Nottingham Core Hma

NOTTINGHAM CORE HMA

HOUSING MARKET NEEDS ASSESSMENT

UPDATE 2009

B.Line Housing Information Ltd

http://www.blinehousing.info/ [email protected] telephone : 0116 270 6441

Table of Contents

1) Introduction...... 1 Figure 1:1 Average annual build rate targets as set out in the East Midlands RSS8 2006 and 2009 ...... 1 Figure 1:2 Bramley affordability model – summary...... 2 2) Key Figures and Comparisons ...... 5 Figure 2:1 Key Variable settings for LA and submarket model outputs...... 5 Figure 2:2 Local Authority Level Needs Estimates...... 6 2.2. Comparisons with results from 2006...... 6 2.2.2. Lower Quartile Prices ...... 6 Figure 2:3 Change in lower quartile price 2006/2009 (LA Level) ...... 6 2.2.3. Affordability...... 6 Figure 2:4 Percentage of emerging households unable to afford market purchase...... 7 Figure 2:5 Split of main income bands...... 7 2.2.4. Gross Need, Supply and Net Need...... 7 Figure 2:6 Need and Supply ...... 7 3) House Prices ...... 8 Figure 3:1 Property Sales Count (June 2007 to March 2009)...... 8 Figure 3:2 Change in count of sales, June 2007 to March 2009...... 9 Figure 3:3 Count of sales – month on month comparison (June 2007 – March 2009)...... 9 Figure 3:4 Average House Prices (Comparison) Nottingham Core LAs ...... 10 3.1.2. Lower Quartile Price Fluctuations by Bed Count and Property Type...... 11 Figure 3:5 Lower Quartile price by bed count and type - Broxtowe Local Authority...... 11 Figure 3:6 Lower Quartile price by bed count and type - Erewash Local Authority...... 12 Figure 3:7 Lower Quartile price by bed count and type - Gedling Local Authority...... 13 Figure 3:8 Lower Quartile price by bed count and type - Nottingham Unitary Authority...... 14 Figure 3:9 Lower Quartile price by bed count and type - Rushcliffe Local Authority ...... 15 Figure 3:10 Comparison of lower quartile prices by property type and size – all Local Authorities 15 Figure 3:11 Comparison of property prices (March 2009) by type and size, LA level ...... 16 Figure 3:12 Local authority level number of sales by property type and size...... 16 4) Supply...... 17 Figure 4:1 Local authority level annual supply ...... 17 5) Households in need...... 17 5.1. Backlog Need ...... 17 Figure 5:1 HSSA data (2005 – 2008)...... 17 Figure 5:2 Comparison of average and trend figures from HSSA data (2009 projection) ...... 18 Figure 5:3 Waiting List Growth (HSSA data), 2009 value takes trend figure...... 18 5.2. Emerging Households ...... 18 Figure 5:4 Emerging Households by local authority...... 18 5.3. Owner Occupier Need ...... 19 Figure 5:5 Estimated levels of owner occupation by local authority...... 19 5.4. Migrations ...... 20 Figure 5:6 Migration statistics by local authority...... 20 6) Key Variables ...... 21 Figure 6:1 Scenarios changing 3 key variables and results ...... 21 6.2. Rent and Purchase Price Differentials...... 21 Figure 6:2 Impact of Private Rental Sector monthly cost variations on affordability percentages by LA ...... 22 Figure 6:3 Comparison of need figures - lower quartile purchase and rent (Key Variables set as above)...... 23 7) Results by Local Authority and Submarket Level Outputs...... 24 7.1.2. Deriving submarket level needs figures...... 24

Figure 7:1 Discussion of sources and methodology for model outputs ...... 24 7.2. Broxtowe...... 27 Figure 7:2 Model outputs at Local Authority level - Broxtowe ...... 27 7.2.3. Submarket Outputs - Broxtowe...... 28 Figure 7:3 Broxtowe submarkets ...... 29 Figure 7:4 Key Outputs by submarket (Broxtowe), including cross-border submarkets...... 29 7.3. Erewash...... 30 Figure 7:5 Model outputs at Local Authority level - Erewash ...... 30 7.3.4. Submarket Outputs – Erewash...... 31 Figure 7:6 Erewash submarkets ...... 31 Figure 7:7 Key Outputs by submarket (Erewash), including cross-border submarkets...... 31 7.4. Gedling ...... 32 Figure 7:8 Model outputs at Local Authority level - Gedling...... 32 7.4.5. Submarket Outputs - Gedling ...... 33 Figure 7:9 Gedling submarkets...... 33 Figure 7:10 Key Outputs by submarket (Gedling) ...... 33 7.5. Nottingham ...... 34 Figure 7:11 Model outputs at Local Authority level - Nottingham...... 34 7.5.6. Submarket Outputs - Nottingham ...... 35 Figure 7:12 Nottingham City Bigger Submarkets...... 35 Figure 7:13 Key Outputs by submarket (Nottingham City Council Bigger Submarkets)...... 35 Figure 7:14 Nottingham City Smaller Submarkets ...... 37 Figure 7:15 Key Outputs by submarket (Nottingham City)...... 37 7.6. Rushcliffe...... 38 Figure 7:16 Model outputs at Local Authority level - Rushcliffe...... 38 7.6.7. Submarket Outputs - Rushcliffe...... 40 Figure 7:17 Rushcliffe Submarkets...... 40 Figure 7:18 Key outputs by submarket (Rushcliffe) ...... 40 8) Submarket Output Comparisons by Local Authority, 2006 and 2009 outputs...... 41 Figure 8:1 Submarket Level Output comparisons, Broxtowe ...... 41 Figure 8:2 Submarket Level Output comparisons, Erewash ...... 41 Figure 8:3 Submarket Level Output comparisons, Gedling...... 42 Figure 8:4 Nottingham City Submarket Structure: Comparison 2006/7 to 2009...... 43 Figure 8:5 Submarket Level Output comparisons, Nottingham...... 44 9) Key Conclusions and Recommendations ...... 46 9.1.1. Broxtowe...... 46 9.1.2. Erewash...... 46 9.1.3. Gedling ...... 46 9.1.4. Nottingham ...... 47 9.1.5. Rushcliffe...... 47 9.1.6. Area Summary – Nottingham Core Housing Market Area ...... 47

1) Introduction

1.1.1.a. The UK housing market has undergone a turbulent and well publicised period of change since the Nottingham Core HMA 2006/7 was carried out. Although house prices have reduced to some extent across the board, in many areas they remain well above long term incomes multipliers and now combine with the additional barrier of increasingly stringent mortgage application criteria, as well as demands for larger deposits (many of the best mortgage deals require a deposit of 15 to 25%). The delay in a market adjustment may be exacerbated by a lingering determination among vendors to achieve peak values for their homes, as well as a need to reach a certain price to repay mortgages (particularly if a purchase was made during the 2004 – 2007 boom). The onset of economic recession has been accompanied by increased unemployment, though housing need as a result of this more recent development may not yet have filtered through to the data sources used for this analysis.

1.1.1.b. All evidence indicates that the need for affordable housing has not disappeared and imbalances continue to be evident across housing markets. Recent observations include continued growth in the number of repossessions; an increase in competition across the private rental sector; owner occupiers unable to sell turning increasingly to renting their homes (and moving into the sector themselves) to cover costs; and a slowdown in the rate of new developments.

1.1.1.c. The review of the East Midlands Regional Plan (RSS8) 2009 contains significant increases to the build targets across the Nottingham Core Housing Market Area. This may be a reflection both of the continued pressure on the public sector to provide new housing as well as a build up effect, because of the slowdown in the number of new developments. A continued recession is likely to reduce the possibility of meeting these increased targets, as developers and land owners wait for a market recovery.

Figure 1:1 Average annual build rate targets as set out in the East Midlands RSS8 2006 and 2009 Local Authority Annual Build Rate Target 2006 Annual Build Rate Target 2009 Broxtowe 270 340 Erewash 290 360 Gedling 310 400 Nottingham 945 1000 Rushcliffe 555 750

1.1.1.d. Increased build rate targets are coupled with a requirement to ensure the economic viability of affordable housing provision on any new development sites (following the Blythe Valley case 1), which is also delaying the provision of new housing as sites are examined.

1.1.1.e. The reliability of the outputs contained in this update can only be as good as the data itself. There are often discrepancies between the level of detail at which data is available, the timescale or categorisation by which it is gathered, as well as inevitable gaps in data relating to factors like migration or the private rental sector.

1.1.1.f. Several housing needs spreadsheet models were developed as part of the 2006/7 Nottingham core Strategic Housing Market Assessment, based on the ‘Bramley’ model. This captures the main components of housing need of:-

1 See http://www.bailii.org/ew/cases/EWCA/Civ/2008/861.html

• New emerging households that cannot afford market housing, with the ability to afford estimated by comparing entry level house prices or private sector rents to incomes • Backlog need based on local authority housing registers • A factor for owner occupiers falling into need • An element for need from migrations

1.1.1.g. This is then compared to the supply of affordable lets and sales from local authorities and housing associations.

1.1.1.h. The model can be summarised as:-

Figure 1:2 Bramley affordability model – summary

The basic model for estimating affordable housing need is as follows:-

Net Need (units per year) = Gross Household Formation x % aged under 35 unable to buy (adjusted for wealth) + proportion (33%) x net migration (household equiv) x % <35 unable to buy + proportion (0.345 %) x owner occupier households (moving to social renting) + proportion over the ‘policy period’ (e.g. 20% over 5 years, 10% over 10 years) x waiting list ‘backlog’ above need threshold

Less net annual new and relets of social rented housing

1.1.1.i. It is a simplified, systematised model which does not capture all aspects of need, although many of them will be partially reflected in the main components. For example households living in unsuitable accommodation are not specifically included, but many of them will be in the backlog need on local authority housing registers. The model will therefore tend to under-estimate need, and other methods have been consistently shown to give higher needs estimates.

1.1.1.j. However while very high levels of need may be justifiable by the evidence, in current housing market circumstances they are unlikely to be delivered by the Planning system, and many alternative and inventive methods will be required to have any substantial impact on the level of need. The basic problem is a dysfunctional, volatile housing market, and seeking ever higher contributions to affordable housing provision through S106 agreements will not address that, and indeed may make it worse.

