Experience of Automated Valuation Modelling (AVM) in England

1. Experience of Automated Valuation Modelling (AVM)in England Tim Eden BSc MRICS IRRV Deputy Director of Council Tax Valuation Office Agency

2. Presentation overview • A “fly through” of the VOA AVM experience including: • Background • The challenge • The extent of data capture • Changes in data management • Process improvement and change • Model development • Training • Achievements

3. To be a world class organisation providing valuation and property services for the public sector

4. VOA - Our purpose • To provide a fair and robust basis for taxes, which help to pay for public services; and to help drive better use of property in the public sector by: • compiling and maintaining accurate and comprehensive valuation lists for local taxation • providing accurate valuations for national taxes • delivering expert advice on valuations and strategic property management • developing and maintaining a comprehensiveand up to date property database • advising policy makers on valuation andproperty issues

5. VOA - Our structure

6. “Recent” Taxation History

7. Council Tax (CT) - Context • CT + Non-Domestic Rates together raise c. £40bn pa • CT raises £18.5bn pa • 22m domestic properties in England • 1.3m domestic properties in Wales • Clients • Department for Communities & Local Gov’t (DCLG) in England • Welsh Assembly Gov’t (WAG) in Wales

8. CT – 1993 List Bands (England) • A - up to £40,000 • B - £40,001 to £52,000 • C - £52,001 to £68,000 • D - £68,001 to £88,000 • E - £88,001 to £120,000 • F - £120,001 to £160,000 • G - £160,001 to £320,000 • H – over £320,001

9. CT - Revaluation • Local Government Act 2003 included requirement to undertake revaluations in Wales (2005) and England (2007) • In Wales AVD 1 April 2005; list came into force on 1 April 2005 • In England AVD 1 April 2005; list was to come into force on 1 April 2007 • Draft lists were to be published 1 September 2006

10. The Challenge • New banding scheme not known • Necessary to produce individual valuations & then overlay new bands • Scale of task • Complexity of housing types • Vagaries of the market • Inconsistent, paper-based records

11. The Challenge – Market Vagaries • Market analysis across England • Free flow of money • Historically low interest rates • Fluctuating volumes in sales

12. The Challenge – Housing Types • Profile of Housing Stock • 80% Houses/Bungalows • 20% Flats/Maisonettes • Over 1/3rd of all flats in Greater London • Around 1/6th of properties at least 100 years old • BUT high proportion of new properties in sales evidence!

13. The Solution! • Develop an AVM • Skill-up staff • Learn from others • Cole Layer Trumble (CLT) – computer modelling expertise, AVM software • KPMG – programme & project management support • IAAO – statistical training • Digitise data

14. data enhancement • business case • political issues • staff planning and allocation • AVM design • Improving Business Decisions • value reviews • data collection • 22 million values • data cleansing • MRA models VOA - AVM Journey • Began in 2002; worldwide research • Mass data capture/enhancement • Model development • Many innovations and lessons • These aspects have been very important to the success of the project

15. Attribute Data Investigation • Investigation commences in 2002 considering: • Use of existing “attribute” codes already available on VOA IT database (“Group”, “Type”, “Area”) • Valuer and caseworker engagement to determine useful codes • Data availability & maintainability • CT List Maintenance work • External standards e.g. IAAO (6 year cycle) • Impact of differing local practices • Ability to undertake sales investigation activity • Pre-contractor appointment; so limited AVM expertise

16. Main Attributes to be digitised • Architectural Style and construction quality – “Group” • Property Type e.g. House Semi-Detached – “Type” • Age (coded as era e.g. G – 1965-72) • Area m² - external area (houses); internal area (flats) • No. of rooms • No. of bedrooms • No of bathrooms • Number of floors (houses); floor level (flats) • Parking • Conservatory (type and size) • Outbuilding details • Value Significant Codes

17. What about Sales Data? • Access to Land Registry and Stamp Duty Land Tax information • Address irregularities – creates matching problems and manual effort • Need to establish whether transaction at “Market Value”, special circumstances etc. • Data understanding significantly assists • AVM geared to support this process • “Value” understanding working with AVM is key to the process

