Explaining private rental growth

Explaining private rental growth Rhys Lewis 1. Summary This article focuses on the difference between ONS private rental price indices 1 and Valuation Office Agency (VOA) private rental market (PRM) statistics, both of which are based on the same underlying rental data collected by VOA rent officers. While a price index seeks to make pure price comparisons, VOA PRM statistics are simple averages over a 12 month period which can be influenced by other factors, for example changes in the composition and quality of the stock of privately rented properties. This article builds on the analysis published in January 2015 which focused on improvements to the ONS rental indices 2. It presents additional evidence to show that differences in growth between ONS rental indices and VOA PRM statistics since 2010 can be largely attributed to compositional changes in the private rented sector, in particular in London. Overall, the sample average rent 3 grows by around 35% between 2010 and 2015, 11% of this can be explained by index growth while an additional 17% is explained by changes in the composition of the sample between years. This is because more rental properties in more affluent areas are now included in the sample. The impact of this is to increase an average rents measure, however these effects are intentionally excluded from a price index measure which compares like with like. This highlights that care should be taken when making comparisons of simple level based growth rates with that of an index. 2. Private rents data This section explores differences between VOA PRM statistics and ONS rental indices. ONS rental indices are used to produce a publication called the Index of Private Housing Rental Price Indices (IPHRP) and they are used for the private rental component in the suite of consumer prices inflation statistics. The indices are also used in to calculate owner occupiers housing costs (OOH) in CPIH (a measure of consumer price inflation including OOH), which are calculated using a rental equivalence approach. The first part of this section focuses on coverage differences between VOA PRM statistics and ONS rental indices, methodological differences are then considered. The 12-month growth rates for the two series are presented in Figure 1 below, demonstrating the differences between the series. The VOA PRM statistics are only available from June 2011 onwards. 1 Henceforth referred to as ONS rental indices. 2 http://www.ons.gov.uk/ons/rel/cpi/consumer-price-indices/improvements-to-the-measurement-of-owneroccupiers--housing-costs-and-private-housing-rental-prices/index.html 3 Weighted to reflect the private rental sector. Office for National Statistics 1

Figure 1: VOA PRM and ONS rental indices (OOH weighted), England, 12-month growth Per cent 4 ONS rental indices VOA PRM - unadjusted 3 2 1 0-1 -2 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 2.1 Coverage The coverage of VOA PRM statistics and ONS rental indices are broadly similar, however, there are two slight differences; 1. Single rooms in a House in Multiple Occupation (HMO) are excluded from ONS s rental price indices. The indices were initially developed to calculate OOH and as owner occupiers do not own single rooms within a shared house, these properties were excluded from the indices. 2. Services such as heating, lighting, hot water and council tax can sometimes be included in a rental price. These service costs are excluded from ONS rental indices so that only the price to rent the property is included. VOA PRM statistics were adjusted to replicate the coverage of ONS s rental indices, the impact of this adjustment on the annual growth rate is shown in Figure 2 below. The PRM series in Figures 1 and 2 are both based on the same underlying data; the data in Figure 2 is presented on a quarterly, rather than monthly basis, consistent with its publication. Office for National Statistics 2

Figure 2: VOA PRM and adjusted to ONS rental coverage 12 month growth Per cent 3.5 3.0 Difference PRM - adjusted to ONS rental coverage VOA PRM - unadjusted 2.5 2.0 1.5 1.0 0.5 0.0-0.5 2012Q3 2012Q4 2013Q1 2013Q2 2013Q3 2013Q4 2014Q1 2014Q2 2014Q3-1.0-1.5 Note: VOA data from 2013Q2 are published on a bi-annual basis. Coverage differences reduce the growth in VOA s PRM statistics by an average of 0.2 percentage points over the period shown. This difference is primarily driven by the growth in room rental prices which is above that of the other property categories during the period; this is particularly true in the London region. The exclusion of services has had a negligible impact on the difference in the growth rates. The remainder of this section uses the VOA monthly data for the analysis. These monthly data match the data used to calculate the VOA PRM statistics (once the differences in coverage have been accounted for) and are the input data used to calculate ONS rental indices. Using these data allows for the construction of a longer monthly time series which can be used to further explore the differences between average rents and a rental price index. 2.2 Methodology Once differences in coverage are accounted for, the VOA monthly data used to calculate ONS s rental prices indices align well with data used in VOA s PRM statistics. However, the application of price index methodology to calculate price indices introduces other differences, these include; Sample composition: VOA PRM statistics are based on a simple average of all records collected over a 12 month period. ONS rental indices use a matched sample which is reselected every January 4 and followed for the next 12 months 4 The sample is selected every January using half of all properties collected by VOA rent officers in the past 14 months Office for National Statistics 3

