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The structural characteristics of properties in the dataset were gathered from two sources; from records kept by the Valuation Office Agency (VOA) and from the digital map data provided by Land-Line.Plus.

The Valuation Office Agency is an executive agency o f the Inland Revenue, one o f whose main functions is to value property for Council tax purposes. In order to

perform this function, the VO A maintain a database describing the structural characteristics o f every residential property in England. This is the first hedonic price study in the UK to make use o f VO A data.

Unfortunately, the VOA data sources are currently held as paper records. As such the most up-to-date information for each property were obtained by manually searching through VOA files and recording details in a spreadsheet application.

As detailed in Table 1, the data collected from the VOA provided the basic structural characteristics of each property. Furthermore, the VOA classifies properties according to age and style o f construction into one o f around 30 property types called Beacon Groups. This information was also recorded as it provides a useful additional indication of property quality that cannot be determined from size and age alone.

Table 1: Structural variables obtained from the VOA

Variable Description

Beacon group 33 nationally defined property groups defined by the VOA. These identify similar properties in terms of style and age

Floor area Floor area in square metres.

Property type 20 property types (e.g.detached, semi-detached)

Property age 7 age bands with properties built after 1973 coded with the actual year of construction

Number of

bedrooms Number of bedrooms at each property Number of WCs Number of internal WC’s at each property Central heating Central heating classification; full, partial or none

Central heating type Central heating type; recorded as either radiators, warm air or night storage heaters

Double glazing Double glazing classification; full, partial or absent

Garage Garage classification; coded as either single, double, car port or none.

Parking Other parking facilities recorded as car space, shared drive or rear entry

For further information see: Dwellinghouse Coding; an illustrated guide, The Valuation Office

Using the Land-Line.Plus digital map automatic procedures were developed to define and extract the outline o f each property’s plot and buildings. Each property

was then visually inspected using the GIS, in order to ensure that the building and plot area had been properly delineated. Subsequently, measures o f ground floor area and garden were calculated for each property.

Descriptions o f the structural characteristics of properties included in the specification o f the HPF are to be found in Table 2. Also included in that table are the researcher’s a priori expectations concerning each variables impact on property prices.

Table 2: Structural variables included in the Hedonic Price Models

Variable Code Description and a priori Expectations

Floor Area ( m ) Properties in the data set have between 1 and 12 bedrooms.

We create 12 constants one for each number of bedrooms.

3 bedrooms is taken as the baseline and this constant is dropped from the regressions.

Properties with more bedrooms command higher prices.

Properties in the data set have between 1 and 5 WCs. We create 5 constants, one specific to each number of WCs.

One WC is taken as the baseline and this constant is dropped from the regressions.

Properties with more WCs command higher prices.

Properties in the data set have between 1 and 7 storeys. We create 6 constants specific to each number of stories between 2 and 7 inclusive. We include a separately labelled set of indicator variables for bungalows and set 2 storeys as the baseline category.

Given that two properties have the same floor area it is expected that those with less storeys will be preferred to those with more storeys.

Indicator variable identifying properties with a garage.

Properties with a garage command higher prices.

Indicator variable identifying properties with a central heating. Properties with central heating will command higher prices.

Age of the property in decades prior to 1997.

The relationship between property age and property price is not entirely clear. Older properties may be desired for their “character” and “original features”, more modem properties for their state of repair and more up-to-date facilities. What is clear, however, is that property age proxies for a number of property characteristics not least of which will be the architectural design of the house.

Semi-detached houses are taken as the baseline property type since all market segments contain properties of this

Variable Code Description and a priori Expectations

Beacon Group (dummy vars.)

End Terrace House type. The coefficients estimated on the other property type dummy variables, reflect the relative difference in price Terrace House between that property type and a semi-detached house with Detached Bungalow exactly ^ same characteristics.

• „ It is expected that houses will fetch more than bungalows, emi- etac e unga ow Moreover, properties will increase in value from terraces End Terrace Bungalow through end terraces and semi-detached properties through

to detached properties.

BG 9 (Large “villas” pre in the models, BG 21 (standard houses built between the 1919) War) is taken as the baseline beacon group since all market BG 10 (Large detached pre se8ments contain properties of this type. The coefficients

Iqiqx estimated on the other beacon group dummy variables, reflect the relative difference in price between that BG 19 (Houses 1908 to properties of that beacon group and a property in beacon

1930) group 21 with similar characteristics.

BG 20 (Subsidy houses

1920s & 30s) The beacon group data collected from the VOA provides a detailed categorisation of properties according to their age, i qi Q4<a°USeS s*ze’ architectural type and quality. As such, we would

to 9 5) expect these dummy variables to be important descriptors BG 24 (Large houses 1919 that add significantly to the explanatory power of the

to 1945) model.

To introduce the maximum flexibility into the specification of the hedonic function a series of dummy variables have been created to represent each different quantity o f bedrooms, each different quantity o f WCs and each different number of storeys. One important omission from the model is an indicator o f whether a property had double- glazing. Unfortunately, this information was not recorded by the VOA for every property and was considered too unreliable to include in the analysis.

The variables listed in Table 2 provide a comprehensive description o f each property’s structural characteristics. Indeed, with reference to structural variables alone, the Birmingham dataset rivals all other published hedonic datasets.