A Teaching License Case Study-
Understanding Rent-Seeking Preferences in Bhubaneswar Using Discrete Choice Modelling.
Author: Ankit Nayak, BiswaJeet Rath, Srikumar Tripathi, Students
Supervised by: Dr. Piyusa Das, Associate Professor, KIIT School of Management, KIIT
Course: Introduction to Marketing Analytics (MBA, 2023-25)
Number of students: 50
Institution: KIIT School of Management, Bhubaneswar
Technical Course Sponsor: SurveyEngine GmbH
Dr. Piyusa Das from KIIT School of Management used the SurveyEngine software to teach choice modelling methods and design choice experiments as part of the Introduction to Marketing Analytics course. The students learned how to define attributes and levels, generate experimental designs, and analyze consumer preferences using a choice-based conjoint experiment. Many thanks to Dr. Piyusa Das for using SurveyEngine and selecting the best case study from his class, summarized below.
Research Context
With Bhubaneswar (Odisha, India) experiencing rapid urbanization due to economic growth and an increasing presence of IT companies, the demand for rental housing among working professionals is rising. This study aimed to identify key attributes influencing rental decisions, helping property owners and developers better understand tenant preferences.
Choice Experiment Setup: SurveyEngine Software
The Business Problem
Understanding the most important factors influencing rent-seekers’ decisions is crucial for optimizing rental offerings. The study sought to determine which features (e.g., house type, rent, parking availability) are most preferred by potential tenants, thereby enabling property owners to tailor their offerings to maximize occupancy and profitability.
The attributes and levels considered in the experiment were:
- House Type: Fully furnished, Semi-furnished, Unfurnished
- Rooms: 1 BHK, 2 BHK
- Parking: Yes, No
- Floor Level: Ground Floor, First Floor
- Distance from Railway Station: 4 km, 8 km
- Rent: ₹6,000, ₹14,000, ₹22,000
Using SurveyEngine, a total of 21 hypothetical rental profiles were generated, and a choice experiment was conducted among respondents to assess their preferences
Summary of findings
The results revealed that house type was the most influential factor in rental decisions, followed by rent price and floor level. The rankings of attribute importance were:
- House Type
- Rent Price
- Floor Level
- Distance from Railway Station
- Number of Rooms
- Parking Availability
Key insights from the experiment include:
- Furnished apartments are highly preferred, with 80.3% of respondents favoring fully furnished units over unfurnished ones.
- Rent plays a significant role, with 67.8% of respondents opting for lower rental prices (₹6,000) over higher ones (₹22,000).
- Ground floor preference is notable, as 64.7% of respondents chose a ground-floor apartment over a first-floor one.
- Proximity to the railway station matters, with 63.6% preferring residences closer (4 km) rather than farther (8 km).
- The number of rooms and parking availability had relatively low impact on decisions.
Conclusion
This study highlights the strong preference for affordability, convenience, and comfort among rent-seekers in Bhubaneswar. Property owners can optimize rental offerings by focusing on furnishing quality and competitive pricing while considering the demand for accessibility and lower-floor apartments. The findings serve as a valuable guide for real estate developers and landlords aiming to improve rental occupancy and tenant satisfaction.