Weighting and stratification: VOA PRM statistics are not explicitly weighted to reflect the private rental sector. The ONS rental price indices are weighted and stratified to reflect the rental sector (or owner occupiers sector for OOH) To account for some of these differences, the VOA monthly series presented in Figure 3 below is adjusted to match the weighting and stratification applied to ONS s rental indices when they are used to calculate OOH 5. In addition, the VOA monthly series has been smoothed over a 14 month period to reduce the volatility in the series; this also matches the validity period 6 used to calculate the ONS rental price indices.. A large difference in growth rates is seen between the VOA monthly input and the ONS rental index (weighted for OOH) during 2008 and 2009. This period is difficult to interpret due to changes in VOA s collection practices as a result of the introduction of the Local Housing Allowance (LHA) in April 2008. As a direct result of the introduction of the LHA, the size of the sample used to construct the private rent indices grew from around 115,000 in 2008 to around 230,000 in 2010. The increased sample size improved the quality of the data from 2010 onwards, therefore this article will primarily focus on differences in the series post 2010. Since 2010, the difference in growth between the two series has averaged around 0.8 percentage points, although the difference is greater in more recent years. 5 Only unfurnished properties are used to calculate OOH, furnished properties have been excluded. The stratification uses 9 English regions and 4 property types. 6 When a rental price is collected, it is assumed to be valid for 14 months from its entry date to the system, or until an update is received. Office for National Statistics 4

Figure 3: VOA monthly input and ONS rental indices, OOH weighted, England, 12-month growth rate 7 Per cent 12.0 10.0 Difference VOA monthly input ONS rental indices 8.0 6.0 4.0 2.0 0.0-2.0-4.0 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 ONS rental indices use a matched sample which is reselected every January. It is difficult to precisely account for differences introduced through the use of a matched sample. Figure 4 below shows the averages of the matched samples against the average of the VOA monthly data (smoothed over 14 months). Both series are weighted to reflect the owner occupiers sector. 7 A similar chart to this was presented in Figure 6.14 of the previous article (http://www.ons.gov.uk/ons/rel/cpi/consumer-price-indices/improvements-to-the-measurement-of-owneroccupiers--housing-costs-and-private-housing-rental-prices/index.html). Data used in the previous chart was constructed using data extracted when the methodology was still undergoing development and not final. Figure 3 above has been constructed based on the most available data and current OOH methodology. Office for National Statistics 5

Figure 4: VOA monthly average, matched sample average and ONS rental indices, OOH weighted, England, 2005-2014 per month 1000 950 ONS rental indices, with reference to Jan 2005 prices Matched sample average VOA monthly average 900 850 800 750 700 650 600 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 The matched sample average series in Figure 4 (black line) above shows the impact of reselecting the sample each January against the VOA monthly series aggregated using the same weights. Between certain years, for example 2012 and 2013, there are large increases in the average rent of the sample. In total, rents rose from around 810 per month at the start of 2010 to around 930 per month by March 2015. Around 45 of the 120 per month (or 40%) increase in rent can be attributed to changes between the samples, rather than growth within years. Removing these sample jumps through chain-linking, described in Box 2.2 below, and comparing its resulting growth to that of the index for England, Figure 5 shows that the growth in both series since 2010 are broadly in line, suggesting that between year, rather than within year changes are responsible for overall differences in growth. Box 2.2: Chain-linking Chain-linking is a process applied to all items in the construction of consumer price statistics. UK consumer price statistics are constructed as measures of price changes within year using a specified basket of goods and services. Each January the basket is refreshed and the series starts again. A continuous time-series is then formed by chain-linking on to the existing series. Further information on this process can be found in the Consumer Price Indices- Technical Manual, 2014 Office for National Statistics 6

