A PROPERTY VALUATION MODEL FOR RURAL VICTORIA

Kelly Nicole HaylesAssociate Diploma of Engineering (Surveying &

Mapping)Bachelor of Applied Science (Land Information)

Research Aims

• to develop a conceptual rural property valuation model, and

• to develop and evaluate quantitative models for rural property valuation based on the

variables identified in the conceptual model that appear most likely to influence price.

• to enhance existing rural valuation methods by helping to automate the valuation process to develop numerical models using statistical regression analysis.

Hypotheses

• that statistical regression techniques can be used to accurately estimate property values in rural Victoria

• that using statistical measures to re-group properties into sub-market areas will lead to higher modelling accuracy than numerical models which primarily segregate property into sub-markets using geographical techniques.

In order to test the above hypotheses

Research Area

• The research is applicable to rural property valuation in Victoria. The development of numerical models and subsequent testing was undertaken for selected properties in the four Victorian LGAs: Horsham, Yarriambiack, Northern

• Grampians and West Gippsland.• Lastly, numerical valuation models were

developed to evaluate the manual valuation techniques currently used by licensed valuers.

Research Methods

• The research involved five main stages; selection of the study areas, conceptualising

• the value of rural property in terms of measurable land characteristics, database

• development, development of the numeric rural property valuation models using ‘apriori’

• LGA sub-markets, and cluster analysis to determine sub-markets using statistical

• methods.

• Stage one involved the selection of the two study areas in Victoria. This involved

• selecting regions where agricultural productivity was high and where there was some

• form of land degradation problems apparent within the regions. Stage two, the

• conceptual model stage involved reviewing existing research to determine suitable land

• characteristics that were applicable to rural valuation in Victoria. Stage three, the database development stage involved the

• creation of a database using the variables conceptualised to model rural property

• prices from stage two. Stage four involved the development and testing of the numeric

• rural property valuation models using LGAs to assign properties to particular

• geographic areas. Stage five involved the use of cluster analysis to re-define

• properties into more homogenous groupings using statistical measures. These five

• stages are outlined below.

Study AreaIn this study the LGAs of Horsham, Yarriambiack, Northern Grampians and Wellington were the smaller geographical areas from which the majority of data was obtained for valuation modelling.

Conceptualising the Value of Rural Property

• This involved analysis of various research papers, standards presently in operation for rural valuation.

• The number of variables that have been used amongst the numerous studies for rural

• valuation is large. With each study using different combinations of property

• characteristics, the conceptual model I develop later in this thesis aims to specify only

• the most significant property characteristics that appear most likely to influence

• property values in Victoria.

Database Development

• The aim of this stage was to obtain the required data sets and integrate these into a

• single GIS database. After integration of all data sets, spatial analyses were performed to enhance the

• final database by providing additional measurement variables for use in the study.

The Numeric Rural Property Valuation Models

• Multiple Regression Analysis (MRA) was first used in the 1960’s in the USA to generate models for residential property valuation.

• This technique was used to develop a model or equation that attempts to explain the relationship between a dependent variable (sale price) and the independent variables that are characteristic of the property (Gallimore et al., 1996).

• Once a model has been developed the technique enables the value of a property to be estimated in situations where there is no sale price (and where the characteristics of the property are known).

• MRA has been• used for rating valuations in the United Kingdom and the USA (Reid Schott &

White,• 1977; Palmquist & Danielson, 1989; Elad et al., 1994; Adair et al., 1996; Kettani et

al.,• 1998). Automated Valuation Models (AVM’s) and Computer Assisted Mass

Appraisal• (CAMA) are increasingly used in the valuation industry and the mortgage

origination• process (Poor's, 2004). The two major AVM’s used in the USA for the mortgage• origination process are based on MRA and hedonic theory. These models are used

in• approximately 10% of all commercial mortgage originations (Poor's, 2004).

• Variations were made to the dependent variables by implementing the

• model with a ‘sale price’ as well as a ‘sale price per hectare’ dependent variable.

Approaches to Land Valuation

• The three most common approaches to valuation are the Cost, Sales Comparison and Income Capitalisation approach.

• The Cost Approach• The Cost Approach involves estimating replacement costs of

farm buildings and• machinery on the property to determine an estimated value.

This approach requires• the creation of a detailed inventory of all machinery and

buildings within a property.

• The Sales Comparison Approach• This method is dependent on the availability

of sales data and having adequate numbers of properties with similar characteristics.

• A number of properties are selected so• that they have similar characteristics to the subject property and can then be

compared• more easily. Based on differences between the comparable and subject

properties, the• valuer makes adjustments to the sale prices to arrive at a valuation for the subject• property

• There can also be greater diversity amongst properties, such that

• obtaining enough sales over a similar time frame can be difficult. This can affect the

• degree of comparison that can be made between properties.

• The Income Capitalisation Approach• The Income Capitalisation Approach is based on the assumption that

those wishing to• purchase a property are mostly concerned with its current and future

income producing• Capacity.• This is mainly due to the technique• requiring more detailed data regarding property income. Income over one

year may• also fluctuate. Thus, to predict future income, production rates from

previous years are• required to compensate for any market driven influences.

Manual Valuation Methods and Valuation Best Practice Standards

• The• difficulties that arise with valuation of rural

property is that there are often fewer sales,• greater time between sales and rural property can

be influenced by more factors than• other types of valuation.• manual• valuation can also require site visits to obtain

additional property characteristics.

