Site Rating Scorecard
The site rating scorecard is an alternative to regression that produces a single intuitive score (0–100) for any location. Instead of fitting coefficients statistically, you assign weights to each factor based on their known importance, then score each site.
The Method
Section titled “The Method”Site Score = Σ (wᵢ × sᵢ) where Σwᵢ = 100
Each attribute is weighted by importance and scored on a normalized scale.
Example weights (from Birkin & Clarke):
- Market Size: 50 points
- Store Size: 30 points
- Affluence: 10 points
- Competition: 10 points
“A ratings approach may be used to provide single scores for each site on the basis of the quality of attributes at that location. Each variable may be weighted according to its relative importance.” — Birkin & Clarke, Ch. 7
How It Works
Section titled “How It Works”- Choose attributes — the 4–6 factors that drive success in your category
- Assign weights — based on regression results or domain expertise (must sum to 100)
- Normalize each attribute — convert raw values to 0–10 scale across your portfolio
- Multiply and sum — weight × score for each factor = total site rating
Example from the Book
Section titled “Example from the Book”Birkin & Clarke scored UK retail sites on 4 attributes:
| City | Market Size (×50) | Store Size (×30) | Affluence (×10) | Competition (×10) | Total |
|---|---|---|---|---|---|
| Manchester | 10.0 → 50 | 6.0 → 18 | 5.0 → 5 | 4.0 → 4 | 77 |
| Leeds | 7.0 → 35 | 8.0 → 24 | 4.0 → 4 | 3.0 → 3 | 66 |
| Guildford | 4.0 → 20 | 7.0 → 21 | 9.0 → 9 | 7.0 → 7 | 57 |
| Cambridge | 3.0 → 15 | 5.0 → 15 | 8.0 → 8 | 8.0 → 8 | 46 |
Manchester wins on raw score because it has by far the largest market. But Guildford and Cambridge outperform their ratings — their actual turnover exceeds what the scorecard predicts, suggesting strong local factors the model doesn’t capture.
Applied to 14 Wa In Fong East
Section titled “Applied to 14 Wa In Fong East”Using HK open data to score our Sheung Wan location:
| Factor | Weight | Data Source | Raw Value | Score (0–10) | Weighted |
|---|---|---|---|---|---|
| Market Size | 50 | Census (500m radius) | ~28,000 | 7.5 | 37.5 |
| Foot Traffic | 20 | MTR Sheung Wan exits | ~45,000/day | 8.0 | 16.0 |
| Competition | 15 | FEHD licenses (C&W) | 208 restaurants | 4.0* | 6.0 |
| Affluence | 15 | Median HH income | HK$35,000/mo | 7.0 | 10.5 |
| Total | 70.0 / 100 |
*High competition = lower score (inverse relationship)
Strengths & Limitations
Section titled “Strengths & Limitations”Strengths:
- Dead simple — anyone can understand a score out of 100
- No training data needed (unlike regression)
- Transparent — you can see exactly why a site scored high or low
- Easy to adjust weights as you learn more
- Perfect for comparing sites in a portfolio
Limitations:
- Weight assignment is subjective without regression backing
- Assumes factors are independent (ignores interactions)
- Linear scoring may miss thresholds (e.g., “minimum 10,000 market size or don’t bother”)
- Doesn’t model consumer behaviour — just attributes
Regression vs. Scorecard
Section titled “Regression vs. Scorecard”| Regression | Scorecard | |
|---|---|---|
| Training data needed | Yes (20+ stores) | No |
| Objectivity | Statistical | Expert judgment |
| Output | Revenue estimate ($) | Score (0–100) |
| Best for | Revenue prediction | Site comparison |
| Transparency | Medium | High |
Source
Section titled “Source”📖 Birkin, M. & Clarke, G. (2023). Retail Geography. Chapter 7: Store Performance Modelling — Site Ratings.