House Price Effects of Commercial Entry: Event Study Evidence from London

Abstract

Restaurants, cafes, and other commercial amenities are among the most visible markers of neighborhood change, yet whether their arrival drives house price appreciation or merely follows rising demand remains an open empirical question. This study investigates the causal effect of commercial entry on residential property values in Greater London. Exploiting the staggered timing of 21,189 restaurant and cafe openings across 4,835 Lower Layer Super Output Areas (LSOAs)--identified through Energy Performance Certificate records--we implement an event study design with LSOA-specific linear trends that passes the parallel trends test (F = 1.04, p = 0.384). We find that house prices rise monotonically after commercial entry, reaching +4.1% at four years post-treatment (p < 0.01). The effect is gradual and cumulative, consistent with amenity capitalisation. By matching EPC records to Google Places API price tier data at the building level, we further show that the effect is driven by upmarket commercial entry (+7.4%, clean pre-trends) rather than budget establishments (questionable pre-trends, unreliable post-treatment effect), establishing that the quality of commercial clustering--not merely its presence--drives neighborhood price dynamics. Results are robust to heterogeneity-robust estimation, alternative treatment thresholds, broader commercial category definitions, and a permutation-based placebo test.

Figures (10)

• Figure 1: Bivariate LISA: RCCI $\times$ house prices. (a) Cluster map: HH = high commercial density and high prices (gentrified cores); HL = high commercial, low prices (potentially gentrifying); LH = low commercial, high prices (residential affluent); LL = low commercial, low prices. Only LSOAs significant at $p < 0.05$ (999 permutations) are coloured. (b) Bivariate Moran scatterplot ($I_{BV} = 0.261$, $p < 0.001$).
• Figure 2: Event study: house price response to commercial entry. Preferred specification (TWFE + LSOA-specific linear trends) with 95% CI. Pre-trend test: $F = 1.04$, $p = 0.384$. Post-treatment effects are monotonically increasing, reaching $+4.1\%$ at $\tau = +4$. $N = 300{,}392$; $N_{\text{treat}} = 1{,}084$; $N_{\text{ctrl}} = 3{,}747$.
• Figure 3: Heterogeneous effects of commercial entry by price tier. (a) Upmarket entry (Google price level $\geq 2$): clean pre-trends ($p = 0.623$) and $+7.4\%$ at $\tau = +4$. Budget entry (price level $= 1$): visible downward pre-trend ($p = 0.085$), post-treatment effect unreliable. (b) High-RCCI neighborhoods: $+8.5\%$ but pre-trends fail ($p = 0.017$); low-RCCI neighborhoods: no significant effect. All specifications use TWFE + LSOA-specific linear trends.
• Figure 4: Spatial distribution of neighborhood indicators across Greater London LSOAs. Education (Level 4+ qualifications), Income (mean household), RCCI (retail price tier), Green Space (park access), Transport (PTAL score). All values are z-scores.
• Figure 5: Geographically Weighted Regression: spatially varying effects of neighborhood indicators on house prices. Each panel maps the local coefficient ($\beta$) for one indicator; the final panel maps local $R^2$. Bandwidth $= 109$ nearest neighbors (AICc-optimised). Warm colours indicate stronger positive effects; cool colours indicate weaker or negative effects. RCCI effects concentrate in inner London; education and income effects are spatially broader. Global GWR $R^2 = 0.696$.