS) - No codingnecessary