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Giles Heywood, Senior Quantitative Analyst at, shows how analysis of the right datasets can help identify variations and trends in local residential property markets, offering property investment and lending professionals significant opportunities to outperform the market.

Read time: 10 mins 

We’re all aware that the property market in the UK follows a long and relatively predictable cycle. We also know that various areas of the country experience the stages of that cycle at different times and with differing intensity. 

It is these local variations in timing and volatility that affect the fortunes of individual property developments. Understanding of these local market conditions is a basic requirement for any investment and lending decision. But analysis like this often looks at incorrect or incomplete information. This leads to an over-reliance on factors such as price momentum, or even simple intuition. 

However, the same factors that drive the national cycle also drive performance at a local level. Being able to identify the current stage of the cycle in a specific local area makes for a better informed investment decision. 

Spotting the ripples in the cycle

Every ripple in the UK property market begins at the top of the property price pyramid: Prime Central London. And these ripples (or local changes in the pace of price growth) are much more to do with price (£/m2) than with location, although the two are related. The ripples, of course, travel slowly away from the highest-priced areas. This is why some areas of a city usually show a peak in growth rate at the same time as others are growing slowly – this relative performance is a key local variation for investors and lenders to identify. 

To illustrate this effect, I’ve explored the data in a very specific way, identifying a range of contiguous metro areas that traverse a range of prices. Each trail has been selected not so much for its natural beauty, but instead to steadily descend in price over as many postcode sectors as possible. 


In London, the SW postcode area has a very broad spectrum of prices. The map above shows a trail from the mansions of Knightsbridge to the more affordable suburbs bordering Croydon. This illustrates not only a fairly steep price descent but – crucially for investors and lenders – also a great diversity of capital gain throughout the cycle. Relative performance in higher and lower priced areas varies greatly depending where on the growth cycle a particular city happens to be. This is of special interest given that we are now – after a long breather in Prime Central London – just past a turning point in the cycle. 

Convergence and divergence phases

The line chart below shows performance since 1995 on a log scale for Knightsbridge, Putney, and Norbury. We see evidence of the ripple effect through about 1.5 cycles over 25 years. Over the period 2005-2014 the high-priced sectors gained value faster than low price areas, which increases the price disparity. This is a divergence phase. The ripple of price growth then moved outwards and starting in mid-2014 this trend reversed, with properties in lower-priced areas gaining a much higher percentage of their value than the high-priced area. This second convergence phase ended in mid-2019. 


Over the recent divergence phase Norbury returned 70%, Knightsbridge 170%, a geometric outperformance of 60%. These are the extremes, so other areas lie in between these measurements of performance. The outperformance was subsequently in large part retraced with Norbury returning over 20% and Knightsbridge down over 10% after 2014. In mid-2019 divergence began once more.

Analysing performance disparities

These are not trivial disparities. To avoid these pitfalls, the professional investor or lender should have access both to all the necessary data, and to the means of analysing it effectively. For example, consider an investor backing a developer in buying a building with space for 20 units in Knightsbridge at the highs of 2014 – at the average price of over £3M per unit back then. They would have subsequently underperformed an investor with the same value of property in Norbury by 30%, at a cost of some £20M. 


Next consider Birmingham, a very different market, where the top-priced sectors just match the bottom-priced sectors in our London trail. While the timings and values may be very different, similar patterns can be observed. Here the largest divergence occurred 1995 to 2001, when the leafy suburbs to the south of Solihull (turquoise on the map above – currently£4400/m2) and beyond more than doubled in price. Over the same period the lower-priced inner districts such as Saltley (purple on the map above – currently £1400/m2) returned less than 20%. The subsequent 8-year convergence largely reversed this, as the cheaper areas returned well over 100% and the same southern suburbs some 30-40%. Fast-forward to the end of 2018 and Birmingham has entered a new convergence phase. 


Analysing regional areas of the UK

Heading either north or west in the UK, we find lower property prices, but the same patterns persist. Consider Liverpool, which has a wide spectrum of house prices. In its strong convergence phase late 2001 to mid-2005 the bottom-priced sectors towards Bootle (pink on the map below – currently £700/m2) returned over 150%, while higher-priced southeastern sectors (blue on the map – currently £3000/m2) returned just under 100%. 


This dizzy frenzy was followed by a long hangover, so in the divergence from 2005 to 2017 the very highest returns were clocked up in the more expensive parts, but even here did not reach 20%. Up towards Bootle, over 20% was lost over this nearly 12-year period. Since 2017 convergence has resurfaced and the cheapest areas have returned more than 5% over the last year. 


These three examples show how the responses of local areas to macro factors are dictated by price, or more precisely by £/m2. The only significant complications arise from differential sensitivities to the market as a whole, sometimes called the beta. (And how we use data to define that will have to wait for a future article!) 

Comprehensive datasets offer focused analysis

At the most basic level of decision making, we can gain a lot from knowing whether we are in convergence or divergence phases, how long to the next turning point, and a probable dispersion of performance before we get there. Using repeat sales indices that optimally track not just a sample of transactions but the entire population, we have a comprehensive dataset which supports analysis of return and risk at each level of aggregation. Even without further elaboration, that is a more focused and useful analysis than many more traditional methods. 

So what actionable points can we draw from this analysis? Well, on a purely cyclical basis, hit the road to Wigan for the tail end of this ripple, or perhaps Chelsea for the new wave. To take just one user case, consider an investor looking to use a portion of their fund to back a BTR development for the next five years. Now is not the time to be looking at developments in the outer suburbs – instead, shun Croydon and Thurrock for Prime Central London. 


Unlike, say, securities markets, residential property in the UK behaves in a regular fashion. While the relative predictability and stability of the national market may appear to break down at a very local level, that is not the whole story. While, there is clearly much more to discuss than we have space for here, we have shown that analysis of comprehensive data to identify local variations in the pace of growth offers property investment and lending professionals significant opportunities to outperform the market. 

Giles Heywood, Senior Quantitative Analyst at

‘REalyse (Treex Ltd) does not provide any form of investment advice or property advice or any other regulated function. Note that any information or opinions, presented or referred to in this article are for information purposes only. Any actions taken by a reader are done entirely at their own discretion, you are responsible for your own investment decisions and hold Treex Ltd harmless from the results of any such decisions’. Whilst every effort has been made to ensure the accuracy of the information herein some inaccuracies may remain.’

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