Which home loan in India offers the best interest rate for first-time home buyers?

Compare home loans from SBI, HDFC, ICICI, LIC Housing Finance, and others based on interest rates (fixed vs. floating), processing fees, prepayment charges, and government schemes for first-time buyers.

# Aplly.xyz Case Study Submission

## Title
Which home loan in India offers the best interest rate for first-time home buyers?

## Type
Multi-Criteria Product Comparison

## Difficulty
Intermediate

## Estimated Time
60 minutes

## Overview
Compare home loans from SBI, HDFC, ICICI, LIC Housing Finance, and others based on interest rates (fixed vs. floating), processing fees, prepayment charges, and government schemes for first-time buyers.

## Case Details

Function Focus: Multi-Criteria Product Comparison — manual reasoning, decomposition, and critical judgment (no spreadsheet or AI tool permitted in Phase 1)

Scenario:
You are the analyst at a fictional consultancy ("Praxis Advisors") tasked with answering: Which home loan in India offers the best interest rate for first-time home buyers?. You have a clean, synthetic dataset described below. You must produce a defensible answer using structured reasoning — no tool-assisted shortcut on the first pass.

Dataset Structure:
- project
- builder
- location
- price_per_sqft_rs
- carpet_area_sqft
- bedrooms
- possession_status
- rera_registered
- amenities_score
- price_cr
- near_metro_km

Tasks:
1. Define the evaluation criteria for the choice and assign weights to each, justified explicitly.
2. Score each alternative by hand against the criteria using the dataset columns.
3. Compute a weighted total and rank the alternatives; show the arithmetic.
4. Run a sensitivity check: change the top two weights by +/10 points and note whether the winner changes.
5. After the manual scoring, redo it in a spreadsheet and reconcile any ranking differences.

Expected Output:
A one-page recommendation memo: criteria & weights, scorecard table, final ranking, sensitivity result.

Evaluation Criteria:
Explicit and justified weights, arithmetic correctness, meaningful sensitivity analysis, defensible final ranking.

## Data Sources

| project | builder | location | price_per_sqft_rs | carpet_area_sqft | bedrooms | possession_status | rera_registered | amenities_score | price_cr | near_metro_km |
|---|---|---|---|---|---|---|---|---|---|---|
| Palm Grove | Godrej Properties | Mumbai | 28500 | 850 | 3 | Ready | True | 8 | 2.42 | 0.5 |
| Lavender Fields | Lodha | Mumbai | 32000 | 650 | 2 | Ready | True | 9 | 2.08 | 1.2 |
| Sanskriti | DLF | Gurgaon | 12500 | 1200 | 3 | Ready | True | 7 | 1.5 | 2.5 |
| The Iconic | Prestige Group | Bangalore | 11500 | 950 | 3 | Ready | True | 8 | 1.09 | 1 |
| Emerald Isle | Brigade Group | Chennai | 8500 | 1050 | 3 | Ready | True | 7 | 0.89 | 3 |
| Riverdale | Tata Housing | Pune | 9800 | 780 | 2 | Ready | True | 8 | 0.76 | 2 |
| Green Valley | Mantri | Hyderabad | 7500 | 1350 | 3 | Ready | True | 6 | 1.01 | 4 |
| Puri Tower | Puri Constructions | Kolkata | 5500 | 1000 | 3 | Ready | True | 5 | 0.55 | 2 |
| Neopolis | Hiranandani | Chennai | 7800 | 1250 | 3 | Ready | True | 8 | 0.98 | 5 |
| Ashiana Nava | Ashiana Housing | Jaipur | 4800 | 1100 | 3 | Ready | True | 6 | 0.53 | 6 |


Full dataset: https://github.com/arora200/aplly_case_db/datasets/057-real-estate-housing (synthetic, 10 records)

## Solution Frameworks
Multi-criteria weighted scoring, pairwise trade-off analysis, cost-benefit decomposition, sensitivity/robustness check

## Solver Guidance & Tutorials
_Solver guidance added by the pipeline (tutorial links) — see `solver_guidance` field._

## What You'll Learn
- Turn vague preferences into weighted criteria
- Build a transparent scorecard
- Test how fragile your winner is

## Tags
Home Loan, Interest Rate, First-time Buyer, India

## Registration Links
Register as Solver / Register as Evaluator