18. Process Development • Consider existing process • The impact of AVM – scale and profile • Skills • Existing skills and abilities of targeted staff • The need to re-train certain staff • Resource availability • New process to reflect Revaluation needs and business as usual • IT constraints and capabilities • Understand relationship between sales and model performance

19. After Locality Definition (area from which comparables derived) AVM analysis output reviewed by qualified staff AVM Sales Data 1)New Sales continually updating analysis set 2) Value significance considered Models (MRA, algorithms and variables) Property Data (Group, type, floor area, age, bedrooms, rooms, garage) Decision to change, retain or include current data and coding The Analysis Process Before Sales received Suspect sales coded Sales searched to support each valuation case “on paper” At a Revaluation “Beacon Sales” reviewed and recorded “on paper”

20. Initial Issues with Modelling Process • No first hand knowledge of sale • Data time lag • Data gaps due to • “Permissible development” – the public need not inform the Billing Authority (BA) • BAs internally not acting in a joined-up way – billing, planning & building control • BAs not acting in joined-up way with VOA • Are we maintaining lists or data or both? • “Condition” not an attribute

21. Integrating the AVM • Investigation work commenced late 2002 • Procurement process 2003 • Collaboration of in-house team and successful contractors/partners - Cole Layer Trumble (CLT) • Working on BA areas where data capture is advanced (note had only started in 2003!) • Challenge of integrating AVM technology with existing VOA IT systems

22. Getting Started • Started with a simple additive model • £ = B0 + B1 * size + B2 * Detached * Size + B3 * Terraced * Size + … + Bn * Date of Sale * Size • 1993 Band initially used to support analysis, but quickly stripped out of valuation models • Postcode sectors used as proxy for location – VOA developed “localities” • Postal areas are too crude • Designed to support mail delivery, not to reflect influences on the property market!

23. Performance Improvement (1) • Better understanding of subject/sale data helped to: • Determine usability rules for raw data • Determine usability rules for a sale • This understanding fed • Comparable selection • Model specification • Consideration of rarely occurring variables • Consideration of locality relationships

24. High Correlations Floors/ Floor Level Type 32 separate codes Parking Group 55 separate codes Age 11 separate codes Correlation among variables High correlation compromises modelling stability Area Bathrooms Rooms Bedrooms

25. Privately built housing estate Local Authority housing estate Localities – illustrative photograph • Created bespoke localities (neighbourhoods)

26. Mapping & Localities • In excess of 10,000 localities • Regular boundary review required • Thematic mapping as part of process • X-Y co-ordinate becomes necessary data • X-Y suitable for comparable selection • Issue of who maintains the data

27. Mapping Example

28. Performance Improvement (2) • Move to multiplicative (log linear) model structure Log (£) = B0 + B1 * log (size) + B2 * Detached + B3 * Terraced + … + Bn * Date of Sale + Bp * Log (Locality Adjustment Factor) where Bi determined from sales set using MRA • This enabled improvements in: - • Locality Adjustment Factor (time adjusted median price per square metre) • Locality Grouping – support for comparable selection • Central Modelling across the whole country

29. Benefits of Central Modelling • Central Modelling enabled: • Central recognition of national modelling patterns and data issues • Modelling “constraints” could be imposed by the centre ensuring model coefficients are consistently applied • Effective direction of effort • Calculation of market trend information to support VOA modelling and wider government market appreciation

30. Performance Improvement (3) • Integrating X-Y & Mapping allowed mass calculation of “plot size” • Issues with map plots are: • Plot bleed • Non-alignment with the property transferred • Maintenance of data

31. Model Development - Lessons • Create a multi-skilled and focused R&D team • Select several representative areas to test • Ensure proper debate on proposals • Predict overall likely modelling gains • Sense check & gather feedback from local staff • Business model to relate model performance and cost/benefit • Produce individual cost/benefit for each proposal • Promote external verification e.g. IAAO

32. Supporting Decision Making (1) • VOA recognised management of a national valuation delivery required consistency in valuation decisions • Property level confidence score required • COD/COV inadequate existing banding too remote • Score needed to support decisions at several levels: • Strategic: • Business case development • Process definition • Data collection & enhancement • Tactical: • Resource planning • Operational: • Resource allocation • Value review or band review

33. Supporting Decision Making (2) • Confidence estimate for each subject property needed to reflect the aspects which would reduce accuracy: • Data quality • Data availability • Market variability • Model accuracy • Adequate coverage for accuracy in the MRA • Adequate comparables

