Automated valuation model (AVM) is the name given to a service that can provide real estate property valuations using mathematical modelling combined with a database. Most AVMs calculate a property’s value at a specific point in time by analyzing values of comparable properties. Some also take into account previous surveyor valuations, historical house price movements and user inputs (e.g. number of bedrooms, property improvements, etc.).
Appraisers, investment professionals and lending institutions use AVM technology in their analysis of residential property. An AVM is a residential valuation report that can be obtained in a matter of seconds. It is a technology-driven report. The product of an automated valuation technology comes from analysis of public record data and computer decision logic combined to provide a calculated estimate of a probable selling price of a residential property. An AVM generally uses a combination of two types of evaluation, a hedonic model and a repeat sales index. The results of each are weighted, analyzed and then reported as a final estimate of value based on a requested date.
An AVM typically includes:An indicative market value for many residential properties nationwide.
The tax assessor's indication of value, if available.
Information on a subject property and recent sales history.
Comparable sales analysis of like properties.
In the late 1990s, this technology was used primarily by institutional investors to determine risk when purchasing collateralized mortgage loans.
Automated valuation model Wikipedia
AVMs are increasingly used by mortgage lenders to determine what a property might be worth in order for them to lend against the valuation. The advantages of using AVMs over traditional chartered surveyors are that they save time, money and resources (e.g. there are no transport requirements), thus lowering the cost of valuing a property. Many AVMs can be made and used with little cost, so more choices in valuation methodology are also possible. It is claimed that unlike traditional surveyor valuations, AVM outputs do not suffer from the same fraud risk although certain providers can have their systems manipulated intentionally or otherwise if property features are incorrectly entered. AVMs remove the human element from the valuation process and rely on computer automation so as to remove human bias and subjectivity.
AVMs are particularly useful in assessing the value of a property portfolio. Using an automated model can also be useful for valuing an individual property where the provider can deliver a suitable level of accuracy.
The disadvantages are that they do not take into account the property condition, as a physical inspection of the property does not occur and therefore the valuation produced assumes an average condition which may not reflect reality. Purchasers relying on an AVM-backed mortgage application will need to get separate advice to establish the true condition of the property. New build property is particularly difficult to value due to the lack of comparable properties and historic data; however, an advantage of AVMs is that they pull on a larger pool of comparables and as such are not prone to incorporating the claimed 'new-build premium', although it would rely on comparables from physical inspections to achieve this. Other data sources used are sometimes misleading due to concealed incentives in recorded sales prices (e.g. Land Registry). AVMs also do not work particularly well on large blocks of flats where aspect can have a significant effect on value.
Initial concern over the effectiveness of AVMs in falling markets have now been answered as the best performing models have remained highly effective throughout the latest downturns although their use for "retrospective valuations" has contributed to wasteful activity in some areas – this is not a fault of the tools but a lack of appreciation by some of those using them.
Many AVMs are also using transactional data, which may lag anywhere from three to six months. This is a good starting point but still does not account for changes in current market conditions.