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An Analysis on Commercial Real Estate Property Assessment and Property Tax in BC

STAT 450/550 Techniques of Statistical Consulting

Researcher: Harry Xu (Altus Group)

Graduate consultants Undergraduate consultants Instructors
Gian Carlo Diluvi Peter Han Gabriela Cohen Freue
Mallory Flynn Yuetong Liu Lang Wu
Sophia Li Leonie Lu Estella Qi
Vittorio Romaniello Yuting Wen Miguel Birón

Executive Summary

Every year, all property owners in BC have to pay property taxes—the single greatest operating expense. The property tax is determined based on property assessment and property tax rate (mill rates). This project will be a value-add to the business in projecting commercial real estate property taxes, and providing tax and assessment insights for different neighborhoods/and or property types. This project will quantify the relationship between property tax and assessment value over the past few years of data, and construct a predictive model to estimate property taxes for a given property for the upcoming year.

Project objectives

  • To predict the property tax for a given property for the upcoming year. This may lead to an automated Model/Report to show insights on assessment values and trend for a given area and/or property type.
  • Hypotheses/Question: What is the relationship between property tax and assessment value in the past few years? For example, if the assessment has increased/decreased (for an area/municipality), will the property tax increase/decrease as well? If so, by how much?

Directory roadmap

(We may want to edit this once we have better-defined folders. Below an example.) Each directory includes its own README file. In general:

  • data is for locally saving data and other miscelaneous files. This folder will be empty in the Github repo.
  • doc includes the written portions of the project: main, sections, appendices, and project outline tex/pdfs/aux files.
  • misc contains miscelaneous files.
  • ref has the bib file for generating the project's references.
  • src includes the R code developed for the project.

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Real Estate consulting project

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  • R 90.4%
  • Python 6.9%
  • TeX 2.7%