1 OVERVIEW

This interactive report provides evidence-based insights into residential property investment opportunities across Conwy County Borough, combining transaction-level data with predictive analytics to evaluate price performance, appreciation potential, and investment viability across key neighbourhoods.

1.1 Data Foundation and Scope

The analysis draws on an integrated dataset linking:

UK Land Registry Price Paid Data (PPD) — capturing verified residential property transactions from 2021 to 2025.The historical data dates back to 1995.

EPC Register (Energy Performance Certificates) — providing property-level attributes such as floor area, construction age band, energy efficiency rating, and number of habitable rooms.

ONS Geography Reference (LSOA 2021) — enabling spatial alignment of each transaction to Lower Super Output Areas (LSOAs) within Conwy County Borough.

Together, these sources form a spatially enabled database of approximately 5,000–8,000 unique transactions for selected LSOA in the past 5 years (2021 - 2025), each enriched with property, energy, and locational metadata.

1.2 Analytical Framework

A Hedonic Regression Model was developed to isolate the determinants of property value, accounting for:

Structural features: property type, total floor area, room count, and construction age band.

Energy performance: current EPC efficiency rating.

Locational effects: LSOA-based fixed factors to capture neighbourhood variation.

Temporal trend: transaction date converted into a continuous “time in years” variable to quantify price appreciation.

The model explains approximately 78–80% of price variance (R² ≈ 0.78) across Conwy, providing a statistically robust basis for both predictive and comparative assessments.

1.3 Report Focus

This report presents:

  1. Predicted vs Actual Market Prices across selected LSOAs we intend to scope for properties to flip. The choice of the LSOAs were informed by the knowledge of the property market in the area and proximity to home.

  2. Short-term Repeat Sale Appreciation (properties sold more than once within 24 months)

  3. Holding Period Returns for transactions occurring within any two-year interval since 2021

  4. Comparative Market Benchmarking, highlighting high-performing submarkets and property archetypes

All analyses are limited to 3–5-room residential properties (terraced, semi-detached, and detached types), representing the most active and investable market segment within the borough.

2 SEMI-DETACHED PROPERTY MARKET — CONWY COUNTY BOROUGH

The analysis of semi-detached properties across Conwy County Borough from 2015 to the present reveals a consistent upward trajectory in average transaction prices, highlighting the segment’s sustained strength and stability within the local housing market.

Despite short-term cyclical fluctuations—partly reflecting national economic conditions, lending rates, and seasonal market dynamics—the overall trend indicates steady capital appreciation. This resilience suggests that semi-detached homes, which typically balance affordability with desirable space and utility, remain a core component of Conwy’s residential demand base.

When structural attributes such as floor area, room count, and construction age are controlled for (as in the hedonic model), the price growth pattern persists, underscoring that the appreciation is not solely driven by compositional shifts in property quality but also by genuine market value gains.

This trend positions semi-detached properties as a reliable mid-market investment class, offering both capital growth potential and sustained demand from owner-occupiers and rental investors. The data aligns with broader Welsh market indicators showing continued upward pressure on suburban and coastal residential values.

3 PREDICTED PROPERTY PRICES — MODEL-BASED EVAULATION ACROSS CONWY

The predicted price distribution, derived from the hedonic regression model (2021–2025), illustrates how property characteristics and location jointly influence market value across Conwy’s Lower Layer Super Output Areas (LSOAs).

Each data point represents a model-based valuation for a specific property type and room count within an LSOA, adjusted for structural and locational factors such as total floor area, energy efficiency rating, and construction age. The model’s predictions highlight a clear spatial differentiation in value, with higher-priced zones concentrated in coastal and high-amenity neighbourhoods such as Penrhyn, Deganwy, and Craig-y-Don, while more affordable segments appear inland and toward less densely developed areas.

Across room categories, detached and semi-detached properties dominate the upper price ranges, reflecting their greater size and desirability. Flats and terraced homes, while comparatively lower in predicted value, exhibit narrower price variability, suggesting stable demand in more compact housing sub-markets.

This interactive visualisation provides a data-backed benchmark for investors and policymakers, enabling location-specific comparisons and the identification of potential undervalued or overperforming neighbourhoods relative to model expectations.

