Document Type
Thesis
Degree Name
Master of Science (MSc)
Department
Mathematics
Program Name/Specialization
Mathematics for Science and Finance
Faculty/School
Faculty of Science
First Advisor
Dr. Sunny Wang
Advisor Role
Supervisor
Second Advisor
Dr. Yang Liu
Advisor Role
Supervisor
Abstract
This thesis introduces a novel approach to analyzing residential property sales through the lens of stochastic processes by employing point processes. Herein, property sales are treated as point patterns, using self-exciting point process models and a variety of statistical tools to uncover underlying patterns in the data. Key findings include the identification and explanation of clustering in both space and time, and the efficacy of a temporal Hawkes process with a sinusoidal background in predicting home sale occurrences. The temporal analysis starts by employing the state of art techniques for time series data like regression, autoregressive, and autoregressive integrated moving average (ARIMA) models, extending into more sophisticated point process models with self-excitation features. Spatial analysis delves into clustering and dispersion patterns within specific geographic boundaries, utilizing homogeneous and inhomogeneous processes with covariate analysis to describe these patterns. Further, the spatiotemporal exploration sets a precedent for future comprehensive models in this domain. This exploratory research establishes a foundation for further investigation into the dynamic field of real estate analytics under the framework of point processes.
Recommended Citation
Fraser, Ian, "Self-Exciting Point Processes in Real Estate" (2024). Theses and Dissertations (Comprehensive). 2630.
https://scholars.wlu.ca/etd/2630
Convocation Year
2024
Convocation Season
Spring
Included in
Applied Statistics Commons, Data Science Commons, Dynamical Systems Commons, Longitudinal Data Analysis and Time Series Commons, Real Estate Commons, Statistical Models Commons