AT MANIFOLD WE LIVE FOR DATA We take numbers and turn them into actionable results

Neighbourhood Scores

Neighbourhood Scores provide a holistic measurement of a neighbourhood’s living environment. It is a quantification of the popular phrase: “Location, Location and Location”. This quantification enhances the traditional geo-demographic description of neighbourhoods and reveals nonlinear relationships among geography, demography, environment, crime, climate, and consumer behavours. 

This product is designed to provide machine learning and AI algorithms with high predictive power

Categories listed below are scored on a scale from 1 to 10 to empower you to make better business marketing decisions. The categories include:

  • Road Types
  • Business Types
  • Affordability
  • Housing Suitability
  • Income Equality
  • Debt Service
  • Socio-Economics
  • Population Diversity
  • Home Improvement
  • Amenities
  • Crimes
  • Climate

Companies with advanced analytical capabilities use this data product in their predictive models. Insurance companies use this data product to mitigate risk. Brick and mortar-based businesses use this data product during the site selection process. Developers and governments/development agencies also use Neighbourhood Scores to determine the best mix of residential and commercial buildings for development, evaluate inequality, and more.

It enables companies and organizations to tap into the versatile characteristics of neighbourhoods to grow their business and mitigate risks more effectively.

Most Recent Update

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Available Geographic Levels

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6-digit postal code, FSA, DA, CT, CSD, CD, and custom geography

Update Frequency

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Data sources used for this product include Royal LePage’s quarterly Survey of Canadian Housing Price, Canadian Mortgage and Housing Corporation (CMHC), Statistics Canada, Cleanlist, available statistics and trends from municipalities, transit agencies, and other sources, and Numeris RTS survey. Each metric in “Neighbourhood Scores” is a consolidation of multiple variables to increase its differentiating power. We designed the scores so that most machine learning and AI algorithms can use it directly.  We validated with third party survey data (Vividata and Numeris) and observed overwhelmingly high lifts in predictive models.

Data Format

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Sample Reports

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Data Dictionary

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How To Get It

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