FYP Portfolio

Weak Overall Relationship

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• Total crime and house prices show only a weak overall correlation

• Crime alone is not a strong general predictor of housing values

• Aggregate crime statistics obscure the patterns that really matter

Short-Term vs Long-Term Effects

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• Within-district analysis shows a stronger relationship (r ≈ −0.32) vs overall (r ≈ −0.03)

• Recent local changes in crime have a greater impact than long-term averages

Perception vs Reality

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• Spearman shows anti-social behaviour has a stronger negative association (ρ ≈ −0.30) than most crime types

• Visible disorder has a greater influence on housing demand than overall crime levels

• Buyer perception of neighbourhood quality drives price sensitivity

Evidence of Urban Complexity

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• Theft from the person shows a positive correlation (r ≈ +0.37) with house prices

• Bicycle theft also positive (r ≈ +0.28); both reflect urban concentration effects

• High-value areas experience higher activity-related crime, not higher risk

Limitations of Single-Metric Analysis

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• Total crime shows a very weak relationship (r ≈ −0.03) with house prices

• Crime-type analysis reveals stronger patterns up to |ρ| ≈ 0.30

• Aggregated metrics hide meaningful relationships present at a more detailed level

Statistical Significance vs Practical Impact

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• Most results are statistically significant (p < 0.001) due to large sample size (N ≈ 34,000)

• Even correlations as small as |r| < 0.10 appear significant at this scale

• Statistical significance ≠ real-world impact; effect size must be interpreted carefully