Fixed-Asset Register Discipline and Physical Verification Practices in Bengaluru Private Schools and Nursing Homes

Schools and nursing homes are asset-heavy businesses. A mid-size school owns buses, laboratory equipment, computers, furniture and a building; a nursing home owns beds, ventilators, X-ray and ultrasound machines, autoclaves and generators. Every rupee of that appears in the accounts under one or two asset headings — and, unlike stock, fixed assets sit on the balance sheet for years without anyone checking whether the register still matches the room.

This survey project studies fixed-asset register discipline and physical-verification practices in 60 private schools and nursing homes in Bengaluru. Using a structured questionnaire answered by the person responsible for accounts, and a checklist modelled on AS 10 (Property, Plant and Equipment) and clause (i) of CARO 2020, the study records whether institutions maintain quantitative particulars of assets, whether they run a regular programme of physical verification, how depreciation is computed, how disposals are recorded, and which software (Tally Prime, Excel or manual ledgers) carries the asset data.

The analysis uses percentage analysis, cross-tabulations, chi-square tests and an independent-samples t-test, done in MS Excel with SPSS/Jamovi as confirmation. Two illustrative case profiles — a trust-run school and a proprietor-run nursing home — are presented as modelled composites built from the questionnaire patterns; no real institution is named. The project is written for the BCU B.Com SEP 2024 course 5.6 Survey Project (Semester 5, 4 credits; 100 marks = report 60 + viva-voce 20 + log book 20).

Fixed assets are the largest single block on the balance sheet of most schools and nursing homes, yet their supporting records are the least examined part of the accounting system. When no register carries quantity, location and condition details, depreciation is computed on balances nobody has verified, insurance covers a guessed list, disposals are made without journal entries, and purchases made from donation funds are sometimes never capitalised at all. Auditors of companies must report on exactly this under clause (i) of CARO 2020, but most private schools and nursing homes are trusts, societies or proprietorships where no such reporting exists.

There is no recent local evidence on how well these institutions in Bengaluru maintain fixed-asset registers, how often they verify assets physically, how depreciation and disposals are recorded, and which management type does it better. Without such evidence, administrators cannot benchmark themselves and accountants cannot design practical register formats. This study fills that gap for 60 institutions and tests whether register discipline is associated with management type and with institution size.

Abstract

Private schools and nursing homes invest heavily in fixed assets, but asset records are widely believed to be weaker than their books of account: a balance-sheet figure exists, while quantity, location and condition details do not. This study surveys 60 private schools and nursing homes in Bengaluru to measure how well fixed-asset registers are maintained, whether physical verification is performed at reasonable intervals, how depreciation and disposals are handled, and what software supports the process. Data are analysed with percentages, cross-tables, chi-square tests and a t-test. The study closes with practical suggestions for administrators, accountants and auditors.

Introduction

Fixed assets serve an institution for years, so errors in their records accumulate silently: equipment scrapped long ago stays on the books, donated equipment never enters them, and a physical count surprises everyone. Accounting Standard AS 10 — Property, Plant and Equipment requires proper recognition, measurement, depreciation and disclosure of PPE; for companies, clause (i) of CARO 2020 obliges the auditor to report whether proper records showing quantitative details and situation of PPE exist and whether management verifies assets physically at reasonable intervals. Trust-run schools and proprietor-run nursing homes generally fall outside CARO, which makes their voluntary discipline worth measuring.

Review of literature (summary)

Indian audit literature consistently reports fixed-asset weaknesses — missing quantity particulars, no linkage between register and ledger, verification done only for inspections — particularly in trust and proprietor entities. Studies of asset-management practice in education and healthcare exist, but Bengaluru-specific, institution-level evidence is scarce. Gap: no recent local measurement of register discipline and verification frequency across these two institution types.

Existing vs proposed understanding

• Existing: anecdotes — "the ambulance on the books was sold two years ago"; "the microscopes were counted once, for an inspection."
• This study: measured evidence on register content, verification frequency, depreciation method, disposal recording and software use, tested for association with management type and institution size.

Feasibility

• Time: 12 weeks inside Semester 5; field work of three weeks; institutions are approachable through administrative offices.
• Cost: printing and travel only (about ₹1,200).
• Ethics: participation voluntary and anonymous; no fee, donation or enrolment data collected; no tax identification numbers recorded — only operational facts about asset records.
• Skill fit: questionnaire design, chi-square and t-test, and Tally Prime asset ledgers are all within the B.Com syllabus.

