GY458      Half Unit
Real Property Market Practice

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Additional requisites:

Background (UG level) in basic statistics is required. Basic or Intermediate courses in econometrics (at the UG level) will be advantageous.

The course aims to introduce students to the analysis of real estate markets from two separate but related perspectives: that of academic empirical research and that of practitioners and professionals. In doing so, the course introduces students to the types of methods, questions and language that are employed in both fields when investigating the operation of Real Estate Markets. It will also introduce students to the types of data available and provide substantive training in econometrics and data science methods for the analysis of this data. Finally, it provides a stepping stone in the development of skills for applied economics research.

In the Department of Geography and Environment, teaching will be delivered through a combination of classes/seminars, pre-recorded lectures, live online lectures, in-person lectures and other supplementary interactive live activities.

This course is delivered through a combination of seminars, workshops and lectures across Autumn Term

There is no course text. Specific readings (papers and textbook chapters) will be provided in class. Readings on econometric methods will be mostly based on the following texts:

1) Angrist, J. D., & Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton university press.
2) Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics. Princeton university press.
3) Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage learning.

Essay (100%, 3000 words)

The course is assessed with a 3,000 word research project identifying a research problem in real estate markets on a specific issue (e.g., ‘Does Mortgage Credit Supply Affect House Prices in Spain?’, ‘Are Council Tax Differentials Capitalised into Housing Prices?’, ‘Which Side of Beijing-Tianjin High-Speed Railway Benefits Most in terms of Housing Prices?’) which will require the student to investigate data sources and implement quantitative methods in order to provide a suitable answer. Students should discuss their intended topics with their academic advisors and the teachers responsible.

Research projects should include a submission of estimation codes, the data used in the final estimation and a log-file or some other source that allows markers to check that the statistical output corresponds to the results reported in the submitted report. Students who use restricted access data (data that cannot be shared with the LSE) need to submit a document including a description of the dataset, the source of the data provided and an active contact point with the institution that provided the data which enables markers to confirm the data was indeed provided under restricted access agreement. Estimation files exceeding 120Mb in size need to be submitted separately. Students using this type of large dataset in estimation should submit a form indicating how they can make the data available to markers after submission.