Figure 5: Chained matched sample average and ONS rental indices, OOH weighted, England, 12-month growth rate Per cent 5 4 Chained matched sample average ONS rental indices 3 2 1 0-1 -2-3 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 2.3 Between year changes Applying the same methodology of aggregating stratum sample averages using weights to reflect the OOH sector, Figure 6 presents sample averages for England excluding the London region. Focusing on the series post 2010 (as the VOA sample size has been stable and is a better quality over this time period), some small differences are still seen across years, however these are now much less pronounced. This suggests that London is the region predominantly driving the difference seen at the England level, particularly differences between 2012-2013 and 2013-2014. Office for National Statistics 7

Figure 6: Matched Sample average and ONS rental indices, OOH weighted, England minus London per month ONS rental indices, with reference to Jan 2005 prices 900 Matched sample average 850 800 750 700 650 600 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 3. The London effect This section focuses on the London region which is a driver of the between year sample average differences post 2010 at the England level. As already mentioned, between year sample average differences do exist for other regions but to a much lesser extent compared to that of London, hence why London is considered here. 3.1 Sample jumps Aggregating the sample monthly rents of London property types using weights to reflect the OOH sector, Figure 7 below presents the sample averages for the London region. Differences broadly tally with those in Figure 4 but are much larger. Office for National Statistics 8

Figure 7: Matched sample average, OOH weighted, London per month 2400 2200 2000 1800 1600 1400 1200 1000 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 During the construction of ONS rental indices, each region is stratified by property type. To investigate these jumps between years, the analysis will focus on one particular type, in this case flats within the London region. Flats account for over half of the private rented stock in London 8. 8 2011 Census for England and Wales Office for National Statistics 9

Figure 8: Matched sample average, unfurnished, London flats 1600 per month 1500 1400 1300 1200 1100 1000 900 800 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 Similarly London flats, shown in Figure 8, show jumps similar to those seen at the London (all property type) level. Other property types also show similar jumps but are not presented as an individual series within this article. There are various possible explanations for these jumps in average rents which are explored in the remainder of this section. 3.2 Compositional changes Compositional change relates to how the mix and location of properties in the private rental market can change. For example, over time, the relative number of flats could increase within an area and the relative number of houses could decrease, as areas become more or less attractive for private renters. If the composition of the underlying stock has changed over time then our samples should also reflect this. To ensure a representative sample, rent officers in VOA and the other devolved governments are given targets to collect rental data in each area sufficient to represent 10% of the private rental market based on the 2011 census. Rent Officers are expected to maintain a high standard of knowledge of the private rental market in their area and over time the collection is refined using local market knowledge to reflect the changing rental market. This combined approach of regular and targeted collection based on market intelligence results in a representative sample across each area. 3.3 Evaluating Compositional change To estimate the impact of compositional changes, the rental data which form the matched samples each year were analysed at the Local Authority level in London. The Local Authority level was Office for National Statistics 10