• The major statistical tests used within Valuation Best Practice for the evaluation of• results are the median sales ratio, Coefficient of Dispersion (COD), Coefficient of• Variation (COV) and Price Related Differential (PRD). These statistical measures• adhere to that of the Standard on Automated Valuation Models (AVM’s) (IAAO,

2003)• and the Standard on Ratio Studies (IAAO, 1999). Mapping outputs produced are• primarily used to show location of sales and value shift maps which depict the

change• in values between dates. They can also show the percentage change between site• value for previous valuations and that of the current valuation (Valuation Best

Practice,• 2005).

Computer Assisted and Automated Valuation

• Automated valuation, also known as computer-assisted valuation was first used

• during the 1960's in the form of regression analysis.• Regression analysis and comparable selection still feature prominently in

current• research with some residential research achieving a similar accuracy to

manual• techniques (Adair et al., 1996). Data availability is a• key issue to address in the determination of valuations for any type of

property and in• the generation of automated models.

• AVM’s have typically used regression, either linear or multiple; expert systems or

• neural networks in their modelling for the determination of property estimates (Waller,

• 1999). The three most commonly used models for automated valuation are repeat

• sales, tax assessment and hedonic models (Nattagh & Ross, 2000).

• Repeat sales models use sale prices of properties to develop indices based on

• historical sale price data which can then be used to estimate property values. Tax

• assessment models use assessed values that are based on tax values (Nattagh &

• Ross, 2000). Hedonic models are based on the theory that the price of a property is a

• function of the property’s various characteristics and that the purchaser is only willing to

• pay a set amount for these characteristics (Powe et al., 1997, Rosen, 1974).

Criteria Weighting and Decision Rules

• Weighting of criteria is important in property valuation due to the differences in the

• influence that each criterion may have on the valuation price. Weights are determined

• by assigning a ranking, with a larger weight implying a greater dependency on the

• values of the criteria. This provides a basis to indicate the degree of variation between

• the different criterions (Malczewski, 1999b).

• Techniques to determine weights for criteria include ranking, rating, pair-wise• comparison and trade-off analysis as methods (Malczewski 1999b). Ranking and• rating, though they are easy to use and can be calculated in a spreadsheet and

then• imported into a GIS environment, are not considered to be precise or suitably

accurate• (Malczewski et al., 1997). Trade-off analysis is considered precise, but relatively• difficult to use. Pair-wise comparison is considered most effective for spatial

decisions• (Malczewski et al., 1997). Given a requirement of high accuracy in determining

criteria• weights for property valuation, the pair-wise method might be considered more• appropriate due to its underlying statistical theory (Malczewski et al., 1997).

Multi-Criteria Decision-Making• Key elements of MCDM include defining the alternative and criteria sets and

analysis of• the impact of the alternatives on each criterion• The decision-maker is able to weight• or rank the criteria based on their expertise and experience in the area• Property values are affected by numerous factors. Some of these include the age,

size• and quality of the buildings, special features, and proximity to schools and shops

(Adair• & McGreal, 1988). These factors can be deemed as ‘multiple criteria’ that may• influence property values to varying degrees. The factors that influence value can

be• thought of as a function of a property’s value

• The valuation methods proposed by Kettani et al. (1998) utilised a data set consisting

• of 108 properties from a one year time period. The selected study area was Alberta,

• Canada and ‘residential bungalows’ were the sole property type under analysis. After

• consultation with a team of real estate agents, 11 criteria were identified that were

• deemed to have a significant influence on sale prices in this area. These criteria• included house age, house size, number of garages, ease of access to the garage,• presence of a basement, presence of a fireplace within the region. Kettani et al.,• (1998) assumed that the sale prices of these bungalows represent a multi-

criteriaapproach taken by the buyers and that each criteria contribute to the sale price realised through the property sale.

• The technique outlined by Kettani et al. (1998) required a valuer or real estate agent to• determine the relevant ranking of importance of each property characteristic prior to• further analysis. The technique is subject to human judgement in determining which• property characteristics are important. The statistical allocation of features into ranges• to determine an adjustment of price can limit the reliability of the results due to some• pricing factors only having a few instances of property within each range. This could• give an unrealistic view of the monetary effect of each value driver as the sample is not• representative of a wider range of property. The method used is static in that new• results cannot be updated after new prices are estimated for properties thus new prices• cannot be added to the database. The process is limited as it uses statistical results to• segregate the data into ranges for each criteria and to develop the relationship of its• effect on price. In this sense it does not search for comparable properties first and then• make adjustments based on the differences in features between the subject and the• comparable properties. This could lead to less reliable results due to properties being• selected from different locations or prices based on statistical relationships on the• whole data set instead of on a selection of similar properties.

Case Based Reasoning• Case Based Reasoning (CBR) uses a decision making approach to select examples• from a database which are similar to a subject property• Bonissone & Cheetham (1997) used fuzzy logic with CBR to estimate residential• property values in California.• The• selection of comparable properties was based on the sale date, distance of

properties• from one another, the lot size, living area, number of bedrooms and number of• bathrooms. Each property characteristic was assigned a maximum allowable

deviation• between the subject and the comparable property with those falling within the

deviation• selected as a ‘comparable’. These characteristics and the deviations selected were

• based on consultations with two appraisers.

Artificial Neural Networks• Artificial Neural Networks (ANN’s) involve training to learn relationships

and patterns• from the data to mimic the learning that a human appraiser performs

during appraisal• ANN’s have been used in residential and commercial valuation since the

early 1990's• the authors concluded that the ANN is best used as a support tool for• valuation or as a preliminary analysis tool