Evolution of Residential Real Estate in Toronto – 2014 to 2022

Shashank Prabhu, Geovis Project Assignment, TMU Geography, SA8905, Fall 2024 

Introduction
Toronto’s residential real estate market has experienced one of the most rapid price increases among major global cities. This surge has led to a significant affordability crisis, impacting the quality of life for residents. My goal with this project was to explore the key factors behind this rapid increase, while also analyzing the monetary and fiscal policies implemented to address housing affordability.

The Approach: Mapping Median House Prices
To ensure a more accurate depiction of the market, I used the median house price rather than the average. The median better accounts for outliers and provides a clearer view of housing trends. This analysis focused on all home types (detached, semi-detached, townhouses, and condos) between 2014 and 2022.

Although data for all years were analyzed, only pivotal years (2014, 2017, 2020, and 2022) were mapped to emphasize the factors driving significant changes during the period.

Data Source
The Toronto Regional Real Estate Board (TRREB) was the primary data source, offering comprehensive market watch reports. These reports provided median price data for Central Toronto, East Toronto, and West Toronto—TRREB’s three primary regions. These regions are distinct from the municipal wards used by the city.

Creating the Maps

Step 1: Data Preparation
The Year-to-Date (YTD) December figures were used to capture an accurate snapshot of annual performance. The median price data for each of the years across the different regions was organized in an Excel sheet, joined with TRREB’s boundary file (obtained through consultation with the Library’s GIS department), and imported into ArcGIS Pro. WGS 1984 Web Mercator projection was used for the maps.

Step 2: Visualization with 3D Extrusions
3D extrusions were used to represent price increases, with the height of each bar corresponding to the median price. A green gradient was selected for visual clarity, symbolizing growth and price.

Step 3: Overcoming Challenges

After creating the 3D extrusion maps for the respective years (2014, 2017, 2020, 2022), the next step was to export those maps to ArcOnline and then to Story Maps, the easiest way of doing so was to export it as a Web Scene, from which it would show up under the Content section on ArcOnline.

  • Flattened 3D Shapes: Exporting directly as a Web Scene to add onto Story Maps caused extrusions to lose their 3D properties. This was resolved using the “Layer 3D to Feature Class” tool.

  • Lost Legends: However, after using the aforementioned tool, the Legends were erased during export. To address this, static images of the legends were added below each map in Story Maps.

Step 4: Finalizing the Story Map
After resolving these issues, the maps were successfully exported using the Export Web Scene option. They were then embedded into Story Maps alongside text to provide context and analysis for each year.

Key Insights
The project explored housing market dynamics primarily through an economic lens.

  • Interest Rates: The Bank of Canada’s overnight lending rate played a pivotal role, with historic lows (0.25%) during the COVID-19 pandemic fueling a housing boom, and sharp increases (up to 5% by 2023) leading to market cooling.
  • Immigration: Record-breaking immigration inflows also contributed to increased demand, exacerbating the affordability crisis.

While earlier periods like 2008 were critical in shaping the market, boundary changes in TRREB’s data made them difficult to include.

Conclusion
Analyzing real estate trends over nearly a decade and visualizing them through 3D extrusions offers a profound insight into the rapid rise of residential real estate prices in Toronto. This approach underscores the magnitude of the housing surge and highlights how policy measures, while impactful, have not fully addressed the affordability crisis.

The persistent rise in prices, even amidst various interventions, emphasizes the critical need for increased housing supply. Initiatives aimed at boosting the number of housing units in the city remain essential to alleviate the pressures of affordability and meet the demands of a growing population.

Link to Story Map (You will need to sign in through your TMU account to view it): https://arcg.is/WCSXG

Under Construction Commercial Real Estate in Toronto Market

GeoVis Project @RyersonGeo, SA8905, Fall 2021, Mirza Ammar Shahid

Introduction

Commercial real estate is crucial part of the economy and is a key indicator of a region’s economic health. In the project different types of Under constriction projects within the Toronto market will be assessed. Projects that are under construction or are proposed to be completed within the next few years will be visualized. Some property types that will be looked at are, hospitality, office, industrial, retail, sports and entertainment etc. The distribution of each property type within the regions will be displayed. To determine the proportional distribution within each region by property type. Software that will be used is Tableau to create a visualization of the data which will be interactive to explore different data filters.

Data

The data for the project was obtained from the Costar group’s database. The data used was exported using all properties within the submarket of Toronto (York region, Durham region, Peel Region, Halton region). Under construction or proposed properties above the size of 7000 sqft were exported to be used for the analysis. Property name, address, submarket, size, longitude, latitude and the year built were some of the attributes exported for each property project.

Method

  1. Once data was filtered and exported from the source, the data was inserted into Tableau as an excel file.
  2. The latitude and longitude were placed in rows and columns in order to create a map in tableau for visualization.
  3. Density of mark was used to show the density and a filter was applied for property type.
  4. Second sheet was created with same parameters but instead of density circle marks were used to identify locations of each individual project (Under Construction Projects).
  5. Third sheet was created with property type on x axis and proportion of each in each region in y axis. To show the proportions of each property type by region.
  6. The three worksheets were used to compile an interactive dashboard for optimal visualization of the data.
Figure 1: rows, columns and marks

Results

Density Map Showing Industrial Property type
All Under construction project locations
Regional Distribution by Property type

The results are quite intriguing as to where certain property type constriction dominant over the rest. Flex is greatest in Peel region, Health care in Toronto, Hospitality in Halton, Industrial in Peel, Multifamily in Toronto, Office in downtown Toronto, retail in York region, specialty in York region and sports and entertainment in Durham with new casino opening in Ajax.

The final dashboard can be seen below, however due to sharing restrictions, the dashboard can only be accessed if you have a Tableau account.

Click here to view dashboard

Conclusion

In conclusion, using under construction commercial real estate dashboard can have positive impact on multiple entities within the sector. Developers can use such geo visualizations to monitor ongoing projects and find new projects within opportunity zones. Brokerages can use this to find new leads, potential listings and manage exiting listings. Governments of all three levels, municipal, provincial and federal can use these dashboard to monitor health conditions of their constituency and make insightful policy changes based on facts.