selected for practical reasons as the classification was available on the dataset. It is important to note that there could be further compositional effects at work below this level which have not been accounted for. Analysing the implicit weights of Local Authorities in London for the matched samples found that for years when a large price increase is seen between samples (for example between 2012 and 2013), the sample has shifted towards areas with higher levels of average rental price (which are generally more affluent areas) and away from areas with lower levels of average rental price. For example, between 2012 and 2013, the top eight London Local Authorities with the highest rental levels; Kensington and Chelsea, Westminster, City of London, Camden, Hammersmith and Fulham, Islington, Tower Hamlets and Wandsworth, saw an increase in their relative weight. This shift does not impact a price index which aims to measure like with like and intentionally excludes these compositional changes. A simple representation of this is presented in Figure 9 below. In the example there are two Local Authorities (LA); LA 1 has monthly rent of 500, LA 2 has double the monthly rent at 1000. The monthly rent is the same in both year 1 and year 2 for the two LA s. However, the relative weights between the two years change, with LA 2 accounting for 50% of the total market in year one but 60% in year 2. The average rent has increased from 750 in year 1 to 800 in year 2 (a 6.7% increase), all due to a compositional change in the population. But there has been no increase in rent prices; which means that a rent price index would show no increase in price between periods 1 and 2. Figure 9: Example showing how compositional change can impact on average rents Local Authority 1 Local Authority 2 Rent Weight Rent Weight Average rent Year 1 500 50 1,000 50 750 Year 2 500 40 1,000 60 800 Albeit a very simple example, Figure 9 illustrates the impact compositional change can have. Focusing on unfurnished flats within London, Figure 10 below presents the average rent and implicit weight, as a percentage, for the top and bottom eight London Local Authorities based on their 2015 rental levels. A full breakdown of this Figure by Local Authority can be found in Annex A1. Office for National Statistics 11

Figure 10: Matched sample average rent 9 and implied weight (%) Top 8 10 Middle Bottom eight 11 Average rent ( ) Implied weight Average rent ( ) Implied weight Average rent ( ) Implied weight 2010 1570 10 980 62 770 29 2011 1990 15 1020 60 780 25 2012 1760 21 1090 57 810 21 2013 1840 32 1210 51 850 17 2014 1830 43 1270 43 890 14 2015 1950 38 1290 48 930 15 Figure 10 re-iterates how the sample for unfurnished flats had gradually moved away from less affluent areas and towards more affluent areas. By 2015, rental prices collected for unfurnished flats, within the top 8 affluent areas, make up around 40% of the sample. As is seen, the average rent of the top eight Local Authorities is also more than double that of the bottom eight Local Authorities. The impact of this is that between 2010 and 2015, the average rent for unfurnished flats in London has increased by more than half, but no individual category has grown by more than a third. This shift and difference in levels demonstrates how an average rental measure can move very differently to that of a price index. There could be many factors driving this change in the composition of the rental market in London. It is possible that more properties are now available for rent in more expensive areas. Land Registry data 12 provide evidence of a number of new developments (since 2011) within some of these affluent areas such as the City of London. Areas such as Hackney have seen the private rental market more than double between 2001 and 2011 13 of which this shift could be linked to the gentrification of this and other areas such as Tower Hamlets. In addition, these shifts in recent years could be partially influenced by the movement of households claiming the housing benefit. VOA rental officers do not collect rental prices for housing benefit supported tenancies 14. In April 2011, a cap was introduced to the Local Housing Allowance (LHA) and a subsequent report by DCLG 15 found that the London Boroughs that have experienced the largest decreases in LHA caseloads between 2011 and 2013 are largely in the central boroughs of Camden, Hammersmith and Fulham, Islington, Kensington and Chelsea, Tower Hamlets and Westminster. Those that have experienced the largest increases in LHA 9 Average rent reflect the sample averages each January, data is rounded to nearest 10 10 Camden. City of London, Hackney, Islington, Kensington and Chelsea, Tower Hamlets, Wandsworth, Westminster 11 Barking and Dagenham, Bexley, Croydon, Havering, Hillingdon, Redbridge, Sutton, Waltham Forest 12 http://landregistry.data.gov.uk/ 13 http://www.hackney.gov.uk/assets/documents/final-report-including-appendices.pdf 14 The author could not find definitive evidence that households claiming housing benefit in private rented accommodation experienced different rental growth to other tenants in private rented accommodation. So while it is possible that they may be experiencing lower rental growth it is unlikely that they would be experiencing higher rental growth given the LHA cap in place. 15 https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/329794/rr873-lha-impact-ofrecent-reforms-differences-by-place.pdf Office for National Statistics 12

