Illustrative engagement. A representative scenario showing how we approach this kind of work in real estate. It does not describe a specific client; outcomes are described qualitatively.
Industry overview
What is shaping real estate
Homebuyers make one of their largest decisions with a mix of online research, site visits and trusted advice. Developers and brokers need quality leads and strong follow-up.
- Buyers shortlist online before visiting sites
- Lead quality and speed of follow-up decide conversion
- Residents' satisfaction drives referrals
Client background
A housing finance company, working in Tier-2 India. This is an illustrative engagement: a representative scenario showing how we approach this kind of work, not a named client project.
- Organisation
- A housing finance company
- Sector
- Real estate
- Team engaged
- IT & Development
- Services
- Offline data collection (CAPI), Quantitative research
Client challenge
The company wanted to understand demand for affordable home loans.
Research objectives
- Deliver on the brief: demand for affordable housing.
- Design around the real needs of households.
- Define requirements and success criteria the solution would be tested against.
- Deliver a reliable, secure solution the team can run and extend.
Target audience
The work focused on 3,000 households.
- households3,000
Audiences we typically reach in real estate
Homebuyers and investorsTenantsBrokers and channel partnersResidentsDevelopers' sales teams
Geographic coverage
Coverage: Tier-2 India. Market: India.
- India
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Household survey
- 3Insight
We surveyed renting households about aspirations, income and barriers.
Offline data collection (CAPI)Quantitative research
Sampling strategy
- households
- 3,000
- Users and stakeholders from every affected team took part in discovery workshops and acceptance testing.
Data collection
- Requirements were gathered through workshops, process walk-throughs and a review of existing systems and data.
Data quality & validation
- Peer code review on every change
- Automated and manual testing
- User-acceptance testing with the client
- Release checklist and rollback plan
Data analysis
- Usage and performance monitoring after launch
- Defect and support-ticket trends
- Process time before and after
Key findings
- Many lacked income documents.
Design products for real incomes.
Recommendations
- Build on the core insight: design products for real incomes.
- Focus effort where the evidence was strongest: many lacked income documents.
- Re-measure after the changes to confirm the effect.
Business impact
- The company developed products for informal earners.
What changed in the business as a result of the work. Impact is described qualitatively.
Deliverables & next steps
- Insight report and management presentation
- Cleaned data file and data tables
- Deployed solution with documentation and handover
- Support and maintenance plan
- Repeat the measure after changes are in place to track progress.
- A natural follow-on question: what do homebuyers want in this location?