The Cycle

Tracking winners and losers in residential property

Giles Heywood



Dispersion produces winners and losers - it is quite common for the dispersion of returns to be quite wide even locally, and at the national level this is more significant. Currently in early 2022 this effect is very strong with a range of 23% over 12 months. Investors in the most affordable districts have experienced 21% return versus -2% in Prime London. The rapid re-appearance of significant return dispersion starting in 2020 confirms that the cycle which had been somewhat ‘on pause’ since 2018 is now continuing, the so-called ‘ripple effect’ propagating through ever-lower-priced regions. Return dispersion is the focus of attention in this document. Several analytical insights in conjunction reveal exactly what is happening, and the parameters selected in the first steps are guided by insights only clarified in later steps, but let us start at the beginning.

Repeat Sales Index


Indices summarise homeowners’ holding period return in the most accurate way possible by minimising the deviation of the fitted returns across the entire dataset in each locality. Because the entire set of transactions is available from the Land Registry, we can make sweeping statements about accuracy with full justification based on the entire relevant set of data, not just a sample. Furthermore, two key parameters of the indices - spatial and temporal sampling - can be customised once the systematic drivers have been revealed via factor analysis. The reference methodology for repeat sales index (RSI) construction is S&P Case-Shiller 1 Shiller, R.J., 1994. Macro markets. OUP Oxford. The benchmark UK indices for accuracy comparisons are the Land registry UK HPI, which also uses a repeat sales method.


UK HPI is less accurate. Accuracy is quantified across multiple major cities using the standard deviation of the difference between an index-derived return over a homeowner’s holding period and the return they actually achieved. The same metric is used for both our index and the benchmark UK HPI index. Comparing the error using our RSI methodology we find very similar and consistently slightly lower error rate averaging 5.9% versus a UK HPI rate of 6.3%. UK HPI is not seasonally adjusted whereas our indices are adjusted and so errors are measured accordingly, therefore seasonality effects are neutralised to minimise any impact on accuracy metrics. One credible reason as to why higher accuracy is achieved may be differences in definition of city boundaries.

Signal and noise

UK HPI is more volatile. Comparing our monthly RSI against UK HPI confirms that the trend is extremely similar, but UK HPI has both a strong seasonal cycle and high-frequency noise, so our monthly return volatility is 0.8% versus UK HPI of 1.8% for the example shown. The noise component is the result of over-fitting the data, and normally increases in-sample accuracy to the detriment of out-of-sample ‘true’ accuracy - hence the term over-fitting. A second reason for our higher accuracy is that both for estimation and appraisal we use all relevant holding periods: those prior to a given index date, those spanning it, and those subsequent. For an index provider or government body there are practical reasons for not including subsequent periods, otherwise index revisions never cease. In this document our primary objective is to understand the drivers of return, therefore accuracy trumps timeliness.