1.1.1.k. The 2006/7 SHMA included a development of a needs model at housing submarket area 2 level. This is a geographical structure which aims to capture ‘real’ sub areas within the Nottingham conurbation which influence household choices. It was derived by using:-

• House price and house price change patterns • Short distance moves • Urban morphology which subdivides the built up area , such as major roads, railway lines, parks, commercial and industrial areas, open space, etc • Local knowledge – this more subtle, implicit awareness of differences is often found to be the best indicator of the real urban structure 3

2 http://www.rics.org/NR/rdonlyres/B98D6404-2FB7-45D3-93A8-6F8B18618251/0/39217_Housing_Market_Fibre4.pdf 3 https://www.researchgate.net/publication/23772013_Forecasting_Housing_Prices_under_Different_Submarket_Assumptions

1.1.1.l. For this update the submarket model has been developed further, also using ‘larger’ submarket areas which have proved to be useful in policy development for Nottingham City Council. The updated model also includes several more detailed and accurate data sources, such as using actual housing registers records and lettings data, rather than proxy estimates. But models at these more detailed spatial scales cannot yet, if ever, capture the flows and migrations between submarket areas, which are extremely complex and variable.

1.1.1.m. This means that even if some submarkets show little or no need in themselves, supply within them may meet needs arising from elsewhere, so there may be good reasons for providing additional affordable housing in them, such as available land or lower prices. However it does also mean that local authorities should be particularly cautious about creating a local over supply of affordable housing and concentrating deprivation, or increasing the level of ‘churning’ within an area resulting in increased unpopularity and poor reputation.

1.1.1.n. They remain simply data driven models which must rely on the quality and coverage of the data inputs to them, and which cannot capture the full complexity of needs in a dynamic, shifting and inefficient housing market. The data and models provide part of the evidence base and a decision support system, but policy judgments and interventions should also take into account and balance more up to date qualitative local knowledge, experience and perceptions.

1.1.1.o. The methods used in the 2009 model reiterate those followed in the 2006 HMA report.4 However, there are some differences between the datasets which are likely to have an impact on the resulting outputs. These factors are listed below:

1. Emerging Households: In the 2006/7 submarket model emerging households were derived using Census data, rolled forward to estimate how many people would have reached the 18-35 age group and formed households. Though this method has been used again in this model for the smaller submarkets, where possible figures are derived from ONS population projections by lower super output area, 2007 which should include additional growth from migration and other factors. 2. The income element of the affordability calculations at submarket level in 2006/7 was derived using the ASHECASS model, which attributes earnings from the Annual Survey of Hours and Earnings (given at local authority level by occupation) to socio-economic groups based on the Census. Incomes in the 2009 model are based on CACI incomes data by ward (for LA level data, 2009), or by postcode (2006) for submarket level data. 3. For the 2009 model CACI income bands most sensitive to entry level price changes (i.e. £15K -£30K) were split into four to increase the responsiveness of the model. 4. The 2009 model includes a basic search of rental costs by submarket which adds an additional affordability factor which may be discounted to give ‘urgent’ need. 5. Backlog need in the 2009 model is derived from actual detailed waiting list data where available. Where this is not available, HSSA totals are allocated across submarkets according to the corresponding proportion of households in the private rented sector in each submarket based on the 2001 Census. In 2006 backlog need was estimated by using the Private Rented Sector adjusted for affordability using ASHECASS. As a proxy this matched fairly well with HSSA totals in 2006, but significant growth in waiting lists more recently and the growing time difference makes the proxy less robust. feasible.

4 See http://www.blinehousing.info/NottCore_HMA/SHMA_report_sections/Housing_need.PDF

6. The owner occupier need factor has been increased from 0.234% or 1 in 427 to 0.345% or 1 in 290, based on an increase in the number of repossessions. The figure used is derived from statistics published by the Council of Mortgage Lenders in 2008. 7. An additional element has been added to allow for the increasingly important role of the private rented sector in serving those who are somewhere between purchase and social rent, by choice or otherwise. The model allows the effect of different rent levels on overall affordability to be examined, and provides an indication of the number of households likely to fall into each group (can’t rent/can’t buy).

2) Key Figures and Comparisons

2.1.1.a. The outputs produced by the model are based on the following:

• House Prices from March 2008 to March 2009 • CACI Incomes data, 2009 (LA level only, submarket analysis uses 2006 incomes data) • Emerging households calculations are based on, depending on the level of detail: o Chelmer Model projections by household type/Local Authority o ONS population projections by Lower Super Output Area (for larger submarkets) o Census 2001 population by age group by Output Area, rolled forwards to 2008 (for some smaller submarkets) • Private rental sector rents are based on a basic web search of prices as advertised on www.rightmove.co.uk , rent levels by local authority as published on www.dataspring.org.uk , and cross-tenure affordability data provided by the Hometrack Housing Intelligence System, www.hometrack.co.uk). • Backlog need data is based on HSSA returns and waiting list data where available. Where housing waiting list data has not been available, the HSSA total is allocated across submarkets to match the proportions of private rent in each submarket as at the Census 2001, based on the assumption that need will arise mainly from this tenure. Problems with deriving backlog need in this way are discussed later. • Supply is based on local authority lettings data where available, and CORE data. Totals are compared with HSSA returns to assess accuracy.

2.1.1.b. Key Variables are set as follows, unless stated otherwise:

Figure 2:1 Key Variable settings for LA and submarket model outputs KEY VARIABLES Inputs in white cells House Price Fluctuation 0% Mortgage Multiplier 3.5 Size of Deposit 10% Policy Period (years) 7.5 Proportion unable to access mortgage 51% Owner Occupier Need Factor 0.345% Equity Share in Intermediate Housing Products 40% % with resources from other sources 0% Lower quartile private rent level (Rent Service = 1; Submarket Average = 2; Housing Intelligence = 3 1

Figure 2:2 Local Authority Level Needs Estimates Total Emerging Emerging Lower % can't Emerging Private % can't Households Households Quartile Income afford Households Rent afford LA (10 years) (annual) Price required purchase can't afford LQP rent Broxtowe 8,852 885 £120,000 £30,857 52% 460 £394 17% Erewash 7,853 785 £95,000 £24,429 40% 314 £360 13% Gedling 7,403 740 £100,000 £25,714 39% 289 £416 19% Nottingham 29,956 2,996 £82,500 £21,214 39% 1,168 £373 18% Rushcliffe 8,213 821 £139,995 £35,999 50% 411 £412 14% Annual Owner Need Backlog backlog Owner Occupier from Need (Policy GROSS Occupiers Need migration (HSSA) Period) NEED Broxtowe 21,250 73 80 2,344 313 926 Erewash 23,099 80 8 3,627 484 886 Gedling 23,570 81 19 3,275 437 826 Nottingham 37,498 129 124 17,083 2,278 3,699 Rushcliffe 20,789 72 46 1,452 194 723 Annual Proportion able to Number Supply afford but unable unable to Total unable to afford + (Net of NET to access access unable to access transfers) NEED mortgage mortgage mortgage Broxtowe 481 445 24% 212 672 Erewash 529 357 31% 243 557 Gedling 430 396 31% 229 518 Nottingham 3,410 289 31% 929 2,097 Rushcliffe 361 362 26% 213 624

2.2. Comparisons with results from 2006

2.2.2. Lower Quartile Prices

Figure 2:3 Change in lower quartile price 2006/2009 (LA Level) Lower Quartile Price Lower Quartile Price LA 2005-06 2008-09 Broxtowe £103,000 £120,000 Erewash £93,125 £95,000 Gedling £105,000 £100,000 Nottingham £85,000 £82,500 Rushcliffe £142,000 £139,995

2.2.2.a. Given the level of speculation in the media, on an aggregated basis across the housing market area there is surprisingly little change in lower quartile house prices across each local authority. Broxtowe alone shows a significant change, though house prices have gone up, not down. This corroborates the notion that many of those unable to purchase property in 2006 are now little closer to affording their own home.

2.2.3. Affordability

2.2.3.a. The following table compares the percentage of emerging households unable to afford market purchase, deducting 10% who may have access to financial resources from elsewhere (for example parental help), as applied in the 2006 study. The method of splitting the most highly populated income bands (see figure 2.5 below) means the later model picks up more people below the lower quartile threshold.

Figure 2:4 Percentage of emerging households unable to afford market purchase 2006 unable to afford 2009 unable to afford (minus 10% resources (minus 10% resources LA from elsewhere) from elsewhere) Broxtowe 30% 47% Erewash 32% 36% Gedling 29% 35% Nottingham 38% 35% Rushcliffe 42% 45%

2.2.3.b. As the income range for lower quartile housing so often lies within the most common incomes, the bands containing the largest number of people have been split evenly into 4 as follows:

Figure 2:5 Split of main income bands Income Band Split 1 Split 2 Split 3 Split 4 £15,000 - £20,000 £16,250 £17,500 £18,750 £20,000 £20,000 - £25,000 £21,250 £22,500 £23,750 £25,000 £25,000 - £30,000 £26,250 £27,500 £28,750 £30,000

2.2.3.c. Although splitting in this way will not be an altogether accurate reflection of reality, it will help to give an improved indication of the volatility of this factor. In the most populated income bands, a relatively small change in house prices can move large numbers of households in or out of the need calculation.

2.2.4. Gross Need, Supply and Net Need

Figure 2:6 Need and Supply Gross Gross Waiting Waiting Need Need Supply Supply Net Need Net Need LA List 2006 List 2009 2006 2009 2006 2009 2006 2009 Broxtowe 2,508 2,344 733 882 465 481 168 401 Erewash 1,633 3,627 560 855 238 529 199 326 Gedling 2,700 3,275 675 796 450 430 153 366 Nottingham 14,270 17,083 3,484 3,580 3,190 3,410 192 170 Rushcliffe 1,442 1,452 701 681 298 361 236 320

2.2.4.a. The main area of change over the period has been the growth in size of the housing registers across Erewash, Gedling and Nottingham. Though the waiting list has decreased in Broxtowe, house prices have gone up bringing more emerging households into need. Overall, although supply does show growth, it is not enough to reverse the trend of growing need for more affordable housing.

3) House Prices

3.1.1.a. House prices have been under constant scrutiny, first because they seemed to be rising inextricably beyond the reach of almost all average incomes, then since the market has begun to correct at the expense of the economy.

3.1.1.b. The Hometrack Housing Intelligence System is used below to give an overview of prices across the 4 local authority areas.

