The hidden economics of academic housing: Trading on trust?

Are you interested in the intersection of economics, digital trust, and platform behaviour? This project explores how pricing decisions are made using SabbaticalHomes.com, a platform where academics rent, swap, or share homes with others in the scholarly community, as an empirical setting. Designed as a trusted space for real estate transactions built on shared values, the platform suggests an environment of fairness. But does that trust influence pricing? Rather than go for “market pricing” or even lower than market rates, could hosts strategically set rents higher than the fair market rate, knowing that users don’t know the local rent market, and assume fairness within the community?

This research will investigate how several attributes shape pricing. First, whether people listing homes from lower-cost locations inflate rents when advertising, potentially aware of the arbitrage available when renting to academics from wealthier cities and institutions. Second, it will examine whether pricing differs between scholars and non-academics who are using the platform to target an academic audience (the platform distinguishes user types according to this dichotomy). Third, are certain disciplines—such as business professors—more entrepreneurial in setting (higher) prices? Finally, do academics in high-demand locations charge premiums, and how do they justify their pricing in listing descriptions?

The project involves scraping and analysing data from live listings, extracting details about pricing and host identity. Whether your background is in finance, computer science, real estate, or data analysis, your data analysis and hand coding skills can be applied here. You’ll gain hands-on experience in data analysis, web scraping, and behavioural research while working with an interdisciplinary team at King’s College London. If you’re curious about how people price trust, we’d love to hear from you!

You will be responsible for data collection, processing, and initial analysis. This includes:

• Scraping listings from SabbaticalHomes.com and extracting relevant information such as price, location, and host details.
• Potentially cross-referencing host identities with academic databases (e.g., Google Scholar, ORCID) to classify academic discipline (according to the UNESCO Education Field Codes)
• Review the literature regarding housing pricing, sourcing additional data (e.g., from AirBnB) with the ultimate goal of getting an estimate of what the market rates for rent should be in a city
• Conducting statistical analysis to determine key predictors of pricing, including location, discipline, and host type.
• The goal is to work towards a tier 1 publication and media pieces.

i) Methodological training: Many students are proficient in either econometric analyses or the technical aspects, classically trained more in informatics, such as web scraping and API work. Our aim is to provide students with training in the area they are less familiar with, thereby equipping them with a unique skill set that will give them a competitive edge.

ii) Interdisciplinary exposure: The project requires a combination of computational and social science skills, and it is inherently interdisciplinary. We bring together expertise from two departments and believe that this exposure will encourage the student to step outside of their disciplinary silo, also providing them with insights into the business school work.

iii) Outcome-driven: Beyond skill building, we see the student’s involvement culminating in a Tier 1 publication and, potentially, media coverage. We have a record of securing attention in both academic and popular press, and we believe this particular topic is very likely to be of interest to general media.

The ideal candidate should have experience or a strong interest in at least one of the following (Experience can be coursework or research based):

• Data analysis (e.g., Python, R or Stata)
• Web scraping (using tools like BeautifulSoup)
• Statistical or econometric analysis (familiarity with multivariate regression models, hypothesis testing)

The goal is to work towards a tier 1 publication and media pieces.

1. Please send your CV and a brief cover letter outlining your interest and suitability to the project supervisor(s). Please contact the project supervisor(s) in advance of submitting the application with any questions.

2. If selected by the project supervisor, the student must then complete the Placement /Internship Application form. This ensures that there is approval of PhD supervisor, and the necessary information is obtained to extend funding (for DTP1 students) or confirm placement requirement fulfilled (for DTP2 students), and to fulfil ESRC reporting obligations.  

Please note:

• Research Assistant Internships must not be undertaken with the student’s current supervisor and/or home department.
• DTP1 students (those whose funding commenced before Oct24): a maximum of 4 Research Assistant internships will be funded. These will be filled on a first-come, first-served basis. Once the 4 DTP1 places are filled, we will inform PIs that only DTP2 students are eligible for the Research Assistant internships. PLEASE NOTE THAT ALL DTP1 PLACES HAVE NOW BEEN FILLED.
• DTP2 students (those whose funding commenced from Oct24): are required to complete a 3-month placement, which is funded through their studentship. No limits to number that can be funded.
• Reports: at the conclusion of the internship, the student will be required to complete an internship report, which will include a question for the internship host to feedback on the internship.

Contact liss-dtp@kcl.ac.uk with any questions.