caseload since the reform are mainly in the outer Boroughs - especially Barking and Dagenham, Ealing and Enfield. As Housing Benefit supported tenancies are moving from inner London Boroughs to outer London Boroughs it could be freeing up rental properties within the inner Boroughs for non HB tenancies (which mean VOA rent officers can now collect rental prices for these properties) and absorbing some of the private rental properties in the outer Boroughs (which means VOA rent officers can no longer collect rental prices for these properties). 3.4 Estimating compositional change To evaluate the impact of the changing rental market distribution within London for unfurnished flats, average rents at the Local Authority level were aggregated using weights from one year previous. Presented in Figure 11 below, compositional change at the Local Authority level is estimated to account for, around 75% of the difference between samples from 2010 to 2015. Earlier periods are slightly more difficult to interpret due to the expansion of the VOA sample and growth in the rental market during this period, but it suggests that other effects, perhaps quality, are operating in the opposite direction. Figure 11: Impact of compositional change, London flats, OOH weighed per month 150 Remainder Compositional change at LA level Sample difference 100 50 0-50 -100 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Note: 2005/06 relates to the sample jumps observed between 2005 and 2006 A similar picture is seen for unfurnished terraced properties in London with compositional effects at the Local Authority level estimated to account for, around 75%, of the jump in the sample average between 2010 and 2015. The impact of compositional change for detached and semi detached properties within London is more difficult to estimate and interpret given the unavailability of rental prices collected within some Local Authorities for these property types. However, applying the Office for National Statistics 13

same methodology and aggregating all property types within London using weights to reflect the OOH sector, compositional change at the Local Authority level within London is estimated to account for around 55% of the jump between 2012 and 2013, the period with the largest difference. An additional chart, weighted to reflect the private rental sector can be found in the Annex A2. Applying the same methodology to the other English regions and aggregating using OOH weights, the impact of compositional change at the England level is presented in Figure 12 below. Figure 12: Impact of compositional change, England, OOH weighted per month Remainder 90 Compositional change at LA level Sample difference 70 50 30 10-10 -30-50 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Figure 12 shows that compositional change at the Local Authority level accounts for; the entire difference in sample averages between 2011 and 2012, 55% of 2012/13, 90% of 2013/14 and 45% of the sample averaged between 2014 and 2015. Figure 13 below shows the aggregated impact of compositional change on England using weights to reflect the private rental sector rather than the owner occupied sector. Using these weights, compositional effects are estimated to account for, around 75% of the jumps in the sample average between 2010 and 2015. Office for National Statistics 14

Figure 13: Impact of compositional change, England unfurnished, private rental weighted per month Remainder 90 Compositional change at LA level Sample difference 70 50 30 10-10 -30-50 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Differences seen between Figures 12 and 13 reflect the different weights attributed to property types within regions to reflect the private rented sector or owner occupied sector. These charts show that since the introduction of the LHA in 2008, which led to an increase in the size and stability of the sample, changes in the composition of the rental sector within London are driving differences in growth between an average rental measure and a rental price index. These compositional changes can explain a large proportion of the jumps seen between samples, compositional effects below the Local Authority level could explain some of the remaining difference. 4. Quality This section considers how changes in the quality of the stock of rental properties can impact an average rent measure, an effect which prices indices aim to exclude. Quantifying quality change is difficult, primarily due to the unavailability of data, although some attempt is made within this section. 4.1 Improvement in quality An improvement in the quality of the private rental sector has been proposed as one of the reasons for the difference between growth in average rents and that of a price index. Some of the evidence presented as part of the January 2015 article points towards an increase in quality in the private rents sector. For example, in recent times, the size of the private rented housing stock has more the doubled, and some of this supply has probably come from the owner occupied market which is Office for National Statistics 15

