Moving to NCR for a Job: A First-90-Days Cash Worksheet
In this article (5 sections)
Related programme: the Data Analytics with Generative AI course (3–4 months; ₹90,000 (inclusive of GST) · EMI from ₹4,000/month).
The riskiest point in a move is rarely the day you pay the deposit. It is usually the week before your second salary: the first pay cheque covers only part of a month, while the second month's rent arrives in full. In the hypothetical move below, ₹1,20,000 of savings goes negative by ₹4,071 on 30 November, a month after the deposit has been paid. The fix is to list every outflow and inflow with a date, then find the lowest running balance before you accept a joining date.
The move
All figures are hypothetical placeholders, not estimates for any part of NCR.
- Joining date: Thursday 15 October 2026.
- Monthly gross: ₹60,000, of which basic is ₹30,000.
- Salary assumed to be credited on the 1st or 2nd of the following month. Check your employer's pay date; it moves every figure below.
- Savings on the day you travel: ₹1,20,000.
Monthly in-hand
Employee PF is 12% of EPF wages up to the ₹25,000 ceiling in EPFO's wage ceiling FAQ, so ₹3,000 a month on a ₹30,000 basic. Salary for tax year 2026-27 is ₹32,903 for October plus five months at ₹60,000, or ₹3,32,903. That is well within the new-regime rebate on the department's slab page, so no TDS applies. A full month's in-hand is ₹57,000, before any professional tax your state levies.
October is prorated. From 15 to 31 October is 17 days, so gross is ₹60,000 × 17 ÷ 31 = ₹32,903. PF on prorated basic of ₹16,452 is ₹1,974, and in-hand is ₹30,929. First-month salary proration shows how a 30-day or working-day basis would change that figure.
The dated worksheet
Living costs are booked at each month-end for simplicity. If you spend them day by day, balances fall sooner, not later.
| Date | Item | Amount | Balance |
|---|---|---|---|
| 8 Oct | Train and luggage | −₹6,000 | ₹1,14,000 |
| 10 Oct | Guest house, 5 nights | −₹10,000 | ₹1,04,000 |
| 14 Oct | Security deposit, two months | −₹36,000 | ₹68,000 |
| 14 Oct | First month's rent | −₹18,000 | ₹50,000 |
| 14 Oct | Brokerage, one month | −₹18,000 | ₹32,000 |
| 18 Oct | Bed, kitchen, basics | −₹25,000 | ₹7,000 |
| 31 Oct | Living costs, part month | −₹9,000 | −₹2,000 |
| 2 Nov | October salary | +₹30,929 | ₹28,929 |
| 14 Nov | November rent | −₹18,000 | ₹10,929 |
| 30 Nov | Living costs | −₹15,000 | −₹4,071 |
| 1 Dec | November salary | +₹57,000 | ₹52,929 |
| 14 Dec | December rent | −₹18,000 | ₹34,929 |
| 31 Dec | Living costs | −₹15,000 | ₹19,929 |
| 1 Jan | December salary | +₹57,000 | ₹76,929 |
The balance dips below zero twice. The first dip, −₹2,000 on 31 October, is the one people plan for. The second, −₹4,071 on 30 November, is deeper and comes after the first salary has arrived. On these figures you need at least ₹1,24,071 of savings, plus a buffer for anything that goes wrong.
from datetime import date
# Hypothetical relocation. Replace every figure with your own quotes.
def cash_low_point(opening, events):
balance, low, low_day = opening, opening, None
for day, label, amount in sorted(events):
balance += amount
if balance < low:
low, low_day = balance, day
return low, low_day, balance
first_pay = round(60000 * 17 / 31) - round(0.12 * min(30000 * 17 / 31, 25000))
moves = [
(date(2026, 10, 8), "travel", -6000),
(date(2026, 10, 10), "guest house", -10000),
(date(2026, 10, 14), "deposit", -36000),
(date(2026, 10, 14), "rent Oct", -18000),
(date(2026, 10, 14), "brokerage", -18000),
(date(2026, 10, 18), "furniture", -25000),
(date(2026, 10, 31), "living Oct", -9000),
(date(2026, 11, 2), "salary Oct", first_pay),
(date(2026, 11, 14), "rent Nov", -18000),
(date(2026, 11, 30), "living Nov", -15000),
(date(2026, 12, 1), "salary Nov", 57000),
(date(2026, 12, 14), "rent Dec", -18000),
(date(2026, 12, 31), "living Dec", -15000),
(date(2027, 1, 1), "salary Dec", 57000),
]
low, when, end = cash_low_point(120000, moves)
print("first salary:", first_pay, "low point:", low, "on", when, "after 90 days:", end)
print("savings needed for a zero low point:", 120000 - min(low, 0))Levers that move the low point
| Change | Low point | When |
|---|---|---|
| None | −₹4,071 | 30 Nov |
| No brokerage | ₹13,929 | 30 Nov |
| Furniture bought on 3 Nov, after the first salary | −₹4,071 | 30 Nov |
| Savings of ₹1,35,000 instead of ₹1,20,000 | ₹10,929 | 30 Nov |
The furniture row is a warning: moving a cost later does not remove it. Here it simply moves the October dip into November, and the November low point is unchanged. Only a cost that goes away, like the brokerage, or more savings at the start, raises the low point.
The joining date is a stronger lever, but it moves everything. A 1 October start would give a full first salary, but the deposit and rent would fall in September. Rerun the worksheet with those dates rather than assuming it helps.
Before you sign
- Ask HR the exact pay date and whether a mid-month joiner is paid in the first cycle or the next.
- Ask whether any relocation support is an advance, a reimbursement against bills, or a payment through salary. The timing matters more for this worksheet than the label. Relocation allowance negotiation covers how to ask.
- Get the deposit, brokerage and notice terms of the flat in writing before paying, including when and how the deposit is returned.
A dated running balance is one of the first things a working analyst builds, whether for a household or a finance team. Our Data Analyst programme runs 3–4 months, live online with optional in-person labs at our Noida centre.
Frequently asked questions
Are the rent and deposit figures here typical for Noida or Gurugram?
No. They are placeholders chosen to make the arithmetic easy to follow. Replace them with quotes for the specific flats you are considering.
My employer offers a relocation payment. How is it taxed?
We have not verified the treatment for this article, so it is not in the worksheet. Ask payroll whether it is paid as a reimbursement against bills or as part of salary, and check how it appears on your payslip.
Pankit Kumar has 10 years in Data Science & AI, building and shipping production systems in regulated pharma and clinical environments. He is a freelance trainer at Boston Institute of Analytics, AnalytixLabs and Scaler, and has taught this material to thousands of working professionals.
This article is part of our Data Analytics with Generative AI programme — 3–4 months. The full analyst stack — Excel, SQL, Power BI and Python pipelines — then a generative-AI layer you can prove is right.
Explore Data Analytics with Generative AI