In scope

• 60 private unaided schools and nursing homes located in Bengaluru (roughly 30 of each), reached through administrative offices.
• Respondent: the accountant, administrative officer or office manager responsible for asset records.
• Topics: existence and content of asset registers, quantity/location particulars, physical verification frequency and discrepancy treatment, depreciation method, acquisition and disposal recording, software used, reports generated.
• Two modelled composite case profiles (one trust-run school, one proprietor-run nursing home) used to illustrate good and weak practice — stated as fictional.

Out of scope

• Government and aided institutions, and standalone diagnostic labs or pharmacies.
• Valuation or re-valuation of assets.
• Verification of actual amounts beyond what the respondent shows; findings are self-reported.
• Intangible assets (software licences) and capital work-in-progress accounting.

Research design

Descriptive survey with a documentary-checklist element. Primary data through a structured questionnaire plus a tick-box checklist completed by inspecting (with permission) the register format the institution uses; secondary data from ICAI standards, audit literature and textbooks.

Sample

60 institutions — 30 private schools and 30 nursing homes — drawn from different zones of Bengaluru using convenience sampling with quotas by zone (North, South, East, West) and management type (trust/society vs proprietor), since no complete list exists. The sample is adequate for chi-square tests with 2×2 and 2×3 tables; findings are described as indicative, not generalisable.

Instrument

Parts B and C are scored into a Register Discipline Index (0–8) and a Verification Score (0–3). The questionnaire was pre-tested on 6 institutions; Cronbach's alpha for Part E was checked (≥ 0.70 accepted).

Hypotheses (α = 0.05)

• H1₀: register discipline is not associated with management type (trust/society vs proprietor) — chi-square on Register Discipline Index bands (0–4 vs 5–8).
• H2₀: physical verification frequency is not associated with institution size — chi-square.
• H3₀: there is no difference in mean Register Discipline Index between institutions using Tally Prime and those using manual registers/Excel — independent-samples t-test.
• H4₀: there is no association between depreciation method and asset-class mix — chi-square.

Tools of analysis

Percentages, bar charts, weighted-mean ranking of Likert items, chi-square (CHISQ.TEST in Excel; cross-tabs in SPSS/Jamovi), t-test (T.TEST) and correlation between years of software use and Register Discipline Index.

Timeline (12 weeks)

Weeks 1–2 topic approval and literature · Week 3 instrument design and pilot · Weeks 4–6 field survey and checklist completion · Weeks 7–8 coding and testing · Weeks 9–10 report writing · Week 11 guide review · Week 12 submission and viva preparation.

The study proceeds as a pipeline: every stage produces a named artefact that appears in the report or the log book.

flowchart TD
  A["Topic approval and college letter"] --> B["Literature: AS 10, CARO 2020 clause (i), audit texts"]
  B --> C["Instrument: Parts A-E plus register checklist"]
  C --> D["Pilot on 6 institutions; reliability check"]
  D --> E["Field survey: 30 schools + 30 nursing homes"]
  E --> F["Code responses into Excel master sheet"]
  F --> G["Register Discipline Index (0-8) per institution"]
  G --> H["Percentage analysis and charts"]
  G --> I["Chi-square: H1, H2, H4"]
  G --> J["t-test and correlation: H3"]
  H --> K["Modelled composite case profiles"]
  I --> L["Findings, register format, verification calendar"]
  J --> L
  K --> L
  L --> M["Report, log book, viva"]

Variables

• Grouping variables: management type (trust/society vs proprietor), institution type (school vs nursing home), size band, software used.
• Outcome variables: Register Discipline Index (0–8), Verification Score (0–3), depreciation method, disposal-recording practice, perceived-benefit score.

Data flow in Excel

One master sheet holds one row per institution and one column per checklist item, scored so the Index computes automatically with SUM. Analysis sheets use COUNTIF and pivot tables for frequencies and cross-tabulations, CHISQ.TEST against a computed expected-frequency block for H1, H2 and H4, and T.TEST for H3. Because raw data and analysis are separate, correcting one response refreshes every table.

• Extend the survey to government and aided institutions, where purchase and verification rules differ.
• Track institutions over two years to see whether discipline improves after audit observations or inspections.
• Study computerised asset management: barcode/QR tagging and asset-management add-ons for Tally Prime.
• Examine insurance adequacy against the verified asset list — a natural sequel study.
• Develop and field-test the suggested register format as a printable template and measure adoption.