3.1.1.c. The overall frequency of sales has fallen drastically across all areas since 2007, towards the end of the housing boom. Both linear and month to month comparisons show a steady decline in the number of purchases, although the seasonal nature of the market is still evident. This fall in the number of sales reflects, among other things, continued affordability problems, barriers to access for potential purchasers, consumers waiting for further price drops before they enter the market, and fewer properties remaining on the market while prices are falling. In addition, continued economic recession and widespread instability in the employment sector are likely to undermine the confidence of potential buyers.

Figure 3:1 Property Sales Count (June 2007 to March 2009) Date Rushcliffe Nottingham Broxtowe Gedling Erewash Jun-07 698 1,516 627 626 624 Jul-07 729 1,582 672 679 692 Aug-07 790 1,732 712 802 759 Sep-07 676 1,727 667 742 728 Oct-07 638 1,696 604 709 681 Nov-07 545 1,517 623 614 595 Dec-07 512 1,334 568 600 543 Jan-08 427 1,158 504 532 472 Feb-08 361 1,019 365 458 432 Mar-08 316 942 327 375 408 Apr-08 345 961 318 354 433 May-08 357 917 320 352 433 Jun-08 355 863 336 360 445 Jul-08 348 804 329 358 393 Aug-08 338 723 319 319 339 Sep-08 324 673 276 315 279 Oct-08 304 605 281 288 289 Nov-08 263 565 243 249 271 Dec-08 236 524 252 240 256 Jan-09 192 440 191 175 192 Feb-09 147 320 148 137 127 Mar-09 84 168 63 46 60

Figure 3:2 Change in count of sales, June 2007 to March 2009

Increase/Decrease in Sales by LA (June 2007 = 0)

200 Rushcliffe Nottingham 150 Broxtowe Gedling

100 Erewash

0 Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- 07 07 07 07 07 07 07 08 08 08 08 08 08 08 08 08 08 08 08 09 09 09 -50

-100

-150

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Figure 3:3 Count of sales – month on month comparison (June 2007 – March 2009)

Count of Sales - comparing month/month

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0 Rushcliffe Nottingham Broxtowe Gedling Erewash

Jun-07 Jun-08 Jul-07 Jul-08 Aug-07 Aug-08 Sep-07 Sep-08 Oct-07 Oct-08

Nov-07 Nov-08 Dec-07 Dec-08 Jan-08 Jan-09 Feb-08 Feb-09 Mar-08 Mar-09

Source: Housing Intelligence System. Hometrack ( www.hometrack.co.uk )

3.1.1.d. The trend of average price over time for the whole study area shows decreasing prices have had little impact on the steep inflation shown across all five local authorities since 2002.

Figure 3:4 Average House Prices (Comparison) Nottingham Core LAs

3.1.2. Lower Quartile Price Fluctuations by Bed Count and Property Type

Figure 3:5 Lower Quartile price by bed count and type - Broxtowe Local Authority

3.1.2.e. Lower quartile prices in Broxtowe are behaving similarly across all property types and sizes, though to different extents. The fall in price of 1 and 2 bed flat prices has been most pronounced, and corresponds with similarly notable increases over 2003/4. Houses, though more expensive, have fluctuated much less over the period and are suffering a less severe price drop. Land Registry prices as used in the revised housing needs model show an overall increase in lower quartile prices for Broxtowe.

3.1.2.f. Erewash lower quartile prices again show much more volatility in the prices of flats compared to houses. There is also an intriguing spike in the price of 2 bed flats in the area during September 2008, though this could have been caused by a very few sales.

Figure 3:6 Lower Quartile price by bed count and type - Erewash Local Authority

3.1.2.g. Prices in Gedling are fairly smooth across all property types in the lower quartile until around March 2008. After this it looks as though prices for 2 bed flats and 2 and 3 bed houses are converging, and there is a fairly substantial drop in price for 1 bed flats (returning to just above 2003 levels). There is no data for 1 bed flats in March 2009, suggesting there were no sales of that property type during that month. Four bedroom houses increased in price significantly from March 2004 to March 2005, and since then look to have been following a repetitive pattern of rising and correcting, to stay at around £180 – £190,000. Generally the larger properties tend to demonstrate different patterns to those which are more accessible to the wider market.

Figure 3:7 Lower Quartile price by bed count and type - Gedling Local Authority

3.1.2.h. Lower quartile prices in Nottingham City are volatile, reflecting the complex urban housing market. However, like the neighbouring authorities, the general direction of property prices is downwards. In Nottingham the decline is more severe across all property types, including larger houses. 1 and 2 bed flats in particular show the sharpest decline. Two bed flat prices are particularly interesting, having been benefiting from a premium (probably because of all the new developments) until March 2007, when they drop to compete with 2 bed houses (which arguably is where they should be as a viable substitute).

Figure 3:8 Lower Quartile price by bed count and type - Nottingham Unitary Authority

3.1.2.i. Prices in Rushcliffe are the highest across the whole study area, for all property types. The gaps between each property type (or each ‘rung’ on the property ladder) are more comparatively stable than the other authorities. As prices for 2 bed flats and 2 and 3 bed houses have all declined, 1 bed flat prices show an upturn over the last quarter, converging with 2 bed houses. However, 1 bed flat prices generally seem more volatile than other property types, so it is unsafe to make any judgement based on price movement during only one quarter.

Figure 3:9 Lower Quartile price by bed count and type - Rushcliffe Local Authority

Figure 3:10 Comparison of lower quartile prices by property type and size – all Local Authorities Compare Lower Quartile Prices by Property Type & Size March 2009 Broxtowe Erewash Gedling Nottingham Rushcliffe 1 bed flat £55,000* £68,000* £52,200* £50,000 £101,000 2 bed flat £68,500 £73,000 £88,500 £75,000 £80,000 2 bed house £90,000 £83,500 £95,000 £71,000 £106,000 3 bed house £109,300 £105,000 £100,000 £80,000 £140,000 4 bed house £163,000 £152,000 £175,000 £105,000 £235,000 *Sep-08 price has been used where Mar-09 price is unavailable Source: Hometrack Housing Intelligence System

Figure 3:11 Comparison of property prices (March 2009) by type and size, LA level

Price Comparison by property type & size

250000

200000

150000

100000

50000

0 1 bed flat 2 bed flat 2 bed house 3 bed house 4 bed house

Nottingham Broxtowe Gedling Erewash Rushcliffe

Source: Hometrack Housing Intelligence System

3.1.2.j. There continues to be a clear hierarchy between prices in the city and prices in the more suburban authorities. Rushcliffe clearly carries a premium which is probably attributable to its more rural character. The hierarchy is least evident in relation to 2 bed flats, for which prices are very similar and Gedling shows as the most expensive. In terms of frequency there are far fewer sales of flats than houses, which is a clear reflection of market demand (particularly since we know there is currently an abundant supply of flats available). Three bed houses remain the most commonly purchased property in all local authorities.

Figure 3:12 Local authority level number of sales by property type and size Property Count by property type and size Sept 2008 Nottingham Broxtowe Gedling Erewash Rushcliffe 1 bed flat 68 9 10 9 10 2 bed flat 175 44 39 8 40 2 bed house 537 284 204 386 156 3 bed house 1,161 683 510 716 433 4 bed house 265 172 154 143 270 Mar-09 count is unavailable

4) Supply

4.1.1.a. Supply figures at LA level are based on CORE RSL lettings and HSSA data. The CORE entries for LA stock are included in the model for reference but are not used at this level (to avoid duplication with HSSA data). However, where LAs currently holding social housing stock do not participate in CORE this poses a problem with disaggregating the data to submarket level.

4.1.1.b. Across all local authorities the total yearly lettings recorded in the 2008 HSSA data were less than those recorded in 2007, indicating a downward trend. Erewash and Rushcliffe have transferred all their stock to RSLs, so their HSSA entry is zero. In this example, the trend has been used to estimate annual supply from local authorities, rather than the average (which is higher and does not reflect the apparent reduction in annual lets).

Figure 4:1 Local authority level annual supply Result Total (select Lettings Net Lettings Net of from Average appropriate results to of Transfers Transfers CORE Annual HA combine for annual LA 2007 2008 Trend Average data Lettings supply) 2007 2008 2009 2009 Broxtowe 401 375 349 388 175 132 481 Erewash 0 0 0 0 0 529 529 Gedling 313 299 285 306 184 145 430 Nottingham 2,780 2,696 2,612 2,738 0 798 3,410 Rushcliffe 0 0 0 0 60 301 361

5) Households in need

5.1. Backlog Need

5.1.0.a. The numbers on the waiting list are taken from HSSA returns, and move differently for each local authority. The trend has been used rather than the average – this decision must be judgment based and which figure is appropriate may vary for each authority. Apart from Broxtowe and Gedling, all authorities show an increase in numbers on their waiting lists, though the numbers in Rushcliffe have remained fairly stable. The figure used in the needs calculation will need to take account of internal knowledge of waiting list changes. In all cases, the number of applicants for housing far outstrips supply.

Figure 5:1 HSSA data (2005 – 2008) Section C: Housing waiting list and choice-based lettings

Households on the housing waiting list at 1st April

1a. Total households on the housing waiting list at 1st April Trend LA 2005 2006 2007 2008 2009 Broxtowe 2,502 2,508 2,293 2,448 2,344 Erewash 0 1,633 2,482 2,386 3,627 Gedling 4,849 5,218 3,251 4,074 3,275 Nottingham 11,329 13,201 14,159 15,668 17,083 Rushcliffe 1,534 1,442 1,535 1,451 1,452

Figure 5:2 Comparison of average and trend figures from HSSA data (2009 projection) LA Average annual waiting list Trend Broxtowe 2,438 2,344 Erewash 1,625 3,627 Gedling 4,348 3,275 Nottingham 13,589 17,083 Rushcliffe 1,491 1,452

Figure 5:3 Waiting List Growth (HSSA data), 2009 value takes trend figure

Waiting List Growth

18000 2005 2006 16000 2007 2008 2009 14000

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0 Broxtowe Erewash Gedling Nottingham Rushcliffe

5.2. Emerging Households

5.2.0.a. A proportion of emerging households will be unable to afford accommodation at open market cost. Data for emerging households is taken from the DCLG/Chelmer Household Projections, 2004. These projections are particularly useful in needs analysis as they give a breakdown by age and household type, as well as providing an estimate of average household size. This allows the emerging households (given to be between 18 and 35) to be isolated fairly effectively (in theory). Results are shown below. The period considered is 10 years, based on the projections for 2011 and 2021. The model calculates how many households over that time will move through the emerging households age group, and apportions them annually. Numbers are similar for all authorities except Nottingham, which obviously has a greater number of new households as a large city.

