Hospitality Insight
Smart exposure: optimising hotel market combinations for returns, stability and balanceĀ
In todayās fragmented and fast-evolving hospitality landscape, selecting the right markets for hotel investment and operation is both a strategic imperative and a complex challenge. Unlike other asset classes, the hotel sector lacks a unified, transparent dataset for total returns ā making it harder to assess performance, compare opportunities, or build diversified portfolios with confidence.
Those responsible forāÆportfolio strategy and income generationāÆā whether investors, lenders, owners, developers, or operators ā must often rely on a mix ofāÆproxies,āÆpattern recognition, andāÆstock availabilityāÆto guide decisions. This blend of data and market reality creates a unique challenge: how to build exposure that is not only high-performing, but also resilient and aligned with long-term goals.
While RevPAR is an imperfect proxy ā it does not account for operating costs, capital expenditure, or asset value appreciation ā it remains one of the most consistently available and comparable metrics across markets, including the UK. In the absence of granular asset-level data, it offers a practical lens through which to assess market performance and simulate portfolio strategies.
Authors
In this piece, we use RevPAR to model how different hotel markets interact in terms ofāÆperformance and risk. We explore how strategic exposure can be shaped to deliver:
- High-return portfolios: combinations that maximise RevPAR growth
- Stable portfolios: combinations that minimise volatility
- Balanced portfolios: combinations that optimise risk-adjusted returns
While driving alpha through operational excellence and asset-level strategy is often the focus of hotel investment, this report highlights the critical role of market selection ā or beta ā in shaping portfolio performance. Choosing the right markets can allow investors to ride structural demand growth and cyclical recovery, delivering material returns even without aggressive asset-level intervention. Conversely, selecting the wrong markets can significantly weigh on performance, regardless of how well individual assets are managed. This analysis underscores that strategic market allocation is not just a backdrop to alpha generation ā itās a powerful lever in its own right.
This isnāt just about chasing returns. Itās about applying a clear, data-led framework to identify opportunities that fit the strategy. In a market shaped by limited stock and real-world constraints, repeatable success depends on disciplined decision-making.
Methodology:
A Data-Led Approach to Market Selection
Our framework combines historical RevPAR performance with portfolio simulation, structured around three key steps:
1.
Correlation Analysis
We mapped how RevPAR trends align across markets to identify combinations that offer diversification. Highly correlated markets move together, limiting risk mitigation; low or negative correlations suggest potential for more resilient portfolios.
2.
Volatility Assessment
Using data from 2010 onwards, we calculated the standard deviation of annual RevPAR to gauge market stability. This highlights which markets deliver consistent performance versus those with higher upside and greater uncertainty.
3.
Portfolio Simulation
We simulated thousands of three-market combinations to identify strategic profiles:- High-return: strongest cumulative RevPAR growth
ā Stable: lowest aggregate volatility
ā Balanced: best risk-adjusted returns
Each portfolio reflects not just individual market strength, but how markets interact ā capturing the benefits of diversification and strategic fit.
Findings
Understanding Market Interdependence: The Role of Correlation
Before constructing portfolios, itās essential to understand how hotel markets behave in relation to one another. Correlation analysis provides a window into these dynamics ā revealing which markets tend to move together and which offer diversification benefits.
The matrix below illustrates theāÆRevPAR correlation coefficientsāÆacross UK hotel markets. A high correlation suggests that markets are influenced by similar demand drivers, economic cycles, or travel patterns. Lower correlations indicate more independent performance, which can help reduce portfolio risk.
