I teach across three programmes in the Department of Land Economy and one methods module in the wider University. The modules below run from a core master’s course in real estate finance to a new option on infrastructure investment, alongside research methods training for practitioners and for doctoral students. Where a topic is better handled than watched, I build the teaching material as something students can operate themselves.
RE02 Real Estate Finance and Investment
RE02 is the core module of the MPhil in Real Estate Finance and runs across Michaelmas and Lent. It sets out the principles of investment and finance in commercial property, with an emphasis on direct investment, drawing on financial, urban, network and institutional economics as well as on valuation theory. The Michaelmas half concerns the structure of real estate markets and the formation of prices in the occupier and investment sectors, moving from macroeconomic analysis through the interaction of space and capital markets to performance metrics, property indices, sector specifics and regional growth. Lent turns to investment in the transition to a net zero built environment, to climate risk, to valuation methods and to the structures and dynamics of private equity real estate. Assessment is by coursework and a 48-hour examination, supported by four supervisions built around modelling exercises in Excel.
In Michaelmas 2026 I teach two of the eight lectures. Space and capital markets, pricing processes builds the DiPasquale and Wheaton four-quadrant model, which ties the market for space to the market for the assets that provide it. Because the model is easier to understand by operating it than by watching it drawn, I have built an interactive version calibrated to real British market data, in which choosing a local authority and a base year fixes the observed rent, price and yield, the student supplies the behavioural parameters that no dataset contains, and the resulting shock can be compared against what rents in that market actually did. Urban and regional growth and real estate pricing covers bid rent theory, regional equilibrium, land rent and land leverage, and the re-pricing of urban space after the pandemic, closing on current work in the Department that measures housing pressure before it reaches prices.
RE07 Global Infrastructure Investment and Management
RE07 is a new optional module on the MPhil in Land Economy, approved and running for the first time in Lent 2027. It treats infrastructure as an asset class and examines how governments, investors, financiers and developers coordinate to deliver projects across different institutional settings. Seven taught weeks pair an hour of theory with an applied hour built around practice, covering institutional frameworks and stakeholder governance, the mobilisation of private capital, financing and deal structuring, intermediation and brokerage, performance and valuation, and sustainability. The module is designed to be read by all three of the Department’s taught cohorts, in real estate finance, in planning and regeneration, and in environmental policy, so each week states in full the ideas it borrows before naming the module in which they are developed.
I designed this module and coordinate it. I wrote the proposal, the syllabus and the reading list, built the lecture material for the seven taught weeks, and designed the applied hour around invited practitioners. The weeks run from introduction to infrastructure, through institutional frameworks and stakeholder governance, private investment in infrastructure, infrastructure financing and deal structuring, intermediation and deal brokerage and performance and valuation, to sustainability, ESG and emerging trends. I teach the theory hours and run the group project that carries through the term to the presentations in the final week.
REM2 Research Methods
REM2 is one of seven modules on the MSt in Real Estate, a two-year part-time master’s for people already working in the industry. The programme is delivered through distance learning and five intensive residential sessions in Cambridge, and REM2 runs during the first of them. It covers the research process and research design, the reading and critiquing of research literature, the interpretation of real estate data and the limitations of it, the statistical and econometric tools used for analysis, modelling and simulation, and the writing of essays and of the dissertation. The module is taught as eight sessions by a team drawn from the Department, and the work students produce for it feeds the 12,000-word dissertation that carries almost half of the degree.
I teach five of the eight sessions, which together form the quantitative core of the module: critical reading, the problems that arise with real estate data, hedonic price modelling part I and part II, and time series analysis. Each is paired with an exercise worked in Excel or Stata on market data of the kind the students handle in their own work, so that the methods are used rather than only described. The remaining sessions, on the research process, on qualitative methods and on research design and implementation, are taught by colleagues. I also supervise dissertations arising from the programme.
Time Series Analysis, Cambridge Research Methods
Time Series Analysis is one of the advanced statistics modules in Cambridge Research Methods, the training programme run by the Social Sciences Research Methods Centre and open by booking to graduate students from across the social sciences. It introduces the time series techniques used in forecasting, together with their implementation in software, and assumes a working knowledge of statistics up to linear regression. The module runs as a single intensive day in Lent term, lectures in the morning and practice sessions in the afternoon, and moves from regression on stationary series through non-stationarity, unit root tests and cointegration to vector error correction and vector autoregressive models, impulse responses and variance decomposition, and the ARCH and GARCH family of models for time-varying volatility. Applied work is emphasised throughout, because the module is written for social scientists who need to use these methods rather than to derive them. Assessment is by one written exercise.
I wrote this module and I teach it. It runs as four topics, each with lecture notes, Stata code and worked outputs, and a set of exercises that students run themselves during the afternoon: regression with stationary time series, regression with non-stationary time series, vector error correction and vector autoregressive models, and time-varying volatility and ARCH models. The slides for all four are published as a single viewer that can be searched across its full text and that carries the Stata code and the datasets alongside, which is more use to somebody returning to a method months later than a set of files would be. I mark the written exercise that follows.