Figure 5:4 Emerging Households by local authority LA Emerging households over 10 years Annual emerging households Broxtowe 8852 885 Erewash 7853 785 Gedling 7403 740 Nottingham 29956 2996 Rushcliffe 8213 821 18

5.3. Owner Occupier Need

5.3.0.a. The model derives owner occupation levels by taking the number of owner occupiers (who live in their home, with a mortgage, including shared ownership) at the 2001 Census as a baseline, then increasing this by 1% per year (x 1.08), based on the addition of new stock, to give an estimate of the number of owner occupiers in the housing market today. This level of growth could be argued up or down, because:

a) During the beginning of the boom, more people were entering the property market than before with easier access to capital and a huge number of incentives. b) Towards the end of the boom, fewer people could afford to enter the property market because of unprecedented house price inflation.

5.3.0.b. A report by Savills Research (February 2009)5, based on the Survey of English Housing suggests a fairly steep decline in owner occupation, which is being replaced by private renting. Would-be buyers are kept in the private rental sector while they are priced out of the purchase market. This situation is upheld by the continued (though lately more limited) availability of buy-to-let mortgages. However, many transactions during the boom were remortgages or purchases by existing owner occupiers using capital from their own home, which could contribute significantly to need if these households are unable to service their debt.

Figure 5:5 Estimated levels of owner occupation by local authority Owner Proportion Increase to Occupied with of 2009 (+1% LA All Households mortgage households per year) Ashfield 46,601 18,870 40% 20,380 Broxtowe 45,422 19,676 43% 21,250 Erewash 46,219 21,388 46% 23,099 Gedling 47,546 21,824 46% 23,570 Nottingham 116,070 34,720 30% 37,498 Rushcliffe 43,648 19,249 44% 20,789

5.3.0.c. According to the Council of Mortgage Lenders, 1 in 290 mortgages were repossessed during 2008. They have predicted a rise in this proportion during 2009, though because of technical issues with data compatibility the final number of repossessions predicted will be revised downwards 6. The 1 in 290 (or 0.345%) figure has been used to assess owner occupier need in the latest model, though this figure can be revised up or down if new figures emerge. This is an increase from the figure originally used in the previous adaptation of the Bramley model (0.234%, or 1 in 427), and reflects the increased risk to owner occupiers in today’s market.

5 “The decline of owner-occupation” , Savills Research (February 2009) last accessed 29 May 2009, http://www.grantmanagement.co.uk/media/resources/Savills%20Feb%2009.pdf 6 See CML Press Release, 15 May 2009 (last accessed 29 May 2009), http://www.cml.org.uk/cml/media/press/2262

5.4. Migrations

5.4.0.a. Migration statistics are difficult to incorporate, as they tend to be slightly behind other population data given the transient nature of the group, but may be already accounted for to some extent in emerging households and population growth projections. As the model uses 2004 projections in deriving emerging households, more recent net migration may add to the total number of households potentially in need.

5.4.0.b. The migration figures used in the model are therefore based on ‘ONS population projections: Natural change and migration summaries for local authorities and higher administrative areas (2006)’, and take the ‘all migration net’ figure for each local authority. This is then divided by the average household size (using the 2011 projection figure) to give an estimate of the number of migrant households. Based on the Bramley model, it is assumed that around a third of migrant households who are in need will apply for affordable housing.

5.4.0.c. The accuracy of these figures is questionable in relation to housing need, as it is very difficult to know how many migrants remain in the area long term, how many actually apply for housing, or whether there is a significant difference in household size. The assumption that their socio-economic status, incomes and affordability criteria will be the same as other residents is also debatable.

Figure 5:6 Migration statistics by local authority ONS Net Migration LA ONS Net Migration (2008) ONS Net Migration (2009) (2010) Broxtowe 1,100 1,000 1,000 Erewash 100 100 200 Gedling 300 300 400 Nottingham 2,400 2,000 1,700 Rushcliffe 600 600 700 % % likely Ave. hsehld Number of unable to size (from migrant Annual to afford apply Need Migrations over 3 years 2011 households migrant market for from in- LA (people) projections) over 3 years households housing AH migrants Broxtowe 3,100 2.221 1396 465 52% 33% 80 Erewash 400 2.22 180 60 40% 33% 8 Gedling 1,000 2.21 452 151 39% 33% 19 Nottingham 6,100 2.107 2895 965 39% 33% 124 Rushcliffe 1,900 2.291 829 276 50% 33% 46

6) Key Variables

6.1.1.a. The model allows key variables to be altered to assess the potential impact on need. The significance of each variable can be considerable. Fluctuations in house price, the policy period over which backlog need is addressed, and the number of households with resources from other sources have the largest impact on the resulting need figure. A figure for the proportion of households able to afford but unable to access mortgage capital 7 is given in the table below, and has a significant impact on the overall number of households who cannot owner occupy, but is not applied to the needs figure as it is unlikely that those households would apply for or want social housing. Four scenarios are given below.

Figure 6:1 Scenarios changing 3 key variables and results Base Scenario Scenario Scenario KEY VARIABLES scenario 1 2 3 House Price Fluctuation 0% -15% 0% 0% Mortgage Multiplier 3.5 3.5 3.5 3.5 Size of Deposit 10% 10% 10% 10% Policy Period 7.5 7.5 5 7.5 Proportion unable to access mortgage 51% 51% 51% 51% Owner Occupier Need Factor 0.345% 0.345% 0.345% 0.345% Equity Share in Intermediate Housing Products 40% 40% 40% 40% % with resources from other sources 10% 10% 10% 0%

Broxtowe Erewash Gedling Nottingham Rushcliffe Net Need (Base Scenario) 561 342 405 419 411 Net Need (Scenario 1) 408 273 333 172 321 Net Need (Scenario 2) 717 583 623 1,558 507 Net Need (Scenario 3) 605 373 435 538 453

6.2. Rent and Purchase Price Differentials

6.2.0.a. The needs figures given throughout this report are based on affordability of lower quartile house prices. However, there is a clear and relatively wide gap between social renting and owner occupation which is filled frequently, and often effectively, by private renting. Although private renting in Britain has historically been seen as a ‘stopgap’, temporary tenure,

7 Based on Council of Mortgage Lenders statistic, 51% decline in the number of loans since January 2008

it has been argued that the continued imbalance in housing markets and increasing separation of the link between earnings and ownership have led to a shift in its role 8.

6.2.0.b. As a result, the use of lower quartile house prices as a measure of affordability may give an unrealistically high indication of the demand for social housing. In actuality, people may be happy to remain in private renting for the longer term, rather than apply for social housing after a period if unable to buy.

6.2.0.c. To attempt to account for this argument within the model, a secondary need figure has been generated which deducts those households who can afford lower quartile private rent 9 from the total figure unable to afford lower quartile purchase. The remaining households left in need are those that are more likely to more urgently require social housing, being unable to afford anything else. A judgment is then required as to which figure most accurately reflects the true picture, as not all people in private renting will be happy or suitably housed (though this is true of all tenures). This also prompts an argument for further focus on the private rented sector to ensure that those who do remain in the tenure long term are well treated and protected.

6.2.0.d. Establishing a realistic lower quartile rental cost figure (for which the supply and quality is an adequate substitute for the alternative of social housing) is difficult. Reported rent levels vary, and variation can potentially make a large difference to the residual need figures. In the example below, the monthly rent levels for each local authority have been derived using data from Dataspring, which refers to the Rent Service. Because these rents are used to calculate local housing allowance, they are more likely to be at the lower end of the market. Using the Rent Service figure gives a much lower (and in some cases negative) result.

6.2.0.e. The model includes two other figures for private rental costs which may be applied to assess the different impacts on residual need (those who cannot afford to rent or buy will be the most urgent candidates for social housing). The two alternative figures are taken from an average of submarket private rent levels (based on a search of www.rightmove.co.uk ), and cross-tenure affordability data provided by the Hometrack Housing Intelligence System (www.hometrack.co.uk ). The impact of changing private rental costs on the model is shown below.

Figure 6:2 Impact of Private Rental Sector monthly cost variations on affordability percentages by LA % unable to afford based on LA Rent Service Submarket Average Housing Intelligence System Broxtowe 17% 23% 28% Erewash 13% 24% 24% Gedling 19% 16% 24% Nottingham 18% 28% 36% Rushcliffe 14% 17% 24%

Residual need figure (can't rent, can't buy) based on: LA Rent Service Submarket Average Housing Intelligence System Broxtowe 135 189 233

8 See “ The Private Rented Sector: its contribution and potential ”, Julie Rugg and David Rhodes, Centre for Housing Policy, The University of York (2008), at http://www.york.ac.uk/inst/chp/publications/PDF/prsreviewweb.pdf and http://www.homesandcommunities.co.uk/private_rented_sector_initiative 9 Based on private rent levels by LA provided by dataspring ( www.dataspring.org.uk ). Submarket lower quartile rents are based on a search of Rightmove for properties within the area, top of the first 25% of entries is selected. Although not the most exact method, it gives a reasonably good indication of supply in the sector.

Erewash 145 231 231 Gedling 248 225 285 Nottingham -340 -40 200 Rushcliffe 66 91 148

Figure 6:3 Comparison of need figures - lower quartile purchase and rent (Key Variables set as above)

Lower Private Total can't NET NET Quartile Income % can't Rent LQP % can't afford NEED NEED LA Price required afford (monthly) afford PRS (Purchase) (PRS) Broxtowe £120,000 £30,857 52% £394 17% 150 605 135 Erewash £95,000 £24,429 40% £360 13% 63 373 145 Gedling £100,000 £25,714 39% £416 19% 141 435 248 Nottingham £82,500 £21,214 39% £373 18% 330 538 -340 Rushcliffe £139,995 £35,999 50% £412 14% 115 453 66

6.2.0.f. In addition to the problem of establishing accurate lower quartile rental prices to use in the model is the issue of supply within the private rental sector. Despite the appearance of affordability in the private sector, the general upward trend of growth in local authority waiting lists suggests the stock on offer is not adequate to meet demand. This may be due to a combination of factors such as:

• Short supply of the property types and sizes that people want or need • Difficulty raising deposits or acquiring references to access private rent • Bad experiences or similar prompting people to apply for social housing where they may otherwise have remained in private renting • Low security of tenure (fear of unfair treatment, rent increases or eviction) creating an impetus to apply for social housing (particularly in times of economic instability) • Low supply for emerging households (much of the activity and movement within the private rented sector occurs among those households already established in it).