generally in better repair that the rental market. Evidence from the English Housing Survey suggests that rented properties are now better maintained than they were a few years ago. People in more affluent areas are now more likely to rent than they were in 2001, which leads to a higher average rent paid, but without the rent prices of individual properties increasing to the same extent. Given the available data, it is difficult to quantify the direct impact of improvements in the quality of the rental stock, but the evidence mentioned provides some support that the rental stock has been improving. 4.2 Impact of new properties One way the rental stock could be improving is through the introduction of new properties. Focusing on the London region again, if new properties are entering the rental market that are of a higher quality and price than the existing private rental stock, then this could increase the overall rental level without affecting the growth of a price index. According to research from Beauchamp Estates 16 on purchase prices; the City has become a hub for new residential towers and new homes in both the West End and City enjoy a price premium. Evaluating sold property prices of new builds against existing builds available on Land Registry confirm this. During 2013, a peak of 165 new flats were registered on Land Registry within the City of London which achieved, on average, sold prices of 150,000 higher than existing flats in the area 17. If these new properties are also enjoying a rental price premium, as they are of a higher quality, this might help explain some of the remaining difference seen between the growth in an average measure of rents and that of a price index. In an attempt to evaluate this, new properties identified from Land Registry data were linked to rental properties in the VOA sample by postcode. Exact matches were not possible as full addresses were not always available on the VOA data. Therefore, only London unfurnished flats were considered for postcode matching, as Royal Mail allocate a different postcode to a building with several units e.g. a new block of flats. Averaging across the Local Authorities in London, new properties 18 do seem to achieve a rental price premium when compared against existing properties. Focussing on the January samples, this amounts to around 150 for 2012, 160 for 2013 and 70 for 2014, driven by Local Authorities such as Westminster in 2012 and 2013 and City of London in 2014. However when evaluating the impact of these new properties towards the overall jumps between samples they account for around 10 of the 2012-13 difference and 5 of the 2013-14 difference (so explain around 5% of the difference in sample averages), as new properties only make up around 5% of the VOA sample for unfurnished London flats 19. The introduction of new properties into the private rental sector has been shown to be an additional factor in explaining the difference between growth in an average measure of private rentals and that of a price index, using unfurnished flats as an example. Quality changes from other sources, such as properties leaving the owner occupiers sector and entering the private rental sector could also be contributing to this quality change; however we are not able to identify these properties in order to test this assumption. 16 http://www.propertywire.com/news/europe/city-london-new-homes-201409029537.html 17 http://landregistry.data.gov.uk/ 18 Defined in this analysis as a property built in the last 3 years. 19 Based on the January samples each year. Office for National Statistics 16

Acknowledgment The author would like to thank the Valuation Office Agency who provided the data presented within this article, particularly Philippe Guiblin, Anna McReady and Stephanie Astley for their analysis. Office for National Statistics 17

Annex: Compositional change additional data Section 3 discussed and presented Figures showing how the composition of the VOA sample has changed over time. This annex provides some additional data relating to that section. A1. Local Authority composition Figure A1 below presents implied weights and sample averages for unfurnished flats in London Local Authorities. As discussed in the main article, it can be seen that the implied weight of more affluent areas, such as Westminster and Wandsworth, has gradually increased over time from around 3.2% and 1.8% of the sample in 2010 to 8.7% and 6.7% of the sample in 2015. Less affluent areas such as Bexley and Croydon have seen their relative sample weight decrease over the same period. Office for National Statistics 18