| Geography Name | Bedfordshire and Hertfordshire GBR | Birmingham GBR | Bristol GBR | Buckinghamshire and Oxfordshire GBR | Cardiff GBR | Channel Islands/Cornwall/Devon GBR | Cheshire GBR | Coventry GBR | Cumbria GBR | Derbyshire and Nottinghamshire GBR | Dorset and Somerset GBR | East Anglia and Essex GBR | East Sussex/Surrey/West Sussex GBR | East and North Yorkshire GBR | Edinburgh GBR | England Northeast Provincial GBR | Glasgow GBR | Gloucestershire GBR | Hampshire and Isle of Wight GBR | Herefordshire/Warwickshire/Worcestershire GBR | Kent GBR | Lancashire GBR | Leeds GBR | Leicestershire and Northamptonshire GBR | Lincolnshire GBR | Liverpool GBR | London GBR | M4 Corridor GBR | Manchester GBR | Merseyside GBR | Newcastle GBR | Northern Ireland GBR | Scotland Provincial GBR | Shropshire and Staffordshire GBR | South Yorkshire GBR | Southampton GBR | Swindon and Wiltshire GBR | Wales Provincial GBR | West Yorkshire GBR | York GBR | |
| Bedfordshire and Hertfordshire GBR | |||||||||||||||||||||||||||||||||||||||||
| Birmingham GBR | |||||||||||||||||||||||||||||||||||||||||
| Bristol GBR | |||||||||||||||||||||||||||||||||||||||||
| Buckinghamshire and Oxfordshire GBR | |||||||||||||||||||||||||||||||||||||||||
| Cardiff GBR | |||||||||||||||||||||||||||||||||||||||||
| Channel Islands/Cornwall/Devon GBR | |||||||||||||||||||||||||||||||||||||||||
| Cheshire GBR | |||||||||||||||||||||||||||||||||||||||||
| Coventry GBR | |||||||||||||||||||||||||||||||||||||||||
| Cumbria GBR | |||||||||||||||||||||||||||||||||||||||||
| Derbyshire and Nottinghamshire GBR | |||||||||||||||||||||||||||||||||||||||||
| Dorset and Somerset GBR | |||||||||||||||||||||||||||||||||||||||||
| East Anglia and Essex GBR | |||||||||||||||||||||||||||||||||||||||||
| East Sussex/Surrey/West Sussex GBR | |||||||||||||||||||||||||||||||||||||||||
| East and North Yorkshire GBR | |||||||||||||||||||||||||||||||||||||||||
| Edinburgh GBR | |||||||||||||||||||||||||||||||||||||||||
| England Northeast Provincial GBR | |||||||||||||||||||||||||||||||||||||||||
| Glasgow GBR | |||||||||||||||||||||||||||||||||||||||||
| Gloucestershire GBR | |||||||||||||||||||||||||||||||||||||||||
| Hampshire and Isle of Wight GBR | |||||||||||||||||||||||||||||||||||||||||
| Herefordshire/Warwickshire/Worcestershire GBR | |||||||||||||||||||||||||||||||||||||||||
| Kent GBR | |||||||||||||||||||||||||||||||||||||||||
| Lancashire GBR | |||||||||||||||||||||||||||||||||||||||||
| Leeds GBR | |||||||||||||||||||||||||||||||||||||||||
| Leicestershire and Northamptonshire GBR | |||||||||||||||||||||||||||||||||||||||||
| Lincolnshire GBR | |||||||||||||||||||||||||||||||||||||||||
| Liverpool GBR | |||||||||||||||||||||||||||||||||||||||||
| London GBR | |||||||||||||||||||||||||||||||||||||||||
| M4 Corridor GBR | |||||||||||||||||||||||||||||||||||||||||
| Manchester GBR | |||||||||||||||||||||||||||||||||||||||||
| Merseyside GBR | |||||||||||||||||||||||||||||||||||||||||
| Newcastle GBR | |||||||||||||||||||||||||||||||||||||||||
| Northern Ireland GBR | |||||||||||||||||||||||||||||||||||||||||
| Scotland Provincial GBR | |||||||||||||||||||||||||||||||||||||||||
| Shropshire and Staffordshire GBR | |||||||||||||||||||||||||||||||||||||||||
| South Yorkshire GBR | |||||||||||||||||||||||||||||||||||||||||
| Southampton GBR | |||||||||||||||||||||||||||||||||||||||||
| Swindon and Wiltshire GBR | |||||||||||||||||||||||||||||||||||||||||
| Wales Provincial GBR | |||||||||||||||||||||||||||||||||||||||||
| West Yorkshire GBR | |||||||||||||||||||||||||||||||||||||||||
| York GBR | |||||||||||||||||||||||||||||||||||||||||
Source: HHTL analysis, CoStar Group
In this visualisation:
- Red tonesāÆindicateāÆhigh correlation, meaning markets tend to move in sync.
- Blue tonesāÆindicateāÆlow correlation, suggesting more independent or counter-cyclical behaviour.
Observations:
- LondonāÆshows strong correlations with many regional markets, suggesting its performance is broadly influential.
- Channel Islands/Cornwall/DevonāÆandāÆScotland ProvincialāÆshow lower correlations with some urban centres, indicating potential diversification value.