7) Results by Local Authority and Submarket Level Outputs

7.1.1.a. Key Variables for each local authority are set as follows:

KEY VARIABLES House Price Fluctuation 0% Mortgage Multiplier 3.5 Size of Deposit 10% Policy Period 7.5 Proportion unable to access mortgage 51% Owner Occupier Need Factor 0.345%

Equity Share in Intermediate Housing Products 40% % with resources from other sources 0%

7.1.2. Deriving submarket level needs figures

7.1.2.a. Finding data which is detailed and recent enough to allow submarket modeling is very difficult and involves many assumptions, proxy calculations and substitutions where the ideal data is unavailable. The following table details the output requirements we want, the data needed to provide them, and the substitutes or proxies used in the final model with accompanying methods and potential inaccuracies. Whenever the model is updated this ‘shopping list’ should be referred to and the closest available dataset supplied wherever possible to achieve a more accurate picture. Where submarkets cross administrative local authority boundaries these are included within the model twice, under each local authority for reference, and highlighted so users will be aware need for those submarkets will be duplicated.

Figure 7:1 Discussion of sources and methodology for model outputs Output Data Required Data obtained Data used Household size from Chelmer Model (2011 Chelmer Model (LA Level) figure); Population Household Projections; projections aggregated ONS 2007 population to submarket. Census projections by LSOA, counts rolled forward to Number of new households Census data where today to estimate requiring housing annually by submarkets do not fit LSOA emerging households Emerging households submarket data. in smaller submarkets. Age groups in 2007 population projections are 16-29 and 30-44. The numbers in each age group are disaggregated apportioning an equal number of people to each year of the age group and reaggregated to include only the ages in question (i.e. 18-29 and 30-35). This figure is then divided by the average household size as taken from the Chelmer projections. The household figure is then divided by 10, assuming new households will emerge from this age group over 10 years. Census age groups who will have reached the emerging households group since 2001 are selected and the total divided by the average household size for those submarkets which do not contain Method Applied an LSOA total.

Using a uniform average household size across all submarkets probably creates an ‘ecological fallacy’, assuming most households are the average size when in fact, most are either larger or smaller, and there is almost certainly variation between submarkets. Separating the age groups in the projections equally ignores the probability that there are more people in one age group than another. The average household size in 2011 is subject to change, in fact some sources indicate that households are now growing Potential Inaccuracies due to affordability problems. House prices selected by submarket and lower quartile derived Land Registry House Price (top of first 25%), data, postcode level, Affordability (owner- (Jan08-Feb09); CACI occupation) = count of Incomes data by output area households unable to (2006); Private rental lower afford LQP borrowing quartile price derived using 3.5 x income; Rightmove search (by Affordability (PRS) = submarket where possible, count of households Number of households or using sample postcode + unable to afford unable to afford to buy/rent 1/4 mile), selecting top of LQRentalP paying 30% Affordability by submarket lowest 25% of returns. of income.

Rank sales by submarket/price then derive lower quartile for each submarket. Assume 10% deposit and 3.5 times borrowing for purchase. Assume access to deposit for rent and affordability at 30% of income. Total number of households in each income band by submarket. Split most common income bands to allow for greater sensitivity in affordability calculations (i.e. split households with income of £15,000 - £30,000). Total number in each submarket unable to rent/buy at lower quartile price. Affordability proportion is this number as a percentage of the total households in the submarket. (A factor has been added to account for the number of households with resources from Method Applied elsewhere, though this figure is also unknown locally).

The lower quartile price can be distorted by some very low end sales which may not reflect market reality for most households and can provide an underestimation of prices. Land registry data does not include bedroom counts, so lower quartiles can only be derived by property type, not size. Ideally a lower quartile by property size could tell us what kind of affordability levels there are by household type (i.e. singles, couples, families, large families etc), however it is also difficult to access data on household types and sizes more recent than the Census. We might assume that emerging households (to which this factor will be applied) are naturally smaller and will Potential Inaccuracies begin by purchasing or renting smaller (therefore cheaper) properties. Migrations are not included in the submarket calculations as ONS population estimates by Number of migrants unable to LSOA (2007) have been afford market used to estimate emerging accommodation who are households, and should In migrant households eligible and would apply for include a migration element and affordability affordable housing in their totals n/a Method Applied n/a

Migration figures can vary greatly especially in periods of economic volatility. The current economic downturn has reportedly seen many migrant workers leaving the country, unable to find work or unsatisfied with the cost of living. A large exodus of migrants can have a significant impact on the private rental sector in particular, for which migrants have been a key market in certain areas. This could both increase supply in the private sector, and raise issues with stock condition (as conditions of overcrowding and poor quality stock have been argued to be more acceptable to transient communities). There may have been a change in the level of migration since the 2007 projections came out, although the overall impact of migration on needs figures is small. However, if certain submarkets are more affected than others by Potential Inaccuracies migrant movements, this may not be reflected. Tenure aggregated to submarket level and Number of owner occupiers inflated based on Owner occupier group (with a mortgage) by Tenure by output area, additional stock growth size submarket Census 2001 (Table KS18) since 2001

The number of owner occupiers in each submarket at the 2001 Census is taken as a baseline. The number with mortgages (including shared ownership) is extracted and inflated by 1.08 (1% per year) to give an estimate of owner occupation levels today. Method Applied The 1% represents the average additional stock growth.

Since the Census 2001 households have been getting older in most areas. This suggests that many of the households with a mortgage in 2001 may now wholly own their properties. In addition to this, there has been a steep drop in the number of first time buyers entering the market. It is not possible to know the true levels of owner occupation today. Some submarkets may have become hotspots for private renting (particularly in student areas), leading to a decline in owner occupation, while others may have had a boom in owner occupation because of a new scheme or development. Potential Inaccuracies These variations at local level cannot be picked up by the model without local input. The 1 in 290 figure Number of owner occupiers (0.345%) is applied to falling into housing need as a the projected number result of arrears, eviction or National figure from CML (1 of owner occupiers in Owner occupier need repossession by submarket in 290, 2008) each submarket.

The number of owner occupiers falling into housing need is quite a large unknown. Due to growth in unemployment and a high number of households bearing high debt levels it has been projected that there will be a rise in repossessions before the economy starts to recover. However, moves by the Bank of England to keep rates low and pressure on financial institutions to support financially troubled households may have a downward impact on the number in this category. Deriving a figure for each submarket is almost impossible without detailed information on repossessions at a local level. It may be that repossessions are more common in certain areas, or for Potential Inaccuracies certain property types or sizes. For Nottingham City For Nottingham CC, Council, waiting list data by Rushcliffe BC and submarket (where Broxtowe BC, waiting households live now); For list by submarket; for Rushcliffe Borough Council, other councils proxy waiting list data by postcode based on the Private (where households live Rented Sector as at the now); for Broxtowe Borough 2001 Census, which Number of households on the Council, waiting list data by apportions the HSSA waiting list by submarket sub-area (where households waiting list by (ideally giving which wish to live); Gedling submarket according to submarket they want to live Borough Council/Erewash the distribution of in, rather than where they live Borough Council, no data private renting as at the Backlog need now), excluding transfers available Census 2001.

Where detailed waiting list data is provided, a count of backlog households by submarket is derived and spread over the policy period. Where no data has been provided a proxy has been used which allocates the total number of households on the HSSA waiting list (based on 2009 trend projections) across each submarket proportionately, based on the proportions in the private rented sector in each submarket in 2001(based on the Census). The private rented sector is used as a proxy, as it is assumed that this is a temporary tenure from which households will want Method Applied to either buy or move into social housing. Without genuine waiting list data we cannot be sure where need is emerging from, or for. The private rented sector (PRS) is increasing less a temporary form of tenure for many households, who do want to buy but do not want or need the option of social housing. Government initiatives have encouraged the development of the PRS as a more secure tenure, and some initial steps are being taken to improve the tenure and make it more secure for households through greater support and regulation. There has been very significant growth in the PRS over recent years, particularly through buy to let mortgages and increased interest in property investment. The situation of Nottingham as a university location also affects the distribution of the private rented sector in a manner which may not accurately reflect the need for social housing based on the proxy used in the model. However, because of the lack of available data as discussed, the 2001 PRS distribution is the best proxy we have available to estimate need at submarket level. Where waiting list data is used there are also discrepancies, where totals do not match those given in the HSSA returns. There is also no way of telling where people want to live as opposed to where they are living when they apply, Potential Inaccuracies and the data collected in waiting lists varies. For NCC and Broxtowe BC, lettings by submarket; for Erewash and Gedling BC CORE lettings by submarket increased CORE lettings data; proportionately to Nottingham UA lettings by match HSSA totals RSL and LA annual lettings postcode; Broxtowe BC lets where there is a Supply by submarket by sub-area significant discrepancy.

Since not all local authorities participate fully in CORE there are some discrepancies between the lettings count in the HSSA and in CORE (see tab CompareHSSA_CORE). In the case of Erewash and Rushcliffe, all stock has been transferred to RSLs so should all be accounted for in CORE. Nottingham UA has provided lettings data by postcode which has been aggregated to submarket. No lettings data has been made available for Gedling, so the proportionate difference between HSSA and CORE totals Method Applied has been applied to the CORE totals to inflate them for each submarket. Without full supply data for all local authorities applying the HSSA increase equally across all submarkets is likely to result in a greater or lesser view of actual supply in some areas. In reality, the difference in the stock count will be distributed unevenly Potential Inaccuracies across the different sub-areas.

7.2. Broxtowe

Figure 7:2 Model outputs at Local Authority level - Broxtowe Net Need 445

Number of emerging households per year 85 Entry Level Price 120,000

Total % able to unable to No. afford but No. afford + Proportion households unable to unable to unable to unable to unable to access access access Tenure/Product afford afford mortgage mortgage mortgage Number unable to afford IH market scope housing (as % but able of AH to afford provide Purchase 52% 460 IH d) 24% 212 672 Intermediate at 70% 34% 301 159 34.6% 34% 301 602 Intermediate at 50% 20% 177 283 61.5% 41% 363 540 Variable proportion for Intermediate Housing 40% 12% 106 354 76.9% Private Rent 17% 150 Number of in migrant households 465 Number likely to apply for housing 80 Number of owner occupiers 21,250 Source: CML (2008) "1 in 290 mortgage s reposses Proportion falling into sed in the need 0.345% 73 year" Uses waiting list Backlog need 2,344 trend figure Policy Period 7.5 years Waiting list annual backlog 313 Gross Need 926 Net Need 445 Lets of existing supply 481

Annual apportionment - Total Provision (Regional Plan) 340 Source: East Midlands Regional Plan (March Proportion affordable 131% 2009) Actual provision proposed (HSSA) 0

7.2.3. Submarket Outputs - Broxtowe

7.2.3.a. Several of Broxtowe’s submarkets cross the administrative local authority boundary – (Cotmanhay, Sandiacre and Stapleford, Wollaton and Long Eaton).