34. Confidence Model • Confidence model based upon indicators from MRA and comparables • Related actual errors to the dispersion amongst the comparables • Comparable sales approach provides a number of other indicators for confidences: • comparability distance • weighted estimate (average adjusted) vs. MRA estimate • the overall COV for the model on the sales set which tells us something about the underlying uniformity in the market and sales base • MRA modelling includes a control model which considers current CT band • So Confidence Model is: Likely Error = A + B  dispersion of comparables + C x average distance between comparables + D x absolute value of (ln (mkt est. / control model est.) ) + E x model standard error + F x absolute value of (ln (weighted est. / MRA est.) )

35. Confidence Model Maintenance • Re-calibrated with every calibration iteration • Periodically calibrated to valuer judgement of estimate output • Can be used individually or aggregated for decisions at varying levels • Developed between VOA, John Thompson (Cole Layer Trumble), Dr Jim Abbott (EDS) • Presented at IAAO CAMA/GIS conference February 2006

36. Summary of Models (1) • Multiplicative MRA and comparable selection • Operational delivery • Undertaken locally • Specified centrally • Broad Based Model • Market Trends • Linearisations e.g. Group and Type variables to allow wider comparability • Develop constraints for local models • Quality assurance on operational delivery

37. Summary of Models (2) • Control Model • Operates in background of local model • Highlights data irregularities • Confidence Model • Uses data from all models • Calibrated to valuer opinion • Provides information for use at all levels

38. Managing Business Change • Addressing the time lag • Review whole data process • Use electronic transfer to bring in sales data • Consider how to engage with taxpayer • New skills • Control modelling and train professionals • Develop support and maintenance staff • Rigorous application of project management methodology

39. IT Development • VOA has successfully integrated AVM technology with existing IT; this required: • Improving data flow and management • Enhanced mapping tools • “Customising” 3rd party “off-the-shelf” product • Introducing workflow to manage delivery • Lead-in time has created tensions • IT supplier’s understanding of business and new technology does not happen overnight!

40. Conclusions/Lessons Learned (1) • Other data sources create issues: • Often aggregated • Currency – frequency of update • Niche provider with limited depth • Definition – what creates the data • Cost – Is it worth it to you? • Your records may be adequate and better than elsewhere anyway!

41. Conclusions/Lessons Learned (2) • How do you measure data quality • Sale data quality • Mostly measured by model • Dependent upon proper sales review process • Subject data quality • Adoption of external standards not always possible e.g. IAAO 6 year cycle • Data sampling using existing maintenance activities can support the view

42. Conclusions/Lessons Learned (3) • Time lag • Understand the delivery requirement • Address steps in the data process • Work closer with data “partners” • Central modelling can identify areas of concern • Proximity to data • You cannot easily replace local knowledge of market and meaning of sales.

43. Conclusions/Lessons Learned (4) • Modelling • Define “attributes” with knowledge of AVM techniques • Balance modellers desire for more data of imperfect market and cost to complete/maintain • Stabilise data prior to commencement of modelling • Valuing to “Band” does not loosen the rigour of modelling and data management

44. Conclusions/Lessons Learned (5) • Use AVM to direct effort • Raw data analysis • COD & COV are not the only measures • “Frequently used comparables” • Thematic mapping • Use formal modelling hierarchy • Don’t rush to deliver. Inefficiencies result • Stabilise Model approach to ensure consistency • Data stability and consistency

45. Conclusions/Lessons Learned (6) • Create clear lines of communication • Local Management • Local technical/modelling • Training • Make timely • Consider delivery mechanism (e-learning, workshop) • Project Management Structure • A quality model can be let down by a poorly defined and delivered process!

46. Some VOA achievements • Digitisation of over 22 Million records • Data completeness approaching 100% • Over 120 surveyors trained in AVM techniques • Market analysis nationwide, including mapping localities across the whole country • Model performance well within recognised standards e.g. median COD of 353 BAs is 9.97 • Proven IT platform for mass appraisal at national level.

47. Questions? Tim Eden BSc MRICS IRRV Deputy Director of Council Tax tim.g.eden@voa.gsi.gov.uk

48. Achieving World Class