- ClustersāÆof highly correlated markets (e.g. M4 Corridor, South East, and London) may offer high relative RevPAR but limited risk dispersion.
Volatility: Risk, Reward, and Strategic Fit
Volatility in RevPAR is often viewed through a risk lens ā but it can also signalāÆopportunity. High volatility doesnāt necessarily mean a market is ābad.ā In fact, it can reflectāÆstrong growth potential, especially in markets that are dynamic, international, or undergoing structural change. The key is understanding whether the volatility isāÆproductiveāÆ(e.g. driven by surging demand or pricing power) orāÆdisruptiveāÆ(e.g. due to over-reliance on a single segment or external shocks)
The chart above ranks UK hotel markets by their RevPAR volatility since 2010. Notably:
- EdinburghāÆtops the list, reflecting its exposure to seasonal tourism and international events.
- London, traditionally seen as a relatively stable market, now ranks second ā a shift that may reflectāÆpost-pandemic demand swings, andāÆsupply additions.
- West Yorkshire,āÆNewcastle, andāÆEngland Northeast ProvincialāÆremain among the most stable, underpinned by domestic demand and less seasonality.
Ultimately, the relevance of volatility depends onāÆcorporate objectives:
- Investors seekingāÆyield and growthāÆmay tolerate more volatility.
- Those prioritisingāÆincome stabilityāÆorāÆlong-term hold strategiesāÆmay lean toward lower-volatility markets.
- Operators and developers should also consider whether volatility isāÆunderstood and manageableāÆā for example, through brand positioning, pricing strategy, or operational flexibility.
Portfolio Simulation:
Designing Exposure Through Market Combinations
To explore how different hotel markets interact in terms of performance and risk, we simulated thousands of three-market combinations using RevPAR as a proxy for total returns. This reflects the reality that hotel portfolios are often anchored by a limited number of core locations ā whether for operational efficiency, brand strategy, or capital allocation.
While our model focuses on three-market groupings for clarity, the approach is highly adaptable. It can be scaled to assess smaller or larger sets of markets, or portfolios of assets, and tailored to reflect specific corporate strategies, investment horizons, or operational models.
Each simulated portfolio was evaluated on two dimensions:
- Average Return: cumulative RevPAR growth across the three markets.
- Volatility: standard deviation of RevPAR, used as a proxy for risk.
To reflect real-world investment decision-making, we applied aāÆweighted scoring modelāÆāāÆ60% return and 40% volatility (inverted)āÆā to identify combinations that offer the best balance of performance and stability. Again, this weighting can be adapted to specific corporate objectives.
This produced three distinct strategic profiles:
Top-Return Portfolios
These combinations delivered the highest RevPAR growth, typically featuring high-demand, internationally exposed markets such as London, Edinburgh, and York. While these markets do exhibit volatility, they are also characterised by strong long-term fundamentals and high barriers to entry.
London and Edinburgh, in particular, are not just cyclical performers ā they are core holdings in many institutional portfolios due to their global connectivity, depth of demand, and constrained supply in central locations. Volatility in these markets may reflect dynamic pricing and seasonal/events demand, but over time, they tend to outperform due to their resilience and strategic importance.
Most Stable Portfolios
These portfolios are built aroundāÆregional, domestic-driven marketsāÆwith consistent demand patterns ā such asāÆWest Yorkshire,āÆNewcastle, andāÆLancashire. They offer predictability and resilience, making them ideal for long-hold strategies, income-focused investors, or public-private partnerships.
Best-Balanced Portfolios
Using the weighted scoring model, these combinations sit on the āhotel portfolio frontierā ā delivering strong returns without excessive risk. Markets likeāÆLondon,āÆYork,āÆBristol, and theāÆM4 CorridorāÆfeature prominently, offering diversified exposure across business, leisure, and regional demand drivers.