Figure 7:3 Broxtowe submarkets

Figure 7:4 Key Outputs by submarket (Broxtowe), including cross-border submarkets

Lower % can't % can't NET Quartile Income afford Private afford NET NEED NEED Submarket Name Price required mortgage Rent LQP rent (Purchase) (Rent) Beeston £ 115,000 £ 29,571 57% £420 27% 141 -6 Cotmanhay £ 75,000 £ 19,286 40% £595 54% -38 -22 Eastwood £ 94,000 £ 24,171 45% £425 26% 15 -23 Kimberley £ 108,400 £ 27,874 52% £400 22% 8 -27 Long Eaton £ 102,000 £ 26,229 45% £450 27% 1186 1096 Sandiacre & Stapleford £ 99,500 £ 25,586 49% £495 33% 621 581 Wollaton (cross-border) £ 151,000 £ 38,829 61% £495 23% 102 44 *No waiting list data is available for Long Eaton (which is based on LA level data for Erewash Borough Council), so the PRS proxy has been used, giving a particularly high level of need. Though the actual figure may be inaccurate, Erewash and Broxtowe Borough Councils confirm that there is high need in this submarket.

7.3. Erewash

Figure 7:5 Model outputs at Local Authority level - Erewash Net Need 357

Number of emerging households per year 785 Entry Level Price £95,000

Total % able to unable to No. afford but No. afford + Proportion households unable to unable to unable to unable to unable to access access access Tenure/Product afford afford mortgage mortgage mortgage Number unable to afford IH market scope housing (as % but able of AH to afford provide Purchase 40% 314 IH d) 31% 243 557 Intermediate at 70% 24% 188 126 40.0% 39% 306 494 Intermediate at 50% 13% 102 212 67.5% 44% 345 447 Variable proportion for Intermediate Housing 40% 8% 63 251 80.0% Private Rent 13% 102 Number of in migrant households 60 Number likely to apply for housing 8 Number of owner occupiers 23,099 Source: CML (2008) "1 in 290 mortgages Proportion falling into repossessed need 0.345% 80 in the year" Uses waiting list Backlog need 3,627 trend figure Policy Period 7.5 years Waiting list annual backlog 484 Gross Need 886 Net Need 357 Lets of existing supply 529

Annual apportionment - Total Provision (Regional Plan) 360 Source: East Midlands Regional Plan (March Proportion affordable 99% 2009) Actual provision proposed (HSSA) 73

7.3.4. Submarket Outputs – Erewash

Figure 7:6 Erewash submarkets

Figure 7:7 Key Outputs by submarket (Erewash), including cross-border submarkets

Lower % can't % can't NET Quartile Income afford Private afford NET NEED NEED Submarket Name Price required mortgage Rent LQP rent (Purchase) (Rent) Cotmanhay £ 75,000 £ 19,286 40% £595 54% -38 -22 Long Eaton £ 102,000 £ 26,229 45% £450 27% 1186 1096 North Derby rural fringe £ 127,500 £ 32,786 56% £350 13% 173 135 Sandiacre & Stapleford £ 99,500 £ 25,586 49% £495 33% 621 581 South Erewash Rural Derby fringe £ 122,000 £ 31,371 54% £425 23% 226 194 South Ilkeston £ 85,000 £ 21,857 41% £475 35% 754 736

7.3.4.a. The private rental sector proxy has been used to estimate submarket need in Erewash. The indication is that house prices rise moving away from Nottingham, though this is not necessarily the case for rental prices, based on a simple web search. Long Eaton and South Ilkeston stand out as generating the highest levels of need, and only Cotmanhay appears to have an adequate level of supply. A good portion of need may be met by the rental market, particularly in the North Derby rural fringe, where there look to be a good supply of properties at a reasonable cost.

7.4. Gedling

Figure 7:8 Model outputs at Local Authority level - Gedling Net Need 396

Number of emerging households per year 740 Entry Level Price £100,000

Total % able to unable to No. afford but No. afford + Proportion households unable to unable to unable to unable to unable to access access access Tenure/Product afford afford mortgage mortgage mortgage Number unable to afford IH market scope housing (as % but able of AH to afford provide Purchase 39% 289 IH d) 31% 229 518 Intermediate at 70% 24% 178 111 38.5% 39% 289 467 Intermediate at 50% 11% 81 208 71.8% 45% 333 414 Variable proportion for Intermediate Housing 40% 7% 52 237 82.1% Private Rent 19% 141 Number of in migrant households 151 Number likely to apply for housing 19 Number of owner occupiers 23,570 Source: CML (2008) "1 in 290 mortgages Proportion falling into repossessed need 0.345% 81 in the year" Uses waiting list Backlog need 3,275 trend figure Policy Period 7.5 years Waiting list annual backlog 437 Gross Need 826 Net Need 396 Lets of existing supply 430

Annual apportionment - Total Provision (Regional Plan) 400 Proportion affordable 99% Source: East Midlands Regional Plan (March 2009) Actual provision proposed (HSSA) 69

7.4.5. Submarket Outputs - Gedling

Figure 7:9 Gedling submarkets

Figure 7:10 Key Outputs by submarket (Gedling)

% Lower % can't Private can't NET Quartile Income afford Rent afford NET NEED NEED Submarket Name Price required mortgage LQP rent (Mortgage) (Rent) Arnold - Arnos/Mapperley £113,500 £29,186 50% 325 14% 212 129 Arnold/Bestwood £91,000 £23,400 47% 300 13% 64 15 Bestwood St.Albans £84,000 £21,600 35% 325 15% 9 -3 Calverton £112,000 £28,800 56% 460 29% 49 32 Carlton £95,000 £24,429 45% 450 29% 297 244 Colwick & Netherfield £83,500 £21,471 36% 450 27% 72 67 Newstead £73,000 £18,771 34% 375 18% 8 6 North Gedling rural £165,000 £42,429 59% 390 12% 47 20 South Gedling rural £155,000 £39,857 64% 350 12% 79 50

7.4.5.a. House prices in Gedling are lower generally than Erewash or Broxtowe, but being the other side of Nottingham may be unlikely to cater for people living in those markets. The HSSA waiting list totals have reduced over time in Gedling, indicating that some headway has been made in meeting demand. The private rented sector proxy has been used to determine backlog need in the borough, and continues to show a positive net need across all submarkets.

7.5. Nottingham

Figure 7:11 Model outputs at Local Authority level - Nottingham Net Need 289

Number of emerging households per year 2,996 Entry Level Price £82,500

Total % able to unable to No. afford but No. afford + Proportion households unable to unable to unable to unable to unable to access access access Tenure/Product afford afford mortgage mortgage mortgage Number unable to afford IH market scope housing (as % but able of AH to afford provide Purchase 39% 1168 IH d) 31% 929 2097 Intermediate at 70% 25% 749 419 35.9% 38% 1138 1887 Intermediate at 50% 11% 330 838 71.8% 45% 1348 1678 Variable proportion for Intermediate Housing 40% 7% 210 958 82.1% Private Rent 18% 539 Number of in migrant households 2,895 Number likely to apply for housing 373 Number of owner occupiers 37,498 Source: CML (2008) "1 in 290 mortgages Proportion falling into repossessed need 0.345% 129 in the year" Uses waiting list Backlog need 17,083 trend figure Policy Period 7.5 years Waiting list annual backlog 2,278 Gross Need 3,699 Net Need 289 Lets of existing supply 3,410

Annual apportionment - Total Provision (Regional Plan) 1,000 Proportion affordable 29% Source: East Midlands Regional Plan (March 2009) Actual provision proposed (HSSA) 174

7.5.6. Submarket Outputs - Nottingham

7.5.6.a. Nottingham City Council are currently using 2 sets of submarkets for strategic planning. Smaller Submarkets, which are based on the findings of the 2006/7 SHMA study, and Bigger Submarkets, which combine some of the original areas more broadly.

Figure 7:12 Nottingham City Bigger Submarkets

Figure 7:13 Key Outputs by submarket (Nottingham City Council Bigger Submarkets)

Lower % Private % Quartile Income can't Rent can't GROSS NET Positive Submarket Name Price required afford LQP afford NEED NEED Need Beechdale, Ainsley and Southwold £90,000 £23,143 54% 400 31% 60 10 10 Bestwood Park £82,500 £21,214 47% 450 38% 63 -90 Bilborough, Broxtowe, Whitemoor etc £73,000 £18,771 43% 375 22% 325 -99 Bulwell £76,000 £19,543 38% 395 27% 208 -126 Cliff Road #N/A #N/A #N/A 375 12% 1 -6 Clifton £77,000 £19,800 43% 475 43% 162 -5 Clifton Village, Barton Green, Fabis Drive £105,000 £27,000 42% 500 25% 40 27 27 Dunkirk, Hillside and Old Lenton £105,000 £27,000 60% 325 20% 252 163 163 Forest Fields and New Basford £75,000 £19,286 37% 350 19% 125 41 41 Hyson Green, Bobbers Mill and Arboretum £80,000 £20,571 44% 293 13% 159 29 29 Lenton Abbey £82,000 £21,086 49% 280 14% 15 -3

Mapperley £93,000 £23,914 33% 395 15% 30 16 16 Mapperley Park, Sherwood Rise £90,000 £23,143 28% 360 9% 48 27 27 Meadows £78,000 £20,057 48% 500 44% 90 -39 Park and Standard Hill £120,000 £30,857 38% 625 26% 53 17 17 Radford £70,000 £18,000 45% 350 26% 161 -86 Rise Park £138,000 £35,486 60% 400 16% 22 21 21 Sherwood North £96,500 £24,814 37% 525 29% 15 11 11 Sherwood, Old Basford and Edwards Lane £92,000 £23,657 38% 380 19% 167 32 32 Sneinton £74,000 £19,029 43% 375 22% 119 -24 St Anns £66,500 £17,100 35% 350 23% 201 -123 Thorneywood, Bakersfield, Candle Meadow £90,000 £23,143 40% 400 20% 55 42 42 Top Valley and Bestwood £71,000 £18,257 35% 450 35% 104 -109 Wilford and Silverdale £135,000 £34,714 55% 600 36% 26 25 25 Wollaton £140,000 £36,000 57% 495 23% 188 164 164