4 ACTUAL PRICES (LAST 24 MONTHS)

This analysis captures real transaction prices over the past 24 months across 17 selected Lower Layer Super Output Areas (LSOAs) within Conwy County Borough. The dataset integrates verified EPC-linked property transaction records, filtered to include homes with 3 to 5 habitable rooms—a range representing the borough’s mainstream residential segment.

The interactive chart highlights the average sale prices by neighbourhood, property type, and room count, offering a granular view of market dynamics across the area. The differentiation across property types is evident:

Detached and semi-detached houses command the highest values, consistent with their larger floor areas and plot sizes.

Terraced and flats show more affordability and narrower price spreads, indicating stable demand and lower price volatility.

Spatially, premium clusters are visible in localities such as Deganwy, Penrhyn, and Craig-y-Don, reflecting proximity to coastal amenities and stronger buyer competition. In contrast, more moderate pricing is observed inland and in higher-density zones such as Rhiw and Mostyn.

5 RESALE/FLIPPING PROPERTY VALUE APPRECIATION (LAST 24 MONTHS)

Hover over each data point to see property type, LSOA, room count, and average gain. Negative values represent losses (depreciation).

The red dashed line represents the ideal 1:1 match between predicted and actual average prices. Points above the line indicate undervalued opportunities; those below may be overvalued.

6 PROPERTY VALUE APPRECIATION

6.1 Flipping/Resale within 24-Month Holding Periods (2021–Present)

This analysis evaluates repeat property transactions within any 24-month window between January 2021 and the present, focusing on selected Conwy LSOAs and properties with 3 to 5 habitable rooms.

Each observation represents a property sold at least twice during the study period. The computation captures:

First and last sale prices per property,

Absolute gain or loss (£) between transactions,

Percentage appreciation, and

Holding period duration (months) between sales.

The results are aggregated by LSOA, property type, and room count to produce average appreciation percentages, median monetary gains, and average time intervals between sales. The interactive plot displays these metrics, allowing direct comparison of market performance across neighbourhoods and property types.

Both positive and negative appreciations are reflected—price losses are included where resale values fell below the initial transaction price. The y-axis is scaled to range from –200% to +200%, ensuring visibility of both significant losses and high-gain flips within short-term resale cycles.

Negative values indicate depreciation. Hover tooltips show holding duration, median £ gains/losses, and repeat sale counts.

7 SUMMARY STATS

8 CONCLUSIONS

I believe Conwy property market demonstrates strong transactional depth and sustained pricing power across its coastal and suburban neighbourhoods. Data covering the 17 selected LSOAs (including Deganwy, Marl, Penrhyn, Llandrillo yn Rhos, and Craig-y-Don) confirm that the core residential stock — semi-detached and terraced homes — remains the most liquid and consistently appreciating segment of the market.

Across all LSOAs reviewed, median semi-detached property prices cluster around £165,000 – £195,000, while terraced units trade within £120,000 – £150,000, depending on location and floor area. Detached homes exhibit higher volatility, with prices ranging from £180,000 – £370,000 but also a larger capital outlay and longer resale horizon.

The price volatility index (40–60%) recorded in these neighbourhoods reflects healthy turnover and indicates ample scope for short-term refurbishment-led value creation. This profile, combined with verified repeat-sale data showing achievable appreciation within 24-month holding periods, provides a quantitative foundation for a structured flipping strategy.

8.2 Caveat

As this project progresses, all figures and assumptions presented in this report will be reviewed and validated by professional valuers, financial advisors, and real estate experts. This follow-up assessment will help refine cost estimates, adjust for evolving market conditions, and identify optimal areas of focus for capital deployment.

It is important to note that the strategy intentionally targets distressed or undervalued properties, including those requiring refurbishment or repositioning. The investment model seeks to acquire such assets at up to 20% below prevailing market value, implement targeted improvements to restore or enhance their marketability, and subsequently realise gains through resale at or above fair market value.

Therefore, all numerical estimates in this report — including acquisition costs, renovation budgets, and expected returns — are indicative and subject to change as new data, professional input, and local market realities emerge. This report should be regarded as a conceptual and analytical guide, not necessarily as a definitive financial forecast.

8.3 Prepared By

Temidayo Popoola Project Engineer & Data Analyst