Best-Balanced Market Combinations
| Top Balanced | Average Return | Volatility | Return Score | Volatility Score | Balanced Score |
| London GBR, M4 Corridor GBR, York GBR | 85.829 | 17.962 | 0.845 | 0.541 | 0.723 |
| Bristol GBR, London GBR, York GBR | 85.592 | 17.952 | 0.841 | 0.541 | 0.721 |
| London GBR, Manchester GBR, York GBR | 84.550 | 17.647 | 0.823 | 0.556 | 0.716 |
| Bristol GBR, London GBR, M4 Corridor GBR | 81.967 | 16.297 | 0.778 | 0.622 | 0.715 |
| London GBR, M4 Corridor GBR, Manchester GBR | 80.926 | 15.934 | 0.759 | 0.640 | 0.712 |
| Buckinghamshire and Oxfordshire GBR, London GBR, York GBR | 86.380 | 19.045 | 0.855 | 0.488 | 0.708 |
| Bristol GBR, London GBR, Manchester GBR | 80.689 | 16.013 | 0.755 | 0.636 | 0.708 |
| Hampshire and Isle of Wight GBR, London GBR, York GBR | 85.790 | 18.763 | 0.845 | 0.502 | 0.707 |
| Liverpool GBR, London GBR, York GBR | 84.543 | 18.164 | 0.823 | 0.531 | 0.706 |
| Liverpool GBR, London GBR, M4 Corridor GBR | 80.919 | 16.375 | 0.759 | 0.618 | 0.703 |
London and Edinburgh are widely recognised as high-barrier-to-entry hotel markets, where the availability of investable stock can constrain portfolio-building strategies. To provide a broader perspective, weāve excluded both cities from this analysis and selected ten alternative scenarios featuring regional UK markets. This approach offers a clearer comparison of risk and returns dynamics across the country, showcasing a cross-section of combinations based on their balanced score.
| Selected Others | Average Return | Volatility | Return Score | Volatility Score | Balanced Score |
| Aberdeen GBR, Bristol GBR, York GBR | 56.850 | 14.690 | 0.820 | 0.620 | 0.739 |
| Aberdeen GBR, Bedfordshire and Hertfordshire GBR, East Sussex/Surrey/West Sussex GBR | 49.710 | 13.190 | 0.540 | 0.730 | 0.614 |
| Channel Islands/Cornwall/Devon GBR, East Sussex/Surrey/West Sussex GBR, Hampshire and Isle of Wight GBR | 56.260 | 19.470 | 0.800 | 0.270 | 0.586 |
| Bristol GBR, Cardiff GBR, Merseyside GBR | 49.010 | 14.250 | 0.510 | 0.650 | 0.566 |
| Bristol GBR, Coventry GBR, Glasgow GBR | 50.530 | 16.050 | 0.570 | 0.520 | 0.550 |
| Bristol GBR, Coventry GBR, Northern Ireland GBR | 50.650 | 16.680 | 0.580 | 0.470 | 0.535 |
| Coventry GBR, Gloucestershire GBR, Southampton GBR | 49.900 | 16.600 | 0.550 | 0.480 | 0.519 |
| Channel Islands/Cornwall/Devon GBR, Coventry GBR, Southampton GBR | 50.080 | 17.270 | 0.550 | 0.430 | 0.504 |
| Channel Islands/Cornwall/Devon GBR, Cheshire GBR, East and North Yorkshire GBR | 49.570 | 17.440 | 0.530 | 0.420 | 0.487 |
| Shropshire and Staffordshire GBR, South Yorkshire GBR, West Yorkshire GBR | 36.820 | 11.320 | 0.030 | 0.860 | 0.364 |
Strategic Implications and Summary
This analysis offers a structured, data-led approach to understanding how different hotel markets interact in terms of performance and risk. By simulating thousands of three-market combinations using RevPAR as a proxy for total returns, weāve identified clear patterns in how growth, stability, and balance can be achieved through geographic exposure.
However, itās important to recognise theāÆlimitations:
- The model isāÆbackward-looking, based on historical RevPAR performance since 2010.
- It does not account forāÆforward-looking fundamentals, such as pipeline supply, infrastructure investment, or changing demand drivers.
- RevPAR, while useful, is anāÆimperfect proxyāÆfor total returns ā it excludes operating costs, capital expenditure, and asset appreciation.
That said, the analysis provides a valuableāÆthrough-the-cycle perspective, helping investors, lenders, and operators understand how markets behave over time and in relation to one another. It supports strategic conversations around:
- Diversification: avoiding overexposure to correlated markets.
- Resilience: identifying markets that perform consistently across cycles.
- Growth: targeting combinations that deliver strong returns with manageable risk.
At Horwath HTL, we are actively advising investors, owners, operators, and brands on portfolio strategy ā helping them navigate market selection, growth planning, and capital allocation in ways that align with their strategic objectives.
Whether you’re building a new platform, repositioning assets, or expanding across regions, we bring data, insight, and experience to support confident decision-making. Our work is grounded in market intelligence and tailored to the realities of stock availability, brand fit, and long-term value creation.