Overall Need -79 Positive Need 625

Figure 7:14 Nottingham City Smaller Submarkets

Figure 7:15 Key Outputs by submarket (Nottingham City)

% Lower % can't can't NET Quartile Income afford Private Rent afford NET NEED NEED Submarket Name Price required mortgage LQP rent (Purchase) (Rent) Arboretum £96,000 £24,686 50% 293 13% 15 -30 Aspley, Broxtowe, Bells Lane estates £65,500 £16,843 39% 400 34% -13 -25 Bakersfield £93,950 £24,159 46% 425 27% 39 25 Beechdale, Ainsley and Southwold £90,000 £23,143 57% 450 43% 26 13 Bestwood £84,950 £21,844 60% 450 48% -26 -28 Bestwood Estate £71,000 £18,257 43% 450 43% -9 -9 Bestwood Park £76,950 £19,787 46% 425 38% -37 -43 Bilborough & Strelley £82,000 £21,086 51% 475 47% -10 -15 Bulwell £70,000 £18,000 39% 425 35% -26 -28 Bulwell Hall £66,000 £16,971 37% 450 41% -14 -13 Candle Meadow £60,000 £15,429 21% 475 30% 3 4 Carrington £50,000 £12,857 16% 450 28% 2 4 Castle Marina £86,000 £22,114 20% 375 8% 0 -1 Cinderhill £95,000 £24,429 40% 425 22% 5 3 Cliff Road Unknown Unknown Unknown 350 #N/A Clifton Estate £77,000 £19,800 46% 495 46% 62 62 CliftonVillage, Barton Green, Fabis Drive £105,000 £27,000 50% 495 32% 43 26 Crabtree Farm £57,227 £14,716 30% 425 45% -39 -38 Dunkirk,Hillside and Old Lenton £90,000 £23,143 51% 395 29% 68 38

Edwards Lane £75,500 £19,414 44% 475 44% -1 -1 Forest Fields £76,000 £19,543 40% 375 21% 56 34 Hempshill Vale £103,000 £26,486 42% 425 19% 11 7 Highbury Vale £78,000 £20,057 44% 380 29% 19 4 Hyson Green & Bobbers Mill £79,000 £20,314 43% 420 31% 54 37 Lace Market and City Centre £102,000 £26,229 38% 495 24% 5 3 Lenton £115,000 £29,571 64% 325 21% 171 16 Lenton Abbey £82,000 £21,086 46% 450 38% 2 -1 Mapperley £93,000 £23,914 37% 395 18% 14 9 Mapperley Park £96,000 £24,686 32% 350 11% 24 10 Meadows £78,000 £20,057 49% 395 33% -13 -32 Moor Green unknown unknown unknown 475 42% unknown -9 New Basford £75,000 £19,286 35% 375 18% 5 -9 Nobel Road £80,000 £20,571 49% 400 32% -18 -21 Old Basford & Heathfield £83,000 £21,343 43% 395 25% 10 -9 Park and Standard Hill £145,000 £37,286 37% 465 11% 34 14 Radford £70,000 £18,000 40% 375 24% -53 -97 Rise Park £138,000 £35,486 71% 350 16% 25 11 Sherwood £102,000 £26,229 43% 410 22% 63 25 Sherwood North £96,500 £24,814 38% 525 30% 14 11 Sherwood Rise £89,000 £22,886 39% 425 24% 11 7 Snape Wood £64,500 £16,586 34% 425 34% -29 -29 Sneinton £74,000 £19,029 45% 375 25% -1 -26 St Ann's £66,500 £17,100 36% 395 32% -67 -79 Thorneywood £90,000 £23,143 38% 425 23% 14 11 Top Valley £67,000 £17,229 34% 450 38% -46 -43 University and Wollaton Hall Drive £300,000 £77,143 128% 390 32% 269 64 Victoria Centre and City Centre North £133,937 £34,441 63% 475 30% 6 -9 Whitemoor £87,000 £22,371 43% 400 26% 5 -1 Wilford and Silverdale £135,000 £34,714 60% 600 40% 29 22 Wollaton (cross-border) £151,000 £38,829 61% 495 23% 102 44 Wollaton (within NCC) £150,000 £38,571 66% 495 25% 67 29 Wollaton Park Estate £118,000 £30,343 68% 450 35% 38 21

7.6. Rushcliffe

Figure 7:16 Model outputs at Local Authority level - Rushcliffe Net Need 362

Number of emerging households per year 821 Entry Level Price £139,995

Number unable to afford IH market scope housing (as % but able of AH to afford provide Purchase 50% 411 IH d) 25% 205 616 Intermediate at 70% 34% 279 132 32.0% 34% 279 558 Intermediate at 50% 19% 156 255 62.0% 41% 337 493 Variable proportion for Intermediate Housing 40% 12% 99 312 76.0% Private Rent 14% 115 Number of in migrant households 829 Number likely to apply for housing 137 Number of owner occupiers 20,789 Source: CML (2008) "1 in 290 mortgages Proportion falling into repossessed need 0.345% 72 in the year" Uses waiting list Backlog need 1,452 trend figure Policy Period 7.5 years Waiting list annual backlog 194 Gross Need 723 Net Need 362 Lets of existing supply 361

Annual apportionment - Total Provision (Regional Plan) 750 Proportion affordable 48% Source: East Midlands Regional Plan (March 2009) Actual provision proposed (HSSA) 50

7.6.7. Submarket Outputs - Rushcliffe

Figure 7:17 Rushcliffe Submarkets

Figure 7:18 Key outputs by submarket (Rushcliffe)

% Lower % can't Private can't NET Quartile Income afford Rent afford NET NEED NEED Submarket Name Price required mortgage LQP rent (Purchase) (Rent) Bingham £120,000 £30,857 50% 395 17% 26 -3 Compton Acres £152,000 £39,086 58% 395 15% 43 13 Cotgrave £95,400 £24,531 44% 550 38% 23 18 E&W Leake £160,000 £41,143 65% 425 18% 27 3 Keyworth £137,000 £35,229 63% 550 33% 26 11 Radcliffe £134,950 £34,701 56% 375 13% 32 4 Ruddington £124,999 £32,143 53% 475 28% 20 6 Rushcliffe Rural East £150,000 £38,571 59% 350 11% 47 4 Rushcliffe Rural west £160,000 £41,143 67% 475 22% 60 22 WBridgfordE & Gamston £154,000 £39,600 60% 525 26% 56 16 WBridgfordN £129,995 £33,427 50% 475 22% 81 27 WBridgfordS £177,500 £45,643 65% 400 11% 47 5

8) Submarket Output Comparisons by Local Authority, 2006 and 2009 outputs

Figure 8:1 Submarket Level Output comparisons, Broxtowe % unable to Submarket List Submarket List LQHouse Price LQHouse afford % unable to LA 2006 2009 2006 Price 2009 2006 afford 2009 Broxtowe Beeston Beeston £143,626 £115,000 51% 57% Broxtowe/Erewash Cotmanhay Cotmanhay £98,321 £75,000 56% 40% Broxtowe Eastwood Eastwood £120,529 £94,000 47% 45% Broxtowe Kimberley Kimberley £142,935 £108,400 52% 52% Erewash/Broxtowe Long Eaton Long Eaton £143,834 £102,000 54% 45% Sandiacre & Sandiacre & Erewash/Broxtowe Stapleford Stapleford £125,746 £99,500 46% 49% now separated Nottingham/Broxtowe Wollaton into: £216,049 Separated 80% Separated Wollaton (cross- border), NCC Nottingham/Broxtowe and BBC £151,000 61% Wollaton (NCC Nottingham/Broxtowe only) £151,000 61% Gross Gross Net Submarket List Submarket List Need Need Need Net Need 2006 2009 2006 2009 2006 2009 Beeston Beeston 359 222 170 -6 Cotmanhay Cotmanhay 83 79 41 -22 Eastwood Eastwood 137 91 80 -23 Kimberley Kimberley 80 42 52 -27 Long Eaton Long Eaton 327 1271 187 1096 Sandiacre & Sandiacre & Stapleford Stapleford 51 709 15 581 now separated Wollaton into: 80 Separated 80 Separated Wollaton (cross- border), NCC and BBC 121 102 Wollaton (NCC only) 121 102

Figure 8:2 Submarket Level Output comparisons, Erewash % unable to Submarket List Submarket List LQHouse Price LQHouse afford % unable to LA 2006 2009 2006 Price 2009 2006 afford 2009 Broxtowe/Erewash Cotmanhay Cotmanhay £98,321 £75,000 56% 40% Erewash/Broxtowe Long Eaton Long Eaton £143,834 £102,000 54% 45% North Derby North Derby Erewash rural fringe rural fringe £182,008 £127,500 84% 56% Sandiacre & Sandiacre & Erewash/Broxtowe Stapleford Stapleford £125,746 £99,500 46% 49% South Erewash South Erewash Rural Derby Rural Derby Erewash fringe fringe £166,306 £122,000 77% 54% Erewash South Ilkeston South Ilkeston £115,948 £85,000 51% 41%

Gross Gross Net Submarket List Submarket List Need Need Need Net Need 2006 2009 2006 2009 2006 2009 Cotmanhay Cotmanhay 83 79 41 -22 Long Eaton Long Eaton 327 1271 187 1096 North Derby North Derby rural fringe rural fringe 72 166 67 135 Sandiacre & Sandiacre & Stapleton Stapleford 51 709 15 581 South Erewash South Erewash Rural Derby Rural Derby fringe fringe 79 227 72 194 South Ilkeston South Ilkeston 216 861 144 736

Figure 8:3 Submarket Level Output comparisons, Gedling % unable to Submarket List Submarket List LQHouse Price LQHouse afford % unable to LA 2006 2009 2006 Price 2009 2006 afford 2009 Arnold - Arnold - Arnos/Mapperl Arnos/Mapperle Gedling ey y £158,896 £113,500 78% 50% Arnold/Bestwo Arnold/Bestwoo Gedling od d £121,978 £91,000 48% 47% Bestwood Bestwood Gedling St.albans St.albans £123,751 £84,000 41% 35% BURTON No longer No longer Gedling JOYCE No longer used £181,250 used 83% used Gedling Calverton Calverton £149,924 £112,000 59% 56% Gedling Carlton Carlton £134,764 £95,000 44% 45% Colwick & Colwick & Gedling Netherfield Netherfield £107,345 £83,500 43% 36% Gedling Newstead Newstead £136,602 £73,000 58% 34% North Gedling North Gedling Gedling rural rural £313,309 £165,000 100% 59% No longer No longer Gedling RAVENSHEAD No longer used £190,000 used 81% used South Gedling South Gedling Gedling rural rural £272,318 £155,000 100% 64% WOODBOROU No longer No longer Gedling GH No longer used £206,837 used 84% used

