BC Financial Services Authority

Housing price estimates/forecasts for BC real estate markets

Industry Mentor: David Dong, Senior Analyst, BC Financial Services Authority


The opinions as stated in this outline do not represent the opinions of the organisations that I work for and/or with. In addition, all data that is used in this project is open-sourced; no privileged data is used in the project.

Expectation of outcome

It goes without saying that the Real Estate sector represents one of the most important economic activities in the province of British Columbia. In recent years, it has also attracted media spotlight and public attentions as housing prices spike as well as the development of topical issues such as Public Housing and Strata Insurance. All these have generated a high demand for better understanding of the Industry’s operational mechanism and driving factors. This project can be considered as a further step towards more evidence-based decision making, for the benefits of government regulators, industry practitioners, and concerned citizens.

Housing price estimate/forecast is not a new thing. Zillow’s Zestimate1 and Kaggle2 competitions on Housing Prices are two of the better-known venues of such effort to address public concerns on Housing issues by scientists/engineers’ communities. All these estimates have commonalities for practical reasons. For the end users of an estimate, relevant information would include

  1. Explanatory variables such as property-specific features (# of bedrooms and bathrooms for example) and other driving factors (e.g., demographic information, average household income in the area or mortgage rates)

  2. An interpretable model. Earlier models were mostly similar to regression of some kind for this reason. Recent black-box machine learning models can have interpretability issue.

  3. Some form of estimate or forecast of property prices either in the future or in areas with comparable driving factors.

It is to be noted that some of previous efforts have used Root Mean Square Error (RMSE) as measurement for the accuracy of estimate. We can use that as well.

Why this is an interesting to public sector

Consider a hypothetical scenario where there has been an economic downturn in the Province. Unemployment is up and businesses are shut down. A financial industry regulator would be concerned with where the Housing market goes because real properties represent a significant portion of Financial Institutions’ business and therefore their risks.

Consider a hypothetical scenario where a minor Flood or Earthquake just hit a populated municipality. The Mayor and city Councillors would want to find out the indirect economic impact of the natural disaster.


Data Sources

The first type of data to consider would be the geographic boundary data in the Province. It can be found from the Statistics Canada API or website.

The second source of data is the property valuation information. It contains the valuation of land and improvement valuation of each parcel in the studied area. Historical annual information can be sourced all the way back to the year of 2006.

The third source of data is the demographic information from Statistics Canada.

As the modeling goes deeper, the project team may find it interesting to explore deeper. This is where information on school district, local health care-related information, traffic surveillance, historical police report can become worth studying.

Expectation of students

Data skills

Data can be acquired either through API requests or by downloading CSV files. Students will be expected to learn to use Python for data acquisition, cleaning, organization, and manipulation. If the project continues beyond the length of the boot camp, it is reasonable for the students to learn the basics of data integrity and data governance.

Tools and programming languages

Students are expected to program in Python 3.X. Working experiences with libraries such as pandas can be useful. Previous experiences of other programming languages such as Matlab or R can be useful but is not required.

No previous knowledge of database management or operation Is expected.

Dave Dong
Dave Dong
Senior Analyst
Firas Moosvi
Firas Moosvi
Teaching and Learning Fellow
Leimin Gao
Leimin Gao
Graduate Student