Figure A1: Matched sample average rent 20 and implied weight (%), unfurnished flats, London Local Authorities Local Authority Implied weight 2010 2011 2012 2013 2014 2015 Average rent Implied weight Average rent Implied weight Average rent Implied weight Average rent Implied weight Average rent Barking and Dagenham 1.2 700 1.3 730 1.6 750 1.3 780 1.1 820 0.8 790 Barnet 1.3 980 1.0 1,030 1.5 1,100 3.5 1,190 2.9 1,230 3.7 1,230 Bexley 2.8 700 2.3 720 2.1 720 1.1 740 1.1 780 1.2 820 Brent 0.8 940 1.1 1,010 1.2 1,270 2.4 1,330 2.0 1,340 2.7 1,330 Bromley 6.1 820 5.2 820 4.2 890 3.7 940 2.5 950 2.5 1,030 Camden 1.0 1,970 0.7 1,770 2.7 1,680 4.9 1,920 5.8 1,950 4.9 2,040 City of London 0.1 1,490 0.0 1,650 0.1 1,460 0.3 2,030 0.5 1,940 0.4 2,100 Croydon 9.2 760 4.0 750 3.2 810 2.6 830 2.0 890 3.4 930 Ealing 1.7 930 2.7 940 2.3 950 1.6 1,040 0.9 1,140 0.6 1,160 Enfield 0.9 830 0.4 790 0.7 860 0.9 1,000 1.1 1,030 1.9 1,050 Greenwich 4.4 870 3.5 880 3.5 980 2.1 1,070 2.2 1,140 2.2 1,190 Hackney 1.3 1,090 0.8 1,240 1.2 1,440 2.3 1,430 3.3 1,480 3.0 1,630 Hammersmith and Fulham 0.2 1,210 0.6 1,380 0.6 1,490 1.0 1,840 1.0 1,640 0.9 1,510 Haringey 0.7 1,050 0.4 1,020 0.8 930 1.7 1,150 1.7 1,270 2.8 1,280 Harrow 2.2 920 2.0 930 1.2 980 0.8 990 0.6 1,000 1.7 1,120 Havering 1.6 720 1.9 730 2.1 750 1.7 780 1.4 810 1.1 850 Hillingdon 5.4 840 5.5 830 3.9 870 4.1 890 2.1 910 1.6 960 Hounslow 4.6 880 5.2 900 4.7 930 3.3 1,030 2.0 1,050 1.6 1,100 Islington 0.7 1,440 0.7 1,480 1.1 1,450 2.7 1,620 3.0 1,650 6.1 1,780 Kensington and Chelsea 0.8 2,470 4.2 2,780 2.4 2,750 3.0 2,840 4.1 2,760 2.6 2,910 Kingston upon Thames 4.0 990 3.6 990 1.2 1,050 0.8 1,160 1.5 1,150 3.1 1,200 Lambeth 9.4 1,060 9.0 1,150 8.5 1,260 7.3 1,390 6.8 1,450 6.1 1,530 Lewisham 6.3 850 6.0 850 6.6 890 5.9 980 4.0 1,030 4.3 1,060 Merton 4.4 990 3.6 1,000 4.3 1,090 2.5 1,200 1.6 1,330 2.3 1,320 Newham 0.3 830 0.3 850 1.0 940 1.6 1,010 2.4 1,100 1.8 1,100 Redbridge 2.7 820 3.9 830 4.0 850 2.9 900 2.5 940 2.5 940 Richmond upon Thames 4.4 1,130 5.3 1,120 5.9 1,210 4.1 1,330 3.9 1,410 4.3 1,490 Southwark 9.8 1,150 9.9 1,220 9.2 1,290 7.4 1,470 6.0 1,510 5.0 1,560 Sutton 4.4 730 4.5 760 2.3 780 1.1 850 1.5 890 1.7 920 Tower Hamlets 0.8 1,240 0.6 1,290 1.6 1,500 3.7 1,560 8.8 1,530 5.2 1,560 Waltham Forest 1.4 820 1.7 840 2.1 890 2.4 900 2.8 950 2.4 1,030 Wandsworth 3.2 1,160 4.6 1,280 7.9 1,340 9.4 1,520 10.8 1,540 8.7 1,570 Westminster 1.8 2,230 3.6 2,380 4.3 2,290 5.8 2,210 6.2 2,310 6.7 2,580 20 Average rent reflect the sample averages each January, data is rounded to the nearest 10. Implied weight Average rent Office for National Statistics 19

A2: London composition private rental weighted Similar to results presented in Figures 11, 12 and 13, Figure A2 below presents the estimated impact of compositional change, but at the London level, and using weights to reflect the private rental sector. Figure A2: Impact of compositional change, London unfurnished, private rental weighted per month 150 100 50 0-50 -100 Remainder Compositional change at LA Sample difference 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Between 2009 and 2015, compositional change in the private rental sector is estimated to account for, on average, around 70% of the jumps between samples. As noted previously, earlier periods are slightly more difficult to interpret due to the expansion of the VOA sample and growth in the rental market during this period. Office for National Statistics 20