Gross Gross Need Net Need Submarket List 2006 Submarket List 2009 Need 2006 2009 2006 Net Need 2009 Arnold - Arnos/Mapperley Arnold - Arnos/Mapperley 244 161 235 129 Arnold/Bestwood Arnold/Bestwood 115 85 67 15 Bestwood St.albans Bestwood St.albans 30 27 -6 -3 No longer BURTON JOYCE No longer used 10 used 11 No longer used Calverton Calverton 51 38 44 32 Carlton Carlton 217 270 191 244 Colwick & Netherfield Colwick & Netherfield 63 86 53 67 Newstead Newstead 9 8 5 6 North Gedling rural North Gedling rural 20 20 20 20 No longer RAVENSHEAD No longer used 22 used 22 No longer used South Gedling rural South Gedling rural 33 52 33 50 No longer WOODBOROUGH No longer used 3 used 3 No longer used

8.1.1.a. Since the 2006/7 study, Nottingham City Council have further developed their submarket framework and have elaborated on the original findings with the result that the number of submarkets in the city area has risen from 23 to 51. Consequently, the comparisons given below are only between pre-existing submarket areas, and several have changed in size and scope so a direct comparison is impossible.

Figure 8:4 Nottingham City Submarket Structure: Comparison 2006/7 to 2009

Figure 8:5 Submarket Level Output comparisons, Nottingham % unable to Submarket List Submarket List LQHouse Price LQHouse afford % unable to LA 2006 2009 2006 Price 2009 2006 afford 2009 Nottingham Bakersfield Bakersfield £121,951 £93,950 51% 46% Bestwood Bestwood Nottingham Estate Estate £98,366 £71,000 63% 43% Nottingham Bestwood Park Bestwood Park £95,638 £76,950 64% 46% Broxtowe, Aspley, Aspley, Bells Broxtowe, Bells Nottingham Lane, etc Lane estates £87,147 £65,500 66% 39% Nottingham Bulwell Bulwell £95,108 £70,000 59% 39% Chalfont Drive - No longer No longer Nottingham Aspley Lane No longer used £153,863 used 81% used Nottingham Clifton Clifton Estate £92,027 £77,000 68% 46% CliftonVillage, CliftonVillage, Barton Green, Nottingham Fabis Drive Fabis Drive £205,684 £105,000 91% 50% Dunkirk,Hillside Dunkirk,Hillside Nottingham and Old Lenton and Old Lenton £151,248 £90,000 87% 51% Nottingham Forest Fields Forest Fields £148,811 £76,000 92% 40% Hyson Green & Hyson Green & Nottingham Bobbers Mill Bobbers Mill £129,383 £79,000 88% 43% Nottingham Lenton Abbey Lenton Abbey £93,387 £82,000 58% 46% Mapperley Park South, Carrington, now separated Nottingham Sherwood Rise into: £184,777 Separated 80% Separated Mapperley Nottingham Park Mapperley Park £96,000 32% Nottingham Carrington Carrington £50,000 16% Nottingham Sherwood Rise Sherwood Rise £89,000 39% Nottingham Meadows Meadows £91,572 £78,000 61% 49% Nottingham Park and Park and £190,886 £145,000 100% 37% 44

Standard Hill Standard Hill Nottingham Radford Radford £110,108 £70,000 66% 40% Nottingham Rise Park Rise Park £154,186 £138,000 53% 71% Nottingham Sherwood Sherwood £113,841 £102,000 48% 43% Nottingham Sherwood Sherwood £113,841 £102,000 48% 43% Nottingham Sneinton Sneinton £92,096 £74,000 58% 45% Nottingham St Ann's St Ann's £101,403 £66,500 62% 36% Wilford and Wilford and Nottingham Silverdale Silverdale £164,567 £135,000 80% 60% now separated Nottingham/Broxtowe Wollaton into: £216,049 Separated 80% Separated Wollaton (cross- border), NCC Nottingham/Broxtowe and BBC £151,000 61% Wollaton (NCC Nottingham/Broxtowe only) £151,000 61% Gross Gross Net Submarket List Submarket List Need Need Need Net Need 2006 2009 2006 2009 2006 2009 Bakersfield Bakersfield 49 36 27 25 Bestwood Bestwood Estate Estate 104 50 -161 -9 Bestwood Park Bestwood Park 45 58 -76 -43 Broxtowe, Aspley, Aspley, Bells Broxtowe, Bells Lane, etc Lane estates 293 167 -347 -25 Bulwell Bulwell 231 69 -240 -28 Chalfont Drive - No longer No longer Aspley Lane No longer used 44 used 36 used Clifton Clifton Estate 151 178 -95 62 CliftonVillage, CliftonVillage, Barton Green, Fabis Drive Fabis Drive 32 39 32 26 Dunkirk,Hillside Dunkirk,Hillside and Old Lenton and Old Lenton 564 46 438 38 Forest Fields Forest Fields 269 60 183 34 Hyson Green & Hyson Green & Bobbers Mill Bobbers Mill 350 95 129 37 Lenton Abbey Lenton Abbey 29 16 -6 -1 Mapperley Park South, Carrington, now separated Sherwood Rise into: 177 Separated 145 Separated Mapperley Park Mapperley Park 19 10 Carrington Carrington 11 4 Sherwood Rise Sherwood Rise 11 7 Meadows Meadows 104 88 -73 -32 Park and Park and Standard Hill Standard Hill 106 14 49 14 Radford Radford 118 137 -101 -97 Rise Park Rise Park 21 12 21 11 Sherwood Sherwood 180 83 33 25 Sneinton Sneinton 128 112 -74 -26 St Ann's St Ann's 193 222 -368 -79 Wilford and Wilford and Silverdale Silverdale 31 23 31 22 now separated Wollaton into: 80 Separated 80 Separated Wollaton (cross- 121 102 45

border), NCC and BBC Wollaton (NCC only) 121 102

Submarkets newly identified since 2006/7:

• Arboretum • Bilborough & Strelley • Castle Marina • Cinderhill • Hempshill Vale • Highbury Vale • Lace Market and City Centre • Nobel Road • Snape Wood • University and Wollaton Hall Drive

9) Key Conclusions and Recommendations

9.1.1. Broxtowe

9.1.1.a. Prices in Cotmanhay, Beeston, Eastwood and Kimberley have fallen significantly, reducing need in those areas. High need is evident in Long Eaton, Sandiacre and Stapleford and Wollaton. As these are all cross-border submarkets it is important that approaches designed to cater for need in these areas are shared with neighbouring authorities.

9.1.2. Erewash

9.1.2.b. South Ilkeston shows the biggest comparative price drop against 2006, but has lower supply levels so still has significant need. Cotmanhay (shared with Broxtowe) shows low need, but waiting list figures for Erewash are not currently available so this result may be altered if high demand is generated for Cotmanhay from residents in Erewash.

9.1.3. Gedling

9.1.3.c. Gedling shows some substantial price drops, particularly in Newstead and North Gedling rural, where affordability based on income alone has improved. Need figures remain similar to 2006/7 outputs, with the highest need showing in Arnold – Arnos/Mapperley and Carlton. Bestwood St. Albans shows the lowest need. Although affordability in North Gedling rural has improved, the house price : income ratio is still high, and the model shows 59% of people living there would be unable to purchase at current market prices. There is also (based on CORE data only) very limited supply in the submarket, indicating it may benefit from a small scale affordable scheme.

9.1.4. Nottingham

9.1.4.d. House prices in most submarkets in the city are much more affordable than in 2006. However, this has not reduced the waiting list for Nottingham, which has grown by 38% since 2005 (the largest increase of all the local authorities in the housing market area). The model shows that certain areas in Nottingham have an adequate supply of stock for the demand (excluding transfers) they generate, and would therefore be less likely to benefit from additional supply.

9.1.4.e. These include, most notably, St Ann’s estate, Radford, and Top Valley. Areas with higher need in Nottingham include University and Wollaton Hall Drive, Lenton and Wollaton. Some areas in Nottingham may benefit particularly from a focus on improving the quality and level of regulation of private rental properties, including the three previously mentioned, as well as Arboretum, Sherwood and Radford.

9.1.5. Rushcliffe

9.1.5.f. Prices have dropped in Rushcliffe but are still out of reach for around half to two thirds of households in most areas, apart from Cotgrave. Waiting list totals provided for Rushcliffe are significantly lower than the HSSA total and show a substantial number of applicants are from outside the administrative area. As a result, net need totals are lower than in 2006/7. Those submarkets in Rushcliffe with higher need include both the East and West rural areas and all of West Bridgford. Several submarkets in Rushcliffe may also benefit from an approach of increased support for tenants and improved monitoring of the private rental sector. These include the rural areas just mentioned, Bingham and Compton Acres.

9.1.6. Area Summary – Nottingham Core Housing Market Area

9.1.6.g. The Nottingham Core Housing Market Area contains much variation within its boundaries, and should not be treated as a homogenous market. There is much movement both within and between local authorities, and in and out of the whole area. House prices are most volatile in the city, but all parts of the HMA show significant fluctuations over the past few years.

9.1.6.h. Though falling prices are not eliminating issues of affordability, not all areas show positive net need, as some submarkets already have adequate supply to meet the demand generated from within them. The private rental sector is playing an increasingly significant role in filling the gap between social rent and purchase, but growing waiting lists show that the sector is not capable of fully catering to the needs of all households.

9.1.6.i. There is some evidence that a notable number of applicants to the outer authorities are from outside their administrative boundaries, indicating that some people in need of housing may be making multiple applications, which may result in some duplication.

9.1.6.j. Generally the overall number of people on local authority waiting lists is going up, while the number of lets coming available each year is going down. The pressure to increase the supply of social housing to cater for this demand is still evident, though not evenly spread across submarkets. The model shows prominently that demand for additional supply is generally less in areas which already contain higher levels of stock of affordable housing.